An agent-based simulation model of pedestrian evacuation based on Bayesian Nash Equilibrium

TL;DR

This paper introduces a Bayesian Nash Equilibrium-based agent model for pedestrian evacuation, demonstrating that BNE improves evacuation efficiency and pedestrian behavior realism in simulations.

Contribution

It develops a novel agent-based evacuation model incorporating Bayesian game theory, showing BNE's effectiveness in reducing evacuation time and enhancing behavior.

Findings

BNE reduces evacuation time significantly.

BNE leads to more intelligent pedestrian behaviors.

Higher BNE adoption correlates with faster evacuation and higher comfort.

Abstract

This research incorporates Bayesian game theory into pedestrian evacuation in an agent-based model. Three pedestrian behaviours were compared: Random Follow, Shortest Route and Bayesian Nash Equilibrium (BNE), as well as combinations of these. The results showed that BNE pedestrians were able to evacuate more quickly as they predict congestion levels in their next step and adjust their directions to avoid congestion, closely matching the behaviours of evacuating pedestrians in reality. A series of simulation experiments were conducted to evaluate whether and how BNE affects pedestrian evacuation procedures. The results showed that: 1) BNE has a large impact on reducing evacuation time; 2) BNE pedestrians displayed more intelligent and efficient evacuating behaviours; 3) As the proportion of BNE users rises, average evacuation time decreases, and average comfort level increases. A detailed description of the model and relevant experimental results is provided in this paper. Several limitations as well as further works are also identified.