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Authors: Akimasa Murata ; Yuichi Sei ; Yasuyuki Tahara and Akihiko Ohsuga

Affiliation: The University of Electro Communications, Tokyo, Japan

Keyword(s): Agents, Deep Reinforcement Learning, Traffic Control.

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.

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Paper citation in several formats:
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; ISSN 2184-433X, SciTePress, pages 319-325. DOI: 10.5220/0011665000003393

@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},
issn={2184-433X},
}

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
IS - 2184-433X
AU - Murata, A.
AU - Sei, Y.
AU - Tahara, Y.
AU - Ohsuga, A.
PY - 2023
SP - 319
EP - 325
DO - 10.5220/0011665000003393
PB - SciTePress

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