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TheFuriousGunner

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CATraffic_Finetuning_eval

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{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 3 in Yolo, California, USA, along the US50-E freeway, lane 4, direction of eastbound. \n - Today's weather: Sunny. Temperature is 6.0\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, commercial areas and educational areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 19, 44, 98, 150, 156, 178, 208, 246, 248, 257, 263 and 269, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [262, 229, 221, 214, 152, 127, 100, 58, 38, 25, 22, 18]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 3 in Sacramento, California, USA, along the US50-E freeway, lane 4, direction of eastbound. \n - Today's weather: Sunny. Temperature is 5.9\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 42, 124, 250, 332, 296, 287, 307, 334, 359, 376, 381 and 384, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [366, 317, 282, 242, 194, 164, 111, 76, 46, 30, 18, 24]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 3 in Sacramento, California, USA, along the US50-E freeway, lane 3, direction of eastbound. \n - Today's weather: Sunny. Temperature is 5.9\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, commercial areas and educational areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 15, 39, 91, 134, 138, 142, 166, 198, 212, 226, 242 and 260, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [264, 232, 191, 141, 112, 80, 58, 35, 21, 17, 11, 13]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 3 in Yolo, California, USA, along the US50-W freeway, lane 3, direction of westbound. \n - Today's weather: Sunny. Temperature is 6.0\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, commercial areas and educational areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 85, 140, 195, 220, 222, 262, 304, 304, 326, 315, 326 and 286, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [284, 248, 228, 253, 231, 161, 116, 57, 35, 30, 36, 90]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 3 in El Dorado, California, USA, along the US50-W freeway, lane 3, direction of westbound. \n - Today's weather: Sunny. Temperature is 5.9\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas and commercial areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 30, 60, 105, 128, 138, 156, 158, 167, 167, 165, 162 and 177, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [163, 158, 126, 103, 85, 62, 50, 27, 16, 12, 14, 23]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 3 in Sacramento, California, USA, along the SR51-N freeway, lane 3, direction of northbound. \n - Today's weather: Sunny. Temperature is 6.0\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, commercial areas and educational areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 62, 136, 254, 314, 330, 387, 577, 581, 581, 616, 646 and 576, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [488, 420, 385, 332, 256, 220, 155, 107, 69, 48, 40, 45]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 3 in Sacramento, California, USA, along the SR51-N freeway, lane 3, direction of northbound. \n - Today's weather: Sunny. Temperature is 5.9\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 36, 83, 164, 207, 205, 231, 279, 300, 320, 332, 342 and 328, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [314, 307, 269, 241, 188, 169, 112, 74, 47, 69, 31, 27]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 3 in Sacramento, California, USA, along the SR51-S freeway, lane 3, direction of southbound. \n - Today's weather: Sunny. Temperature is 5.9\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 71, 135, 250, 256, 267, 320, 350, 378, 404, 371, 378 and 374, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [359, 365, 344, 314, 246, 198, 136, 95, 68, 54, 50, 62]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 3 in Placer, California, USA, along the I80-W freeway, lane 3, direction of westbound. \n - Today's weather: Sunny. Temperature is 5.9\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 34, 65, 125, 157, 192, 238, 288, 356, 343, 344, 352 and 340, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [292, 291, 282, 271, 237, 202, 67, 35, 26, 19, 17, 34]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 3 in Sacramento, California, USA, along the SR99-N freeway, lane 4, direction of northbound. \n - Today's weather: Sunny. Temperature is 6.0\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, commercial areas and educational areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 78, 153, 272, 270, 278, 318, 370, 384, 384, 400, 408 and 371, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [335, 325, 296, 238, 190, 164, 120, 82, 54, 42, 40, 59]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 3 in Sacramento, California, USA, along the SR99-S freeway, lane 2, direction of southbound. \n - Today's weather: Sunny. Temperature is 6.0\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, residential areas and educational areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 49, 64, 90, 111, 122, 137, 141, 156, 164, 163, 160 and 160, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [156, 156, 151, 137, 116, 99, 78, 66, 52, 47, 46, 49]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 3 in Sacramento, California, USA, along the SR99-S freeway, lane 2, direction of southbound. \n - Today's weather: Sunny. Temperature is 5.6\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 40, 63, 103, 107, 94, 106, 112, 114, 119, 117, 112 and 112, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [116, 106, 95, 65, 49, 38, 22, 14, 11, 9, 11, 