Modellierung und Simulation der Dynamik von Fussg\"angerstr\"omen

TL;DR

This paper develops a microscopic social force model for pedestrian dynamics, demonstrating how individual behaviors lead to complex collective patterns, and introduces optimization and decision-making tools for designing pedestrian-friendly infrastructure.

Contribution

It presents a novel social force-based pedestrian model with integrated optimization, decision-making, and learning capabilities, advancing the simulation and design of pedestrian environments.

Findings

Complex flow patterns emerge from simple individual rules.

Geometric design significantly influences pedestrian flow efficiency.

Optimization can improve infrastructure performance and pedestrian safety.

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.