IDEAS home Printed from https://ideas.repec.org/a/eee/transa/v204y2026ics0965856425004471.html

Heterogeneous fare sensitivity in microtransit: evidence from a natural experiment

Author

Listed:
  • Li, Jia
  • Moul, Chuck

Abstract

Small cities and outer suburbs increasingly turn to on-demand microtransit systems to provide public transportation in low-density areas. As pandemic-era subsidies expire, policymakers need evidence on how fare changes affect ridership and revenue. We study Wilson, North Carolina, which in 2020 replaced its fixed-route bus service with citywide on-demand microtransit. Leveraging detailed rider-level trip data and a natural experiment involving a fare increase, we show that aggregate stability masks large rider-level variation. A two-part structural model further reveals that fare sensitivity differs sharply by usage: low-frequency riders cut usage substantially, while high-frequency riders show only modest reductions. A simulated 40 % fare increase raises revenue by just 7 %, as losses from the large share of price-sensitive riders leave only limited net gains from the smaller group of inelastic riders. Traditional aggregate models that impose constant fare responsiveness risk overstating the fiscal benefits of uniform fare hikes, as our simulations demonstrate. Our framework can be applied to other microtransit systems and technology-enabled transit services with rider records. It also offers guidance for designing stated-preference surveys widely used by transportation agencies, enabling them to incorporate revealed rider heterogeneity into forecasts when rider-level trip data are unavailable. More broadly, this study bridges transportation policy, industrial organization, and marketing by treating microtransit as a market for mobility where pricing, segmentation, and equity interact.

