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{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 3 in Yolo, California, USA, along the US50-E freeway, lane 4, direction of eastbound. \n - Today's weather: Sunny. Temperature is 6.0\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, commercial areas and educational areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 98, 150, 156, 178, 208, 246, 248, 257, 263, 269, 262 and 229, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [221, 214, 152, 127, 100, 58, 38, 25, 22, 18, 27, 75]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 3 in Sacramento, California, USA, along the US50-E freeway, lane 4, direction of eastbound. \n - Today's weather: Sunny. Temperature is 5.9\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 250, 332, 296, 287, 307, 334, 359, 376, 381, 384, 366 and 317, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [282, 242, 194, 164, 111, 76, 46, 30, 18, 24, 64, 188]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 3 in Sacramento, California, USA, along the US50-E freeway, lane 3, direction of eastbound. \n - Today's weather: Sunny. Temperature is 5.9\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, commercial areas and educational areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 91, 134, 138, 142, 166, 198, 212, 226, 242, 260, 264 and 232, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [191, 141, 112, 80, 58, 35, 21, 17, 11, 13, 22, 52]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 3 in Yolo, California, USA, along the US50-W freeway, lane 3, direction of westbound. \n - Today's weather: Sunny. Temperature is 6.0\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, commercial areas and educational areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 195, 220, 222, 262, 304, 304, 326, 315, 326, 286, 284 and 248, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [228, 253, 231, 161, 116, 57, 35, 30, 36, 90, 170, 265]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 3 in El Dorado, California, USA, along the US50-W freeway, lane 3, direction of westbound. \n - Today's weather: Sunny. Temperature is 5.9\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas and commercial areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 105, 128, 138, 156, 158, 167, 167, 165, 162, 177, 163 and 158, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [126, 103, 85, 62, 50, 27, 16, 12, 14, 23, 45, 110]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 3 in Sacramento, California, USA, along the SR51-N freeway, lane 3, direction of northbound. \n - Today's weather: Sunny. Temperature is 6.0\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, commercial areas and educational areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 254, 314, 330, 387, 577, 581, 581, 616, 646, 576, 488 and 420, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [385, 332, 256, 220, 155, 107, 69, 48, 40, 45, 72, 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 3 in Sacramento, California, USA, along the SR51-N freeway, lane 3, direction of northbound. \n - Today's weather: Sunny. Temperature is 5.9\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 164, 207, 205, 231, 279, 300, 320, 332, 342, 328, 314 and 307, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [269, 241, 188, 169, 112, 74, 47, 69, 31, 27, 41, 124]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 3 in Sacramento, California, USA, along the SR51-S freeway, lane 3, direction of southbound. \n - Today's weather: Sunny. Temperature is 5.9\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 250, 256, 267, 320, 350, 378, 404, 371, 378, 374, 359 and 365, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [344, 314, 246, 198, 136, 95, 68, 54, 50, 62, 94, 231]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 3 in Placer, California, USA, along the I80-W freeway, lane 3, direction of westbound. \n - Today's weather: Sunny. Temperature is 5.9\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 125, 157, 192, 238, 288, 356, 343, 344, 352, 340, 292 and 291, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [282, 271, 237, 202, 67, 35, 26, 19, 17, 34, 61, 134]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 3 in Sacramento, California, USA, along the SR99-N freeway, lane 4, direction of northbound. \n - Today's weather: Sunny. Temperature is 6.0\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, commercial areas and educational areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 272, 270, 278, 318, 370, 384, 384, 400, 408, 371, 335 and 325, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [296, 238, 190, 164, 120, 82, 54, 42, 40, 59, 106, 261]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 3 in Sacramento, California, USA, along the SR99-S freeway, lane 2, direction of southbound. \n - Today's weather: Sunny. Temperature is 6.0\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, residential areas and educational areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 90, 111, 122, 137, 141, 156, 164, 163, 160, 160, 156 and 156, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [151, 137, 116, 99, 78, 66, 52, 47, 46, 49, 56, 74]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 3 in Sacramento, California, USA, along the SR99-S freeway, lane 2, direction of southbound. \n - Today's weather: Sunny. Temperature is 5.6\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 103, 107, 94, 106, 112, 114, 119, 117, 112, 112, 116 and 106, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [95, 65, 49, 38, 22, 14, 11, 9, 11, 28, 64, 115]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 4 in Contra Costa, California, USA, along the SR24-W freeway, lane 4, direction of westbound. \n - Today's weather: Sunny. Temperature is 7.9\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas and commercial areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 273, 228, 293, 337, 391, 361, 400, 425, 416, 378, 394 and 377, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [334, 