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Forecasting ridership for a metropolitan transit authority

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

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    Bibliographic Info

    Article provided by Elsevier in its journal Transportation Research Part A: Policy and Practice.

    Volume (Year): 45 (2011)
    Issue (Month): 7 (August)
    Pages: 696-705

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    Handle: RePEc:eee:transa:v:45:y:2011:i:7:p:696-705

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    Related research

    Keywords: Public transit ridership Forecasting models Scenario analysis;

    References

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    1. Wang, George H. K. & Skinner, David, 1984. "The impact of fare and gasoline price changes on monthly transit ridership: Empirical evidence from seven U.S. transit authorities," Transportation Research Part B: Methodological, Elsevier, vol. 18(1), pages 29-41, February.
    2. Zhang, G. Peter & Qi, Min, 2005. "Neural network forecasting for seasonal and trend time series," European Journal of Operational Research, Elsevier, vol. 160(2), pages 501-514, January.
    3. Robert Engle, 2001. "GARCH 101: The Use of ARCH/GARCH Models in Applied Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 157-168, Fall.
    4. Marco Aiolfi & Carlos Capistrán & Allan Timmermann, 2010. "Forecast Combinations," CREATES Research Papers 2010-21, School of Economics and Management, University of Aarhus.
    5. Clemen, Robert T., 1989. "Combining forecasts: A review and annotated bibliography," International Journal of Forecasting, Elsevier, vol. 5(4), pages 559-583.
    6. Taylor, Brian D. & Fink, Camille N.Y., 2003. "The Factors Influencing Transit Ridership: A Review and Analysis of the Ridership Literature," University of California Transportation Center, Working Papers qt3xk9j8m2, University of California Transportation Center.
    7. Chiang, W. -C. & Urban, T. L. & Baldridge, G. W., 1996. "A neural network approach to mutual fund net asset value forecasting," Omega, Elsevier, vol. 24(2), pages 205-215, April.
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    Cited by:
    1. 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.
    2. Fullerton, Thomas M., Jr. & Walke, Adam G., 2012. "Border zone mass transit demand in Brownsville and Laredo," MPRA Paper 42990, University Library of Munich, Germany.

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