Simulation of cross-pedestrian flow in intersection based on direction fuzzy visual field

Pedestrian flow refers to the spatiotemporal distribution of people moving in a defined area. At crosswalks, pedestrian dynamics exhibit complex self-organization patterns resulting from interactions between individuals. This paper proposes a novel crosswalk pedestrian flow model based on the concept of directional fuzzy visual field (DFVF) to capture pedestrian heterogeneity. The DFVF defines fuzzy distributions of personal space and information processing capabilities, enabling improved representation of diversity compared to previous models. Incorporating k-nearest neighbour rules in the DFVF pedestrian network topology also better mimics real-world interactions. Using a cellular automata framework, pedestrian self-organization effects like stratification and bottleneck oscillation are simulated at intersections. The model replicates empirically observed dynamics of density, velocity, and evacuation time. Results demonstrate that controlling pedestrian conflicts can effectively enhance crosswalk flow efficiency. This research introduces new techniques for simulating pedestrian psychology and behaviour, providing a valuable contribution to pedestrian flow theory and supporting crosswalk design optimization.