Deep reinforcement learning for addressing disruptions in traffic light control
Document Type
Article
Publication Date
1-1-2022
Abstract
This paper investigates the use of multi-agent deep Q-network (MADQN) to address the curse of dimensionality issue occurred in the traditional multi-agent reinforcement learning (MARL) approach. The proposed MADQN is applied to traffic light controllers at multiple intersections with busy traffic and traffic disruptions, particularly rainfall. MADQN is based on deep Q-network (DQN), which is an integration of the traditional reinforcement learning (RL) and the newly emerging deep learning (DL) approaches. MADQN enables traffic light controllers to learn, exchange knowledge with neighboring agents, and select optimal joint actions in a collaborative manner. A case study based on a real traffic network is conducted as part of a sustainable urban city project in the Sunway City of Kuala Lumpur in Malaysia. Investigation is also performed using a grid traffic network (GTN) to understand that the proposed scheme is effective in a traditional traffic network. Our proposed scheme is evaluated using two simulation tools, namely Matlab and Simulation of Urban Mobility (SUMO). Our proposed scheme has shown that the cumulative delay of vehicles can be reduced by up to 30% in the simulations.
Keywords
Artificial intelligence, Traffic light control, Traffic disruptions, Multi-agent deep Q-network, Deep reinforcement learning
Divisions
fsktm
Funders
Universiti Teknologi MARA, Fundamental Research Grant Scheme (FRGS) [Grant No: 600-IRMI/FRGS 5/3 (342/2019)],Ministry of Higher of Higher Education (MOHE)
Publication Title
CMC-Computers Materials & Continua
Volume
71
Issue
2
Publisher
Tech Science Press
Publisher Location
871 CORONADO CENTER DR, SUTE 200, HENDERSON, NV 89052 USA