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

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  • McDonnell, Simon
  • Zellner, Moira
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    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.

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    Bibliographic Info

    Article provided by Elsevier in its journal Transport Policy.

    Volume (Year): 18 (2011)
    Issue (Month): 6 (November)
    Pages: 825-835

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    Handle: RePEc:eee:trapol:v:18:y:2011:i:6:p:825-835

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    Related research

    Keywords: Bus Rapid Transit Agent-based modeling Travel behavior Mode choice;

    References

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    1. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555, October.
    2. Axelrod, Robert & Tesfatsion, Leigh, 2006. "A Guide for Newcomers to Agent-Based Modeling in the Social Sciences," Staff General Research Papers 12515, Iowa State University, Department of Economics.
    3. 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.
    4. 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.
    5. 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.
    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. John H. Miller & Scott E. Page, 2007. "Complexity in Social Worlds, from Complex Adaptive Systems: An Introduction to Computational Models of Social Life
      [Complex Adaptive Systems: An Introduction to Computational Models of Social Life]
      ," Introductory Chapters, Princeton University Press.
    8. 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.
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    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.

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