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

arXiv: https://arxiv.org/abs/2304.11167 · HTML: https://mlsanity.com/paper/2304.11167/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Apr 2023.

## Built on by (most cited first, 1 of 1)

- [A data-driven approach to predict decision point choice during normal and evacuation wayfinding in multi-story buildings](https://mlsanity.com/paper/2308.03511/) (2023)

## Abstract (arXiv)

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.