  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.
