MULTI-AGENT REINFORCEMENT LEARNING FOR OPTIMIZING TRAFFIC SIGNAL TIMING

Author :  Areej Salaymeh

Affiliation :  Wayne State University

Country :  USA

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  11, 01, January, 2021

Abstract :

Designing efficient transportation systems is crucial to save time and money for drivers and for the economy as whole. One of the most important components of traffic systems are traffic signals. Currently, most traffic signal systems are configured using fixed timing plans, which are based on limited vehicle count data. Past research has introduced and designed intelligent traffic signals; however, machine learning and deep learning have only recently been used in systems that aim to optimize the timing of traffic signals in order to reduce travel time. A very promising field in Artificial Intelligence is Reinforcement Learning. Reinforcement learning (RL) is a data driven method that has shown promising results in optimizing traffic signal timing plans to reduce traffic congestion. However, model-based and centralized methods are impractical here due to the high dimensional state-action space in complex urban traffic network.

Keyword :  Multi-agent, Deep learning, Traffic signal timing, Reinforcement learning.

For More Details :  https://aircconline.com/csit/papers/vol11/csit110102.pdf