264, 221, 193, 133, 17, 2, 1, 1, 20, 257, 500]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 4 in Solano, California, USA, along the I80-W freeway, lane 3, direction of westbound. \n - Today's weather: Sunny. Temperature is 6.0\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, educational areas and residential areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 156, 244, 256, 228, 229, 250, 276, 293, 318, 294, 274 and 288, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [281, 229, 169, 144, 106, 64, 37, 23, 20, 20, 30, 87]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 4 in Santa Clara, California, USA, along the US101-N freeway, lane 4, direction of northbound. \n - Today's weather: Rain. Temperature is 8.4\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, residential areas and educational areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 171, 166, 169, 202, 235, 259, 268, 258, 274, 272, 283 and 293, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [284, 288, 200, 173, 120, 76, 52, 36, 34, 56, 167, 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 4 in San Mateo, California, USA, along the US101-N freeway, lane 5, direction of northbound. \n - Today's weather: Sunny. Temperature is 8.3\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 322, 384, 410, 456, 495, 496, 539, 532, 530, 518, 518 and 491, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [448, 406, 402, 371, 252, 148, 99, 58, 47, 79, 184, 422]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 4 in Marin, California, USA, along the US101-N freeway, lane 4, direction of northbound. \n - Today's weather: Sunny. Temperature is 7.9\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 120, 187, 245, 278, 319, 349, 349, 350, 355, 364, 352 and 330, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [285, 244, 216, 190, 164, 120, 98, 81, 68, 66, 72, 107]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 4 in Santa Clara, California, USA, along the US101-S freeway, lane 4, direction of southbound. \n - Today's weather: Rain. Temperature is 8.4\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 118, 176, 180, 176, 175, 185, 196, 197, 218, 212, 209 and 207, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [204, 172, 137, 115, 86, 60, 35, 19, 16, 16, 26, 69]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 4 in Marin, California, USA, along the US101-S freeway, lane 4, direction of southbound. \n - Today's weather: Sunny. Temperature is 7.9\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 211, 239, 250, 288, 307, 325, 311, 324, 316, 331, 356 and 328, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [279, 216, 177, 158, 120, 91, 75, 68, 66, 78, 125, 225]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 4 in San Mateo, California, USA, along the I280-N freeway, lane 4, direction of northbound. \n - Today's weather: Sunny. Temperature is 8.3\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 158, 228, 270, 305, 377, 402, 395, 413, 434, 470, 520 and 452, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [375, 318, 285, 255, 194, 130, 86, 53, 31, 24, 48, 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 4 in Alameda, California, USA, along the I580-W freeway, lane 4, direction of westbound. \n - Today's weather: Sunny. Temperature is 7.9\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas and commercial areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 208, 324, 342, 303, 306, 333, 367, 390, 424, 393, 365 and 384, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [375, 306, 226, 191, 142, 85, 49, 31, 27, 27, 40, 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 Alameda, California, USA, along the I680-N freeway, lane 3, direction of northbound. \n - Today's weather: Rain. Temperature is 8.4\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 157, 182, 195, 213, 229, 237, 240, 244, 246, 247, 234 and 228, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [208, 190, 165, 134, 105, 81, 66, 57, 55, 66, 106, 160]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 4 in Contra Costa, California, USA, along the I680-N freeway, lane 5, direction of northbound. \n - Today's weather: Sunny. Temperature is 7.9\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, commercial areas and educational areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 278, 382, 449, 498, 622, 682, 732, 750, 810, 803, 809 and 699, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [607, 532, 478, 420, 341, 304, 229, 219, 231, 213, 192, 214]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 4 in Santa Clara, California, USA, along the I680-S freeway, lane 4, direction of southbound. \n - Today's weather: Rain. Temperature is 8.4\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 160, 185, 226, 273, 335, 352, 368, 373, 414, 399, 403 and 383, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [370, 292, 247, 205, 161, 96, 48, 29, 24, 33, 83, 196]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 4 in Contra Costa, California, USA, along the I680-S freeway, lane 6, direction of southbound. \n - Today's weather: Sunny. Temperature is 7.9\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 278, 337, 350, 404, 455, 456, 466, 470, 478, 492, 484 and 433, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [386, 325, 288, 232, 188, 128, 94, 74, 77, 98, 184, 329]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 4 in Santa Clara, California, USA, along the I880-N freeway, lane 6, direction of northbound. \n - Today's weather: Rain. Temperature is 8.4\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 246, 298, 313, 380, 448, 514, 561, 572, 601, 597, 584 and 573, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [514, 486, 394, 321, 229, 170, 104, 70, 62, 88, 176, 320]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 4 in Alameda, California, USA, along the I880-S freeway, lane 4, direction of southbound. \n - Today's weather: Sunny. Temperature is 8.1\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas and commercial areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 280, 282, 308, 384, 418, 497, 470, 475, 468, 453, 452 and 430, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [370, 310, 262, 216, 168, 112, 69, 40, 40, 61, 186, 390]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 5 in Santa Cruz, California, USA, along the SR17-N freeway, lane 3, direction of northbound. \n - Today's weather: Rain. Temperature is 8.4\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including commercial areas, transportation areas and residential areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 117, 126, 0, 102, 244, 258, 263, 276, 300, 308, 300 and 280, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [52, 0, 0, 0, 10, 26, 15, 8, 10, 15, 54, 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 6 in Kern, California, USA, along the SR99-S freeway, lane 3, direction of southbound. \n - Today's weather: Sunny. Temperature is 9.9\u00b0C, and visibility reaches 9.9 miles. \n - Region information: including transportation areas, residential areas and educational areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 210, 146, 145, 147, 190, 212, 216, 258, 260, 278, 284 and 226, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [190, 142, 114, 83, 76, 46, 49, 29, 27, 37, 55, 77]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the SR2-W freeway, lane 4, direction of westbound. \n - Today's weather: Sunny. Temperature is 11.2\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 263, 285, 306, 315, 302, 314, 332, 305, 328, 343, 364 and 339, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [284, 217, 176, 171, 139, 112, 98, 80, 88, 94, 132, 279]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the SR2-W freeway, lane 4, direction of westbound. \n - Today's weather: Sunny. Temperature is 11.2\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 173, 208, 204, 196, 182, 170, 167, 178, 185, 226, 226 and 197, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [156, 107, 75, 70, 48, 27, 16, 9, 7, 14, 42, 155]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the I5-S freeway, lane 5, direction of southbound. \n - Today's weather: Sunny. Temperature is 11.2\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 552, 572, 542, 513, 506, 508, 521, 530, 560, 575, 572 and 568, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [522, 440, 338, 306, 238, 153, 99, 74, 78, 77, 117, 308]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the I10-E freeway, lane 4, direction of eastbound. \n - Today's weather: Sunny. Temperature is 13.9\u00b0C, and visibility reaches 9.7 miles. \n - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 414, 482, 456, 399, 390, 397, 412, 423, 457, 476, 481 and 482, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [437, 358, 267, 238, 185, 117, 77, 58, 53, 56, 82, 204]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the I10-W freeway, lane 4, direction of westbound. \n - Today's weather: Rain. Temperature is 12.3\u00b0C, and visibility reaches 9.1 miles. \n - Region information: including residential areas, transportation areas and educational areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 390, 418, 414, 428, 449, 454, 404, 453, 464, 448, 465 and 458, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [436, 386, 304, 270, 228, 195, 174, 160, 162, 194, 329, 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 7 in Los Angeles, California, USA, along the SR47-S freeway, lane 2, direction of southbound. \n - Today's weather: Sunny. Temperature is 13.9\u00b0C, and visibility reaches 9.7 miles. \n - Region information: including residential areas, commercial areas and transportation areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 38, 54, 60, 56, 60, 84, 61, 69, 82, 87, 114 and 86, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [70, 52, 43, 51, 32, 23, 19, 18, 39, 17, 8, 29]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the SR60-E freeway, lane 4, direction of eastbound. \n - Today's weather: Sunny. Temperature is 13.9\u00b0C, and visibility reaches 9.7 miles. \n - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 297, 364, 346, 353, 408, 438, 492, 501, 489, 478, 478 and 510, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [446, 405, 339, 279, 214, 143, 101, 61, 57, 86, 145, 254]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the SR60-E freeway, lane 5, direction of eastbound. \n - Today's weather: Sunny. Temperature is 13.9\u00b0C, and visibility reaches 9.7 miles. \n - Region information: including residential areas, transportation areas and educational areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 374, 450, 472, 450, 487, 505, 541, 560, 573, 564, 556 and 588, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [531, 448, 416, 358, 305, 227, 187, 149, 151, 183, 254, 327]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the SR60-E freeway, lane 4, direction of eastbound. \n - Today's weather: Sunny. Temperature is 13.9\u00b0C, and visibility reaches 9.7 miles. \n - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 357, 434, 452, 453, 476, 469, 504, 537, 544, 460, 488 and 480, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [465, 424, 388, 348, 286, 198, 164, 117, 120, 140, 222, 328]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the SR60-E freeway, lane 4, direction of eastbound. \n - Today's weather: Rain. Temperature is 13.5\u00b0C, and visibility reaches 9.8 miles. \n - Region information: including residential areas, transportation areas and educational areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 318, 379, 364, 351, 366, 392, 411, 459, 480, 488, 508 and 495, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [440, 381, 350, 300, 228, 144, 93, 65, 63, 76, 140, 272]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the SR91-W freeway, lane 4, direction of westbound. \n - Today's weather: Sunny. Temperature is 13.9\u00b0C, and visibility reaches 9.7 miles. \n - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 