🤖 AI Summary
This study addresses pedestrian-friendly infrastructure design by developing a microscopic pedestrian flow dynamics model grounded in social force theory, which captures self-organized behaviors emerging from individuals’ goal-directed motion and avoidance of obstacles and other pedestrians. The work innovatively integrates evolutionary algorithms to optimize building layouts and introduces mechanisms for dynamic target selection, experience-based learning, and a path load distribution method that accounts for subjective preferences, thereby generating a self-organized path system with minimal detours. Through microsimulation and self-organization theory, the model successfully reproduces complex spatiotemporal patterns such as unidirectional pedestrian flows, reveals a strong dependence of walking efficiency on architectural geometry, and demonstrates that moderately reducing walkable area can enhance overall pedestrian throughput.
📝 Abstract
This work presents a microscopic model to describe pedestrian flows based on the social force theory. The aim of this study is twofold: (1) developing a realistic model that can be used as a tool for designing pedestrian-friendly infrastructure, and (2) verifying a social science theory using a model with sufficient data. The investigation of the pedestrian model shows that despite simple individual behavior patterns, complex spatial and temporal structures emerge through the interactions in pedestrian flows. Collective behavior emerges from individuals following two basic rules: (1) moving directly towards their goal at a certain speed, and (2) maintaining a distance to other pedestrians and obstacles. This self-organized collective behavior manifests itself as trails that are formed by pedestrians moving in one direction. Furthermore, strong dependencies of the properties of pedestrian flows on geometric forms of buildings are shown, and the influence of geometric changes on performance characteristics is investigated. An example demonstrates how efficiency can be increased by reducing walkable areas. This work also presents an evolutionary algorithm for optimizing building layouts based on the social force model. Additionally, a decision-making model is integrated to describe alternative goal selection, and adaptation and learning capabilities are included to improve pedestrian avoidance behavior and decision strategies based on accumulated experience. A method for determining load distributions in individual sections of a path system considering subjective selection criteria is also developed. Finally, a model that describes the self-organization of path systems with minimal detours is presented, similar to natural transport networks where total length and material costs are optimized.