28]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 4 in Contra Costa, California, USA, along the SR24-W freeway, lane 4, direction of westbound. \n - Today's weather: Sunny. Temperature is 7.9\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas and commercial areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 105, 218, 273, 228, 293, 337, 391, 361, 400, 425, 416 and 378, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [394, 377, 334, 264, 221, 193, 133, 17, 2, 1, 1, 20]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 4 in Solano, California, USA, along the I80-W freeway, lane 3, direction of westbound. \n - Today's weather: Sunny. Temperature is 6.0\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, educational areas and residential areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 28, 79, 156, 244, 256, 228, 229, 250, 276, 293, 318 and 294, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [274, 288, 281, 229, 169, 144, 106, 64, 37, 23, 20, 20]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 4 in Santa Clara, California, USA, along the US101-N freeway, lane 4, direction of northbound. \n - Today's weather: Rain. Temperature is 8.4\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, residential areas and educational areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 78, 146, 171, 166, 169, 202, 235, 259, 268, 258, 274 and 272, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [283, 293, 284, 288, 200, 173, 120, 76, 52, 36, 34, 56]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 4 in San Mateo, California, USA, along the US101-N freeway, lane 5, direction of northbound. \n - Today's weather: Sunny. Temperature is 8.3\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 126, 237, 322, 384, 410, 456, 495, 496, 539, 532, 530 and 518, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [518, 491, 448, 406, 402, 371, 252, 148, 99, 58, 47, 79]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 4 in Marin, California, USA, along the US101-N freeway, lane 4, direction of northbound. \n - Today's weather: Sunny. Temperature is 7.9\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 70, 87, 120, 187, 245, 278, 319, 349, 349, 350, 355 and 364, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [352, 330, 285, 244, 216, 190, 164, 120, 98, 81, 68, 66]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 4 in Santa Clara, California, USA, along the US101-S freeway, lane 4, direction of southbound. \n - Today's weather: Rain. Temperature is 8.4\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 25, 66, 118, 176, 180, 176, 175, 185, 196, 197, 218 and 212, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [209, 207, 204, 172, 137, 115, 86, 60, 35, 19, 16, 16]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 4 in Marin, California, USA, along the US101-S freeway, lane 4, direction of southbound. \n - Today's weather: Sunny. Temperature is 7.9\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 89, 126, 211, 239, 250, 288, 307, 325, 311, 324, 316 and 331, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [356, 328, 279, 216, 177, 158, 120, 91, 75, 68, 66, 78]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 4 in San Mateo, California, USA, along the I280-N freeway, lane 4, direction of northbound. \n - Today's weather: Sunny. Temperature is 8.3\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 33, 76, 158, 228, 270, 305, 377, 402, 395, 413, 434 and 470, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [520, 452, 375, 318, 285, 255, 194, 130, 86, 53, 31, 24]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 4 in Alameda, California, USA, along the I580-W freeway, lane 4, direction of westbound. \n - Today's weather: Sunny. Temperature is 7.9\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas and commercial areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 38, 106, 208, 324, 342, 303, 306, 333, 367, 390, 424 and 393, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [365, 384, 375, 306, 226, 191, 142, 85, 49, 31, 27, 27]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 4 in Alameda, California, USA, along the I680-N freeway, lane 3, direction of northbound. \n - Today's weather: Rain. Temperature is 8.4\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 74, 110, 157, 182, 195, 213, 229, 237, 240, 244, 246 and 247, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [234, 228, 208, 190, 165, 134, 105, 81, 66, 57, 55, 66]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 4 in Contra Costa, California, USA, along the I680-N freeway, lane 5, direction of northbound. \n - Today's weather: Sunny. Temperature is 7.9\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, commercial areas and educational areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 162, 196, 278, 382, 449, 498, 622, 682, 732, 750, 810 and 803, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [809, 699, 607, 532, 478, 420, 341, 304, 229, 219, 231, 213]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 4 in Santa Clara, California, USA, along the I680-S freeway, lane 4, direction of southbound. \n - Today's weather: Rain. Temperature is 8.4\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 49, 109, 160, 185, 226, 273, 335, 352, 368, 373, 414 and 399, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [403, 383, 370, 292, 247, 205, 161, 96, 48, 29, 24, 33]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 4 in Contra Costa, California, USA, along the I680-S freeway, lane 6, direction of southbound. \n - Today's weather: Sunny. Temperature is 7.9\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 119, 193, 278, 337, 350, 404, 455, 456, 466, 470, 478 and 492, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [484, 433, 386, 