Robust Traffic Signal Timing Control using Multiagent Twin Delayed Deep Deterministic Policy Gradients

Priya Shanmugasundaram, Shalabh Bhatnagar

2022

Abstract

Traffic congestion is an omnipresent and serious problem that impacts people around the world on a daily basis. It requires solutions that can adapt to the changing traffic environments and reduce traffic congestion not only across local intersections but also across the global road network. Traditional traffic control strategies suffer from being too simplistic and moreover, they cannot scale to real-world dynamics. Multiagent reinforcement learning is being widely researched to develop intelligent transportation systems where the different intersections on a road network co-operate to ease vehicle delay and traffic congestion. Most of the literature on using Multiagent reinforcement learning methods for traffic signal control is focussed on applying multi-agent Q learning and discrete-action based control methods. In this paper, we propose traffic signal control using Multiagent Twin Delayed Deep Deterministic Policy Gradients (MATD3). The proposed control strategy is evaluated by exposing it to different time-varying traffic flows on simulation of road networks created on the traffic simulation platform SUMO. We observe that our method is robust to the different kinds of traffic flows and consistently outperforms the state-of-the-art counterparts by significantly reducing the average vehicle delay and queue length.

Paper Citation

in Harvard Style

Shanmugasundaram P. and Bhatnagar S. (2022). Robust Traffic Signal Timing Control using Multiagent Twin Delayed Deep Deterministic Policy Gradients. In Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-547-0, pages 477-485. DOI: 10.5220/0010889300003116

in Bibtex Style

@conference{icaart22,
author={Priya Shanmugasundaram and Shalabh Bhatnagar},
title={Robust Traffic Signal Timing Control using Multiagent Twin Delayed Deep Deterministic Policy Gradients},
booktitle={Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2022},
pages={477-485},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010889300003116},
isbn={978-989-758-547-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Robust Traffic Signal Timing Control using Multiagent Twin Delayed Deep Deterministic Policy Gradients
SN - 978-989-758-547-0
AU - Shanmugasundaram P.
AU - Bhatnagar S.
PY - 2022
SP - 477
EP - 485
DO - 10.5220/0010889300003116