Traffic Light Control using Reinforcement Learning: A Survey and an Open Source Implementation

Ciprian Paduraru, Miruna Paduraru, Alin Stefanescu

2022

Abstract

Traffic light control optimization is nowadays an important part of a smart city, given the advancement of sensors, IoT, and edge computing capabilities. The optimization method targeted by our work follows a general trend in the community: dynamically switching traffic light phases depending on the current traffic state. Reinforcement learning was lately adopted in the literature as it has been shown to outperform previous methods. The primary goal of our work is to provide an overview of the state of the art of reinforcement methods for traffic signal control optimization. Another topic of our work is to improve over existing tools that combine the field of reinforcement learning with traffic flow optimization. In this sense, we seek to add more output capabilities to existing tools to get closer to the domain-specific problem, to evaluate different algorithms for training strategies, to compare their performance and efficiency, and to simplify efforts in the research process by providing ways to more easily capture and work with new data sets.

Paper Citation

in Harvard Style

Paduraru C., Paduraru M. and Stefanescu A. (2022). Traffic Light Control using Reinforcement Learning: A Survey and an Open Source Implementation. In Proceedings of the 8th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS, ISBN 978-989-758-573-9, pages 69-79. DOI: 10.5220/0011040300003191

in Bibtex Style

@conference{vehits22,
author={Ciprian Paduraru and Miruna Paduraru and Alin Stefanescu},
title={Traffic Light Control using Reinforcement Learning: A Survey and an Open Source Implementation},
booktitle={Proceedings of the 8th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS,},
year={2022},
pages={69-79},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011040300003191},
isbn={978-989-758-573-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 8th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS,
TI - Traffic Light Control using Reinforcement Learning: A Survey and an Open Source Implementation
SN - 978-989-758-573-9
AU - Paduraru C.
AU - Paduraru M.
AU - Stefanescu A.
PY - 2022
SP - 69
EP - 79
DO - 10.5220/0011040300003191