Coordinated Ramp Metering with Equity Consideration Using Reinforcement Learning

Abstract

Reinforcement learning (RL) has been applied to solve ramp-metering problems and attracted increasing attention in recent studies. However, improving traffic efficiency is the main concern of these applications, and the issue relating to user equity has not been well considered. A new RL-based system is developed in this paper to deal with equity-related problems. With the definition of three RL elements, including reward, action, and state, this system can capture the information of user equity and balance it with traffic efficiency. Simulation experiments using real traffic data collected from a real-world motorway stretch are designed to test the performance of the new system. Compared with a widely used ramp-metering algorithm ALINEA, the new system shows superior performance on improving both traffic efficiency and user equity. Specifically, with suitable parameter settings, the new system can reduce the total time spent (TTS) by motorway users by 18.5% and maintain an equally distributed total waiting time (TWT) with a low standard deviation for TWT across on-ramps close to 0.