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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: Rain. Temperature is 14.9\u00b0C, and visibility reaches 9.7 miles. \n - Region information: including transportation areas, commercial areas and educational areas within a range of 5 km.\n - Current Time: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 300, 289, 349, 338, 288, 194, 157, 122, 105, 70, 40 and 29, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [25, 23, 30, 74, 192, 373, 383, 261, 214, 237, 252, 261]}

{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: Rain. Temperature is 14.9\u00b0C, and visibility reaches 9.3 miles. \n - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.\n - Current Time: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 416, 530, 536, 517, 412, 284, 226, 225, 138, 97, 56 and 33, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [22, 27, 62, 183, 404, 528, 520, 388, 348, 349, 381, 379]}

{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: Rain. Temperature is 14.9\u00b0C, and visibility reaches 9.3 miles. \n - Region information: including transportation areas, commercial areas and educational areas within a range of 5 km.\n - Current Time: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 246, 323, 342, 336, 274, 187, 142, 108, 69, 45, 25 and 15, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [10, 13, 18, 50, 126, 200, 212, 175, 172, 198, 216, 217]}

{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: Rain. Temperature is 14.9\u00b0C, and visibility reaches 9.7 miles. \n - Region information: including transportation areas, commercial areas and educational areas within a range of 5 km.\n - Current Time: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 287, 319, 403, 395, 283, 184, 148, 144, 89, 55, 34 and 27, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [38, 75, 147, 246, 332, 357, 335, 288, 267, 275, 284, 301]}

{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: Rain. Temperature is 14.9\u00b0C, and visibility reaches 9.3 miles. \n - Region information: including transportation areas and commercial areas within a range of 5 km.\n - Current Time: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 145, 164, 160, 170, 119, 66, 56, 40, 30, 20, 14 and 12, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [12, 20, 40, 103, 222, 272, 234, 181, 154, 146, 145, 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 3 in Sacramento, California, USA, along the SR51-N freeway, lane 3, direction of northbound. \n - Today's weather: Rain. Temperature is 14.9\u00b0C, and visibility reaches 9.7 miles. \n - Region information: including transportation areas, commercial areas and educational areas within a range of 5 km.\n - Current Time: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 662, 675, 618, 519, 533, 446, 313, 254, 178, 128, 88 and 52, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [45, 45, 83, 199, 372, 507, 505, 550, 624, 610, 596, 606]}

{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: Rain. Temperature is 14.9\u00b0C, and visibility reaches 9.3 miles. \n - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.\n - Current Time: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 402, 432, 404, 294, 350, 284, 237, 236, 162, 108, 62 and 75, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [37, 29, 42, 120, 244, 331, 303, 288, 302, 316, 339, 365]}

{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: Rain. Temperature is 14.9\u00b0C, and visibility reaches 9.3 miles. \n - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.\n - Current Time: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 394, 396, 342, 404, 406, 290, 225, 200, 142, 107, 73 and 62, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [51, 59, 101, 238, 468, 506, 466, 428, 410, 400, 406, 391]}

{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: Rain. Temperature is 14.9\u00b0C, and visibility reaches 9.3 miles. \n - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.\n - Current Time: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 281, 318, 289, 235, 206, 136, 104, 82, 53, 37, 24 and 14, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [13, 23, 50, 123, 239, 294, 286, 266, 261, 262, 264, 253]}

{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: Rain. Temperature is 14.9\u00b0C, and visibility reaches 9.7 miles. \n - Region information: including transportation areas, commercial areas and educational areas within a range of 5 km.\n - Current Time: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 430, 424, 412, 364, 338, 257, 208, 178, 139, 95, 65 and 44, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [39, 59, 107, 267, 466, 539, 498, 430, 432, 365, 387, 457]}

{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: Rain. Temperature is 14.9\u00b0C, and visibility reaches 9.7 miles. \n - Region information: including transportation areas, residential areas and educational areas within a range of 5 km.\n - Current Time: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 139, 129, 130, 135, 124, 38, 22, 22, 16, 11, 8 and 4, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [4, 4, 8, 14, 89, 121, 118, 130, 127, 125, 134, 124]}

