Simulation of Passenger Flows on Urban Rail Transit Platform based on Adaptive Agents

Modeling and simulation of passenger flows on urban rail transit platform is a key issue in improving operation efficiency and service of level of urban rail transit, which should consider architectural environment, facilities implementation, and transportation organization. To simulate this kind of passenger for planning or evaluation, 3-layer architecture adaptive agent model is proposed to simulate passenger microscopic behaviors, which is based on visual perception module, making-decisions module, and action execution module. In respect of perception of agents, we construct a neuron-model-based perception model of environmental crowding to examine how individual URT passengers on the move represent the visual information of environmental crowding. Then, we define rules for behaviors based on cognitive heuristics for making-decisions module, and propose a discrete rule for the updating of passenger movement state for action execution module. Based on modeling passenger behavior dynamics, a microscopic simulation model for complex passenger flows on urban rail transit platform is developed. As a case study, the passenger flows scenarios of an island platform of urban rail transit station are simulated. Simulation results show that boarding and alighting passengers demand and train departure frequency have a remarkable impact on the maximum number of assembling passengers on platform and efficiency of mustering and evacuating under given conditions of building environment and facilities.

摘要作为提高地铁运行效率与服务水平的基础研究,地铁 客集散行为建模及仿真应综合考虑建, 环境, 设施配置, 运输组织三方面对 客集散效率的影响. 本文构建了 客自适应 Agent 模型, 从环境感知, 行为决策, 动作执行三方面建立基于神经元模型的环境, 感知模型, 考虑, 客的运动行为发规则, 运动状态离散更新规则. 在构建 客微观行为动力学模型的基础上, 以典型的地铁岛式站台 客集散为研究对象 建立地铁站台 客集散仿真模型. 仿真结果表明, 在地铁站台建空间环境和设施设备配置确定的情况下, 降客流需求和列发间隔对地铁站台最大 客数及 客集散效率影响显著.