325, 288, 232, 188, 128, 94, 74, 77, 98]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 4 in Santa Clara, California, USA, along the I880-N freeway, lane 6, direction of northbound. \n - Today's weather: Rain. Temperature is 8.4\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 95, 176, 246, 298, 313, 380, 448, 514, 561, 572, 601 and 597, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [584, 573, 514, 486, 394, 321, 229, 170, 104, 70, 62, 88]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 4 in Alameda, California, USA, along the I880-S freeway, lane 4, direction of southbound. \n - Today's weather: Sunny. Temperature is 8.1\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas and commercial areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 96, 186, 280, 282, 308, 384, 418, 497, 470, 475, 468 and 453, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [452, 430, 370, 310, 262, 216, 168, 112, 69, 40, 40, 61]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 5 in Santa Cruz, California, USA, along the SR17-N freeway, lane 3, direction of northbound. \n - Today's weather: Rain. Temperature is 8.4\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including commercial areas, transportation areas and residential areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 31, 76, 117, 126, 0, 102, 244, 258, 263, 276, 300 and 308, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [300, 280, 52, 0, 0, 0, 10, 26, 15, 8, 10, 15]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 6 in Kern, California, USA, along the SR99-S freeway, lane 3, direction of southbound. \n - Today's weather: Sunny. Temperature is 9.9\u00b0C, and visibility reaches 9.9 miles. \n - Region information: including transportation areas, residential areas and educational areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 61, 165, 210, 146, 145, 147, 190, 212, 216, 258, 260 and 278, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [284, 226, 190, 142, 114, 83, 76, 46, 49, 29, 27, 37]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the SR2-W freeway, lane 4, direction of westbound. \n - Today's weather: Sunny. Temperature is 11.2\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 114, 186, 263, 285, 306, 315, 302, 314, 332, 305, 328 and 343, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [364, 339, 284, 217, 176, 171, 139, 112, 98, 80, 88, 94]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the SR2-W freeway, lane 4, direction of westbound. \n - Today's weather: Sunny. Temperature is 11.2\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 30, 86, 173, 208, 204, 196, 182, 170, 167, 178, 185 and 226, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [226, 197, 156, 107, 75, 70, 48, 27, 16, 9, 7, 14]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the I5-S freeway, lane 5, direction of southbound. \n - Today's weather: Sunny. Temperature is 11.2\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 132, 378, 552, 572, 542, 513, 506, 508, 521, 530, 560 and 575, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [572, 568, 522, 440, 338, 306, 238, 153, 99, 74, 78, 77]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the I10-E freeway, lane 4, direction of eastbound. \n - Today's weather: Sunny. Temperature is 13.9\u00b0C, and visibility reaches 9.7 miles. \n - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 91, 262, 414, 482, 456, 399, 390, 397, 412, 423, 457 and 476, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [481, 482, 437, 358, 267, 238, 185, 117, 77, 58, 53, 56]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the I10-W freeway, lane 4, direction of westbound. \n - Today's weather: Rain. Temperature is 12.3\u00b0C, and visibility reaches 9.1 miles. \n - Region information: including residential areas, transportation areas and educational areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 352, 390, 390, 418, 414, 428, 449, 454, 404, 453, 464 and 448, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [465, 458, 436, 386, 304, 270, 228, 195, 174, 160, 162, 194]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the SR47-S freeway, lane 2, direction of southbound. \n - Today's weather: Sunny. Temperature is 13.9\u00b0C, and visibility reaches 9.7 miles. \n - Region information: including residential areas, commercial areas and transportation areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 6, 15, 38, 54, 60, 56, 60, 84, 61, 69, 82 and 87, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [114, 86, 70, 52, 43, 51, 32, 23, 19, 18, 39, 17]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the SR60-E freeway, lane 4, direction of eastbound. \n - Today's weather: Sunny. Temperature is 13.9\u00b0C, and visibility reaches 9.7 miles. \n - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 126, 222, 297, 364, 346, 353, 408, 438, 492, 501, 489 and 478, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [478, 510, 446, 405, 339, 279, 214, 143, 101, 61, 57, 86]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the SR60-E freeway, lane 5, direction of eastbound. \n - Today's weather: Sunny. Temperature is 13.9\u00b0C, and visibility reaches 9.7 miles. \n - Region information: including residential areas, transportation areas and educational areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 231, 299, 374, 450, 472, 450, 487, 505, 541, 560, 573 and 564, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [556, 588, 531, 448, 416, 358, 305, 227, 187, 149, 151, 183]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the SR60-E freeway, lane 4, direction of eastbound. \n - Today's weather: Sunny. Temperature is 13.9\u00b0C, and visibility reaches 9.7 miles. \n - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 184, 278, 357, 434, 