414, 482, 456, 399, 390, 397, 412, 423, 457, 476, 481 and 482, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [437, 358, 267, 238, 185, 117, 77, 52, 53, 56, 82, 204]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the US101-N freeway, lane 4, direction of northbound. \n - Today's weather: Sunny. Temperature is 10.6\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 298, 391, 365, 385, 460, 453, 471, 462, 439, 475, 472 and 452, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [391, 327, 289, 253, 183, 110, 67, 36, 28, 40, 86, 224]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the US101-S freeway, lane 4, direction of southbound. \n - Today's weather: Sunny. Temperature is 11.2\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including residential areas, commercial areas and transportation areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 323, 295, 322, 372, 398, 400, 392, 378, 372, 371, 376 and 392, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [363, 346, 344, 342, 282, 200, 137, 89, 75, 90, 172, 396]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the I110-N freeway, lane 4, direction of northbound. \n - Today's weather: Sunny. Temperature is 12.9\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 454, 449, 466, 498, 504, 504, 462, 441, 482, 452, 487 and 493, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [476, 466, 414, 387, 295, 236, 173, 118, 104, 138, 253, 476]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the SR134-E freeway, lane 4, direction of eastbound. \n - Today's weather: Sunny. Temperature is 11.2\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 256, 324, 338, 392, 462, 469, 480, 486, 466, 453, 421 and 419, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [406, 381, 303, 261, 194, 123, 74, 38, 32, 37, 69, 180]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the SR134-E freeway, lane 4, direction of eastbound. \n - Today's weather: Sunny. Temperature is 11.2\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 200, 290, 304, 310, 324, 358, 356, 354, 358, 358, 374 and 364, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [333, 300, 248, 208, 174, 133, 112, 84, 87, 88, 100, 154]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the I210-E freeway, lane 5, direction of eastbound. \n - Today's weather: Sunny. Temperature is 11.2\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 306, 424, 440, 500, 551, 597, 610, 563, 632, 628, 602 and 590, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [526, 473, 372, 311, 225, 164, 114, 72, 72, 79, 104, 188]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the I405-N freeway, lane 5, direction of northbound. \n - Today's weather: Sunny. Temperature is 13.9\u00b0C, and visibility reaches 9.7 miles. \n - Region information: including residential areas, transportation areas and educational areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 515, 552, 551, 574, 680, 674, 688, 655, 673, 712, 649 and 673, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [636, 551, 485, 424, 311, 200, 126, 80, 72, 114, 323, 716]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the I405-N freeway, lane 5, direction of northbound. \n - Today's weather: Sunny. Temperature is 13.9\u00b0C, and visibility reaches 9.7 miles. \n - Region information: including residential areas, transportation areas and educational areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 543, 567, 557, 566, 658, 659, 684, 651, 671, 675, 662 and 680, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [628, 537, 488, 426, 330, 233, 160, 119, 118, 152, 319, 717]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the I405-N freeway, lane 4, direction of northbound. \n - Today's weather: Sunny. Temperature is 12.9\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 288, 275, 312, 333, 367, 362, 364, 374, 387, 309, 315 and 312, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [337, 322, 309, 266, 230, 161, 101, 52, 34, 44, 103, 257]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the I405-N freeway, lane 5, direction of northbound. \n - Today's weather: Sunny. Temperature is 10.6\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including residential areas, transportation areas and educational areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 363, 446, 497, 562, 604, 614, 620, 648, 637, 633, 639 and 597, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [565, 550, 506, 446, 393, 306, 232, 146, 132, 126, 168, 298]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the I405-N freeway, lane 6, direction of northbound. \n - Today's weather: Sunny. Temperature is 10.6\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 365, 476, 536, 615, 682, 685, 714, 763, 752, 770, 739 and 692, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [669, 664, 567, 509, 411, 292, 197, 93, 68, 64, 111, 269]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the I405-N freeway, lane 4, direction of northbound. \n - Today's weather: Sunny. Temperature is 10.6\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 250, 346, 362, 370, 406, 448, 468, 502, 487, 418, 448 and 456, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [427, 404, 348, 307, 244, 178, 110, 56, 44, 44, 85, 186]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the I405-S freeway, lane 4, direction of southbound. \n - Today's weather: Sunny. Temperature is 12.9\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 478, 576, 560, 516, 506, 521, 550, 561, 550, 497, 458 and 465, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [457, 542, 464, 368, 282, 185, 127, 69, 58, 51, 120, 302]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the I405-S freeway, lane 5, direction of southbound. \n - Today's weather: Sunny. Temperature is 12.9\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 575, 594, 624, 667, 693, 680, 702, 699, 672, 494, 528 and 611, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [637, 624, 574, 545, 455, 309, 198, 98, 67, 78, 185, 482]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the