{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: Rain. Temperature is 14.5\u00b0C, and visibility reaches 9.5 miles. \n - Region information: including transportation areas within a range of 5 km.\n - Current Time: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 99, 116, 123, 130, 97, 50, 37, 30, 24, 14, 10 and 8, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [12, 22, 52, 109, 213, 220, 184, 128, 102, 97, 99, 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 4 in Contra Costa, California, USA, along the SR24-W freeway, lane 4, direction of westbound. \n - Today's weather: Rain. Temperature is 14.6\u00b0C, and visibility reaches 9.6 miles. \n - Region information: including transportation areas and commercial areas within a range of 5 km.\n - Current Time: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 408, 374, 411, 459, 354, 264, 214, 198, 134, 2, 0 and 2, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [1, 35, 277, 458, 298, 361, 348, 414, 454, 406, 380, 417]}

{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: Rain. Temperature is 14.9\u00b0C, and visibility reaches 9.7 miles. \n - Region information: including transportation areas, educational areas and residential areas within a range of 5 km.\n - Current Time: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 327, 296, 278, 288, 292, 244, 180, 157, 116, 69, 42 and 30, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [26, 26, 32, 63, 134, 242, 268, 234, 226, 247, 276, 294]}

{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: Sunny. Temperature is 16.7\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: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 204, 191, 212, 205, 160, 120, 92, 77, 60, 40, 26 and 19, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [28, 55, 153, 293, 344, 308, 276, 255, 214, 185, 182, 184]}

{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 15.3\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: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 508, 566, 618, 581, 534, 543, 456, 424, 270, 176, 92 and 55, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [46, 84, 179, 436, 607, 647, 553, 564, 505, 525, 485, 524]}

{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: Rain. Temperature is 14.6\u00b0C, and visibility reaches 9.6 miles. \n - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.\n - Current Time: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 402, 433, 404, 408, 419, 350, 287, 255, 209, 143, 108 and 83, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [72, 70, 72, 110, 175, 289, 321, 305, 290, 292, 316, 352]}

{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: Sunny. Temperature is 16.7\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: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 218, 200, 194, 189, 194, 178, 145, 126, 108, 67, 41 and 23, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [17, 17, 28, 66, 117, 177, 190, 182, 179, 179, 190, 199]}

{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: Rain. Temperature is 14.6\u00b0C, and visibility reaches 9.6 miles. \n - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.\n - Current Time: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 268, 278, 284, 315, 270, 197, 160, 141, 118, 85, 72 and 69, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [65, 78, 126, 227, 351, 424, 415, 354, 337, 305, 268, 257]}

{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 15.3\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: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 463, 565, 608, 607, 554, 463, 348, 300, 202, 132, 96 and 53, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [32, 30, 45, 116, 301, 514, 518, 444, 366, 348, 358, 418]}

{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: Rain. Temperature is 14.6\u00b0C, and visibility reaches 9.6 miles. \n - Region information: including transportation areas and commercial areas within a range of 5 km.\n - Current Time: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 436, 393, 370, 383, 390, 325, 240, 209, 154, 92, 56 and 38, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [34, 35, 43, 84, 178, 322, 356, 311, 300, 329, 368, 392]}

{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: Sunny. Temperature is 16.7\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: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 246, 232, 242, 250, 274, 226, 199, 146, 114, 86, 69 and 61, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [56, 69, 109, 171, 250, 276, 296, 252, 204, 199, 206, 228]}

{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: Rain. Temperature is 14.6\u00b0C, and visibility reaches 9.6 miles. \n - Region information: including transportation areas, commercial areas and educational areas within a range of 5 km.\n - Current Time: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 844, 781, 682, 684, 772, 724, 575, 511, 379, 331, 255 and 200, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [190, 152, 167, 263, 426, 663, 699, 643, 626, 645, 720, 800]}

{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: Sunny. Temperature is 16.7\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: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 413, 448, 396, 400, 392, 317, 220, 186, 125, 88, 52 and 38, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [29, 32, 73, 191, 292, 379, 445, 470, 376, 353, 307, 317]}