452, 453, 476, 469, 504, 537, 544 and 460, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [488, 480, 465, 424, 388, 348, 286, 198, 164, 117, 120, 140]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the SR60-E freeway, lane 4, direction of eastbound. \n - Today's weather: Rain. Temperature is 13.5\u00b0C, and visibility reaches 9.8 miles. \n - Region information: including residential areas, transportation areas and educational areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 111, 212, 318, 379, 364, 351, 366, 392, 411, 459, 480 and 488, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [508, 495, 440, 381, 350, 300, 228, 144, 93, 65, 63, 76]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the SR91-W freeway, lane 4, direction of westbound. \n - Today's weather: Sunny. Temperature is 13.9\u00b0C, and visibility reaches 9.7 miles. \n - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 91, 262, 414, 482, 456, 399, 390, 397, 412, 423, 457 and 476, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [481, 482, 437, 358, 267, 238, 185, 117, 77, 52, 53, 56]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the US101-N freeway, lane 4, direction of northbound. \n - Today's weather: Sunny. Temperature is 10.6\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 80, 174, 298, 391, 365, 385, 460, 453, 471, 462, 439 and 475, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [472, 452, 391, 327, 289, 253, 183, 110, 67, 36, 28, 40]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the US101-S freeway, lane 4, direction of southbound. \n - Today's weather: Sunny. Temperature is 11.2\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including residential areas, commercial areas and transportation areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 138, 254, 323, 295, 322, 372, 398, 400, 392, 378, 372 and 371, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [376, 392, 363, 346, 344, 342, 282, 200, 137, 89, 75, 90]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the I110-N freeway, lane 4, direction of northbound. \n - Today's weather: Sunny. Temperature is 12.9\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 218, 408, 454, 449, 466, 498, 504, 504, 462, 441, 482 and 452, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [487, 493, 476, 466, 414, 387, 295, 236, 173, 118, 104, 138]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the SR134-E freeway, lane 4, direction of eastbound. \n - Today's weather: Sunny. Temperature is 11.2\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 55, 124, 256, 324, 338, 392, 462, 469, 480, 486, 466 and 453, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [421, 419, 406, 381, 303, 261, 194, 123, 74, 38, 32, 37]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the SR134-E freeway, lane 4, direction of eastbound. \n - Today's weather: Sunny. Temperature is 11.2\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 92, 132, 200, 290, 304, 310, 324, 358, 356, 354, 358 and 358, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [374, 364, 333, 300, 248, 208, 174, 133, 112, 84, 87, 88]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the I210-E freeway, lane 5, direction of eastbound. \n - Today's weather: Sunny. Temperature is 11.2\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 99, 174, 306, 424, 440, 500, 551, 597, 610, 563, 632 and 628, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [602, 590, 526, 473, 372, 311, 225, 164, 114, 72, 72, 79]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the I405-N freeway, lane 5, direction of northbound. \n - Today's weather: Sunny. Temperature is 13.9\u00b0C, and visibility reaches 9.7 miles. \n - Region information: including residential areas, transportation areas and educational areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 240, 500, 515, 552, 551, 574, 680, 674, 688, 655, 673 and 712, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [649, 673, 636, 551, 485, 424, 311, 200, 126, 80, 72, 114]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the I405-N freeway, lane 5, direction of northbound. \n - Today's weather: Sunny. Temperature is 13.9\u00b0C, and visibility reaches 9.7 miles. \n - Region information: including residential areas, transportation areas and educational areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 261, 513, 543, 567, 557, 566, 658, 659, 684, 651, 671 and 675, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [662, 680, 628, 537, 488, 426, 330, 233, 160, 119, 118, 152]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the I405-N freeway, lane 4, direction of northbound. \n - Today's weather: Sunny. Temperature is 12.9\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 80, 187, 288, 275, 312, 333, 367, 362, 364, 374, 387 and 309, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [315, 312, 337, 322, 309, 266, 230, 161, 101, 52, 34, 44]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the I405-N freeway, lane 5, direction of northbound. \n - Today's weather: Sunny. Temperature is 10.6\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including residential areas, transportation areas and educational areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 161, 254, 363, 446, 497, 562, 604, 614, 620, 648, 637 and 633, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [639, 597, 565, 550, 506, 446, 393, 306, 232, 146, 132, 126]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the I405-N freeway, lane 6, direction of northbound. \n - Today's weather: Sunny. Temperature is 10.6\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 104, 219, 365, 476, 536, 615, 682, 685, 714, 763, 752 and 770, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [739, 692, 669, 664, 567, 509, 411, 292, 197, 93, 68, 