Proposal of a Signal Control Method Using Deep Reinforcement Learning with Pedestrian Traffic Flow

Akimasa Murata, Yuichi Sei, Yasuyuki Tahara, Akihiko Ohsuga

2023

Abstract

In dealing with traffic control problems, there have been studies on learning signal change patterns and timing by using reinforcement learning for signals. In most of them, the focus is on improving the delay time of vehicles, and few of them assume the traffic situation including pedestrians. Therefore, the objective of this study is to provide traffic control to reduce traffic delays for both vehicles and pedestrians in an environment where pedestrian traffic volume varies greatly. Then, we will verify the accuracy with traffic signals considering the temporal changes of the environment. Results of verification, although vehicle wait times increased, a significant reduction in pedestrian wait times was observed.

Paper Citation

in Harvard Style

Murata A., Sei Y., Tahara Y. and Ohsuga A. (2023). Proposal of a Signal Control Method Using Deep Reinforcement Learning with Pedestrian Traffic Flow. In Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART, ISBN 978-989-758-623-1, pages 319-325. DOI: 10.5220/0011665000003393

in Bibtex Style

@conference{icaart23,
author={Akimasa Murata and Yuichi Sei and Yasuyuki Tahara and Akihiko Ohsuga},
title={Proposal of a Signal Control Method Using Deep Reinforcement Learning with Pedestrian Traffic Flow},
booktitle={Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,},
year={2023},
pages={319-325},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011665000003393},
isbn={978-989-758-623-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,
TI - Proposal of a Signal Control Method Using Deep Reinforcement Learning with Pedestrian Traffic Flow
SN - 978-989-758-623-1
AU - Murata A.
AU - Sei Y.
AU - Tahara Y.
AU - Ohsuga A.
PY - 2023
SP - 319
EP - 325
DO - 10.5220/0011665000003393