IDEAS home Printed from https://ideas.repec.org/a/eee/trapol/v101y2021icp133-144.html
   My bibliography  Save this article

Data science and GIS-based system analysis of transit passenger complaints to improve operations and planning

Author

Listed:
  • Yona, Moran
  • Birfir, Genadi
  • Kaplan, Sigal

Abstract

Transit user complaints support system resilience by serving as a data source for service improvements. This study shows how geographic information system (GIS)-based analysis, econometric models, and latent class analysis can improve system-wide understanding of passenger complaints. The analyzed dataset consists of 718 passenger complaints concerning the operation of municipal lines in Jerusalem as the study region. The analytical methods consists of GIS-based analysis and statistical modeling: mapping, recursive bivariate probit estimation, negative binomial model estimation, and latent class analysis. The GIS-based analysis showed that the spatial distribution of complaints changes over time as a function of service disruption type and geographical area. The recursive bivariate probit model results indicated that the most acute sources of frustration are service problem recurrence and monetary loss, with the former caused by overcrowding, delays and line cancellations. The negative binomial model results shows that the number of complaints increases with an increase in the passenger boarding to bus arrivals ratio. Latent class analysis reveals that, in terms of both prevalence and customer frustration, overcrowding delays and line cancellations are the most acute problems in the study region. The proposed interface between transit complaints and GIS databases can readily be implemented by transport operators and authorities.