Suggested Citation

  • Li, Jia & Moul, Chuck, 2026. "Heterogeneous fare sensitivity in microtransit: evidence from a natural experiment," Transportation Research Part A: Policy and Practice, Elsevier, vol. 204(C).
    • Handle: RePEc:eee:transa:v:204:y:2026:i:c:s0965856425004471
    DOI: 10.1016/j.tra.2025.104814
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0965856425004471
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.tra.2025.104814?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Frondel, Manuel & Vance, Colin, 2010. "Fixed, random, or something in between? A variant of Hausman's specification test for panel data estimators," Economics Letters, Elsevier, vol. 107(3), pages 327-329, June.
    2. Guzman, Luis A. & Beltran, Carlos & Bonilla, Jorge & Gomez Cardona, Santiago, 2021. "BRT fare elasticities from smartcard data: Spatial and time-of-the-day differences," Transportation Research Part A: Policy and Practice, Elsevier, vol. 150(C), pages 335-348.
    3. Kenneth L. Judd, 1998. "Numerical Methods in Economics," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262100711, December.
    4. William H. Greene, 1994. "Accounting for Excess Zeros and Sample Selection in Poisson and Negative Binomial Regression Models," Working Papers 94-10, New York University, Leonard N. Stern School of Business, Department of Economics.
    5. Andreas Million & Regina T. Riphahn & Achim Wambach, 2003. "Incentive effects in the demand for health care: a bivariate panel count data estimation," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(4), pages 387-405.
    6. Kholodov, Yaroslav & Jenelius, Erik & Cats, Oded & van Oort, Niels & Mouter, Niek & Cebecauer, Matej & Vermeulen, Alex, 2021. "Public transport fare elasticities from smartcard data: Evidence from a natural experiment," Transport Policy, Elsevier, vol. 105(C), pages 35-43.
    7. Mullahy, John, 1986. "Specification and testing of some modified count data models," Journal of Econometrics, Elsevier, vol. 33(3), pages 341-365, December.
    8. Manuel Frondel & Colin Vance, 2010. "Fixed, Random, or Something in Between? – A Variant of HAUSMAN’s Specifi cation Test for Panel Data Estimators," Ruhr Economic Papers 0160, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Ruhr-Universität Bochum, Universität Dortmund, Universität Duisburg-Essen.
    9. Bhat, Chandra R., 1997. "Covariance heterogeneity in nested logit models: Econometric structure and application to intercity travel," Transportation Research Part B: Methodological, Elsevier, vol. 31(1), pages 11-21, February.
    10. David Hensher & William Greene, 2003. "The Mixed Logit model: The state of practice," Transportation, Springer, vol. 30(2), pages 133-176, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Greene, William, 2007. "Functional Form and Heterogeneity in Models for Count Data," Foundations and Trends(R) in Econometrics, now publishers, vol. 1(2), pages 113-218, August.
    2. William H. Greene & David A. Hensher, 2008. "Modeling Ordered Choices: A Primer and Recent Developments," Working Papers 08-26, New York University, Leonard N. Stern School of Business, Department of Economics.
    3. William Greene, 2014. "Models for ordered choices," Chapters, in: Stephane Hess & Andrew Daly (ed.), Handbook of Choice Modelling, chapter 15, pages 333-362, Edward Elgar Publishing.
    4. William Greene, 2009. "Models for count data with endogenous participation," Empirical Economics, Springer, vol. 36(1), pages 133-173, February.
    5. Agostino Capponi & Zhaonan Qu, 2025. "Handling Sparse Non-negative Data in Finance," Papers 2509.01478, arXiv.org.
    6. Christopher J. W. Zorn, 1998. "An Analytic and Empirical Examination of Zero-Inflated and Hurdle Poisson Specifications," Sociological Methods & Research, , vol. 26(3), pages 368-400, February.
    7. Timothy C. Haab, "undated". "A Utility Based Repeated Discrete Choice Model of Consumer Demand," Working Papers 9611, East Carolina University, Department of Economics.
    8. Carolina Laureti, 2015. "The Debt Puzzle in Dhaka’s Slums: Do Poor People Co-hold for Liquidity Needs?," Working Papers CEB 15-021, ULB -- Universite Libre de Bruxelles.
    9. Niklas Elert, 2014. "What determines entry? Evidence from Sweden," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 53(1), pages 55-92, August.
    10. Schmitz, Hendrik, 2013. "Practice budgets and the patient mix of physicians – The effect of a remuneration system reform on health care utilisation," Journal of Health Economics, Elsevier, vol. 32(6), pages 1240-1249.
    11. Abbas Moghimbeigi & Mohammed Reza Eshraghian & Kazem Mohammad & Brian Mcardle, 2008. "Multilevel zero-inflated negative binomial regression modeling for over-dispersed count data with extra zeros," Journal of Applied Statistics, Taylor & Francis Journals, vol. 35(10), pages 1193-1202.
    12. Buczkowska, Sabina & de Lapparent, Matthieu, 2014. "Location choices of newly created establishments: Spatial patterns at the aggregate level," Regional Science and Urban Economics, Elsevier, vol. 48(C), pages 68-81.
    13. Kovárník Richard & Staňková Michaela, 2023. "Assessing the Technical Efficiency of European Companies in the Automotive Sector," LOGI – Scientific Journal on Transport and Logistics, Sciendo, vol. 14(1), pages 251-262, January.
    14. Daniel Biftu Bekalo & Dufera Tejjeba Kebede, 2021. "Zero-Inflated Models for Count Data: An Application to Number of Antenatal Care Service Visits," Annals of Data Science, Springer, vol. 8(4), pages 683-708, December.
    15. Manuel Frondel & Colin Vance, 2013. "Fuel Taxes versus Efficiency Standards – An Instrumental Variable Approach," Ruhr Economic Papers 0445, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Ruhr-Universität Bochum, Universität Dortmund, Universität Duisburg-Essen.
    16. Carolina Laureti, 2017. "Why do Poor People Co-hold Debt and Liquid Savings?," Working Papers CEB 17-007, ULB -- Universite Libre de Bruxelles.
    17. Jiang, Yuan & House, Lisa A., "undated". "Comparison of the Performance of Count Data Models under Different Zero-Inflation Scenarios Using Simulation Studies," 2017 Annual Meeting, July 30-August 1, Chicago, Illinois 258342, Agricultural and Applied Economics Association.
    18. Isler, Cassiano Augusto & Blumenfeld, Marcelo & Caldeira, Gabriel Pereira & Roberts, Clive, 2024. "Long-Distance railway mode choice in Brazil: Evidence from a discrete choice experiment," Research in Transportation Economics, Elsevier, vol. 104(C).
    19. Gregory N. Price, 2019. "Does Productivity in the Formal Food Sector Drive Human Ebola Virus Infections in Sub‐Saharan Africa?," African Development Review, African Development Bank, vol. 31(2), pages 167-178, June.
    20. Hossein Kavand & Marcel Voia, 2018. "Estimation of Health Care Demand and its Implication on Income Effects of Individuals," Springer Proceedings in Business and Economics, in: William H. Greene & Lynda Khalaf & Paul Makdissi & Robin C. Sickles & Michael Veall & Marcel-Cristia (ed.), Productivity and Inequality, pages 275-304, Springer.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:transa:v:204:y:2026:i:c:s0965856425004471. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/547/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.