IDEAS home Printed from https://ideas.repec.org/a/eee/transa/v163y2022icp126-147.html
   My bibliography  Save this article

A direct demand model for bus transit ridership in Bengaluru, India

Author

Listed:
  • Deepa, L.
  • Rawoof Pinjari, Abdul
  • Krishna Nirmale, Sangram
  • Srinivasan, Karthik K.
  • Rambha, Tarun

Abstract

This study formulates a disaggregate direct demand model of bus transit ridership while addressing the following substantive and methodological issues: (a) endogeneity and non-linearity of the influence of service frequency on ridership, (b) inter-route relationships such as competition and complementarity among routes within the bus transit network and with other transit networks (such as the metro/rail network), (c) relating spatially aggregated demand to disaggregate, stop-level catchment characteristics – although demand data are available only at an aggregation of stop-clusters, and (d) overlapping of catchment areas among closely spaced stops. The proposed model is applied to analyze bus transit ridership (boardings) during weekdays for morning peak period in Bengaluru, India. This study is among the first to develop a comprehensive direct demand model for forecasting bus transit ridership in an Indian city. Yet, the proposed conceptual and methodological framework and the findings from the study are general enough to be of use for transit planning in other cities of India and other countries. Transit agencies with spatially aggregate, fare-stage cluster-level ridership data can employ the proposed approach to examine the influence of disaggregate stop-level catchment characteristics on ridership. Additionally, transit agencies may utilise the proposed model to quantify bus ridership impacts of service network modifications, route alignments, and network connectivity/accessibility, while considering interactions with other transit networks. The empirical results suggest that while increasing service frequency increases ridership along low-frequency routes, the returns from increasing service frequency diminish as current frequency levels increase. Further, it is shown that route-level passenger kilometres, a variable commonly available with transit agencies, serves effectively as an instrument for addressing endogeneity between route-level service frequency and stop-route-level ridership.

Suggested Citation

  • Deepa, L. & Rawoof Pinjari, Abdul & Krishna Nirmale, Sangram & Srinivasan, Karthik K. & Rambha, Tarun, 2022. "A direct demand model for bus transit ridership in Bengaluru, India," Transportation Research Part A: Policy and Practice, Elsevier, vol. 163(C), pages 126-147.
  • Handle: RePEc:eee:transa:v:163:y:2022:i:c:p:126-147
    DOI: 10.1016/j.tra.2022.07.004
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0965856422001732
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.tra.2022.07.004?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 search for a different version of it.

    References listed on IDEAS

    as
    1. Kaushik Deb & Massimo Filippini, 2013. "Public Bus Transport Demand Elasticities in India," Journal of Transport Economics and Policy, University of Bath, vol. 47(3), pages 419-436, September.
    2. Chakour, Vincent & Eluru, Naveen, 2016. "Examining the influence of stop level infrastructure and built environment on bus ridership in Montreal," Journal of Transport Geography, Elsevier, vol. 51(C), pages 205-217.
    3. Taylor, Brian D & Miller, Douglas & Iseki, Hiroyuki & Fink, Camille, 2008. "Nature and/or nurture? Analyzing the determinants of transit ridership across US urbanized areas," University of California Transportation Center, Working Papers qt5w9045hh, University of California Transportation Center.
    4. Berrebi, Simon J. & Joshi, Sanskruti & Watkins, Kari E., 2021. "On bus ridership and frequency," Transportation Research Part A: Policy and Practice, Elsevier, vol. 148(C), pages 140-154.
    5. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, December.
    6. Hausman, Jerry, 2015. "Specification tests in econometrics," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 38(2), pages 112-134.
    7. Terza, Joseph V. & Basu, Anirban & Rathouz, Paul J., 2008. "Two-stage residual inclusion estimation: Addressing endogeneity in health econometric modeling," Journal of Health Economics, Elsevier, vol. 27(3), pages 531-543, May.
    8. Voith, Richard, 1991. "The long-run elasticity of demand for commuter rail transportation," Journal of Urban Economics, Elsevier, vol. 30(3), pages 360-372, November.
    9. Holmgren, Johan, 2007. "Meta-analysis of public transport demand," Transportation Research Part A: Policy and Practice, Elsevier, vol. 41(10), pages 1021-1035, December.
    10. Rahman, Moshiur & Yasmin, Shamsunnahar & Eluru, Naveen, 2019. "Controlling for endogeneity between bus headway and bus ridership: A case study of the Orlando region," Transport Policy, Elsevier, vol. 81(C), pages 208-219.
    11. Felix FitzRoy & Ian Smith, 1999. "Season Tickets and the Demand for Public Transport," Kyklos, Wiley Blackwell, vol. 52(2), pages 219-238, May.
    12. Simon Berrebi & Sanskruti Joshi & Kari E Watkins, 2020. "On Ridership and Frequency," Papers 2002.02493, arXiv.org, revised Apr 2021.
    13. Abdul Rawoof Pinjari & Chandra R. Bhat, 2011. "Activity-based Travel Demand Analysis," Chapters, in: André de Palma & Robin Lindsey & Emile Quinet & Roger Vickerman (ed.), A Handbook of Transport Economics, chapter 10, Edward Elgar Publishing.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Deepa, L. & Pinjari, Abdul Rawoof & Nirmale, Sangram Krishna & Biswas, Mehek & Srinivasan, Karthik K., 2023. "The adverse impact of headway variability on bus transit ridership: Evidence from Bengaluru, India," Transport Policy, Elsevier, vol. 141(C), pages 343-356.

