A Study on Safety Evaluation of Pedestrian Flows Based on Partial Impact Dynamics by Real-Time Data in Subway Stations

Xianing Wang, Zhan Zhang (), Ying Wang, Jun Yang and Linjun Lu
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Xianing Wang: School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Zhan Zhang: School of Design, Shanghai Jiao Tong University, Shanghai 200240, China
Ying Wang: School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Jun Yang: Safety Supervise Department, Shanghai Municipal Commission of Transport, Shanghai 200003, China
Linjun Lu: School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

Sustainability, 2022, vol. 14, issue 16, 1-19

Abstract: With the rapid development of urban rail transit, the scientific assurance of pedestrian safety has become an important issue. Orderly behavior is a crucial factor affecting pedestrian safety. Therefore, in-depth research into pedestrian behavior is needed. This study carries out an evaluation of safety in pedestrian flows by establishing a new force model based on real-time data. In this model, we consider the microscopic characteristics of pedestrians and define four force influence mechanisms for simulating pedestrian behavior. Compared with existing models, this model incorporates partial impact dynamics to make it applicable to the particular environment of subway stations. Through the validation of real-world data, it is demonstrated that the model can accurately describe pedestrian behavior and better reproduce the characteristics of pedestrians. The influence of pedestrians and of environmental factors on the model are also discussed. Using our model, we propose a risk evaluation system based on pedestrian volatility. By using real-time pedestrian information from subway stations, the potential risk to pedestrians can be discerned and assessed in advance. This research advances the management of pedestrian safety and provides a framework for studying behavior models and for safety evaluation.

Keywords: pedestrian behavior; risk evaluation; subway station; social force model; analytic hierarchy process; data-driven (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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