SynTraC: A Synthetic Dataset for Traffic Signal Control from Traffic Monitoring Cameras
<|reference_start|>SynTraC: A Synthetic Dataset for Traffic Signal Control from Traffic Monitoring Cameras: This paper introduces SynTraC, the first public image-based traffic signal control dataset, aimed at bridging the gap between simulated environments and real-world traffic management challenges. Unlike traditional datasets for traffic signal control which aim to provide simplified feature vectors like vehicle counts from traffic simulators, SynTraC provides real-style images from the CARLA simulator with annotated features, along with traffic signal states. This image-based dataset comes with diverse real-world scenarios, including varying weather and times of day. Additionally, SynTraC also provides different reward values for advanced traffic signal control algorithms like reinforcement learning. Experiments with SynTraC demonstrate that it is still an open challenge to image-based traffic signal control methods compared with feature-based control methods, indicating our dataset can further guide the development of future algorithms. The code for this paper can be found in \url{https://github.com/DaRL-LibSignal/SynTraC}.SynTraC<|reference_end|>
@article{chen2024syntrac:,
    title={SynTraC: A Synthetic Dataset for Traffic Signal Control from Traffic
  Monitoring Cameras},
    author={Tiejin Chen, Prithvi Shirke, Bharatesh Chakravarthi, Arpitsinh
  Vaghela, Longchao Da, Duo Lu, Yezhou Yang, Hua Wei},
    journal={arXiv preprint arXiv:2408.09588},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.09588},
    primaryClass={cs.AI}
}