    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. Deepa, L. & Pinjari, Abdul Rawoof & Nirmale, Sangram Krishna & Biswas, Mehek & Srinivasan, Karthik K., 2023. "The adverse impact of headway variability on bus transit ridership: Evidence from Bengaluru, India," Transport Policy, Elsevier, vol. 141(C), pages 343-356.
    2. Jun Li & Serguei Netessine & Sergei Koulayev, 2018. "Price to Compete … with Many: How to Identify Price Competition in High-Dimensional Space," Management Science, INFORMS, vol. 64(9), pages 4118-4136, September.
    3. Minoru Morita & Kazuyuki Iwata & Toshi H. Arimura, 2022. "The rebound effect in air conditioner usage: an empirical analysis of Japanese individuals’ behaviors," Environmental Economics and Policy Studies, Springer;Society for Environmental Economics and Policy Studies - SEEPS, vol. 24(1), pages 99-117, January.
    4. Fang Hai & Miller Nolan H. & Rizzo John & Zeckhauser Richard, 2011. "Demanding Customers: Consumerist Patients and Quality of Care," The B.E. Journal of Economic Analysis & Policy, De Gruyter, vol. 11(1), pages 1-51, September.
    5. Haiyang Lu & Peishan Tong & Rong Zhu, 2020. "Longitudinal Evidence on Social Trust and Happiness in China: Causal Effects and Mechanisms," Journal of Happiness Studies, Springer, vol. 21(5), pages 1841-1858, June.
    6. David T. Frazier & Eric Renault & Lina Zhang & Xueyan Zhao, 2020. "Weak Identification in Discrete Choice Models," Papers 2011.06753, arXiv.org, revised Jan 2021.
    7. Toshi H. Arimura & Kazuyuki Iwata & Hajime Katayama & Mari Sakudo, 2018. "Seemingly Unrelated Interventions:Environmental Management Systems in the Workplace and Energy Conservation Behaviors at Home," RIEEM Discussion Paper Series 1802, Research Institute for Environmental Economics and Management, Waseda University.
    8. Wooldridge, Jeffrey M., 2014. "Quasi-maximum likelihood estimation and testing for nonlinear models with endogenous explanatory variables," Journal of Econometrics, Elsevier, vol. 182(1), pages 226-234.
    9. Rahman, Moshiur & Yasmin, Shamsunnahar & Eluru, Naveen, 2019. "Controlling for endogeneity between bus headway and bus ridership: A case study of the Orlando region," Transport Policy, Elsevier, vol. 81(C), pages 208-219.
    10. 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.
    11. Hummy Song & Elena Andreyeva & Guy David, 2022. "Time Is the Wisest Counselor of All: The Value of Provider–Patient Engagement Length in Home Healthcare," Management Science, INFORMS, vol. 68(1), pages 420-441, January.
    12. Toshi H. Arimura & Kazuyuki Iwata & Hajime Katayama & Mari Sakudo, 2021. "Seemingly Unrelated Interventions: Environmental Management Systems in the Workplace and Energy Saving Practices at Home," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 80(4), pages 761-794, December.
    13. Lippi Bruni, Matteo & Mammi, Irene & Ugolini, Cristina, 2016. "Does the extension of primary care practice opening hours reduce the use of emergency services?," Journal of Health Economics, Elsevier, vol. 50(C), pages 144-155.
    14. MATSUDA Naoko & MATSUO Yutaka, 2014. "Governing Board Interlocks and Probability of an IPO," Discussion papers 14040, Research Institute of Economy, Trade and Industry (RIETI).
    15. Ji Yan & Sally Brocksen, 2013. "Adolescent risk perception, substance use, and educational attainment," Journal of Risk Research, Taylor & Francis Journals, vol. 16(8), pages 1037-1055, September.
    16. Campbell, Randall C. & Nagel, Gregory L., 2016. "Private information and limitations of Heckman's estimator in banking and corporate finance research," Journal of Empirical Finance, Elsevier, vol. 37(C), pages 186-195.
    17. Mehzabin Tuli, Farzana & Mitra, Suman & Crews, Mariah B., 2021. "Factors influencing the usage of shared E-scooters in Chicago," Transportation Research Part A: Policy and Practice, Elsevier, vol. 154(C), pages 164-185.
    18. Peppel-Srebrny, Jemima, 2021. "Not all government budget deficits are created equal: Evidence from advanced economies' sovereign bond markets," Journal of International Money and Finance, Elsevier, vol. 118(C).
    19. Etienne Redor & Magnus Blomkvist, 2021. "Do all inside and affiliated directors hold the same value for shareholders?," Economics Bulletin, AccessEcon, vol. 41(3), pages 882-895.
    20. Tesfaye, Wondimagegn & Tirivayi, Nyasha, 2020. "Crop diversity, household welfare and consumption smoothing under risk: Evidence from rural Uganda," World Development, Elsevier, vol. 125(C).

    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:163:y:2022:i:c:p:126-147. 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.