Decision evolution and heterogeneity aware pedestrian wayfinding behaviour modelling in VR integrated transportation hub

Abstract

AbstractThe dynamic evolution and heterogeneity of passenger wayfinding decisions in integrated transport hubs have a significant impact on both operational efficiency and user experience. However, existing static models fall short in capturing the temporal variability of passengers’ cognitive states in response to environmental and situational changes. This study develops a virtual reality scenario of an integrated transport hub and conducts non-immersive behavioral experiments to support the construction of a novel dynamic modeling framework–Dynamic Hidden Markov Model-Logit (DHMM-Logit), which integrates a multi-state hidden Markov model with a Logit model. For the first time, decision cascading analysis is introduced into this framework, utilizing mutual information theory to uncover the temporal dependency and decay mechanism of historical decisions on current choices. These insights guide both the hyperparameter setting and discretization of decision sequences in the DHMM-Logit model. The framework comprehensively incorporates spatial syntax metrics, the use of 2D navigation tools and travel purposes to account for spatial and individual heterogeneity. In addition, a graph embedding-based high-order semantic encoding of nodes is introduced as explanatory variables, enhancing the model’s ability to fit and generalize sequential pedestrian decision-making processes. Empirical validation in the Shanghai Hongqiao Integrated Transport Hub demonstrates that the proposed DHMM-Logit model significantly outperforms baseline methods. The findings reveal pronounced latent cognitive state transitions during pedestrian wayfinding, with travel mode and navigation usage exerting significant influence on passengers’ spatial sensitivity and cognitive processes. This research provides a solid theoretical and empirical foundation for the optimization of hub spatial design and the implementation of personalized information guidance strategies.