Datasets:

TheFuriousGunner

/

CATraffic_Finetuning_eval

like 0

{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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 349, 338, 288, 194, 157, 122, 105, 70, 40, 29, 25 and 23, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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, 74, 192, 373, 383, 261, 214, 237, 252, 261, 287, 313]}

{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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 536, 517, 412, 284, 226, 225, 138, 97, 56, 33, 22 and 27, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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, 183, 404, 528, 520, 388, 348, 349, 381, 379, 421, 534]}

{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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 342, 336, 274, 187, 142, 108, 69, 45, 25, 15, 10 and 13, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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: [18, 50, 126, 200, 212, 175, 172, 198, 216, 217, 251, 324]}

{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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 403, 395, 283, 184, 148, 144, 89, 55, 34, 27, 38 and 75, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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: [147, 246, 332, 357, 335, 288, 267, 275, 284, 301, 306, 333]}

{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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 160, 170, 119, 66, 56, 40, 30, 20, 14, 12, 12 and 20, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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: [40, 103, 222, 272, 234, 181, 154, 146, 145, 138, 146, 162]}

{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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 618, 519, 533, 446, 313, 254, 178, 128, 88, 52, 45 and 45, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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: [83, 199, 372, 507, 505, 550, 624, 610, 596, 606, 539, 623]}

{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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 404, 294, 350, 284, 237, 236, 162, 108, 62, 75, 37 and 29, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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: [42, 120, 244, 331, 303, 288, 302, 316, 339, 365, 414, 434]}

{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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 342, 404, 406, 290, 225, 200, 142, 107, 73, 62, 51 and 59, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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: [101, 238, 468, 506, 466, 428, 410, 400, 406, 391, 399, 398]}

{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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 289, 235, 206, 136, 104, 82, 53, 37, 24, 14, 13 and 23, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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: [50, 123, 239, 294, 286, 266, 261, 262, 264, 253, 279, 284]}

{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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 412, 364, 338, 257, 208, 178, 139, 95, 65, 44, 39 and 59, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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: [107, 267, 466, 539, 498, 430, 432, 365, 387, 457, 419, 413]}

{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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 130, 135, 124, 38, 22, 22, 16, 11, 8, 4, 4 and 4, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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, 89, 121, 118, 130, 127, 125, 134, 124, 132, 114]}

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

Some important information is listed as follows:\n - Location: District 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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 123, 130, 97, 50, 37, 30, 24, 14, 10, 8, 12 and 22, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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: [52, 109, 213, 220, 184, 128, 102, 97, 99, 103, 106, 117]}

{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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 411, 459, 354, 264, 214, 198, 134, 2, 0, 2, 1 and 35, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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: [277, 458, 298, 361, 348, 414, 454, 406, 380, 417, 426, 404]}

{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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 278, 288, 292, 244, 180, 157, 116, 69, 42, 30, 26 and 26, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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, 63, 134, 242, 268, 234, 226, 247, 276, 294, 312, 306]}

{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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 212, 205, 160, 120, 92, 77, 60, 40, 26, 19, 28 and 55, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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: [153, 293, 344, 308, 276, 255, 214, 185, 182, 184, 196, 203]}

{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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 618, 581, 534, 543, 456, 424, 270, 176, 92, 55, 46 and 84, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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: [179, 436, 607, 647, 553, 564, 505, 525, 485, 524, 525, 598]}

{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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 404, 408, 419, 350, 287, 255, 209, 143, 108, 83, 72 and 70, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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, 110, 175, 289, 321, 305, 290, 292, 316, 352, 413, 431]}

{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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 194, 189, 194, 178, 145, 126, 108, 67, 41, 23, 17 and 17, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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, 66, 117, 177, 190, 182, 179, 179, 190, 199, 221, 212]}

{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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 284, 315, 270, 197, 160, 141, 118, 85, 72, 69, 65 and 78, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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: [126, 227, 351, 424, 415, 354, 337, 305, 268, 257, 276, 284]}

{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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 608, 607, 554, 463, 348, 300, 202, 132, 96, 53, 32 and 30, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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, 116, 301, 514, 518, 444, 366, 348, 358, 418, 530, 579]}