64]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the I405-N freeway, lane 4, direction of northbound. \n - Today's weather: Sunny. Temperature is 10.6\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 72, 160, 250, 346, 362, 370, 406, 448, 468, 502, 487 and 418, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [448, 456, 427, 404, 348, 307, 244, 178, 110, 56, 44, 44]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the I405-S freeway, lane 4, direction of southbound. \n - Today's weather: Sunny. Temperature is 12.9\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 120, 335, 478, 576, 560, 516, 506, 521, 550, 561, 550 and 497, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [458, 465, 457, 542, 464, 368, 282, 185, 127, 69, 58, 51]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the I405-S freeway, lane 5, direction of southbound. \n - Today's weather: Sunny. Temperature is 12.9\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 149, 336, 575, 594, 624, 667, 693, 680, 702, 699, 672 and 494, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [528, 611, 637, 624, 574, 545, 455, 309, 198, 98, 67, 78]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the I605-N freeway, lane 4, direction of northbound. \n - Today's weather: Sunny. Temperature is 13.9\u00b0C, and visibility reaches 9.7 miles. \n - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 70, 166, 241, 308, 342, 378, 388, 424, 432, 403, 508 and 523, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [484, 523, 439, 326, 265, 234, 154, 82, 49, 32, 28, 37]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the I605-N freeway, lane 4, direction of northbound. \n - Today's weather: Sunny. Temperature is 13.9\u00b0C, and visibility reaches 9.7 miles. \n - Region information: including residential areas, transportation areas and educational areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 126, 242, 382, 444, 457, 476, 495, 520, 534, 552, 547 and 513, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [542, 543, 488, 420, 379, 335, 259, 178, 116, 78, 70, 86]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the I605-N freeway, lane 4, direction of northbound. \n - Today's weather: Sunny. Temperature is 11.2\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including residential areas, transportation areas and educational areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 80, 184, 282, 330, 332, 348, 378, 418, 448, 434, 370 and 343, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [354, 316, 285, 289, 254, 209, 157, 102, 68, 45, 42, 49]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the I710-S freeway, lane 4, direction of southbound. \n - Today's weather: Sunny. Temperature is 13.9\u00b0C, and visibility reaches 9.7 miles. \n - Region information: including residential areas, transportation areas and educational areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 144, 272, 332, 324, 290, 304, 327, 338, 352, 334, 364 and 380, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [366, 383, 322, 287, 234, 225, 184, 140, 114, 81, 84, 96]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the I710-S freeway, lane 6, direction of southbound. \n - Today's weather: Sunny. Temperature is 13.9\u00b0C, and visibility reaches 9.7 miles. \n - Region information: including residential areas, transportation areas and educational areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 91, 262, 414, 481, 456, 399, 390, 397, 412, 423, 458 and 476, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [481, 482, 437, 358, 267, 238, 186, 117, 78, 52, 53, 56]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the I710-S freeway, lane 4, direction of southbound. \n - Today's weather: Sunny. Temperature is 13.9\u00b0C, and visibility reaches 9.7 miles. \n - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 91, 262, 414, 482, 456, 399, 390, 397, 412, 423, 457 and 476, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [481, 482, 437, 358, 267, 238, 185, 117, 77, 52, 53, 56]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the I710-S freeway, lane 5, direction of southbound. \n - Today's weather: Sunny. Temperature is 12.9\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 57, 168, 277, 325, 302, 254, 244, 246, 253, 258, 286 and 322, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [337, 339, 287, 214, 158, 142, 109, 70, 48, 34, 36, 35]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 8 in San Bernardino, California, USA, along the I10-W freeway, lane 4, direction of westbound. \n - Today's weather: Rain. Temperature is 12.3\u00b0C, and visibility reaches 9.1 miles. \n - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 262, 302, 326, 370, 377, 416, 451, 468, 258, 466, 496 and 474, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [516, 492, 438, 358, 317, 275, 211, 154, 103, 81, 86, 130]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 8 in San Bernardino, California, USA, along the I10-W freeway, lane 4, direction of westbound. \n - Today's weather: Rain. Temperature is 12.3\u00b0C, and visibility reaches 9.1 miles. \n - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 339, 416, 433, 420, 378, 416, 464, 481, 482, 459, 488 and 436, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [418, 386, 365, 320, 279, 226, 145, 103, 67, 62, 77, 179]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 8 in San Bernardino, California, USA, along the I10-W freeway, lane 4, direction of westbound. \n - Today's weather: Rain. Temperature is 12.3\u00b0C, and visibility reaches 9.1 miles. \n - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 216, 298, 382, 349, 334, 367, 422, 436, 441, 448, 485 and 466, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [448, 409, 363, 302, 268, 