Suggested Citation

  • Yona, Moran & Birfir, Genadi & Kaplan, Sigal, 2021. "Data science and GIS-based system analysis of transit passenger complaints to improve operations and planning," Transport Policy, Elsevier, vol. 101(C), pages 133-144.
    • Handle: RePEc:eee:trapol:v:101:y:2021:i:c:p:133-144
    DOI: 10.1016/j.tranpol.2020.12.009
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0967070X2030946X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.tranpol.2020.12.009?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. Rainer Winkelmann, 2012. "Copula Bivariate Probit Models: With An Application To Medical Expenditures," Health Economics, John Wiley & Sons, Ltd., vol. 21(12), pages 1444-1455, December.
    2. Friman, Margareta, 2004. "The structure of affective reactions to critical incidents," Journal of Economic Psychology, Elsevier, vol. 25(3), pages 331-353, June.
    3. Heckman, James J, 1978. "Dummy Endogenous Variables in a Simultaneous Equation System," Econometrica, Econometric Society, vol. 46(4), pages 931-959, July.
    4. Tamara Kerzhner & Sigal Kaplan & Emily Silverman, 2018. "Physical walls, invisible barriers: Palestinian women's mobility in Jerusalem," Regional Science Policy & Practice, Wiley Blackwell, vol. 10(4), pages 299-314, November.
    5. Wittman, Michael D., 2014. "Are low-cost carrier passengers less likely to complain about service quality?," Journal of Air Transport Management, Elsevier, vol. 35(C), pages 64-71.
    6. Yap, Menno & Munizaga, Marcela, 2018. "Workshop 8 report: Big data in the digital age and how it can benefit public transport users," Research in Transportation Economics, Elsevier, vol. 69(C), pages 615-620.
    7. Weng-Kun Liu & Chia-Chun Yen, 2016. "Optimizing Bus Passenger Complaint Service through Big Data Analysis: Systematized Analysis for Improved Public Sector Management," Sustainability, MDPI, vol. 8(12), pages 1-21, December.
    8. Hensher, David A., 2017. "Future bus transport contracts under a mobility as a service (MaaS) regime in the digital age: Are they likely to change?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 98(C), pages 86-96.
    9. Sarker, Rumana Islam & Kaplan, Sigal & Mailer, Markus & Timmermans, Harry J.P., 2019. "Applying affective event theory to explain transit users’ reactions to service disruptions," Transportation Research Part A: Policy and Practice, Elsevier, vol. 130(C), pages 593-605.
    10. Thao, Vu Thi & Wegelin, Philipp & von Arx, Widar, 2017. "Are statutory passenger watchdogs effective in representing passenger interests in public transport?," Transport Policy, Elsevier, vol. 58(C), pages 1-9.
    11. Jun, Chae Nam & Chung, Chung Joo, 2016. "Big data analysis of local government 3.0: Focusing on Gyeongsangbuk-do in Korea," Technological Forecasting and Social Change, Elsevier, vol. 110(C), pages 3-12.
    12. Major, Wesley L. & Hubbard, Sarah M., 2019. "An examination of disability-related complaints in the United States commercial aviation sector," Journal of Air Transport Management, Elsevier, vol. 78(C), pages 43-53.
    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. Giampiero Marra & Rosalba Radice & Silvia Missiroli, 2014. "Testing the hypothesis of absence of unobserved confounding in semiparametric bivariate probit models," Computational Statistics, Springer, vol. 29(3), pages 715-741, June.
    2. Han, Sukjin & Vytlacil, Edward J., 2017. "Identification in a generalization of bivariate probit models with dummy endogenous regressors," Journal of Econometrics, Elsevier, vol. 199(1), pages 63-73.
    3. Marra Giampiero & Radice Rosalba, 2017. "A joint regression modeling framework for analyzing bivariate binary data in R," Dependence Modeling, De Gruyter, vol. 5(1), pages 268-294, December.
    4. Gilenko, Evgenii & Chernova, Aleksandra, 2021. "Saving behavior and financial literacy of Russian high school students: An application of a copula-based bivariate probit-regression approach," Children and Youth Services Review, Elsevier, vol. 127(C).
    5. John S. Heywood & W.S. Siebert & Xiangdong Wei, 2011. "Estimating the Use of Agency Workers: Can Family-Friendly Practices Reduce Their Use?," Industrial Relations: A Journal of Economy and Society, Wiley Blackwell, vol. 50(3), pages 535-564, July.
    6. Marc Henry & Ismael Mourifié, 2013. "Euclidean Revealed Preferences: Testing The Spatial Voting Model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(4), pages 650-666, June.
    7. James J. Heckman, 1991. "Randomization and Social Policy Evaluation Revisited," NBER Technical Working Papers 0107, National Bureau of Economic Research, Inc.
    8. Federico Ciliberto & Elie Tamer, 2009. "Market Structure and Multiple Equilibria in Airline Markets," Econometrica, Econometric Society, vol. 77(6), pages 1791-1828, November.
    9. Klege, Rebecca A. & Amuakwa-Mensah, Franklin & Visser, Martine, 2022. "Tenancy and energy choices in Rwanda. A replication and extension study," World Development Perspectives, Elsevier, vol. 26(C).
    10. Fossen, Frank M. & Glocker, Daniela, 2017. "Stated and revealed heterogeneous risk preferences in educational choice," European Economic Review, Elsevier, vol. 97(C), pages 1-25.
    11. Abu S. Shonchoy, 2015. "Seasonal Migration and Microcredit During Agricultural Lean Seasons: Evidence from Northwest Bangladesh," The Developing Economies, Institute of Developing Economies, vol. 53(1), pages 1-26, March.
    12. Lurkin, Virginie & Garrow, Laurie A. & Higgins, Matthew J. & Newman, Jeffrey P. & Schyns, Michael, 2017. "Accounting for price endogeneity in airline itinerary choice models: An application to Continental U.S. markets," Transportation Research Part A: Policy and Practice, Elsevier, vol. 100(C), pages 228-246.
    13. repec:zbw:rwirep:0200 is not listed on IDEAS
    14. Banal-Estañol, Albert & Duso, Tomaso & Seldeslachts, Jo & Szücs, Florian, 2022. "R&D Spillovers through RJV Cooperation," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 51(4), pages 1-10.
    15. Roger Bandick & Holger Görg, 2016. "Foreign acquisition, plant survival, and employment growth," World Scientific Book Chapters, in: MULTINATIONAL ENTERPRISES AND HOST COUNTRY DEVELOPMENT Volume 53: World Scientific Studies in International Economics, chapter 7, pages 115-141, World Scientific Publishing Co. Pte. Ltd..
    16. Pedersen, Tore & Friman, Margareta & Kristensson, Per, 2011. "The role of predicted, on-line experienced and remembered satisfaction in current choice to use public transport services," Journal of Retailing and Consumer Services, Elsevier, vol. 18(5), pages 471-475.
    17. Michelle A. Danis, 2003. "A Discrete Choice Approach to Measuring Competition in Equity Option Markets," FHFA Staff Working Papers 03-05, Federal Housing Finance Agency.
    18. Jaeun Choi & A. James O'Malley, 2017. "Estimating the causal effect of treatment in observational studies with survival time end points and unmeasured confounding," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(1), pages 159-185, January.
    19. Mundaca, Gabriela, 2015. "Multi-product firms, exports and exchange rate policies. Evidence from an emerging economy," MPRA Paper 65751, University Library of Munich, Germany.
    20. Chesher, Andrew, 2013. "Semiparametric Structural Models Of Binary Response: Shape Restrictions And Partial Identification," Econometric Theory, Cambridge University Press, vol. 29(2), pages 231-266, April.
    21. Souvik Datta & Massimo Filippini, 2012. "Analysing the Impact of ENERGY STAR Rebate Policies in the US," CEPE Working paper series 12-86, CEPE Center for Energy Policy and Economics, ETH Zurich.

    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:trapol:v:101:y:2021:i:c:p:133-144. 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/30473/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.