{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: Rain. Temperature is 14.6\u00b0C, and visibility reaches 9.6 miles. \n - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.\n - Current Time: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 410, 405, 441, 456, 400, 284, 226, 192, 124, 85, 66 and 49, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [49, 60, 100, 238, 431, 518, 477, 421, 380, 374, 390, 429]}

{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: Sunny. Temperature is 16.7\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: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 644, 612, 618, 611, 519, 493, 449, 396, 272, 174, 109 and 84, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [72, 92, 190, 328, 422, 482, 474, 456, 410, 461, 482, 597]}

{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: Rain. Temperature is 16.0\u00b0C, and visibility reaches 9.9 miles. \n - Region information: including transportation areas and commercial areas within a range of 5 km.\n - Current Time: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 456, 445, 442, 444, 404, 331, 242, 207, 157, 96, 61 and 43, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [45, 64, 192, 401, 456, 400, 390, 416, 449, 424, 330, 436]}

{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: Sunny. Temperature is 16.7\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: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 220, 223, 238, 253, 194, 144, 110, 84, 41, 27, 16 and 8, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [10, 18, 55, 157, 244, 250, 257, 236, 224, 214, 212, 218]}

{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 16.2\u00b0C, and visibility reaches 9.6 miles. \n - Region information: including transportation areas, residential areas and educational areas within a range of 5 km.\n - Current Time: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 210, 228, 224, 217, 144, 120, 111, 100, 69, 46, 41 and 28, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [29, 44, 74, 182, 239, 216, 172, 144, 148, 165, 187, 208]}

{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 16.2\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: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 310, 367, 398, 386, 333, 263, 203, 191, 152, 120, 101 and 92, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [90, 94, 128, 289, 494, 424, 393, 446, 354, 325, 322, 327]}

{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 16.2\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: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 190, 240, 242, 230, 186, 136, 86, 82, 58, 27, 17 and 10, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [8, 14, 45, 162, 395, 405, 350, 357, 242, 183, 175, 171]}

{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 16.2\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: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 575, 588, 582, 578, 532, 449, 344, 320, 257, 172, 104 and 72, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [60, 65, 134, 382, 573, 592, 562, 535, 520, 526, 530, 546]}

{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 15.8\u00b0C, and visibility reaches 9.9 miles. \n - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km.\n - Current Time: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 469, 487, 494, 498, 451, 369, 278, 252, 199, 133, 81 and 54, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [44, 48, 92, 262, 426, 494, 470, 418, 398, 405, 419, 433]}

{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: Sunny. Temperature is 16.3\u00b0C, and visibility reaches 5.7 miles. \n - Region information: including residential areas, transportation areas and educational areas within a range of 5 km.\n - Current Time: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 485, 500, 496, 496, 426, 322, 320, 315, 282, 250, 234 and 223, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [225, 259, 382, 454, 470, 481, 469, 446, 422, 422, 421, 427]}

{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 15.8\u00b0C, and visibility reaches 9.9 miles. \n - Region information: including residential areas, commercial areas and transportation areas within a range of 5 km.\n - Current Time: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 98, 108, 148, 122, 91, 64, 52, 69, 46, 24, 30 and 26, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [56, 24, 8, 28, 64, 102, 94, 79, 62, 94, 72, 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 7 in Los Angeles, California, USA, along the SR60-E freeway, lane 4, direction of eastbound. \n - Today's weather: Sunny. Temperature is 15.8\u00b0C, and visibility reaches 9.9 miles. \n - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km.\n - Current Time: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 480, 466, 450, 358, 388, 469, 450, 348, 294, 181, 116 and 82, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [78, 95, 153, 272, 370, 502, 439, 382, 403, 438, 491, 510]}

{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 15.8\u00b0C, and visibility reaches 9.9 miles. \n - Region information: including residential areas, transportation areas and educational areas within a range of 5 km.\n - Current Time: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 538, 496, 628, 607, 594, 585, 498, 425, 378, 270, 209 and 175, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [172, 195, 257, 349, 436, 542, 562, 482, 478, 506, 550, 562]}

{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 15.8\u00b0C, and visibility reaches 9.9 miles. \n - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km.\n - Current Time: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 270, 412, 417, 415, 424, 470, 447, 376, 327, 233, 176 and 142, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [134, 150, 226, 357, 452, 536, 538, 481, 469, 488, 499, 499]}