{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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 370, 383, 390, 325, 240, 209, 154, 92, 56, 38, 34 and 35, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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, 84, 178, 322, 356, 311, 300, 329, 368, 392, 416, 408]}

{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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 242, 250, 274, 226, 199, 146, 114, 86, 69, 61, 56 and 69, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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: [109, 171, 250, 276, 296, 252, 204, 199, 206, 228, 242, 251]}

{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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 682, 684, 772, 724, 575, 511, 379, 331, 255, 200, 190 and 152, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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: [167, 263, 426, 663, 699, 643, 626, 645, 720, 800, 794, 732]}

{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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 396, 400, 392, 317, 220, 186, 125, 88, 52, 38, 29 and 32, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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: [73, 191, 292, 379, 445, 470, 376, 353, 307, 317, 384, 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 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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 441, 456, 400, 284, 226, 192, 124, 85, 66, 49, 49 and 60, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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: [100, 238, 431, 518, 477, 421, 380, 374, 390, 429, 430, 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 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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 618, 611, 519, 493, 449, 396, 272, 174, 109, 84, 72 and 92, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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, 328, 422, 482, 474, 456, 410, 461, 482, 597, 674, 621]}

{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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 442, 444, 404, 331, 242, 207, 157, 96, 61, 43, 45 and 64, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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: [192, 401, 456, 400, 390, 416, 449, 424, 330, 436, 454, 452]}

{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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 238, 253, 194, 144, 110, 84, 41, 27, 16, 8, 10 and 18, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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: [55, 157, 244, 250, 257, 236, 224, 214, 212, 218, 220, 234]}

{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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 224, 217, 144, 120, 111, 100, 69, 46, 41, 28, 29 and 44, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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, 182, 239, 216, 172, 144, 148, 165, 187, 208, 199, 234]}

{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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 398, 386, 333, 263, 203, 191, 152, 120, 101, 92, 90 and 94, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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: [128, 289, 494, 424, 393, 446, 354, 325, 322, 327, 341, 394]}

{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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 242, 230, 186, 136, 86, 82, 58, 27, 17, 10, 8 and 14, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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, 162, 395, 405, 350, 357, 242, 183, 175, 171, 208, 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 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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 582, 578, 532, 449, 344, 320, 257, 172, 104, 72, 60 and 65, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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, 382, 573, 592, 562, 535, 520, 526, 530, 546, 573, 574]}

{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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 494, 498, 451, 369, 278, 252, 199, 133, 81, 54, 44 and 48, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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: [92, 262, 426, 494, 470, 418, 398, 405, 419, 433, 467, 486]}

{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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 496, 496, 426, 322, 320, 315, 282, 250, 234, 223, 225 and 259, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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: [382, 454, 470, 481, 469, 446, 422, 422, 421, 427, 439, 459]}

{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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 148, 122, 91, 64, 52, 69, 46, 24, 30, 26, 56 and 24, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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, 28, 64, 102, 94, 79, 62, 94, 72, 68, 106, 116]}

{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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 450, 358, 388, 469, 450, 348, 294, 181, 116, 82, 78 and 95, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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: [153, 272, 370, 502, 439, 382, 403, 438, 491, 510, 446, 398]}

{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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 628, 607, 594, 585, 498, 425, 378, 270, 209, 175, 172 and 195, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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: [257, 349, 436, 542, 562, 482, 478, 506, 550, 562, 600, 620]}

{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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 417, 415, 424, 470, 447, 376, 327, 233, 176, 142, 134 and 150, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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: [226, 357, 452, 536, 538, 481, 469, 488, 499, 499, 478, 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 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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 476, 503, 490, 506, 443, 328, 263, 168, 114, 82, 75 and 82, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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: [144, 280, 438, 386, 420, 382, 365, 346, 424, 440, 496, 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 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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 494, 498, 451, 369, 278, 252, 199, 133, 81, 54, 44 and 48, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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: [92, 262, 426, 494, 470, 418, 398, 405, 419, 433, 467, 486]}

