Exploring urban rail transit station-level ridership growth with network expansion

Urban rail transit (URT) is experiencing rapid network expansion in metropolises in China. The network expansion not only improves accessibility, but also motivates the surrounding land use development, which may have important effects on the ridership of existing stations. Considering the network scale, station-level accessibility increment, station characteristics, etc., this study develops an approach to explore the URT station-level ridership growth with network expansion, which can provide transit resource allocation guidance for URT agencies. Instead of collecting land use and socioeconomic data with huge labor and cost, the authors make good use of Automatic Fare Collection (AFC) data to develop proxy variables. Based on the temporal distribution of station-level ridership, a proxy for land use type is proposed. Then, multiple explanatory variables representing network expansion are introduced, and further the multivariate regression models are established to explore station-level ridership growth with network topology change and surrounding land use development. The results show that the proposed approach has good abilities to explain station-level ridership growth with network expansion and can make a response to network topology change and surrounding land use change.

• Record URL:
  • https://doi.org/10.1016/j.trd.2018.04.006
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S1361920916300487
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/13619209
• Supplemental Notes:
  • © 2018 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Liu, Shasha
  • Yao, Enjian
  • Li, Binbin
• Publication Date: 2019-8

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: pp 391-402
• Serial:
  • Transportation Research Part D: Transport and Environment
  • Volume: 73
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 1361-9209
  • Serial URL: http://www.sciencedirect.com/science/journal/13619209

Subject/Index Terms

• TRT Terms: Accessibility; Automatic fare collection; Data mining; Land use; Metropolitan areas; Networks; Rail transit; Rail transit stations; Ridership; Urban transit
• Geographic Terms: China
• Subject Areas: Operations and Traffic Management; Planning and Forecasting; Public Transportation; Terminals and Facilities;

Filing Info

• Accession Number: 01717565
• Record Type: Publication
• Files: TRIS
• Created Date: Sep 19 2019 3:07PM