221, 148, 106, 70, 60, 72, 126]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 8 in San Bernardino, California, USA, along the I10-W freeway, lane 4, direction of westbound. \n - Today's weather: Rain. Temperature is 12.3\u00b0C, and visibility reaches 9.1 miles. \n - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 165, 344, 433, 476, 500, 468, 469, 478, 491, 506, 551 and 591, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [570, 589, 500, 396, 335, 294, 233, 170, 112, 80, 72, 88]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 8 in Riverside, California, USA, along the I10-W freeway, lane 4, direction of westbound. \n - Today's weather: Rain. Temperature is 9.8\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 104, 112, 146, 185, 214, 247, 274, 282, 294, 290, 282 and 300, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [284, 263, 212, 189, 174, 152, 134, 111, 98, 99, 98, 103]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 8 in Riverside, California, USA, along the I15-N freeway, lane 4, direction of northbound. \n - Today's weather: Rain. Temperature is 9.8\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 138, 172, 216, 248, 287, 326, 372, 383, 355, 348, 353 and 298, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [306, 340, 368, 378, 269, 230, 195, 156, 128, 118, 109, 112]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 8 in San Bernardino, California, USA, along the I15-N freeway, lane 4, direction of northbound. \n - Today's weather: Rain. Temperature is 12.3\u00b0C, and visibility reaches 9.1 miles. \n - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 120, 152, 194, 212, 229, 249, 264, 275, 284, 313, 344 and 315, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [327, 370, 335, 306, 260, 240, 208, 169, 150, 107, 88, 81]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 8 in San Bernardino, California, USA, along the I15-N freeway, lane 4, direction of northbound. \n - Today's weather: Rain. Temperature is 12.3\u00b0C, and visibility reaches 9.1 miles. \n - Region information: including transportation areas and commercial areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 64, 102, 143, 193, 209, 250, 290, 323, 327, 366, 384 and 394, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [418, 450, 352, 277, 238, 194, 155, 106, 80, 56, 56, 58]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 8 in Riverside, California, USA, along the SR60-E freeway, lane 2, direction of eastbound. \n - Today's weather: Rain. Temperature is 9.8\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 55, 90, 136, 150, 176, 208, 244, 250, 244, 242, 254 and 273, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [214, 240, 241, 214, 186, 161, 125, 94, 63, 52, 39, 46]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 8 in San Bernardino, California, USA, along the SR71-S freeway, lane 3, direction of southbound. \n - Today's weather: Rain. Temperature is 13.5\u00b0C, and visibility reaches 9.8 miles. \n - Region information: including transportation areas, residential areas and educational areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 60, 116, 158, 206, 213, 209, 230, 246, 251, 290, 278 and 284, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [284, 319, 273, 215, 174, 147, 106, 60, 39, 26, 26, 32]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 8 in Riverside, California, USA, along the SR91-W freeway, lane 3, direction of westbound. \n - Today's weather: Rain. Temperature is 9.8\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, residential areas and educational areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 182, 183, 270, 332, 316, 328, 350, 360, 364, 374, 386 and 361, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [365, 358, 336, 289, 256, 201, 156, 102, 66, 47, 48, 106]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 8 in San Bernardino, California, USA, along the I210-W freeway, lane 4, direction of westbound. \n - Today's weather: Rain. Temperature is 12.3\u00b0C, and visibility reaches 9.1 miles. \n - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 234, 241, 272, 291, 291, 314, 329, 345, 364, 378, 374 and 349, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [348, 308, 327, 274, 238, 202, 161, 116, 68, 58, 63, 112]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 8 in San Bernardino, California, USA, along the I215-N freeway, lane 3, direction of northbound. \n - Today's weather: Rain. Temperature is 12.3\u00b0C, and visibility reaches 9.1 miles. \n - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 73, 146, 220, 237, 254, 259, 286, 291, 303, 318, 336 and 345, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [338, 339, 284, 227, 190, 152, 119, 73, 49, 32, 25, 42]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 8 in San Bernardino, California, USA, along the I215-N freeway, lane 5, direction of northbound. \n - Today's weather: Rain. Temperature is 12.3\u00b0C, and visibility reaches 9.1 miles. \n - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 92, 156, 207, 266, 289, 316, 343, 370, 410, 440, 480 and 548, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [548, 541, 429, 355, 274, 236, 180, 134, 93, 63, 59, 68]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 8 in Riverside, California, USA, along the I215-S freeway, lane 3, direction of southbound. \n - Today's weather: Rain. Temperature is 12.3\u00b0C, and visibility reaches 9.1 miles. \n - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 186, 179, 251, 273, 260, 266, 278, 294, 279, 276, 285 and 284, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [275, 300, 299, 258, 233, 210, 180, 153, 122, 110, 116, 139]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 8 in San Bernardino, California, USA, along