{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 15.6\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: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 450, 523, 476, 503, 490, 506, 443, 328, 263, 168, 114 and 82, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [75, 82, 144, 280, 438, 386, 420, 382, 365, 346, 424, 440]}

{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 15.8\u00b0C, and visibility reaches 9.9 miles. \n - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km.\n - Current Time: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 469, 487, 494, 498, 451, 369, 278, 252, 199, 133, 81 and 54, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [44, 48, 92, 262, 426, 494, 470, 418, 398, 405, 419, 433]}

{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 15.8\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: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 309, 396, 401, 462, 369, 323, 264, 228, 171, 139, 98 and 84, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [79, 84, 112, 182, 304, 401, 418, 361, 336, 346, 342, 362]}

{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 16.2\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: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 362, 354, 368, 362, 354, 328, 304, 307, 321, 228, 152 and 103, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [103, 91, 165, 393, 488, 389, 356, 453, 416, 340, 388, 321]}

{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 15.1\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: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 508, 510, 508, 498, 475, 461, 421, 405, 350, 239, 187 and 132, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [112, 143, 264, 480, 380, 259, 244, 302, 435, 470, 476, 460]}

{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 16.2\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: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 518, 484, 426, 408, 373, 419, 416, 313, 262, 157, 101 and 59, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [46, 41, 74, 189, 394, 554, 546, 494, 487, 519, 482, 510]}

{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 16.2\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: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 423, 406, 434, 425, 439, 379, 309, 264, 224, 148, 124 and 91, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [86, 90, 100, 156, 269, 404, 430, 376, 358, 360, 376, 387]}

{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 16.2\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: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 606, 596, 667, 657, 635, 581, 445, 372, 272, 185, 106 and 69, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [62, 66, 97, 196, 413, 607, 627, 552, 554, 579, 605, 659]}

{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 15.8\u00b0C, and visibility reaches 9.9 miles. \n - Region information: including residential areas, transportation areas and educational areas within a range of 5 km.\n - Current Time: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 713, 700, 719, 696, 672, 577, 488, 426, 315, 190, 133 and 86, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [80, 115, 318, 653, 755, 714, 639, 679, 689, 680, 697, 677]}

{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 15.8\u00b0C, and visibility reaches 9.9 miles. \n - Region information: including residential areas, transportation areas and educational areas within a range of 5 km.\n - Current Time: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 642, 650, 674, 658, 624, 528, 474, 420, 342, 243, 190 and 152, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [148, 176, 322, 599, 708, 666, 605, 632, 634, 622, 636, 628]}

{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 15.1\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: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 324, 306, 257, 220, 224, 264, 246, 244, 211, 134, 96 and 54, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [38, 46, 102, 240, 354, 386, 406, 372, 357, 313, 340, 348]}

{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 15.8\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: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 638, 626, 550, 516, 523, 594, 608, 564, 498, 354, 257 and 178, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [140, 131, 172, 272, 420, 552, 589, 560, 560, 576, 596, 641]}

{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 15.8\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: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 780, 717, 617, 597, 635, 770, 760, 657, 563, 360, 236 and 146, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [108, 102, 145, 274, 434, 624, 676, 640, 636, 668, 691, 750]}

{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 15.8\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: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 478, 457, 457, 457, 449, 479, 464, 380, 320, 214, 129 and 78, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [64, 48, 85, 180, 298, 444, 386, 360, 406, 423, 445, 495]}

{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 15.1\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: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 402, 324, 320, 290, 306, 366, 388, 331, 237, 161, 125 and 96, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [76, 74, 113, 254, 348, 442, 450, 375, 366, 414, 438, 418]}

{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 15.1\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: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 632, 523, 403, 344, 349, 506, 552, 585, 513, 309, 202 and 118, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [78, 84, 184, 443, 708, 783, 744, 739, 703, 666, 707, 721]}

{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 15.8\u00b0C, and visibility reaches 9.9 miles. \n - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km.\n - Current Time: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 538, 582, 598, 596, 514, 412, 302, 251, 169, 98, 57 and 39, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [34, 37, 95, 261, 420, 532, 484, 462, 394, 424, 433, 427]}

