Experimental study on the following behavior of pedestrians encountering those who go against the flow

Pedestrian following behavior has a significant impact on crowd dynamics within transportation hubs, where dense and heterogeneous passenger flows pose substantial traffic risk challenges. However, empirical research on this behavior in such complex environments remains scarce, and many existing models still rely on subjective assumptions. This study bridges this gap through controlled experiments to investigate pedestrian following behavior during walking and running, which are typical movement states in transportation hub scenarios.Theresults demonstrate that pedestrians exhibitalower willingness to follow others when running compared to walking. After ceasing one following behavior, more than 70% of pedestrians will initiate the next following within 0.5 s. Walking pedestrians tend to follow the individual within 2.49 m ahead with an angle rang of -53.77° to 50.25°, while the running pedestrians prefer to follow the one within 1.99 m ahead with an angle range of -80.83° to 57.21°, parameters that can inform spatial risk assessment in hub functional zones. Notably, no clear evidence shows that followers prefer pedestrians with larger speed differences or those whose movement directions align closely with their desired velocity direction. Additionally, during the following process, followers may switch their targets or cease following them. The study finds that "the distance between the follower and the leader" and "the number of pedestrians within the rectangular area formed by the positions of the follower and the leader" are the main driving factors for changes in the following behavior of followers in both walking and running states. Specifically, as these two factors increase, the probability of followers changing their following behavior also rises, which is vital for developing safety control strategies during passenger transfers. These findings are further compared with the assumptions about following behavior in previous models. This study enhances the understanding of pedestrian dynamics and aims to facilitate the integration of following behavior into crowd dynamics models, thereby improving the accuracy of evacuation models and supporting traffic risk prevention in hub systems.