The increasing traffic congestion problem can be solved by an adaptive traffic signal control (ATSC) system as it utilises real‐time traffic information to control traffic signals. Recently, deep reinforcement learning (DRL) has shown its potential in solving the traffic signal timing. However, one of the main challenges of DRL is to design a proper reward function and special attention needs for a multi‐objective reward design. Since the feedback to the agent depends on the reward function, a proper design of reward function is needed for fast and stable learning. In this study, the authors proposed a new reward architecture called composite reward architecture (CRA) for multi‐objective ATSC to optimise multiple objectives. It calculates multiple rewards in parallel for each action and applies the majority voting method to choose the desired action. Since the traffic signal of one intersection affects the adjacent intersections, a new coordination approach is proposed to get the overall smooth traffic flow. The proposed reward architecture CRA is compared with several existing reward functions used in the literature for different traffic scenarios. The new coordinated approach is compared with the non‐coordinated approach. The authors demonstrated that the proposed approaches outperform the others concerning waiting time, halting the number of vehicles, and so on.
Adaptive traffic signal control system using composite reward architecture based deep reinforcement learning
IET Intelligent Transport Systems ; 14 , 14 ; 2030-2041
2020-12-01
12 pages
Article (Journal)
Electronic Resource
English
traffic congestion problem , traffic scenarios , coordinated approach , deep learning (artificial intelligence) , CRA , real‐time traffic information , multiple rewards , road traffic control , multiobjective reward design , composite reward architecture , traffic engineering computing , traffic signal timing , reward functions , smooth traffic flow , adaptive traffic signal control system , majority voting method , adaptive control , deep reinforcement learning , DRL , coordination approach , fast learning , stable learning , optimisation , multiobjective ATSC
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