I605-N freeway, lane 4, direction of northbound. \n - Today's weather: Sunny. Temperature is 13.9\u00b0C, and visibility reaches 9.7 miles. \n - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 241, 308, 342, 378, 388, 424, 432, 403, 508, 523, 484 and 523, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [439, 326, 265, 234, 154, 82, 49, 32, 28, 37, 103, 261]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the I605-N freeway, lane 4, direction of northbound. \n - Today's weather: Sunny. Temperature is 13.9\u00b0C, and visibility reaches 9.7 miles. \n - Region information: including residential areas, transportation areas and educational areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 382, 444, 457, 476, 495, 520, 534, 552, 547, 513, 542 and 543, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [488, 420, 379, 335, 259, 178, 116, 78, 70, 86, 148, 330]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the I605-N freeway, lane 4, direction of northbound. \n - Today's weather: Sunny. Temperature is 11.2\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including residential areas, transportation areas and educational areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 282, 330, 332, 348, 378, 418, 448, 434, 370, 343, 354 and 316, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [285, 289, 254, 209, 157, 102, 68, 45, 42, 49, 92, 252]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the I710-S freeway, lane 4, direction of southbound. \n - Today's weather: Sunny. Temperature is 13.9\u00b0C, and visibility reaches 9.7 miles. \n - Region information: including residential areas, transportation areas and educational areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 332, 324, 290, 304, 327, 338, 352, 334, 364, 380, 366 and 383, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [322, 287, 234, 225, 184, 140, 114, 81, 84, 96, 160, 354]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the I710-S freeway, lane 6, direction of southbound. \n - Today's weather: Sunny. Temperature is 13.9\u00b0C, and visibility reaches 9.7 miles. \n - Region information: including residential areas, transportation areas and educational areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 414, 481, 456, 399, 390, 397, 412, 423, 458, 476, 481 and 482, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [437, 358, 267, 238, 186, 117, 78, 52, 53, 56, 82, 204]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the I710-S freeway, lane 4, direction of southbound. \n - Today's weather: Sunny. Temperature is 13.9\u00b0C, and visibility reaches 9.7 miles. \n - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 414, 482, 456, 399, 390, 397, 412, 423, 457, 476, 481 and 482, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [437, 358, 267, 238, 185, 117, 77, 52, 53, 56, 82, 204]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 7 in Los Angeles, California, USA, along the I710-S freeway, lane 5, direction of southbound. \n - Today's weather: Sunny. Temperature is 12.9\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 277, 325, 302, 254, 244, 246, 253, 258, 286, 322, 337 and 339, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [287, 214, 158, 142, 109, 70, 48, 34, 36, 35, 50, 123]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 8 in San Bernardino, California, USA, along the I10-W freeway, lane 4, direction of westbound. \n - Today's weather: Rain. Temperature is 12.3\u00b0C, and visibility reaches 9.1 miles. \n - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 326, 370, 377, 416, 451, 468, 258, 466, 496, 474, 516 and 492, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [438, 358, 317, 275, 211, 154, 103, 81, 86, 130, 330, 448]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 8 in San Bernardino, California, USA, along the I10-W freeway, lane 4, direction of westbound. \n - Today's weather: Rain. Temperature is 12.3\u00b0C, and visibility reaches 9.1 miles. \n - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 433, 420, 378, 416, 464, 481, 482, 459, 488, 436, 418 and 386, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [365, 320, 279, 226, 145, 103, 67, 62, 77, 179, 433, 512]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 8 in San Bernardino, California, USA, along the I10-W freeway, lane 4, direction of westbound. \n - Today's weather: Rain. Temperature is 12.3\u00b0C, and visibility reaches 9.1 miles. \n - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 382, 349, 334, 367, 422, 436, 441, 448, 485, 466, 448 and 409, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [363, 302, 268, 221, 148, 106, 70, 60, 72, 126, 270, 365]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 8 in San Bernardino, California, USA, along the I10-W freeway, lane 4, direction of westbound. \n - Today's weather: Rain. Temperature is 12.3\u00b0C, and visibility reaches 9.1 miles. \n - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 433, 476, 500, 468, 469, 478, 491, 506, 551, 591, 570 and 589, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [500, 396, 335, 294, 233, 170, 112, 80, 72, 88, 169, 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 8 in Riverside, California, USA, along the I10-W freeway, lane 4, direction of westbound. \n - Today's weather: Rain. Temperature is 9.8\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 146, 185, 214, 247, 274, 282, 294, 290, 282, 300, 284 and 263, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [212, 189, 174, 152, 134, 111, 98, 99, 98, 103, 108, 127]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 8 in Riverside, California, USA, along the I15-N freeway, lane 4, direction of northbound. \n - Today's weather: Rain. Temperature is 9.8\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 216, 248, 287, 326, 372, 383, 355, 348, 353, 298, 306 and 340, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [368, 378, 269, 230, 195, 156, 128, 118, 109, 112, 144, 192]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 8 in San