针对高峰时期城市轨道交通有限能力不足以满足乘客出行需求而引发的安全问题,需要采取客流控制策略来调节进入车站的客流量,缓解车站拥挤。本文提出了一种基于强化学习深度Q网络的多站协同控制模型,来优化每个车站在一定时间内的进站量,以最小化地铁车站乘客的站台超限量,平均等待时间以及客流控制强度的综合效益。并使用北京地铁八通线为例,进行了仿真实验,验证了该方法的有效性。仿真结果表明,本文提出的模型可以在客流控制强度较低条件下有效地降低乘客等待时间,提高乘客出行效率,有助于缓解车站的乘客拥堵且不会降低乘客的出行效率。

In view of the urban rail transit safety problems caused by limited transport capacity for passenger demand in peak period, it is necessary to adopt passenger flow control strategy to adjust the inbound passenger flow and alleviate station congestion. In this paper, a multi-station cooperative control model based on reinforcement learning deep-q network is proposed to optimize the arrival number of each station in a certain period of time. The goal is to minimize the comprehensive benefits of platform overrun, average waiting time and passenger flow control intensity. Taking Beijing Metro Batong line as an example, the simulation results verify the effectiveness of the method. The simulation results show that the model can effectively reduce the waiting time of passengers and improve the travel efficiency of passengers under the condition of low passenger flow control intensity. The method is helpful to alleviate the passenger congestion at the station without reducing the travel efficiency of passengers.

U293.1

国家重点研发计划