Kinetic Monte Carlo simulations of bi-direction pedestrian flow with different walk speeds

Abstract

This paper presents a two-dimensional (2D) cellular automaton model for bi-direction pedestrian flows with different walk speeds based on the exclusion principle and Arrhenius microscopic dynamics. This model implements pedestrians’ movement rules based on each pedestrian’s surrounding conditions and their walking preferences and speeds. Although the decision-making process of pedestrians is more complex and adaptive to dynamic conditions than vehicular flows, our rules can reflect the behaviors of pedestrians at the microscale, such as moving forward, stopping to wait, lane switching, passing others, back stepping, etc. while attaining realistic emergent macroscale activity. We employ an efficient list-based kinetic Monte Carlo (KMC) algorithm to evolve the pedestrian system. The simulation results exhibit transitions between three phases: freely flowing, lane formation, and fully jammed phases as a function of initial density of pedestrians. In the phase of lane formation, we can observe the phenomenon that faster pedestrians exceed the slower ones through a narrow walkway. At different phases the relationships of density–flow and density–velocity are different from each other. The KMC simulations reported here are compared with those from other well-known pedestrian flow models and the corresponding empirical results from real traffic.