IDEAS home Printed from https://ideas.repec.org/a/eee/transa/v87y2016icp11-21.html
   My bibliography  Save this article

The effect of slow zones on ridership: An analysis of the Chicago Transit Authority “El” Blue Line

Author

Listed:
  • Bernal, Margarita
  • Welch, Eric W.
  • Sriraj, P.S.

Abstract

Transit agencies frequently upgrade rail tracks to bring the system to a state of good repair (SGR) and to improve the speed and reliability of urban rail transit service. For safety during construction, agencies establish slow zones in which trains must reduce speed. Slow zones create delays and schedule disruptions that result in customer dissatisfaction and discontinued use of transit, either temporarily or permanently. While transit agencies are understandably concerned about the possible negative effects of slow zones, empirical research has not specifically examined the relationship between slow zones and ridership. This paper partially fills that gap. Using data collected from the Chicago Transit Authority (CTA) Customer Experience Survey, CTA Slow Zone Maps, and, the Automatic Fare Collection System (AFC), it examines whether recurring service delays due to slow zones affect transit rider behavior and if the transit loyalty programs, such as smart card systems, increase or decrease rider defections. Findings suggest that slow zones increase headway deviation which reduces ridership. Smart card customers are more sensitive to slow zones as they are more likely to stop using transit as a result of delay. The findings of this paper have two major policy implications for transit agencies: (1) loyalty card users, often the most reliable source of revenue, are most at risk for defection during construction and (2) it is critical to minimize construction disruptions and delays in the long run by maintaining state of good repair. The results of this paper can likely be used as the basis for supporting immediate funding requests to bring the system to an acceptable state of good repair as well as stimulating ideas about funding reform for transit.

Suggested Citation

  • Bernal, Margarita & Welch, Eric W. & Sriraj, P.S., 2016. "The effect of slow zones on ridership: An analysis of the Chicago Transit Authority “El” Blue Line," Transportation Research Part A: Policy and Practice, Elsevier, vol. 87(C), pages 11-21.
  • Handle: RePEc:eee:transa:v:87:y:2016:i:c:p:11-21
    DOI: 10.1016/j.tra.2016.02.007
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0965856416000410
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.tra.2016.02.007?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. A. Higgins & E. Kozan, 1998. "Modeling Train Delays in Urban Networks," Transportation Science, INFORMS, vol. 32(4), pages 346-357, November.
    2. Fosgerau, Mogens, 2009. "The marginal social cost of headway for a scheduled service," Transportation Research Part B: Methodological, Elsevier, vol. 43(8-9), pages 813-820, September.
    3. 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.
    4. Bagchi, M. & White, P.R., 2005. "The potential of public transport smart card data," Transport Policy, Elsevier, vol. 12(5), pages 464-474, September.
    5. Morency, Catherine & Trépanier, Martin & Agard, Bruno, 2007. "Measuring transit use variability with smart-card data," Transport Policy, Elsevier, vol. 14(3), pages 193-203, May.
    6. Bhat, Chandra R. & Sardesai, Rupali, 2006. "The impact of stop-making and travel time reliability on commute mode choice," Transportation Research Part B: Methodological, Elsevier, vol. 40(9), pages 709-730, November.
    7. Carey, Malachy & Kwiecinski, Andrzej, 1994. "Stochastic approximation to the effects of headways on knock-on delays of trains," Transportation Research Part B: Methodological, Elsevier, vol. 28(4), pages 251-267, August.
    8. Yuan, Jianxin & Hansen, Ingo A., 2007. "Optimizing capacity utilization of stations by estimating knock-on train delays," Transportation Research Part B: Methodological, Elsevier, vol. 41(2), pages 202-217, February.
    9. Jeffrey R. Bedell & John C. Ward JR & Robert P. Archer & M. Kirk Stokes, 1985. "An Empirical Evaluation of a Model of Knowledge Utilization," Evaluation Review, , vol. 9(2), pages 109-126, April.
    10. Páez, Antonio & Trépanier, Martin & Morency, Catherine, 2011. "Geodemographic analysis and the identification of potential business partnerships enabled by transit smart cards," Transportation Research Part A: Policy and Practice, Elsevier, vol. 45(7), pages 640-652, August.
    11. Bates, John & Polak, John & Jones, Peter & Cook, Andrew, 0. "The valuation of reliability for personal travel," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 37(2-3), pages 191-229, April.
    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. 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.
    2. Xin, Mengwei & Shalaby, Amer & Feng, Shumin & Zhao, Hu, 2021. "Impacts of COVID-19 on urban rail transit ridership using the Synthetic Control Method," Transport Policy, Elsevier, vol. 111(C), pages 1-16.

