Modeling Pedestrian Behavior in Metro Stations with Commercial Facilities: An Attractiveness-Based Approach

Abstract

This study is focused on understanding and optimizing pedestrian behavior in metro stations with commercial facilities. Recognizing that both transit and commercial interests influence pedestrian behavior in these stations, we developed an improved attractiveness-based route choice model. This model incorporates subjective perceptions of distance and waiting time, alongside the utility of facilities, to predict pedestrian behavior and passenger flow more accurately. We implemented the model in AnyLogic using the social force model for simulation. Our findings show that the incorporation of subjective perceptions enhances the predictive accuracy of pedestrian behavior in metro stations with commercial facilities. We validated the model using benchmarking methods against real-world data from Shanghai's Jing'an Temple station. The simulation results highlighted how strategic placement and configuration of transit and commercial facilities can optimize operational performance and enhance passenger experience. We applied the model to various scenarios, revealing critical insights for spatial design, such as the benefits of repositioning gates, adding barriers near escalators, and adjusting escalator speeds. The study provides actionable recommendations for station layout optimization to improve transportation efficiency and commercial viability. Future research should be designed to expand sample sizes, incorporate more diverse commercial types, and utilize advanced tools, such as virtual reality, for data collection to further refine pedestrian behavior models in complex environments.