An intelligence-based optimization model of passenger flow in a transportation station

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

22 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Article number6514084
Pages (from-to)1290-1300
Journal / PublicationIEEE Transactions on Intelligent Transportation Systems
Volume14
Issue number3
Online published6 May 2013
Publication statusPublished - Sept 2013

Abstract

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. © 2000-2011 IEEE.

Research Area(s)

  • Artificial neural network (ANN), human factors, neural network applications, route choice, transportation