    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. Agarwal, Sumit & Diao, Mi & Keppo, Jussi & Sing, Tien Foo, 2020. "Preferences of public transit commuters: Evidence from smart card data in Singapore," Journal of Urban Economics, Elsevier, vol. 120(C).
    2. Leachman, Robert C. & Jula, Payman, 2012. "Estimating flow times for containerized imports from Asia to the United States through the Western rail network," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 48(1), pages 296-309.
    3. Gert Janssenswillen & Benoît Depaire & Sabine Verboven, 2018. "Detecting train reroutings with process mining," EURO Journal on Transportation and Logistics, Springer;EURO - The Association of European Operational Research Societies, vol. 7(1), pages 1-24, March.
    4. Krüger, Niclas A. & Vierth , Inge & Fakhraei Roudsari, Farzad, 2013. "Spatial, temporal and size distribution of freight train delays: evidence from Sweden," Working papers in Transport Economics 2013:8, CTS - Centre for Transport Studies Stockholm (KTH and VTI).
    5. Bergström, Anna & Krüger, Niclas A., 2013. "Modeling passenger train delay distributions: evidence and implications," Working papers in Transport Economics 2013:3, CTS - Centre for Transport Studies Stockholm (KTH and VTI).
    6. Carrion, Carlos & Levinson, David, 2012. "Value of travel time reliability: A review of current evidence," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(4), pages 720-741.
    7. Yue Liu & Jun Chen & Weiguang Wu & Jiao Ye, 2019. "Typical Combined Travel Mode Choice Utility Model in Multimodal Transportation Network," Sustainability, MDPI, vol. 11(2), pages 1-15, January.
    8. Fosgerau, Mogens & Engelson, Leonid, 2011. "The value of travel time variance," Transportation Research Part B: Methodological, Elsevier, vol. 45(1), pages 1-8, January.
    9. Wang, Yihong & Correia, Gonçalo Homem de Almeida & de Romph, Erik & Timmermans, H.J.P., 2017. "Using metro smart card data to model location choice of after-work activities: An application to Shanghai," Journal of Transport Geography, Elsevier, vol. 63(C), pages 40-47.
    10. Tao, Sui & Rohde, David & Corcoran, Jonathan, 2014. "Examining the spatial–temporal dynamics of bus passenger travel behaviour using smart card data and the flow-comap," Journal of Transport Geography, Elsevier, vol. 41(C), pages 21-36.
    11. Yin-Yen Tseng & Piet Rietveld & Erik Verhoef, 2012. "Unreliable trains and induced rescheduling: implications for cost-benefit analysis," Transportation, Springer, vol. 39(2), pages 387-407, March.
    12. Hiroaki Nishiuchi & Yasuyuki Kobayashi & Tomoyuki Todoroki & Tomoya Kawasaki, 2018. "Impact analysis of reductions in tram services in rural areas in Japan using smart card data," Public Transport, Springer, vol. 10(2), pages 291-309, August.
    13. Benito Zaragozí & Sergio Trilles & Aaron Gutiérrez & Daniel Miravet, 2021. "Development of a Common Framework for Analysing Public Transport Smart Card Data," Energies, MDPI, vol. 14(19), pages 1-22, September.
    14. Dixit, Vinayak V. & Harb, Rami C. & Martínez-Correa, Jimmy & Rutström, Elisabet E., 2015. "Measuring risk aversion to guide transportation policy: Contexts, incentives, and respondents," Transportation Research Part A: Policy and Practice, Elsevier, vol. 80(C), pages 15-34.
    15. Qingru Zou & Xiangming Yao & Peng Zhao & Heng Wei & Hui Ren, 2018. "Detecting home location and trip purposes for cardholders by mining smart card transaction data in Beijing subway," Transportation, Springer, vol. 45(3), pages 919-944, May.
    16. De Zhao & Wei Wang & Amber Woodburn & Megan S. Ryerson, 2017. "Isolating high-priority metro and feeder bus transfers using smart card data," Transportation, Springer, vol. 44(6), pages 1535-1554, November.
    17. Thomas Spanninger & Beda Büchel & Francesco Corman, 2023. "Train Delay Predictions Using Markov Chains Based on Process Time Deviations and Elastic State Boundaries," Mathematics, MDPI, vol. 11(4), pages 1-23, February.
    18. Flurin S. Hänseler & Nicholas A. Molyneaux & Michel Bierlaire, 2017. "Estimation of Pedestrian Origin-Destination Demand in Train Stations," Transportation Science, INFORMS, vol. 51(3), pages 981-997, August.
    19. Hjorth, Katrine & Börjesson, Maria & Engelson, Leonid & Fosgerau, Mogens, 2015. "Estimating exponential scheduling preferences," Transportation Research Part B: Methodological, Elsevier, vol. 81(P1), pages 230-251.
    20. Amarin Siripanich & Taha Hossein Rashidi & Emily Moylan, 2019. "Interaction of Public Transport Accessibility and Residential Property Values Using Smart Card Data," Sustainability, MDPI, vol. 11(9), pages 1-24, May.

    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:87:y:2016:i:c:p:11-21. 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.