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Forecasting ridership for a metropolitan transit authority

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  • Chiang, Wen-Chyuan
  • Russell, Robert A.
  • Urban, Timothy L.

Abstract

The recent volatility in gasoline prices and the economic downturn have made the management of public transportation systems particularly challenging. Accurate forecasts of ridership are necessary for the planning and operation of transit services. In this paper, monthly ridership of the Metropolitan Tulsa Transit Authority is analyzed to identify the relevant factors that influence transit use. Alternative forecasting models are also developed and evaluated based on these factors--using regression analysis (with autoregressive error correction), neural networks, and ARIMA models--to predict transit ridership. It is found that a simple combination of these forecasting methodologies yields greater forecast accuracy than the individual models separately. Finally, a scenario analysis is conducted to assess the impact of transit policies on long-term ridership.

Suggested Citation

  • Chiang, Wen-Chyuan & Russell, Robert A. & Urban, Timothy L., 2011. "Forecasting ridership for a metropolitan transit authority," Transportation Research Part A: Policy and Practice, Elsevier, vol. 45(7), pages 696-705, August.
    • Handle: RePEc:eee:transa:v:45:y:2011:i:7:p:696-705
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    References listed on IDEAS

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    Cited by:

    1. Fullerton, Thomas M. Jr & Walke, Adam G., 2012. "Border Zone Mass Transit Demand in Brownsville and Laredo," Journal of the Transportation Research Forum, Transportation Research Forum, vol. 51(2).
    2. Du, Qiang & Zhou, Yuqing & Huang, Youdan & Wang, Yalei & Bai, Libiao, 2022. "Spatiotemporal exploration of the non-linear impacts of accessibility on metro ridership," Journal of Transport Geography, Elsevier, vol. 102(C).
    3. Li, Shaoying & Lyu, Dijiang & Huang, Guanping & Zhang, Xiaohu & Gao, Feng & Chen, Yuting & Liu, Xiaoping, 2020. "Spatially varying impacts of built environment factors on rail transit ridership at station level: A case study in Guangzhou, China," Journal of Transport Geography, Elsevier, vol. 82(C).
    4. Lyons, Torrey & Ewing, Reid & Tian, Guang, 2025. "Coverage vs frequency: Is spatial coverage or temporal frequency more impactful on transit ridership?," Journal of Transport Geography, Elsevier, vol. 122(C).
    5. Ahmed Daqrouq & Ardeshir Anjomani, 2019. "Public Transit Ridership and Car-Oriented Cities: The Case of the Dallas Region," Economies, MDPI, vol. 7(3), pages 1-17, August.
    6. Caset, Freke & Blainey, Simon & Derudder, Ben & Boussauw, Kobe & Witlox, Frank, 2020. "Integrating node-place and trip end models to explore drivers of rail ridership in Flanders, Belgium," Journal of Transport Geography, Elsevier, vol. 87(C).
    7. Gao, Fan & Yang, Linchuan & Han, Chunyang & Tang, Jinjun & Li, Zhitao, 2022. "A network-distance-based geographically weighted regression model to examine spatiotemporal effects of station-level built environments on metro ridership," Journal of Transport Geography, Elsevier, vol. 105(C).
    8. Diab, Ehab & Kasraian, Dena & Miller, Eric J. & Shalaby, Amer, 2020. "The rise and fall of transit ridership across Canada: Understanding the determinants," Transport Policy, Elsevier, vol. 96(C), pages 101-112.
    9. Jinbao Zhao & Wei Deng & Yan Song & Yueran Zhu, 2014. "Analysis of Metro ridership at station level and station-to-station level in Nanjing: an approach based on direct demand models," Transportation, Springer, vol. 41(1), pages 133-155, January.

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