An Agent-Based Crowd Simulation Framework Integrating Urban Walkway Networks and Individual Pedestrian Behaviors for Congestion Analysis

Abstract

: High-density crowd accidents in complex urban walkway networks represent significant public safety risks characterized by non-linear dynamics and unpredictable emergence. While existing crowd models have primarily focused on evacuation within controlled facilities, they often lack the capacity to explain how behavioral heterogeneity interacts with real-world walkway networks to generate localized congestion. This paper proposes an Agent-Based Modeling (ABM) framework specifically designed to analyze the impact of diverse pedestrian behavior compositions on network-wide congestion dynamics. The proposed framework adopts a centralized time-stepped execution structure that integrates complex movement factors, including multi-destination trip chains and density-responsive navigation. To demonstrate its practical utility, the framework is applied to a realistic urban scenario in the Itaewon district, Seoul, utilizing empirical datasets on transportation demand and network topology. The study investigates how varying the distribution of behavior profiles—such as shortest-path, route-choice, and crowd-avoid strategies—reshapes emergent crowding patterns. The results highlight the framework’s strength as a robust scenario-based analysis tool for identifying systemic risks and providing actionable insights for crowd management in dense urban environments.