{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 15.8\u00b0C, and visibility reaches 9.9 miles. \n - Region information: including residential areas, transportation areas and educational areas within a range of 5 km.\n - Current Time: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 539, 513, 548, 564, 545, 506, 444, 393, 318, 197, 142 and 98, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [85, 85, 155, 334, 490, 542, 524, 515, 494, 504, 526, 526]}

{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 16.2\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: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 342, 338, 342, 357, 362, 362, 286, 251, 205, 128, 86 and 55, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [48, 52, 99, 256, 346, 392, 395, 361, 339, 366, 404, 405]}

{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 15.8\u00b0C, and visibility reaches 9.9 miles. \n - Region information: including residential areas, transportation areas and educational areas within a range of 5 km.\n - Current Time: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 414, 416, 383, 430, 408, 368, 287, 246, 221, 160, 125 and 97, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [86, 100, 162, 360, 481, 465, 427, 397, 326, 389, 382, 396]}

{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 15.8\u00b0C, and visibility reaches 9.9 miles. \n - Region information: including residential areas, transportation areas and educational areas within a range of 5 km.\n - Current Time: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 468, 487, 494, 498, 451, 369, 278, 251, 200, 133, 81 and 54, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [44, 48, 92, 262, 426, 494, 470, 418, 399, 405, 419, 433]}

{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 15.8\u00b0C, and visibility reaches 9.9 miles. \n - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km.\n - Current Time: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 540, 563, 550, 552, 540, 522, 364, 274, 198, 158, 101 and 75, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [75, 104, 204, 432, 573, 539, 479, 489, 506, 518, 527, 523]}

{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 15.1\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: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 291, 328, 345, 349, 296, 220, 167, 151, 119, 80, 50 and 32, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [27, 30, 59, 174, 284, 337, 308, 264, 250, 252, 259, 264]}

{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: Sunny. Temperature is 16.3\u00b0C, and visibility reaches 5.7 miles. \n - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km.\n - Current Time: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 512, 540, 526, 488, 429, 330, 283, 269, 207, 140, 100 and 78, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [82, 132, 333, 432, 442, 474, 442, 448, 444, 451, 468, 488]}

{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: Sunny. Temperature is 16.3\u00b0C, and visibility reaches 5.7 miles. \n - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.\n - Current Time: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 489, 476, 470, 462, 377, 288, 240, 204, 152, 102, 69 and 63, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [74, 175, 434, 505, 542, 521, 468, 428, 414, 433, 438, 446]}

{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: Sunny. Temperature is 16.3\u00b0C, and visibility reaches 5.7 miles. \n - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.\n - Current Time: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 488, 494, 501, 498, 400, 281, 246, 215, 159, 112, 75 and 65, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [74, 122, 260, 357, 489, 496, 438, 413, 400, 416, 427, 464]}

{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: Sunny. Temperature is 16.3\u00b0C, and visibility reaches 5.7 miles. \n - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.\n - Current Time: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 551, 588, 590, 578, 513, 418, 365, 323, 250, 180, 118 and 86, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [75, 88, 176, 362, 462, 522, 508, 473, 466, 478, 490, 510]}

{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: Sunny. Temperature is 14.0\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: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 551, 588, 590, 578, 513, 418, 365, 323, 250, 180, 118 and 86, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [75, 88, 176, 362, 462, 522, 508, 473, 466, 478, 490, 510]}

{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: Sunny. Temperature is 14.0\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: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 551, 588, 590, 578, 513, 418, 365, 323, 250, 180, 118 and 86, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [75, 88, 176, 362, 462, 522, 508, 473, 466, 478, 490, 510]}

{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: Sunny. Temperature is 16.3\u00b0C, and visibility reaches 5.7 miles. \n - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.\n - Current Time: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 383, 398, 398, 397, 339, 275, 234, 205, 151, 103, 68 and 50, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [46, 61, 134, 258, 352, 392, 356, 332, 326, 334, 343, 356]}

