An improved passenger flow simulation method of urban rail transit based on adaptive social force model and bounded rational path decision mechanism

Abstract

Accurate simulation of passenger gathering and distributing processes is critical for intelligent management in urban rail transit (URT) stations. This paper proposes an enhanced passenger flow simulation method integrating an adaptive social force model (SFM) with a bounded rationality path decision mechanism. In the study, the probability model of route change is established to quantitatively describe the probability of passengers’ route change under the influence of the bounded rationality psychology. It can more realistically reflect the routing behavior of passengers. Explore the impact of the proposed method on passenger flow evacuation in crowded areas by building a simple passenger flow simulation scenario. The experimental results show that compared with the original SFM, the proposed method can improve the evacuation efficiency by more than 26%. Validation through a typical island platform simulation experiment and a case study of a Chinese URT station demonstrates that the proposed method effectively captures key passenger flow trends in critical station areas, showing strong alignment with actual operational observations. This work advances URT crowd simulation methodologies by bridging psychological decision-making with physical movement dynamics, offering theoretical foundations for smart station planning with improved crowd perception capabilities.