IDEAS home Printed from https://ideas.repec.org/a/eee/trapol/v18y2011i6p825-835.html
   My bibliography  Save this article

Exploring the effectiveness of bus rapid transit a prototype agent-based model of commuting behavior

Author

Listed:
  • McDonnell, Simon
  • Zellner, Moira

Abstract

The introduction of Bus Rapid Transit (BRT), typically involving the use of exclusive bus lanes and related bus priority measures, is increasingly advocated as a flexible and cost-effective way of improving the attractiveness of public transit in congested urban areas by reducing travel times and variability. These schemes typically involve the reallocation of road space for exclusive use by buses, presenting commuters with potentially competing incentives: buses on BRT routes can run faster and more efficiently than buses running in general traffic, potentially attracting commuters to public transit and reducing congestion through modal shift from cars. However, a secondary impact may also exist; remaining car users may be presented with less congested road space, improving their journey times and simultaneously acting as an incentive for some bus-users to revert to the car. To investigate the potential for these primary and secondary impacts, we develop a prototype agent-based model to investigate the nature of these interactions and how they play out into system-wide patterns of modal share and travel times. The model allows us to test the effects of multiple assumptions about the behaviors of individual agents as they respond to different incentives introduced by BRT policy changes, such as the implementation of exclusive bus lanes, increased bus frequency, pre-boarding ticket machines and express stops, separately and together. We find that, under our assumptions, these policies can result in significant improvements in terms of individual journey times, modal shift, and length of rush hour. We see that the addition of an exclusive bus lane results in significant improvements for both car users and bus riders. Informed with appropriate empirical data relating to the behavior of individual agents, the geography and the specific policy interventions, the model has the potential to aid policymakers in examining the effectiveness of different BRT schemes, applied to broader environments.

Suggested Citation

  • McDonnell, Simon & Zellner, Moira, 2011. "Exploring the effectiveness of bus rapid transit a prototype agent-based model of commuting behavior," Transport Policy, Elsevier, vol. 18(6), pages 825-835, November.
  • Handle: RePEc:eee:trapol:v:18:y:2011:i:6:p:825-835
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0967070X11000783
    Download Restriction: Full text for ScienceDirect subscribers only

    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. Lei Zhang & David Levinson, 2004. "An Agent-Based Approach to Travel Demand Modeling: An Exploratory Analysis," Working Papers 200405, University of Minnesota: Nexus Research Group.
    2. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555, May.
    3. David Hensher & April Reyes, 2000. "Trip chaining as a barrier to the propensity to use public transport," Transportation, Springer, vol. 27(4), pages 341-361, December.
    4. John H. Miller & Scott E. Page, 2007. "Complexity in Social Worlds, from Complex Adaptive Systems: An Introduction to Computational Models of Social Life," Introductory Chapters,in: Complex Adaptive Systems: An Introduction to Computational Models of Social Life Princeton University Press.
    5. Lei Zhang & David Levinson & Shanjiang Zhu, 2007. "Agent-Based Model of Price Competition and Product Differentiation on Congested Networks," Working Papers 200809, University of Minnesota: Nexus Research Group.
    6. Graham Currie & Majid Sarvi & Bill Young, 2007. "A new approach to evaluating on-road public transport priority projects: balancing the demand for limited road-space," Transportation, Springer, vol. 34(4), pages 413-428, July.
    7. Tesfatsion, Leigh, 2006. "Agent-Based Computational Economics: A Constructive Approach to Economic Theory," Handbook of Computational Economics,in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 16, pages 831-880 Elsevier.
    8. Axelrod, Robert & Tesfatsion, Leigh, 2006. "A Guide for Newcomers to Agent-Based Modeling in the Social Sciences," Staff General Research Papers Archive 12515, Iowa State University, Department of Economics.
    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. Forsey, David & Habib, Khandker Nurul & Miller, Eric J. & Shalaby, Amer, 2013. "Evaluating the impacts of a new transit system on commuting mode choice using a GEV model estimated to revealed preference data: A case study of the VIVA system in York Region, Ontario," Transportation Research Part A: Policy and Practice, Elsevier, vol. 50(C), pages 1-14.
    2. Wan, Dan & Kamga, Camille & Liu, Jun & Sugiura, Aaron & Beaton, Eric B., 2016. "Rider perception of a “light” Bus Rapid Transit system - The New York City Select Bus Service," Transport Policy, Elsevier, vol. 49(C), pages 41-55.

    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:18:y:2011:i:6:p:825-835. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Dana Niculescu). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/30473/description#description .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.