{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: Sunny. Temperature is 16.3\u00b0C, and visibility reaches 5.7 miles. \n - Region information: including transportation areas and commercial areas within a range of 5 km.\n - Current Time: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 359, 416, 446, 447, 380, 311, 270, 238, 183, 130, 90 and 68, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [63, 62, 84, 140, 186, 212, 210, 221, 235, 282, 299, 326]}

{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: Sunny. Temperature is 14.0\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: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 266, 258, 247, 249, 244, 243, 221, 193, 151, 107, 77 and 53, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [48, 46, 65, 116, 178, 205, 207, 221, 226, 246, 250, 256]}

{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: Sunny. Temperature is 15.6\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: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 298, 330, 366, 393, 350, 282, 223, 181, 126, 74, 46 and 34, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [30, 39, 74, 134, 188, 242, 260, 220, 232, 264, 267, 283]}

{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: Sunny. Temperature is 14.0\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: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 420, 398, 395, 386, 372, 303, 251, 216, 158, 106, 68 and 53, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [57, 106, 220, 248, 318, 398, 399, 368, 363, 376, 390, 391]}

{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: Sunny. Temperature is 16.3\u00b0C, and visibility reaches 5.7 miles. \n - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.\n - Current Time: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 334, 364, 381, 368, 310, 217, 187, 156, 123, 90, 66 and 54, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [60, 110, 297, 314, 381, 359, 344, 352, 322, 310, 310, 323]}

{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: Sunny. Temperature is 16.3\u00b0C, and visibility reaches 5.7 miles. \n - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.\n - Current Time: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 330, 349, 355, 364, 331, 232, 178, 138, 117, 74, 52 and 35, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [35, 44, 82, 186, 319, 374, 339, 319, 298, 317, 317, 326]}

{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: Sunny. Temperature is 16.3\u00b0C, and visibility reaches 5.7 miles. \n - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.\n - Current Time: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 527, 606, 608, 589, 545, 386, 336, 274, 212, 154, 105 and 77, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [74, 76, 111, 197, 306, 400, 404, 402, 366, 412, 434, 467]}

{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: Sunny. Temperature is 16.3\u00b0C, and visibility reaches 5.7 miles. \n - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km.\n - Current Time: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 413, 441, 443, 433, 385, 314, 274, 242, 188, 135, 89 and 65, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [56, 66, 132, 272, 347, 391, 381, 355, 350, 358, 368, 382]}

{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: Sunny. Temperature is 16.3\u00b0C, and visibility reaches 5.7 miles. \n - Region information: including transportation areas, educational areas and commercial areas within a range of 5 km.\n - Current Time: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 276, 294, 295, 289, 256, 209, 183, 162, 125, 90, 59 and 43, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [38, 44, 88, 181, 231, 261, 254, 237, 233, 239, 245, 255]}

{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 15.6\u00b0C, and visibility reaches 9.9 miles. \n - Region information: including transportation areas within a range of 5 km.\n - Current Time: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 78, 81, 78, 71, 70, 52, 49, 40, 37, 32, 28 and 22, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [27, 27, 34, 35, 35, 44, 52, 62, 56, 59, 62, 70]}

{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: Rain. Temperature is 16.8\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas within a range of 5 km.\n - Current Time: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 127, 156, 157, 146, 123, 81, 67, 53, 54, 44, 34 and 33, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [33, 42, 53, 70, 76, 93, 97, 87, 92, 96, 96, 104]}

{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 15.6\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: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 165, 176, 178, 175, 118, 98, 80, 70, 55, 42, 30 and 21, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [27, 42, 62, 108, 138, 159, 140, 145, 135, 146, 152, 167]}

{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 16.1\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: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 336, 364, 384, 404, 277, 218, 178, 147, 100, 66, 46 and 43, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [65, 118, 184, 308, 403, 363, 308, 322, 292, 311, 322, 343]}

{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 16.6\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: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 258, 300, 307, 312, 257, 232, 223, 194, 126, 86, 71 and 62, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [57, 58, 72, 122, 166, 201, 210, 235, 252, 257, 262, 267]}

{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 16.6\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: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 138, 162, 180, 136, 106, 88, 76, 58, 60, 34, 26 and 23, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [19, 17, 21, 38, 104, 214, 181, 156, 144, 150, 153, 157]}

