Avoid or Follow? Modelling Route Choice Based on Experimental Empirical Evidences

TL;DR

This paper investigates pedestrian route choice behavior through controlled experiments, revealing influences of path length, congestion, and social imitation, and introduces a new model that captures these factors for improved simulation accuracy.

Contribution

It presents a novel pedestrian route choice model incorporating empirical findings on congestion avoidance and social imitation, calibrated with experimental data.

Findings

Path length and congestion level influence route choices.

Emergent leaders' imitation affects decision-making.

The model accurately reproduces observed pedestrian behaviors.

Abstract

Computer-based simulation of pedestrian dynamics reached meaningful results in the last decade, thanks to empirical evidences and acquired knowledge fitting fundamental diagram constraints and space utilization. Moreover, computational models for pedestrian wayfinding often neglect extensive empirical evidences supporting the calibration and validation phase of simulations. The paper presents the results of a set of controlled experiments (with human volunteers) designed and performed to understand pedestrian's route choice. The setting offers alternative paths to final destinations, at different crowding conditions. Results show that the length of paths and level of congestion influence decisions (negative feedback), as well as imitative behaviour of "emergent leaders" choosing a new path (positive feedback). A novel here illustrated model for the simulation of pedestrian route choice captures such evidences, encompassing both the tendency to avoid congestion and to follow emerging leaders. The found conflicting tendencies are modelled with the introduction of a utility function allowing a consistent calibration over the achieved results. A demonstration of the simulated dynamics on a larger scenario will be also illustrated in the paper.