Bernardino, California, USA, along the I15-N freeway, lane 4, direction of northbound. \n - Today's weather: Rain. Temperature is 12.3\u00b0C, and visibility reaches 9.1 miles. \n - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 194, 212, 229, 249, 264, 275, 284, 313, 344, 315, 327 and 370, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [335, 306, 260, 240, 208, 169, 150, 107, 88, 81, 106, 170]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 8 in San Bernardino, California, USA, along the I15-N freeway, lane 4, direction of northbound. \n - Today's weather: Rain. Temperature is 12.3\u00b0C, and visibility reaches 9.1 miles. \n - Region information: including transportation areas and commercial areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 143, 193, 209, 250, 290, 323, 327, 366, 384, 394, 418 and 450, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [352, 277, 238, 194, 155, 106, 80, 56, 56, 58, 86, 136]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 8 in Riverside, California, USA, along the SR60-E freeway, lane 2, direction of eastbound. \n - Today's weather: Rain. Temperature is 9.8\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 136, 150, 176, 208, 244, 250, 244, 242, 254, 273, 214 and 240, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [241, 214, 186, 161, 125, 94, 63, 52, 39, 46, 62, 113]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 8 in San Bernardino, California, USA, along the SR71-S freeway, lane 3, direction of southbound. \n - Today's weather: Rain. Temperature is 13.5\u00b0C, and visibility reaches 9.8 miles. \n - Region information: including transportation areas, residential areas and educational areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 158, 206, 213, 209, 230, 246, 251, 290, 278, 284, 284 and 319, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [273, 215, 174, 147, 106, 60, 39, 26, 26, 32, 72, 142]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 8 in Riverside, California, USA, along the SR91-W freeway, lane 3, direction of westbound. \n - Today's weather: Rain. Temperature is 9.8\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, residential areas and educational areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 270, 332, 316, 328, 350, 360, 364, 374, 386, 361, 365 and 358, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [336, 289, 256, 201, 156, 102, 66, 47, 48, 106, 219, 262]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 8 in San Bernardino, California, USA, along the I210-W freeway, lane 4, direction of westbound. \n - Today's weather: Rain. Temperature is 12.3\u00b0C, and visibility reaches 9.1 miles. \n - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 272, 291, 291, 314, 329, 345, 364, 378, 374, 349, 348 and 308, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [327, 274, 238, 202, 161, 116, 68, 58, 63, 112, 312, 320]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 8 in San Bernardino, California, USA, along the I215-N freeway, lane 3, direction of northbound. \n - Today's weather: Rain. Temperature is 12.3\u00b0C, and visibility reaches 9.1 miles. \n - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 220, 237, 254, 259, 286, 291, 303, 318, 336, 345, 338 and 339, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [284, 227, 190, 152, 119, 73, 49, 32, 25, 42, 81, 190]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 8 in San Bernardino, California, USA, along the I215-N freeway, lane 5, direction of northbound. \n - Today's weather: Rain. Temperature is 12.3\u00b0C, and visibility reaches 9.1 miles. \n - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 207, 266, 289, 316, 343, 370, 410, 440, 480, 548, 548 and 541, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [429, 355, 274, 236, 180, 134, 93, 63, 59, 68, 111, 201]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 8 in Riverside, California, USA, along the I215-S freeway, lane 3, direction of southbound. \n - Today's weather: Rain. Temperature is 12.3\u00b0C, and visibility reaches 9.1 miles. \n - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 251, 273, 260, 266, 278, 294, 279, 276, 285, 284, 275 and 300, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [299, 258, 233, 210, 180, 153, 122, 110, 116, 139, 204, 281]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 8 in San Bernardino, California, USA, along the I215-S freeway, lane 2, direction of southbound. \n - Today's weather: Rain. Temperature is 12.3\u00b0C, and visibility reaches 9.1 miles. \n - Region information: including transportation areas, educational areas and commercial areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 216, 238, 250, 234, 234, 239, 246, 254, 276, 296, 286 and 295, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [250, 198, 167, 147, 116, 86, 56, 40, 36, 44, 84, 181]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 10 in Merced, California, USA, along the I5-N freeway, lane 2, direction of northbound. \n - Today's weather: Sunny. Temperature is 6.7\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 39, 56, 70, 103, 111, 129, 152, 172, 198, 187, 202 and 219, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [223, 230, 225, 154, 138, 113, 76, 54, 49, 48, 50, 42]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 10 in San Joaquin, California, USA, along the I5-N freeway, lane 3, direction of northbound. \n - Today's weather: Sunny. Temperature is 7.7\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 58, 65, 74, 81, 97, 99, 112, 113, 134, 160, 130 and 130, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [115, 88, 87, 78, 64, 53, 45, 37, 33, 36, 52, 74]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 10 in Merced, California, USA, along the SR99-N freeway, lane 2, direction of northbound. \n - Today's weather: Sunny. Temperature is 6.7\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, residential areas and educational areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 106, 101, 116, 125, 150, 172, 179, 191, 184, 