An Intelligence-Based Optimization Model of Passenger Flow in a Transportation Station

This paper proposes an intelligence-based approach to predict passengers' route choice behavior, which is crucial to the effective utilization of transportation stations and affects passenger comfort and safety. The actual route choice decisions of passengers are extremely difficult to mimic as they involve human behavior. A comprehensive methodology for capturing route choice behavior is still lacking because extensive labor and time resources are required to collect passenger movement data from different stations. In this paper, a four-month site survey was carried out to collect actual route choice behavior information in nine transportation stations in Hong Kong during peak hours. We developed an intelligent model to capture passengers' route choice decision-making that achieved prediction accuracy of 86%. The applicability of this intelligent route choice model is demonstrated by optimizing the number of gates in a transportation station to inform the spatial design of the station.