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.