The aim of this website is to offering comprehensive dataset, simulator, relevant papers, tutorial and survey to anyone who may wish to start investigation or evaluate a new algorithm.
Deep Reinforcement Learning for Traffic Signal ControlIEEE ITSC 2020 |
MetaLight: Value-based Meta-reinforcement Learning for Online Universal Traffic Signal ControlAAAI'20 |
CityFlow: A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic ScenarioWWW'19 Demo |
Learning to Simulate with Sparse Trajectory DataECML-PKDD'20 [Best Applied Data Science Paper Award] |
We provide different traffic datasets, each includes both road network (roadnet.json) and traffic flow file (flow.json), whose formats are defined in Roadnet File Format and Flow File Format respectively.
# | Dataset name | Number of Intersections | Time Span (Seconds) | Description | Referred result* | Referred method |
---|---|---|---|---|---|---|
1 | hangzhou_1x1_bc-tyc_18041607_1h | 1 | 3600 | These datasets are based on camera data in Hangzhou. Due to the lack of records about turning vehicles, the turning ratios of each dataset are fixed, with 10% as turning left, 60% as going straight, and 30% as turning right. The turning-right vehicles are discarded since they are not under the control of traffic lights. There are one left-turn lane and one straight lane in each direction in each roadnet. | 221.03 | SOTL |
2 | hangzhou_1x1_bc-tyc_18041608_1h | 1 | 3600 | 334.72 | SOTL | |
3 | hangzhou_1x1_bc-tyc_18041610_1h | 1 | 3600 | 213.20 | SOTL | |
4 | hangzhou_1x1_kn-hz_18041607_1h | 1 | 3600 | 72.48 | SOTL | |
5 | hangzhou_1x1_kn-hz_18041608_1h | 1 | 3600 | 64.10 | SOTL | |
6 | hangzhou_1x1_qc-yn_18041607_1h | 1 | 3600 | 117.24 | SOTL | |
7 | hangzhou_1x1_qc-yn_18041608_1h | 1 | 3600 | 131.99 | SOTL | |
8 | hangzhou_1x1_sb-sx_18041607_1h | 1 | 3600 | 173.85 | SOTL | |
9 | hangzhou_1x1_sb-sx_18041608_1h | 1 | 3600 | 290.00 | SOTL | |
10 | hangzhou_1x1_tms-xy_18041607_1h | 1 | 3600 | 214.77 | SOTL | |
11 | hangzhou_1x1_tms-xy_18041608_1h | 1 | 3600 | 325.32 | SOTL | |
12 | syn_1x1_uniform_200_1h | 1 | 3600 | These datasets are generated artificially. The vehicles enter the road network uniformly with a fixed entering ratio chosen from 200, 400 and 600 vehicles per hour. | 61.44 | SOTL |
13 | syn_1x1_uniform_400_1h | 1 | 3600 | 133.40 | SOTL | |
14 | syn_1x1_uniform_600_1h | 1 | 3600 | 189.11 | SOTL | |
15 | jinan_3x4_hongqi_16XXXXXX_1h | 12 | 3600 | The road network contains 12 intersections in a 3x4 grid. Each intersection has four incoming approaches and four outgping approaches, and each approach has three lanes (left-turn, through and right-turn respectively). The traffic flow data is based on camera data in Jinan. Necessary simplification is done due to the low quality of the real-world data. | ||
16 | hangzhou_4x4_gudang_18010207_1h | 16 | 3600 | The road network contains 16 intersections in a 4x4 grid. Each intersection has four incoming approaches and four outgping approaches, and each approach has three lanes (left-turn, through and right-turn respectively). The traffic flow data is based on camera data in Hangzhou. Necessary simplification is done due to the low quality of the real-world data. • Traffic volume: the traffic volume is derived from camera data at Hangzhou. • Turning ratio: 10% (turning left), 60%(going straight) and 30% (turning right). This is synthesized from the statistics of taxi GPS data. | 240.97 | MaxPressure |
17 | syn_1x3_gaussian_500_1h | 3 | 3600 | The road network contains 16 intersections in a 4x4 grid. Each intersection has four incoming approaches and four outgping approaches, and each approach has three lanes (left-turn, through and right-turn respectively). • Traffic volume: All the vehicles enter and leave the network from the rim edges.For each entering edge, the number of the vehicles generated is sampled from a Gaussian distribution with mean as 500 vehicles/hour/lane. • Turning ratio: 10% (turning left), 60%(going straight) and 30% (turning right) | 422.95 | MaxPressure |
18 | syn_2x2_gaussian_500_1h | 4 | 3600 | 477.71 | MaxPressure | |
19 | syn_3x3_gaussian_500_1h | 9 | 3600 | 631.75 | MaxPressure | |
20 | syn_4x4_gaussian_500_1h | 16 | 3600 | 689.68 | MaxPressure | |
21 | Manhattan_1 | 2510 | 3600 | The road network contains 2510 intersections in Manhattan, New York. The road network is converted from SUMO default road net into the CityFlow format. • Traffic volume: Vehicles enter and leave the network could appear in every node in the network.For each entering edge, the number of the vehicles generated is sampled from a taxi trajectory data. • Turning ratio: 10% (turning left), 60%(going straight) and 30% (turning right) | ||
22 | Manhattan_2 | 2510 | 3600 | |||
23 | Manhattan_3 | 2510 | 3600 | |||
24 | LA_1x4 | 4 | 3600 | The road network contains 4 intersections in LA. | ||
25 | Atlanta_1x5 | 5 | 3600 | The road network contains 5 intersections in Atlanta. | ||
26 | Manhattan_16x3 | 48 | 3600 | The road network contains 48 intersections in Manhattan. | ||
27 | Manhattan_28x7 | 196 | 3600 | The road network contains 196 intersections in Manhattan. |
If you use the datasets in your paper, please cite the following papers:
@article{wei2019survey, title={A Survey on Traffic Signal Control Methods}, author={Wei, Hua and Zheng, Guanjie and Gayah, Vikash and Li, Zhenhui}, journal={arXiv preprint arXiv:1904.08117}, year={2019} }
@inproceedings{wei2019colight, title={Colight: Learning network-level cooperation for traffic signal control}, author={Wei, Hua and Xu, Nan and Zhang, Huichu and Zheng, Guanjie and Zang, Xinshi and Chen, Chacha and Zhang, Weinan and Zhu, Yanmin and Xu, Kai and Li, Zhenhui}, booktitle={Proceedings of the 28th ACM International Conference on Information and Knowledge Management}, pages={1913--1922}, year={2019} }
@inproceedings{zheng2019frap, title={Learning phase competition for traffic signal control}, author={Zheng, Guanjie and Xiong, Yuanhao and Zang, Xinshi and Feng, Jie and Wei, Hua and Zhang, Huichu and Li, Yong and Xu, Kai and Li, Zhenhui}, booktitle={Proceedings of the 28th ACM International Conference on Information and Knowledge Management}, pages={1963--1972}, year={2019} }
A Survey on traffic signal control
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