Single-line Bus Operations Dynamic Holding Control Strategy Based on Deep Reinforcement Learning
Large headway and fluctuations in bus operations can lead to instability of the bus operation system,such as the bus bunching phenomena.This paper proposes a dynamic holding control strategy based on deep reinforcement learning to improve the stability of bus system operations and avoid bus bunching.A linear bus system is established,and the operating rules for vehicles and passenger behavior are defined.Then,a dynamic control method is introduced based on deep reinforcement learning,the elements of the reinforcement learning framework are defined,and an event-driven simulator environment is developed to train and test the agents.Extensive simulation experiments are conducted to compare the proposed method with traditional methods.Various evaluation metrics are selected for comparative analysis,and the sensitivity analysis is also performed.The experimental results show that the proposed method achieves the most stable vehicle trajectories and the smallest passenger occupancy dispersion.The headway variation was reduced respectively by 61.90%,60.98%,and 37.98%compared to the no control strategy,the schedule-based control strategy,and the headway-based control strategy.The average waiting time was reduced by 28.36%,26.53%,and 23.61%compared to the aforementioned strategies.The proposed method also demonstrates strong robustness under varying travel time variability and headway conditions.
万方数据
维普
