A Passenger Model for Simulating Boarding and Alighting in Spatially Confined Transportation Scenarios

Abstract Crowd simulation has been widely used as a tool to demonstrate the behavior of passengers on public transport. A simulation model allows researchers to evaluate the platform or interior designs without involving real-world experimentation. In this paper, we propose a passenger model to measure the effect of different public transport vehicle layouts on the required time for boarding and alighting. We first model a low level collision avoidance behavior based on an extended Social Force Model aiming at simulating human interactions in confined spaces. The model introduces a mechanism to emulate rotation behavior while avoiding complex geometric computations and is calibrated to experimental data. The model also allows agents to perform collision prediction in low density environments. Strategical behavior of passengers is modeled according to the Recognition-Primed Decision paradigm and combined with the collision avoidance model. We validate our model against real-world experiments from the literature, demonstrating deviations of less than 6%. In a case study, we evaluate the boarding and alighting times required by three autonomous vehicle interior layouts proposed by industrial designers in both low-density and high-density scenarios.