{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 16.6\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: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 421, 419, 391, 446, 409, 350, 284, 229, 150, 101, 61 and 37, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [23, 18, 28, 62, 173, 335, 336, 322, 332, 354, 406, 472]}

{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 16.6\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: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 227, 246, 220, 218, 172, 119, 88, 35, 178, 109, 19 and 16, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [12, 16, 32, 65, 101, 312, 271, 247, 218, 215, 206, 222]}

{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 16.6\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: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 605, 595, 616, 593, 454, 332, 243, 206, 132, 86, 55 and 35, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [37, 64, 184, 507, 776, 706, 688, 564, 445, 450, 484, 506]}

{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 16.6\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: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 770, 789, 764, 782, 742, 723, 499, 370, 238, 161, 89 and 72, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [69, 74, 92, 201, 391, 680, 686, 684, 638, 650, 682, 713]}

{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 16.6\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: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 356, 366, 368, 370, 345, 332, 218, 119, 76, 52, 31 and 18, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [12, 14, 26, 64, 136, 168, 167, 168, 182, 205, 228, 260]}

{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 16.6\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: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 474, 530, 614, 530, 416, 298, 198, 171, 114, 71, 47 and 36, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [45, 94, 246, 651, 730, 663, 600, 552, 464, 434, 428, 439]}

{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 16.6\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: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 680, 700, 650, 744, 726, 795, 636, 444, 302, 197, 119 and 72, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [58, 38, 48, 116, 276, 404, 392, 402, 423, 492, 553, 642]}

{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 16.6\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: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 554, 277, 251, 226, 250, 407, 378, 286, 208, 130, 92 and 57, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [48, 36, 51, 134, 301, 416, 430, 400, 422, 480, 513, 569]}

{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: Sunny. Temperature is 15.6\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: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 708, 717, 736, 670, 654, 572, 515, 484, 366, 223, 130 and 88, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [84, 139, 382, 708, 677, 579, 562, 677, 692, 676, 684, 696]}

{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 15.8\u00b0C, and visibility reaches 9.9 miles. \n - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.\n - Current Time: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 536, 562, 603, 574, 504, 444, 410, 357, 289, 213, 148 and 114, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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, 143, 249, 497, 507, 436, 495, 572, 534, 517, 498, 524]}

{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: Sunny. Temperature is 15.6\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: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 367, 294, 319, 296, 297, 413, 430, 400, 314, 164, 84 and 51, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [43, 36, 82, 220, 427, 566, 549, 525, 512, 524, 490, 410]}

{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: Sunny. Temperature is 15.6\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: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 645, 648, 644, 620, 606, 543, 467, 472, 301, 182, 107 and 70, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [67, 96, 294, 591, 640, 636, 631, 607, 586, 598, 628, 626]}

{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: Sunny. Temperature is 15.6\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: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 74, 94, 98, 104, 77, 42, 26, 20, 13, 6, 7 and 4, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [4, 10, 14, 36, 93, 283, 285, 169, 118, 106, 103, 92]}

{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: Sunny. Temperature is 15.6\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including educational areas within a range of 5 km.\n - Current Time: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 184, 267, 386, 487, 334, 123, 80, 58, 33, 16, 7 and 5, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [4, 2, 6, 20, 56, 139, 176, 143, 112, 118, 135, 146]}

{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: Sunny. Temperature is 15.6\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: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 148, 214, 291, 328, 244, 96, 72, 50, 30, 15, 8 and 5, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [3, 2, 6, 22, 62, 130, 158, 132, 99, 105, 114, 124]}

{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: Sunny. Temperature is 15.6\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: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 492, 502, 468, 425, 428, 325, 247, 231, 174, 124, 82 and 70, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [80, 184, 513, 564, 488, 510, 498, 516, 519, 496, 479, 491]}

{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: Sunny. Temperature is 15.6\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: 1 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 618, 658, 685, 637, 617, 510, 399, 362, 286, 184, 109 and 68, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 2 AM to 1 PM). 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: [49, 50, 98, 273, 523, 600, 594, 540, 520, 538, 559, 582]}