{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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 401, 462, 369, 323, 264, 228, 171, 139, 98, 84, 79 and 84, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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, 182, 304, 401, 418, 361, 336, 346, 342, 362, 391, 425]}

{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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 368, 362, 354, 328, 304, 307, 321, 228, 152, 103, 103 and 91, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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: [165, 393, 488, 389, 356, 453, 416, 340, 388, 321, 306, 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 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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 508, 498, 475, 461, 421, 405, 350, 239, 187, 132, 112 and 143, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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: [264, 480, 380, 259, 244, 302, 435, 470, 476, 460, 473, 501]}

{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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 426, 408, 373, 419, 416, 313, 262, 157, 101, 59, 46 and 41, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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, 189, 394, 554, 546, 494, 487, 519, 482, 510, 470, 400]}

{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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 434, 425, 439, 379, 309, 264, 224, 148, 124, 91, 86 and 90, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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: [100, 156, 269, 404, 430, 376, 358, 360, 376, 387, 415, 416]}

{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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 667, 657, 635, 581, 445, 372, 272, 185, 106, 69, 62 and 66, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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: [97, 196, 413, 607, 627, 552, 554, 579, 605, 659, 627, 610]}

{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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 719, 696, 672, 577, 488, 426, 315, 190, 133, 86, 80 and 115, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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: [318, 653, 755, 714, 639, 679, 689, 680, 697, 677, 708, 723]}

{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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 674, 658, 624, 528, 474, 420, 342, 243, 190, 152, 148 and 176, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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: [322, 599, 708, 666, 605, 632, 634, 622, 636, 628, 631, 658]}

{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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 257, 220, 224, 264, 246, 244, 211, 134, 96, 54, 38 and 46, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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: [102, 240, 354, 386, 406, 372, 357, 313, 340, 348, 357, 289]}

{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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 550, 516, 523, 594, 608, 564, 498, 354, 257, 178, 140 and 131, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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, 272, 420, 552, 589, 560, 560, 576, 596, 641, 650, 584]}

{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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 617, 597, 635, 770, 760, 657, 563, 360, 236, 146, 108 and 102, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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: [145, 274, 434, 624, 676, 640, 636, 668, 691, 750, 781, 671]}

{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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 457, 457, 449, 479, 464, 380, 320, 214, 129, 78, 64 and 48, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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, 180, 298, 444, 386, 360, 406, 423, 445, 495, 446, 441]}

{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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 320, 290, 306, 366, 388, 331, 237, 161, 125, 96, 76 and 74, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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: [113, 254, 348, 442, 450, 375, 366, 414, 438, 418, 342, 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 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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 403, 344, 349, 506, 552, 585, 513, 309, 202, 118, 78 and 84, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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: [184, 443, 708, 783, 744, 739, 703, 666, 707, 721, 618, 462]}

{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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 598, 596, 514, 412, 302, 251, 169, 98, 57, 39, 34 and 37, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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: [95, 261, 420, 532, 484, 462, 394, 424, 433, 427, 532, 575]}

{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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 548, 564, 545, 506, 444, 393, 318, 197, 142, 98, 85 and 85, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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: [155, 334, 490, 542, 524, 515, 494, 504, 526, 526, 526, 544]}

{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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 342, 357, 362, 362, 286, 251, 205, 128, 86, 55, 48 and 52, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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: [99, 256, 346, 392, 395, 361, 339, 366, 404, 405, 366, 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 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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 383, 430, 408, 368, 287, 246, 221, 160, 125, 97, 86 and 100, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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: [162, 360, 481, 465, 427, 397, 326, 389, 382, 396, 388, 403]}

{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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 494, 498, 451, 369, 278, 251, 200, 133, 81, 54, 44 and 48, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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: [92, 262, 426, 494, 470, 418, 399, 405, 419, 433, 468, 486]}

{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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 550, 552, 540, 522, 364, 274, 198, 158, 101, 75, 75 and 104, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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: [204, 432, 573, 539, 479, 489, 506, 518, 527, 523, 554, 571]}

