A Two-Step Model for Predicting Travel Demand in Expanding Subways

Kaipeng Wang, Pu Wang, Zhiren Huang, Ximan Ling, Fan Zhang, Anthony Chen

Research output: Journal article publicationJournal articleAcademic researchpeer-review


In many cities, subways are expanding with new or extended lines being built and put into operations. The prediction of future travel demand in subway with the planned expansion is of significant importance because such information is crucial for new line planning and new network operations. In this study, we identify the determinant features from potential influential factors of passenger travel demand and develop a two-step model for predicting passenger travel demand in expanding subways. The proposed model is tested in an actual subway with a new line being put into operations, and achieves higher prediction accuracy than the benchmark models.

Original languageEnglish
JournalIEEE Transactions on Intelligent Transportation Systems
Publication statusAccepted/In press - 2022


  • Data models
  • line extension.
  • new lines
  • Predictive models
  • Public transportation
  • Rails
  • Sociology
  • Statistics
  • Subway
  • travel demand prediction
  • Urban areas

ASJC Scopus subject areas

  • Automotive Engineering
  • Mechanical Engineering
  • Computer Science Applications

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