190, 196 and 180, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [151, 134, 112, 98, 75, 60, 46, 32, 36, 47, 71, 112]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 10 in Stanislaus, California, USA, along the SR99-N freeway, lane 3, direction of northbound. \n - Today's weather: Sunny. Temperature is 7.6\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 285, 246, 250, 269, 305, 326, 363, 381, 364, 353, 362 and 361, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [295, 258, 211, 176, 118, 78, 52, 50, 72, 137, 195, 300]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 11 in San Diego, California, USA, along the I5-S freeway, lane 4, direction of southbound. \n - Today's weather: Sunny. Temperature is 14.9\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, residential areas and educational areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 147, 203, 236, 313, 364, 378, 379, 371, 378, 385, 361 and 362, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [338, 312, 295, 276, 167, 108, 76, 63, 58, 62, 74, 118]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 11 in San Diego, California, USA, along the I8-E freeway, lane 2, direction of eastbound. \n - Today's weather: Sunny. Temperature is 14.9\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including residential areas, transportation areas and commercial areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 60, 96, 120, 118, 133, 135, 146, 149, 151, 145, 130 and 126, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [125, 81, 67, 51, 36, 23, 23, 18, 15, 16, 19, 39]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 11 in San Diego, California, USA, along the I8-E freeway, lane 4, direction of eastbound. \n - Today's weather: Sunny. Temperature is 14.9\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, commercial areas and educational areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 120, 209, 259, 287, 319, 360, 392, 427, 476, 491, 446 and 428, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [360, 262, 222, 198, 145, 100, 50, 26, 18, 15, 25, 59]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 11 in San Diego, California, USA, along the I8-E freeway, lane 2, direction of eastbound. \n - Today's weather: Sunny. Temperature is 14.9\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas and commercial areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 146, 178, 206, 228, 246, 256, 271, 262, 269, 267, 256 and 228, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [179, 140, 104, 72, 62, 46, 27, 22, 18, 27, 64, 187]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 11 in San Diego, California, USA, along the I15-N freeway, lane 5, direction of northbound. \n - Today's weather: Sunny. Temperature is 14.9\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, educational areas and residential areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 466, 454, 412, 425, 406, 441, 463, 469, 492, 498, 456 and 430, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [368, 288, 224, 189, 134, 96, 62, 44, 37, 64, 175, 498]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 11 in San Diego, California, USA, along the I15-S freeway, lane 6, direction of southbound. \n - Today's weather: Sunny. Temperature is 14.9\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, commercial areas and educational areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 250, 375, 455, 486, 516, 572, 571, 598, 658, 669, 652 and 615, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [502, 387, 315, 256, 189, 131, 276, 204, 183, 238, 397, 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 11 in San Diego, California, USA, along the SR52-E freeway, lane 2, direction of eastbound. \n - Today's weather: Sunny. Temperature is 14.9\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas and commercial areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 96, 127, 138, 152, 170, 194, 206, 225, 262, 297, 269 and 240, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [178, 133, 103, 86, 58, 43, 24, 14, 10, 12, 24, 64]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 11 in San Diego, California, USA, along the I805-N freeway, lane 5, direction of northbound. \n - Today's weather: Sunny. Temperature is 14.9\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, educational areas and residential areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 538, 494, 448, 466, 457, 446, 418, 415, 404, 393, 358 and 358, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [319, 257, 211, 180, 133, 104, 67, 50, 48, 98, 244, 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 11 in San Diego, California, USA, along the I805-S freeway, lane 5, direction of southbound. \n - Today's weather: Sunny. Temperature is 14.9\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, educational areas and residential areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 175, 282, 301, 329, 392, 443, 500, 556, 694, 722, 648 and 603, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [497, 415, 347, 312, 243, 177, 96, 50, 39, 30, 42, 111]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 11 in San Diego, California, USA, along the I805-S freeway, lane 4, direction of southbound. \n - Today's weather: Sunny. Temperature is 14.9\u00b0C, and visibility reaches 10.0 miles. \n - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 202, 294, 304, 337, 390, 422, 457, 474, 550, 539, 485 and 472, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [375, 313, 254, 220, 170, 125, 72, 40, 31, 29, 50, 128]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 12 in Orange, California, USA, along the I5-S freeway, lane 5, direction of southbound. \n - Today's weather: Rain. Temperature is 13.5\u00b0C, and visibility reaches 9.8 miles. \n - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 678, 650, 618, 648, 660, 664, 654, 680, 685, 696, 702 and 692, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [644, 570, 512, 449, 339, 226, 134, 82, 75, 144, 395, 682]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 