IDEAS home Printed from https://ideas.repec.org/a/inm/orinte/v53y2023i2p97-110.html
   My bibliography  Save this article

Adoption of FasTrak on San Francisco Bay Area Bridges: Impact of Operations Research Models in Relieving Congestion

Author

Listed:
  • Ramesh Bollapragada

    (College of Business, San Francisco State University, San Francisco, California 94132)

  • Venoo Kakar

    (College of Business, San Francisco State University, San Francisco, California 94132)

  • John Goodwin

    (Metropolitan Transportation Commission, Metro Center, Oakland, California 94607)

  • Andrew Fremier

    (Metropolitan Transportation Commission, Metro Center, Oakland, California 94607)

Abstract

Bay Area toll bridges are the main transportation link across the nine-county San Francisco Bay Area. These bridges experience extreme congestion and become bottlenecks during peak hours with long backups at the toll plazas. A solution to ensure smooth vehicle throughput at toll plazas is the widespread adoption of the electronic toll collection system called FasTrak. However, the FasTrak system has experienced low usage rates since its inception relative to other toll collection systems in the country. Forecasting, marketing, and operations research models were utilized to make recommendations and collaborate with transportation authorities to increase FasTrak usage during peak hours (5–10 a.m. and 3–7 p.m.) to address traffic congestion. After these recommendations were implemented, FasTrak usage increased from 40% in 2006 to the long-term target of 70% by 2016. This paper presents a synthesis of the challenges and the implementation of the FasTrak Strategic Plan. Furthermore, econometric models are presented that capture the effect on traffic volumes of increased FasTrak usage achieved through congestion pricing. Saved travel time resulted in productivity gains of approximately $569 million per year. This study contributes to an understanding of the role of effective transportation policies in reducing congestion and improving productivity.

Suggested Citation

  • Ramesh Bollapragada & Venoo Kakar & John Goodwin & Andrew Fremier, 2023. "Adoption of FasTrak on San Francisco Bay Area Bridges: Impact of Operations Research Models in Relieving Congestion," Interfaces, INFORMS, vol. 53(2), pages 97-110, March.
    • Handle: RePEc:inm:orinte:v:53:y:2023:i:2:p:97-110
    DOI: 10.1287/inte.2022.1127
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/inte.2022.1127
    Download Restriction: no
    File URL: https://libkey.io/10.1287/inte.2022.1127?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
    ---><---

    References listed on IDEAS

    as
    1. John, Wright & Dahlgren, Joy, 2001. "Using Vehicles Equipped with Toll Tags as Probes for Providing Travel Times," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt9f17h2j0, Institute of Transportation Studies, UC Berkeley.
    2. Foreman, Kate, 2016. "Crossing the bridge: The effects of time-varying tolls on curbing congestion," Transportation Research Part A: Policy and Practice, Elsevier, vol. 92(C), pages 76-94.
    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. Beheshtian, Arash & Richard Geddes, R. & Rouhani, Omid M. & Kockelman, Kara M. & Ockenfels, Axel & Cramton, Peter & Do, Wooseok, 2020. "Bringing the efficiency of electricity market mechanisms to multimodal mobility across congested transportation systems," Transportation Research Part A: Policy and Practice, Elsevier, vol. 131(C), pages 58-69.
    2. Wai Wong & S. C. Wong, 2019. "Unbiased Estimation Methods of Nonlinear Transport Models Based on Linearly Projected Data," Transportation Science, INFORMS, vol. 53(3), pages 665-682, May.
    3. Wong, Wai & Wong, S.C., 2016. "Biased standard error estimations in transport model calibration due to heteroscedasticity arising from the variability of linear data projection," Transportation Research Part B: Methodological, Elsevier, vol. 88(C), pages 72-92.
    4. Schubert, Daniel & Sys, Christa & Vanelslander, Thierry & Roumboutsos, Athena, 2022. "No-queue road pricing: A comprehensive policy instrument for Europe?," Utilities Policy, Elsevier, vol. 78(C).
    5. Coria, Jessica & Zhang, Xiao-Bing, 2017. "Optimal environmental road pricing and daily commuting patterns," Transportation Research Part B: Methodological, Elsevier, vol. 105(C), pages 297-314.
    6. Börjesson, Maria & Kristoffersson, Ida, 2018. "The Swedish congestion charges: Ten years on," Transportation Research Part A: Policy and Practice, Elsevier, vol. 107(C), pages 35-51.
    7. Koh, Wee Ping & Chin, Kian Keong, 2022. "The applicability of prospect theory in examining drivers’ trip decisions, in response to Electronic Road Pricing (ERP) rates adjustments - a study using travel data in Singapore," Transportation Research Part A: Policy and Practice, Elsevier, vol. 155(C), pages 115-127.
    8. Lehe, Lewis J. & Devunuri, Saipraneeth, 2022. "Large Elasticity at Introduction," Research in Transportation Economics, Elsevier, vol. 95(C).
    9. Herrera, Juan C. & Work, Daniel B. & Herring, Ryan & Ban, Xuegang Jeff & Bayen, Alexandre M, 2009. "Evaluation of Traffic Data Obtained via GPS-Enabled Mobile Phones: the Mobile Century Field Experiment," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt0sd42014, Institute of Transportation Studies, UC Berkeley.
    10. Wong, Wai & Wong, S.C., 2015. "Systematic bias in transport model calibration arising from the variability of linear data projection," Transportation Research Part B: Methodological, Elsevier, vol. 75(C), pages 1-18.

    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:inm:orinte:v:53:y:2023:i:2:p:97-110. 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: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    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.