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Evaluating the effects of the I-35W bridge collapse on road-users in the twin cities metropolitan region

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  • Feng Xie
  • David Levinson

Abstract

This study evaluates the effects of the I-35W bridge collapse on road-users in the Minneapolis-St Paul, Minnesota Twin Cities metropolitan area. We adopted the Twin Cities Seven-County travel demand model developed in previous research, re-calibrated it against July 2007 loop detector traffic data, and used this model to carry out an evaluation of economic loss incurred by increased travel delay in alternative scenarios before and after the bridge collapse. We conclude that the failure of the I-35W bridge resulted in an economic loss of US$71,000 to US$220,000 a day, depending on how flexible road-users in the system adjusted their trip destinations in response to the bridge closing. We also estimate that the major traffic restoration projects Minnesota Department of Transportation has implemented in quick response to the bridge collapse can save road-users US$9500--17,500 a day. This translates into a benefit--cost ratio of 2.0--9.0, suggesting these projects are highly beneficial in an economic sense. In this analysis, the use of a simplified, scaled-down travel demand model enabled us to carry out the analysis quickly and accurately, which could see its contributions in transportation planning under situations such as emergency relief and comprehensive design.

Suggested Citation

  • Feng Xie & David Levinson, 2011. "Evaluating the effects of the I-35W bridge collapse on road-users in the twin cities metropolitan region," Transportation Planning and Technology, Taylor & Francis Journals, vol. 34(7), pages 691-703, April.
    • Handle: RePEc:taf:transp:v:34:y:2011:i:7:p:691-703
    DOI: 10.1080/03081060.2011.602850
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    Cited by:

    1. Danczyk, Adam & Di, Xuan & Liu, Henry X. & Levinson, David M., 2017. "Unexpected versus expected network disruption: Effects on travel behavior," Transport Policy, Elsevier, vol. 57(C), pages 68-78.
    2. 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.
    3. Sohouenou, Philippe Y.R. & Christidis, Panayotis & Christodoulou, Aris & Neves, Luis A.C. & Presti, Davide Lo, 2020. "Using a random road graph model to understand road networks robustness to link failures," International Journal of Critical Infrastructure Protection, Elsevier, vol. 29(C).
    4. Nazmul Arefin Khan & Muhammad Ahsanul Habib, 2018. "Evaluation of Preferences for Alternative Transportation Services and Loyalty towards Active Transportation during a Major Transportation Infrastructure Disruption," Sustainability, MDPI, vol. 10(6), pages 1-14, June.
    5. Alireza Mostafizi & Haizhong Wang & Dan Cox & Lori A. Cramer & Shangjia Dong, 2017. "Agent-based tsunami evacuation modeling of unplanned network disruptions for evidence-driven resource allocation and retrofitting strategies," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 88(3), pages 1347-1372, September.
    6. Riukula, Krista & Väänänen, Touko, 2024. "Estimating the Labour Market Impacts of Transport Projects in Finland," ETLA Working Papers 120, The Research Institute of the Finnish Economy.
    7. Karakose, Gokhan & McGarvey, Ronald G., 2018. "Capacitated path-aggregation constraint model for arc disruption in networks," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 109(C), pages 225-238.
    8. Mark Horner & Joni Downs, 2014. "Integrating people and place: A density-based measure for assessing accessibility to opportunities," The Journal of Transport and Land Use, Center for Transportation Studies, University of Minnesota, vol. 7(2), pages 1-18.
    9. Shanjiang Zhu & David M. Levinson, 2012. "Disruptions to Transportation Networks: A Review," Transportation Research, Economics and Policy, in: David M. Levinson & Henry X. Liu & Michael Bell (ed.), Network Reliability in Practice, edition 1, chapter 0, pages 5-20, Springer.
    10. Jin, Kun & Wang, Wei & Li, Xinran & Hua, Xuedong & Chen, Siyuan & Qin, Shaoyang, 2022. "Identifying the critical road combination in urban roads network under multiple disruption scenarios," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
    11. Sohouenou, Philippe Y.R. & Neves, Luis A.C., 2021. "Assessing the effects of link-repair sequences on road network resilience," International Journal of Critical Infrastructure Protection, Elsevier, vol. 34(C).

    More about this item

    JEL classification:

    • R41 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Transportation: Demand, Supply, and Congestion; Travel Time; Safety and Accidents; Transportation Noise
    • R48 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Government Pricing and Policy
    • D63 - Microeconomics - - Welfare Economics - - - Equity, Justice, Inequality, and Other Normative Criteria and Measurement

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