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Forecasting bus ridership using a “Blended Approach”

Author

Listed:
  • Catherine T. Lawson

    (State University of New York, Albany)

  • Alex Muro

    (State University of New York, Albany)

  • Eric Krans

    (State University of New York, Albany)

Abstract

As sources of “Big Data” continue to grow, transportation planners and researchers seek to utilize these new resources. Given the current dependency on traditional transportation data sources and conventional tools (e.g., spreadsheets and propriety models), how can these new resources be used? This research examines a “blended data” approach, using a web-based, open source platform to assist transit agencies to forecast bus ridership. The platform is capable of incorporating new Big Data sources and traditional data sources, using modern processing techniques and tools, particularly Application Programming Interfaces (APIs). This research demonstrates the use of APIs in a transit demand methodology that yields a robust model for bus ridership. The approach uses the Census Transportation Planning Products data, modified with American Community Survey data, to generate origin–destination tables for bus trips in a designated market area. Microsimulation models us a transit scheduling specification (General Transit Feed Specification) and an open source routing engine (OpenTripPlanner). Local farebox data validates the microsimulation models. Analyses of model output and farebox data for the Atlantic City transit market area, and a scenario analysis of service reduction in the Princeton/Trenton transit market area, illustrate the use a “blended approach” for bus ridership forecasting.

Suggested Citation

  • Catherine T. Lawson & Alex Muro & Eric Krans, 2021. "Forecasting bus ridership using a “Blended Approach”," Transportation, Springer, vol. 48(2), pages 617-641, April.
    • Handle: RePEc:kap:transp:v:48:y:2021:i:2:d:10.1007_s11116-019-10073-z
    DOI: 10.1007/s11116-019-10073-z
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    References listed on IDEAS

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    1. Boisjoly, Geneviève & Grisé, Emily & Maguire, Meadhbh & Veillette, Marie-Pier & Deboosere, Robbin & Berrebi, Emma & El-Geneidy, Ahmed, 2018. "Invest in the ride: A 14 year longitudinal analysis of the determinants of public transport ridership in 25 North American cities," Transportation Research Part A: Policy and Practice, Elsevier, vol. 116(C), pages 434-445.
    2. Wei, Ran & Liu, Xiaoyue & Mu, Yongjian & Wang, Liming & Golub, Aaron & Farber, Steven, 2017. "Evaluating public transit services for operational efficiency and access equity," Journal of Transport Geography, Elsevier, vol. 65(C), pages 70-79.
    3. Karner, Alex, 2018. "Assessing public transit service equity using route-level accessibility measures and public data," Journal of Transport Geography, Elsevier, vol. 67(C), pages 24-32.
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    Cited by:

    1. Antonello Cammarano & Vincenzo Varriale & Francesca Michelino & Mauro Caputo, 2022. "Open and Crowd-Based Platforms: Impact on Organizational and Market Performance," Sustainability, MDPI, vol. 14(4), pages 1-26, February.

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