the I215-S freeway, lane 2, direction of southbound. \n - Today's weather: Rain. Temperature is 12.3\u00b0C, and visibility reaches 9.1 miles. \n - Region information: including transportation areas, educational areas and commercial areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 82, 172, 216, 238, 250, 234, 234, 239, 246, 254, 276 and 296, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [286, 295, 250, 198, 167, 147, 116, 86, 56, 40, 36, 44]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 10 in Merced, California, USA, along the I5-N freeway, lane 2, direction of northbound. \n - Today's weather: Sunny. Temperature is 6.7\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 34, 32, 39, 56, 70, 103, 111, 129, 152, 172, 198 and 187, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [202, 219, 223, 230, 225, 154, 138, 113, 76, 54, 49, 48]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 10 in San Joaquin, California, USA, along the I5-N freeway, lane 3, direction of northbound. \n - Today's weather: Sunny. Temperature is 7.7\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 46, 58, 58, 65, 74, 81, 97, 99, 112, 113, 134 and 160, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [130, 130, 115, 88, 87, 78, 64, 53, 45, 37, 33, 36]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 10 in Merced, California, USA, along the SR99-N freeway, lane 2, direction of northbound. \n - Today's weather: Sunny. Temperature is 6.7\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, residential areas and educational areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 49, 86, 106, 101, 116, 125, 150, 172, 179, 191, 184 and 190, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [196, 180, 151, 134, 112, 98, 75, 60, 46, 32, 36, 47]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 10 in Stanislaus, California, USA, along the SR99-N freeway, lane 3, direction of northbound. \n - Today's weather: Sunny. Temperature is 7.6\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 138, 216, 285, 246, 250, 269, 305, 326, 363, 381, 364 and 353, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [362, 361, 295, 258, 211, 176, 118, 78, 52, 50, 72, 137]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 11 in San Diego, California, USA, along the I5-S freeway, lane 4, direction of southbound. \n - Today's weather: Sunny. Temperature is 14.9\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, residential areas and educational areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 63, 91, 147, 203, 236, 313, 364, 378, 379, 371, 378 and 385, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [361, 362, 338, 312, 295, 276, 167, 108, 76, 63, 58, 62]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 11 in San Diego, California, USA, along the I8-E freeway, lane 2, direction of eastbound. \n - Today's weather: Sunny. Temperature is 14.9\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 33, 28, 60, 96, 120, 118, 133, 135, 146, 149, 151 and 145, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [130, 126, 125, 81, 67, 51, 36, 23, 23, 18, 15, 16]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 11 in San Diego, California, USA, along the I8-E freeway, lane 4, direction of eastbound. \n - Today's weather: Sunny. Temperature is 14.9\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, commercial areas and educational areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 38, 53, 120, 209, 259, 287, 319, 360, 392, 427, 476 and 491, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [446, 428, 360, 262, 222, 198, 145, 100, 50, 26, 18, 15]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 11 in San Diego, California, USA, along the I8-E freeway, lane 2, direction of eastbound. \n - Today's weather: Sunny. Temperature is 14.9\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas and commercial areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 44, 101, 146, 178, 206, 228, 246, 256, 271, 262, 269 and 267, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [256, 228, 179, 140, 104, 72, 62, 46, 27, 22, 18, 27]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 11 in San Diego, California, USA, along the I15-N freeway, lane 5, direction of northbound. \n - Today's weather: Sunny. Temperature is 14.9\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, educational areas and residential areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 118, 327, 466, 454, 412, 425, 406, 441, 463, 469, 492 and 498, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [456, 430, 368, 288, 224, 189, 134, 96, 62, 44, 37, 64]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 11 in San Diego, California, USA, along the I15-S freeway, lane 6, direction of southbound. \n - Today's weather: Sunny. Temperature is 14.9\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, commercial areas and educational areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 73, 137, 250, 375, 455, 486, 516, 572, 571, 598, 658 and 669, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [652, 615, 502, 387, 315, 256, 189, 131, 276, 204, 183, 238]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 11 in San Diego, California, USA, along the SR52-E freeway, lane 2, direction of eastbound. \n - Today's weather: Sunny. Temperature is 14.9\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas and commercial areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 21, 48, 96, 127, 138, 152, 170, 194, 206, 225, 262 and 297, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [269, 240, 178, 133, 103, 86, 58, 43, 24, 14, 10, 12]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 11 in San Diego, California, USA, along the I805-N freeway, lane 5, direction of northbound. \n - Today's weather: Sunny. Temperature is 14.9\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, educational areas and residential areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 186, 414, 538, 494, 448, 466, 457, 446, 418, 415, 404 and 393, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [358, 358, 319, 257, 211, 180, 133, 104, 67, 50, 48, 98]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 11 in San Diego, California, USA, along the I805-S freeway, lane 5, direction of southbound. \n - Today's weather: Sunny. Temperature is 14.9\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, educational areas and residential areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 34, 80, 175, 282, 301, 329, 392, 443, 500, 556, 694 and 722, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [648, 603, 497, 415, 347, 312, 243, 177, 96, 50, 39, 30]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 11 in San Diego, California, USA, along the I805-S freeway, lane 4, direction of southbound. \n - Today's weather: Sunny. Temperature is 14.9\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 38, 86, 202, 294, 304, 337, 390, 422, 457, 474, 550 and 539, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [485, 472, 375, 313, 254, 220, 170, 125, 72, 40, 31, 29]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 12 in Orange, California, USA, along the I5-S freeway, lane 5, direction of southbound. \n - Today's weather: Rain. Temperature is 13.5\u00b0C, and visibility reaches 9.8 miles. \n - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 296, 552, 678, 650, 618, 648, 660, 664, 654, 680, 685 and 696, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [702, 692, 644, 570, 512, 449, 339, 226, 134, 82, 75, 144]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 12 in Orange, California, USA, along the I5-S freeway, lane 4, direction of southbound. \n - Today's weather: Sunny. Temperature is 13.9\u00b0C, and visibility reaches 9.7 miles. \n - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 220, 404, 481, 506, 514, 514, 534, 524, 517, 528, 526 and 567, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [573, 556, 505, 438, 394, 352, 264, 199, 134, 105, 94, 139]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 12 in Orange, California, USA, along the SR55-N freeway, lane 4, direction of northbound. \n - Today's weather: Rain. Temperature is 13.5\u00b0C, and visibility reaches 9.8 miles. \n - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 62, 174, 310, 437, 446, 437, 484, 500, 509, 497, 406 and 360, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [376, 377, 410, 386, 341, 301, 237, 139, 63, 45, 30, 32]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 12 in Orange, California, USA, along the SR57-S freeway, lane 6, direction of southbound. \n - Today's weather: Rain. Temperature is 13.5\u00b0C, and visibility reaches 9.8 miles. \n - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 230, 478, 512, 546, 578, 588, 591, 606, 622, 651, 636 and 649, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [612, 600, 554, 445, 385, 326, 242, 151, 84, 62, 59, 97]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 12 in Orange, California, USA, along the SR73-N freeway, lane 3, direction of northbound. \n - Today's weather: Rain. Temperature is 13.5\u00b0C, and visibility reaches 9.8 miles. \n - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 11, 29, 51, 105, 96, 101, 102, 103, 104, 109, 122 and 143, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [125, 112, 76, 52, 41, 22, 14, 10, 16, 14, 14, 15]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 12 in Orange, California, USA, along the SR73-S freeway, lane 4, direction of southbound. \n - Today's weather: Rain. Temperature is 13.5\u00b0C, and visibility reaches 9.8 miles. \n - Region information: including educational areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 5, 14, 35, 66, 88, 92, 94, 114, 111, 128, 138 and 171, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [224, 230, 144, 77, 55, 41, 25, 11, 5, 4, 2, 2]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 12 in Orange, California, USA, along the SR73-S freeway, lane 3, direction of southbound. \n - Today's weather: Rain. Temperature is 13.5\u00b0C, and visibility reaches 9.8 miles. \n - Region information: including transportation areas, residential areas and educational areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 5, 16, 39, 66, 81, 81, 88, 106, 103, 110, 122 and 150, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [195, 196, 120, 72, 49, 39, 24, 12, 7, 4, 3, 2]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 12 in Orange, California, USA, along the SR91-W freeway, lane 5, direction of westbound. \n - Today's weather: Rain. Temperature is 13.5\u00b0C, and visibility reaches 9.8 miles. \n - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 460, 514, 431, 461, 478, 525, 495, 472, 474, 502, 523 and 484, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [483, 463, 452, 381, 330, 312, 222, 142, 89, 68, 82, 186]}

{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays?

Some important information is listed as follows:\n - Location: District 12 in Orange, California, USA, along the I405-S freeway, lane 5, direction of southbound. \n - Today's weather: Rain. Temperature is 13.5\u00b0C, and visibility reaches 9.8 miles. \n - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.\n - Current Time: 3 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 172, 296, 483, 517, 466, 437, 430, 470, 465, 488, 530 and 512, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 PM to 3 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]}

{Traffic volume data in the next 12 hours: [522, 520, 458, 412, 362, 321, 227, 159, 116, 96, 84, 106]}