{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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 345, 349, 296, 220, 167, 151, 119, 80, 50, 32, 27 and 30, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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: [59, 174, 284, 337, 308, 264, 250, 252, 259, 264, 293, 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 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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 526, 488, 429, 330, 283, 269, 207, 140, 100, 78, 82 and 132, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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: [333, 432, 442, 474, 442, 448, 444, 451, 468, 488, 518, 530]}

{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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 470, 462, 377, 288, 240, 204, 152, 102, 69, 63, 74 and 175, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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: [434, 505, 542, 521, 468, 428, 414, 433, 438, 446, 482, 474]}

{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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 501, 498, 400, 281, 246, 215, 159, 112, 75, 65, 74 and 122, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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: [260, 357, 489, 496, 438, 413, 400, 416, 427, 464, 492, 484]}

{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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 590, 578, 513, 418, 365, 323, 250, 180, 118, 86, 75 and 88, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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: [176, 362, 462, 522, 508, 473, 466, 478, 490, 510, 559, 592]}

{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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 590, 578, 513, 418, 365, 323, 250, 180, 118, 86, 75 and 88, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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: [176, 362, 462, 522, 508, 473, 466, 478, 490, 510, 559, 592]}

{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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 590, 578, 513, 418, 365, 323, 250, 180, 118, 86, 75 and 88, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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: [176, 362, 462, 522, 508, 473, 466, 478, 490, 510, 559, 592]}

{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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 398, 397, 339, 275, 234, 205, 151, 103, 68, 50, 46 and 61, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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, 258, 352, 392, 356, 332, 326, 334, 343, 356, 385, 397]}

{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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 446, 447, 380, 311, 270, 238, 183, 130, 90, 68, 63 and 62, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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, 140, 186, 212, 210, 221, 235, 282, 299, 326, 377, 430]}

{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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 247, 249, 244, 243, 221, 193, 151, 107, 77, 53, 48 and 46, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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, 116, 178, 205, 207, 221, 226, 246, 250, 256, 258, 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 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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 366, 393, 350, 282, 223, 181, 126, 74, 46, 34, 30 and 39, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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, 134, 188, 242, 260, 220, 232, 264, 267, 283, 294, 325]}

{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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 395, 386, 372, 303, 251, 216, 158, 106, 68, 53, 57 and 106, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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: [220, 248, 318, 398, 399, 368, 363, 376, 390, 391, 418, 406]}

{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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 381, 368, 310, 217, 187, 156, 123, 90, 66, 54, 60 and 110, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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: [297, 314, 381, 359, 344, 352, 322, 310, 310, 323, 348, 366]}

{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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 355, 364, 331, 232, 178, 138, 117, 74, 52, 35, 35 and 44, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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, 186, 319, 374, 339, 319, 298, 317, 317, 326, 361, 358]}

{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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 608, 589, 545, 386, 336, 274, 212, 154, 105, 77, 74 and 76, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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: [111, 197, 306, 400, 404, 402, 366, 412, 434, 467, 528, 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 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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 443, 433, 385, 314, 274, 242, 188, 135, 89, 65, 56 and 66, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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: [132, 272, 347, 391, 381, 355, 350, 358, 368, 382, 419, 444]}

{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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 295, 289, 256, 209, 183, 162, 125, 90, 59, 43, 38 and 44, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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: [88, 181, 231, 261, 254, 237, 233, 239, 245, 255, 279, 296]}

{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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 78, 71, 70, 52, 49, 40, 37, 32, 28, 22, 27 and 27, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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, 35, 44, 52, 62, 56, 59, 62, 70, 83, 81]}

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

Some important information is listed as follows:\n - Location: District 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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 157, 146, 123, 81, 67, 53, 54, 44, 34, 33, 33 and 42, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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: [53, 70, 76, 93, 97, 87, 92, 96, 96, 104, 124, 164]}

{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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 178, 175, 118, 98, 80, 70, 55, 42, 30, 21, 27 and 42, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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, 108, 138, 159, 140, 145, 135, 146, 152, 167, 169, 182]}

