A distributed, cooperative multi-agent system for real-time traffic signal control

Abstract

This dissertation presents a new algorithm designed to control the traffic signals in real time in a dense city network. This algorithm uses a distributed multi-agent system, in which agents are able to pass information on current traffic conditions to each other. Furthermore, reinforcement learning is used to calibrate the parameters used by the agents and a database of previous traffic conditions helps the agents to predict the future traffic. This Reinforcement Learning Multi-Agent System (RLA) is then compared to other exiting traffic signal control algorithms. Simulations were realised on a network containing 29 signalised intersections, modelling the central business district of Singapore, using real demand data, and under a number of different traffic conditions. The results show that this algorithm performs up to 25% better than other multi-agents systems or actuated control.