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.gitattributes

@@ -78,3 +78,6 @@ road_planning/Data/india.mg filter=lfs diff=lfs merge=lfs -text
 route_planning/Data/Road_Network/Manhattan_od_0.01.rou.alt.xml filter=lfs diff=lfs merge=lfs -text
 route_planning/Data/Road_Network/Manhattan.net.xml filter=lfs diff=lfs merge=lfs -text
 socio_economic_prediction/Data/Guangzhou.json filter=lfs diff=lfs merge=lfs -text
+traffic_od_prediction/Data/Newyork.json filter=lfs diff=lfs merge=lfs -text
+traffic_signal_control/Data/Jinan/3_4/anon_3_4_jinan_synthetic_24h_6000.json filter=lfs diff=lfs merge=lfs -text
+traffic_signal_control/Data/Jinan/3_4/anon_3_4_jinan_synthetic_24h.json filter=lfs diff=lfs merge=lfs -text

traffic_od_prediction/Data/Newyork.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2f016943d06300fa608e45e9430b66d716b23ef6668364dfb1fff13f13b10892
+size 31193796

traffic_od_prediction/Data/task_info.json

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+{
+  "task_description": "You are tasked with predicting urban origin-destination traffic flow in New York, USA. You will be provided with historical traffic flow data from the past 3 days and the most recent 1 hour for a target region and its nearby regions, along with their connectivity information.",
+  "data_schema": "- traffic_in_flow: Time-series of vehicles arriving in the target region within the last 1 hour at 5-minute intervals.\n- traffic_out_flow: Time-series of vehicles departing the target region within the last 1 hour at 5-minute intervals.\n- traffic_in_flow_past_3_days: Time-series of vehicles arriving in the target region over the past 3 days at 1-hour intervals.\n- traffic_out_flow_past_3_days: Time-series of vehicles departing the target region over the past 3 days at 1-hour intervals.\n- connectivity: A list of tuples in the format (region_1, region_2, distance), defining undirected edges between regions with the specified distance in m.",
+  "domain_knowledge": "- You need to analyze the time-series data of traffic in-flow and out-flow across regions, then discover any potential patterns.\n- You need to consider the spatial correlation among neighboring regions, where closer regions may exhibit similar patterns to the target region.\n- Use your reasoning skill to solve this problem. DO NOT write any code or algorithms.",
+  "task_target": "predict the most likely traffic in-flow and out-flow numbers for the target region in the next 1 hour (12 time steps).",
+  "task_output_type": "prediction"
+}

traffic_signal_control/Data/Jinan/3_4/anon_3_4_jinan_synthetic_24h.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:bb1dc969bac95743fba2c3d97e61b062b74d9ae927985ed027b52144b865772e
+size 25382780

traffic_signal_control/Data/Jinan/3_4/anon_3_4_jinan_synthetic_24h_6000.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:42c881c96e358396bebba8b7365cbed169e5334912501e823d9f9437abf53c5c
+size 14988480

traffic_signal_control/Data/task_info.json

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+{
+  "task_description": "You are a traffic signal control agent managing an intersection with traffic coming from four directions: north, south, east, and west. The intersection consists of 12 lanes, labeled as follows: NL (North Left), NT (North Through), NR (North Right), SL (South Left), ST (South Through), SR (South Right), EL (East Left), ET (East Through), ER (East Right), WL (West Left), WT (West Through), and WR (West Right). Vehicles travel at an average speed of 11 meters per second. Real-time traffic conditions for each lane are provided. If a lane's traffic condition is not specified, it means no vehicles are currently present in that lane.",
+  "data_schema": "- queue: Number of vehicles waiting at the lane for a green signal.\n- move: Number of vehicles currently moving through the lane.\n- wait_time: Average wait time (in minutes) for vehicles in the queue.\n- occupancy: Lane occupancy rate (0 = free-flow, 100% = fully congested).\n- connectivity: A list of tuples in the format (lane_1, lane_2, distance), representing directed connections between lanes with the specified distance in meters. Traffic flows from lane_1 (upstream) to lane_2 (downstream).",
+  "domain_knowledge": "- Prioritize lanes with long queues and the congestion at the target intersection.\n- Ensure smooth traffic flow at the target intersection first, then coordinate with neighboring intersections.\n- Use logical reasoning to determine the best traffic signal activation. DO NOT write any code or algorithms.",
+  "task_target": "determine which traffic signal activation for two lanes at the target intersection would most significantly reduce overall queue length. Choose from the following options: ETWT, ELWL, NTST, or NLSL",
+  "task_output_type": "decision"
+}