{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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 384, 404, 277, 218, 178, 147, 100, 66, 46, 43, 65 and 118, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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: [184, 308, 403, 363, 308, 322, 292, 311, 322, 343, 376, 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 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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 307, 312, 257, 232, 223, 194, 126, 86, 71, 62, 57 and 58, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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, 122, 166, 201, 210, 235, 252, 257, 262, 267, 278, 275]}

{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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 180, 136, 106, 88, 76, 58, 60, 34, 26, 23, 19 and 17, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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: [21, 38, 104, 214, 181, 156, 144, 150, 153, 157, 154, 169]}

{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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 391, 446, 409, 350, 284, 229, 150, 101, 61, 37, 23 and 18, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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, 62, 173, 335, 336, 322, 332, 354, 406, 472, 580, 533]}

{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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 220, 218, 172, 119, 88, 35, 178, 109, 19, 16, 12 and 16, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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, 65, 101, 312, 271, 247, 218, 215, 206, 222, 202, 198]}

{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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 616, 593, 454, 332, 243, 206, 132, 86, 55, 35, 37 and 64, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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: [184, 507, 776, 706, 688, 564, 445, 450, 484, 506, 612, 573]}

{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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 764, 782, 742, 723, 499, 370, 238, 161, 89, 72, 69 and 74, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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: [92, 201, 391, 680, 686, 684, 638, 650, 682, 713, 776, 765]}

{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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 368, 370, 345, 332, 218, 119, 76, 52, 31, 18, 12 and 14, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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, 64, 136, 168, 167, 168, 182, 205, 228, 260, 356, 385]}

{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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 614, 530, 416, 298, 198, 171, 114, 71, 47, 36, 45 and 94, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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: [246, 651, 730, 663, 600, 552, 464, 434, 428, 439, 473, 414]}

{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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 650, 744, 726, 795, 636, 444, 302, 197, 119, 72, 58 and 38, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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, 116, 276, 404, 392, 402, 423, 492, 553, 642, 854, 711]}

{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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 251, 226, 250, 407, 378, 286, 208, 130, 92, 57, 48 and 36, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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, 134, 301, 416, 430, 400, 422, 480, 513, 569, 572, 578]}

{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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 736, 670, 654, 572, 515, 484, 366, 223, 130, 88, 84 and 139, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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: [382, 708, 677, 579, 562, 677, 692, 676, 684, 696, 722, 736]}

{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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 603, 574, 504, 444, 410, 357, 289, 213, 148, 114, 116 and 143, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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: [249, 497, 507, 436, 495, 572, 534, 517, 498, 524, 559, 594]}

{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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 319, 296, 297, 413, 430, 400, 314, 164, 84, 51, 43 and 36, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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, 220, 427, 566, 549, 525, 512, 524, 490, 410, 334, 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 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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 644, 620, 606, 543, 467, 472, 301, 182, 107, 70, 67 and 96, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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: [294, 591, 640, 636, 631, 607, 586, 598, 628, 626, 646, 650]}

{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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 98, 104, 77, 42, 26, 20, 13, 6, 7, 4, 4 and 10, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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: [14, 36, 93, 283, 285, 169, 118, 106, 103, 92, 110, 131]}

{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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 386, 487, 334, 123, 80, 58, 33, 16, 7, 5, 4 and 2, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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: [6, 20, 56, 139, 176, 143, 112, 118, 135, 146, 203, 292]}

{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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 291, 328, 244, 96, 72, 50, 30, 15, 8, 5, 3 and 2, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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: [6, 22, 62, 130, 158, 132, 99, 105, 114, 124, 175, 242]}

{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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 468, 425, 428, 325, 247, 231, 174, 124, 82, 70, 80 and 184, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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: [513, 564, 488, 510, 498, 516, 519, 496, 479, 491, 479, 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 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: 3 AM, 2018-3-8, Thursday, not a holiday.\n - Traffic volume data in the past 12 hours were 685, 637, 617, 510, 399, 362, 286, 184, 109, 68, 49 and 50, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 4 AM to 3 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: [98, 273, 523, 600, 594, 540, 520, 538, 559, 582, 633, 660]}