This dataset contains the traffic data in San Bernardino from July to August in 2016, with 170 detectors on 8 roads with a time interval of 5 minutes. This dataset is popular as a benchmark traffic forecasting dataset.
35 PAPERS • 1 BENCHMARK
METR-LA is a dataset for traffic prediction.
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PEMS-BAY is a dataset for traffic prediction.
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PeMSD7 is traffic data in District 7 of California consisting of the traffic speed of 228 sensors while the period is from May to June in 2012 (only weekdays) with a time interval of 5 minutes. This dataset is popular for benchmark the traffic forecasting models.
20 PAPERS • 2 BENCHMARKS
PeMS07 is a traffic forecasting benchmark.
15 PAPERS • 1 BENCHMARK
The dataset refers to the traffic speed data in San Francisco Bay Area, containing 307 sensors on 29 roads. The time span of the dataset is January-February in 2018. It is a popular benchmark for traffic forecasting.
Taxi flow data of New York City with grid 20x10.
8 PAPERS • 1 BENCHMARK
PeMS04 is a traffic forecasting benchmark.
PeMS08 is a traffic forecasting dataset.
7 PAPERS • 1 BENCHMARK
Bike flow data of New York City with grid 16x8.
4 PAPERS • 1 BENCHMARK
Bike flow data of New York City.
Q-Traffic is a large-scale traffic prediction dataset, which consists of three sub-datasets: query sub-dataset, traffic speed sub-dataset and road network sub-dataset.
Taxi speed data in 15min interval from 156 sensors on major roads of Luohu District in Shenzhen, China, from Jan. 1 to Jan. 31, 2015.
EXPY-TKY contains the traffic speed information and the corresponding traffic incident information in 10-minute interval for 1843 expressway road links in Tokyo over three months (2021/10∼2021/12). Compared with other benchmarks for traffic prediction, EXPY-TKY covers a larger scale and more complex incident situations. Potential tasks of EXPY-TKY include traffic prediction, incident detection, and road type classification.
2 PAPERS • 1 BENCHMARK
The Beijing Traffic Dataset collects traffic speeds at 5-minute granularity for 3126 roadway segments in Beijing between 2022/05/12 and 2022/07/25.
1 PAPER • 1 BENCHMARK
This is the static test data from the study "Global Geolocated Realtime Data of Interfleet Urban Transit Bus Iding" collected by GRD-TRT-BUF-4I. test-data-a.csv was collected from December 31, 2023 00:01:30 UTC to January 1, 2024 00:01:30 UTC. test-data-b.csv was collected from January 4, 2024 01:30:30 UTC to January 5, 2024 01:30:30 UTC. test-data-c.csv was collected from January 10, 2024 16:05:30 UTC to January 11, 2024 16:05:30 UTC.
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VLUC (Video-Like Urban Computing) is a benchmark for video-like computing on citywide traffic density and crowd prediction. It consists of two new datasets BousaiTYO and BousaiOSA and existing datasets TaxiBJ, BikeNYC I-II, and TaxiNYC.
The CTV-Dataset (CTV stands for Cyclist Top-View) is a trajectories dataset for cyclist behaviour in mixed-traffic environments (aka. shared spaces). This dataset is meant to enlarge the available datasets in the community, focusing on cyclists as main road users to help the research in understanding and predicting cyclist behaviour in shared spaces. The dataset results from an experiment conducted in TU Clausthal to extract data from possible interaction scenarios with other road users, such as pedestrians and cars, in shared spaces. The scenarios were captured using a drone with 4K (3840×2160) resolution at 29.97 fps to ensure high-quality results. The trajectories were extracted using an in-house developed computer vision algorithm.
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