12 in Orange, California, USA, along the I5-S freeway, lane 4, direction of southbound. \n - Today's weather: Sunny. Temperature is 13.9\u00b0C, and visibility reaches 9.7 miles. \n - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 481, 506, 514, 514, 534, 524, 517, 528, 526, 567, 573 and 556, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [505, 438, 394, 352, 264, 199, 134, 105, 94, 139, 264, 498]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 12 in Orange, California, USA, along the SR55-N freeway, lane 4, direction of northbound. \n - Today's weather: Rain. Temperature is 13.5\u00b0C, and visibility reaches 9.8 miles. \n - Region information: including transportation areas, residential areas and commercial areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 310, 437, 446, 437, 484, 500, 509, 497, 406, 360, 376 and 377, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [410, 386, 341, 301, 237, 139, 63, 45, 30, 32, 84, 219]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 12 in Orange, California, USA, along the SR57-S freeway, lane 6, direction of southbound. \n - Today's weather: Rain. Temperature is 13.5\u00b0C, and visibility reaches 9.8 miles. \n - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 512, 546, 578, 588, 591, 606, 622, 651, 636, 649, 612 and 600, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [554, 445, 385, 326, 242, 151, 84, 62, 59, 97, 278, 548]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 12 in Orange, California, USA, along the SR73-N freeway, lane 3, direction of northbound. \n - Today's weather: Rain. Temperature is 13.5\u00b0C, and visibility reaches 9.8 miles. \n - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 51, 105, 96, 101, 102, 103, 104, 109, 122, 143, 125 and 112, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [76, 52, 41, 22, 14, 10, 16, 14, 14, 15, 23, 48]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 12 in Orange, California, USA, along the SR73-S freeway, lane 4, direction of southbound. \n - Today's weather: Rain. Temperature is 13.5\u00b0C, and visibility reaches 9.8 miles. \n - Region information: including educational areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 35, 66, 88, 92, 94, 114, 111, 128, 138, 171, 224 and 230, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [144, 77, 55, 41, 25, 11, 5, 4, 2, 2, 6, 22]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 12 in Orange, California, USA, along the SR73-S freeway, lane 3, direction of southbound. \n - Today's weather: Rain. Temperature is 13.5\u00b0C, and visibility reaches 9.8 miles. \n - Region information: including transportation areas, residential areas and educational areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 39, 66, 81, 81, 88, 106, 103, 110, 122, 150, 195 and 196, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [120, 72, 49, 39, 24, 12, 7, 4, 3, 2, 6, 22]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 12 in Orange, California, USA, along the SR91-W freeway, lane 5, direction of westbound. \n - Today's weather: Rain. Temperature is 13.5\u00b0C, and visibility reaches 9.8 miles. \n - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 431, 461, 478, 525, 495, 472, 474, 502, 523, 484, 483 and 463, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [452, 381, 330, 312, 222, 142, 89, 68, 82, 186, 528, 566]} |
{Role: You are an expert traffic volume prediction model, that can predict the future volume values according to spatial temporal information. We want you to perform the traffic volume prediction task, considering the nearby environment and historical traffic volume data.\nContext knowledge you could consider:\n - Traffic volume: the number of vehicles passing a specific region in an hour, usually ranging from 0 to 1000.\n - Traffic pattern characteristic: Traffic flow patterns in a city are influenced by various area attributes. Also, traffic volume has a periodic daily and weekly pattern.\n - Spatial temporal factors correlation: Traffic flow in an area will be affected by its nearby infrastructures, during specific periods for different areas. You should think about how the volume will change in a specific area, during a specific time.\n For examples,\n Airports, and train stations - increased volume on weekends and holidays.\n Residential areas - more activities during morning and evening rush hours.\n Commercial areas - busy during lunch hours and after-work periods.\n Educational locations - high volume during peak hours near schools.\nThink carefully about the following questions about how spatial-temporal factors affect traffic flow.\n - What is the attribute of this area and what is the predicted time zone located in special periods (like rush hours, weekdays, weekends, and holidays)?\n - What are the traffic patterns of this area, and what is the change in different time slots?\n - What is the historical temporal trend according to temporal information, considering the weekdays, around holidays? | Some important information is listed as follows:\n - Location: District 12 in Orange, California, USA, along the I405-S freeway, lane 5, direction of southbound. \n - Today's weather: Rain. Temperature is 13.5\u00b0C, and visibility reaches 9.8 miles. \n - Region information: including transportation areas, commercial areas and residential areas within a range of 5 km.\n - Current Time: 5 PM, 2018-2-19, Monday, Washington's Birthday.\n - Traffic volume data in the past 12 hours were 483, 517, 466, 437, 430, 470, 465, 488, 530, 512, 522 and 520, respectively.\nAccording to the above information and careful reasoning, please predict traffic volumes in the next 12 hours (from 6 PM to 5 AM). Format the final answer in a single line as a JSON dictionary like: {Traffic volume data in the next 12 hours: [V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12]} | {Traffic volume data in the next 12 hours: [458, 412, 362, 321, 227, 159, 116, 96, 84, 106, 204, 369]} |
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