Pedestrian wayfinding behavior in a multi-story building: a comprehensive modeling study featuring route choice, wayfinding performance, and observation behavior

TL;DR

This study models pedestrian wayfinding in complex buildings using discrete choice and regression models, revealing how personal, infrastructural, and route factors influence route choice, performance, and observation behaviors through VR experiments.

Contribution

It is the first comprehensive study to analyze multiple aspects of pedestrian wayfinding behavior simultaneously using diverse variables and modeling techniques.

Findings

Route choice is mainly influenced by route characteristics.

Wayfinding performance is affected by personal traits.

Observation behavior depends on task complexity and local route features.

Abstract

This paper proposes a comprehensive approach for modeling pedestrian wayfinding behavior in complex buildings. This study employs two types of discrete choice models (i.e., MNL and PSL) featuring pedestrian route choice behavior, and three multivariate linear regression (MLR) models featuring the overall wayfinding performance and observation behavior (e.g., hesitation behavior and head rotation). Behavioral and questionnaire data featuring pedestrian wayfinding behavior and personal information were collected using a Virtual Reality experiment. Four wayfinding tasks were designed to determine how personal, infrastructure, and route characteristics affect indoor pedestrian wayfinding behavior on three levels, including route choice, wayfinding performance, and observation behavior. We find that pedestrian route choice behavior is primarily influenced by route characteristics, whereas wayfinding performance is also influenced by personal characteristics. Observation behavior is mainly influenced by task complexity, personal characteristics, and local properties of the routes that offer route information. To the best of our knowledge, this work represents the first attempt to investigate the impact of the same comprehensive set of variables on various metrics feature wayfinding behavior simultaneously.