A Comprehensive Analysis of Reward Function for Adaptive Traffic Signal Control

Abstract

Adaptive traffic control systems (ATCS) can play an important role to reduce traffic congestion in urban areas. The main challenge for ATSC is to determine the proper signal timing. Recently, Deep Reinforcement learning (DRL) is used to determine proper signal timing. However, the success of the DRL algorithm depends on the appropriate reward function design. There exist various reward functions for ATSC in the existing research. In this research, a comprehensive analysis of the widely used reward function is presented. The pros and cons of various reward algorithms are discussed and experimental analysis shows that multi-objective reward function enhances the performance of ATSC.