Reinforcement learning approach for coordinated passenger inflow control of urban rail transit in peak hours

In peak hours, when the limited transportation capacity of urban rail transit is not adequate enough to meet the travel demands, the density of the passengers waiting at the platform can exceed the critical density of the platform. Coordinated passenger inflow control strategy is required to adjust/meter the inflow volume and relieve some of the demand pressure at crowded metro stations so as to ensure both operational efficiency and safety at such stations for all passengers. However, such strategy is usually developed by the operation staff at each station based on their practical working experience. As such, the best strategy/decision cannot always be made and sometimes can even be highly undesirable due to their inability to account for the dynamic performance of all metro stations in the entire rail transit network. In this paper, a new reinforcement learning-based method is developed to optimize the inflow volume during a certain period of time at each station with the aim of minimizing the safety risks imposed on passengers at the metro stations. Basic principles and fundamental components of the reinforcement learning, as well as the reinforcement learning-based problem-specific algorithm are presented. The simulation experiment carried out on a real-world metro line in Shanghai is constructed to test the performance of the approach. Simulation results show that the reinforcement learning-based inflow volume control strategy is highly effective in minimizing the safety risks by reducing the frequency of passengers being stranded. Additionally, the strategy also helps to relieve the passenger congestion at certain stations.