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Quantifying freight flow disruption risks from railroad accidents

Author

Listed:
  • Raj Bridgelall

    (North Dakota State University)

  • Denver D. Tolliver

    (North Dakota State University)

Abstract

Workforce shortages during the COVID-19 pandemic and the recent threat of railroad strike in the United States have generated greater public awareness of how freight flow disruptions can harm society. Unlike highways, trains often do not have the ability to take alternative routes that directly connect major metropolitan areas; hence railroad networks are vulnerable to disruptions like accidents. This study developed a data mining workflow to rank commodity movements that are at the highest risk of disruption from railroad accidents and other types of regional disasters that can affect railroad operations. A key finding is that five U.S. metropolitan areas are at least five times more likely than others to experience a railroad accident. Those five areas account for more than 40% of the monetary value in alcoholic beverages, raw meats, gasoline, plastic-based products, and rubber-based products moved by rail. Hence, any disruption in those five areas can lead to widespread shortages of those commodities. The implication is that decision makers should focus risk mitigation and resiliency strategies in those five metropolitan areas and on the top commodity categories at risk.

Suggested Citation

  • Raj Bridgelall & Denver D. Tolliver, 2024. "Quantifying freight flow disruption risks from railroad accidents," Quality & Quantity: International Journal of Methodology, Springer, vol. 58(2), pages 1993-2007, April.
    • Handle: RePEc:spr:qualqt:v:58:y:2024:i:2:d:10.1007_s11135-023-01727-3
    DOI: 10.1007/s11135-023-01727-3
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    References listed on IDEAS

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    1. Jabbarzadeh, Armin & Azad, Nader & Verma, Manish, 2020. "An optimization approach to planning rail hazmat shipments in the presence of random disruptions," Omega, Elsevier, vol. 96(C).
    2. Huang, Ping & Wen, Chao & Fu, Liping & Lessan, Javad & Jiang, Chaozhe & Peng, Qiyuan & Xu, Xinyue, 2020. "Modeling train operation as sequences: A study of delay prediction with operation and weather data," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 141(C).
    3. Zhang, Zhipeng & Liu, Xiang & Holt, Keith, 2018. "Positive Train Control (PTC) for railway safety in the United States: Policy developments and critical issues," Utilities Policy, Elsevier, vol. 51(C), pages 33-40.
    4. Woodburn, Allan, 2019. "Rail network resilience and operational responsiveness during unplanned disruption: A rail freight case study," Journal of Transport Geography, Elsevier, vol. 77(C), pages 59-69.
    5. Adithya Thaduri & Mustafa Aljumaili & Ravdeep Kour & Ramin Karim, 2019. "Cybersecurity for eMaintenance in railway infrastructure: risks and consequences," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 10(2), pages 149-159, April.
    6. Kevin P. Scheibe & Jennifer Blackhurst, 2018. "Supply chain disruption propagation: a systemic risk and normal accident theory perspective," International Journal of Production Research, Taylor & Francis Journals, vol. 56(1-2), pages 43-59, January.
    7. Ke, Ginger Y. & Verma, Manish, 2021. "A framework to managing disruption risk in rail-truck intermodal transportation networks," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 153(C).
    8. Ray Y. Zhong & Xun Xu & Olga Battaïa, 2020. "Special issue on sustainability with innovation for manufacturing and supply chain management," International Journal of Production Research, Taylor & Francis Journals, vol. 58(24), pages 7311-7313, December.
    9. Majbah Uddin & Nathan Huynh, 2019. "Reliable Routing of Road-Rail Intermodal Freight under Uncertainty," Networks and Spatial Economics, Springer, vol. 19(3), pages 929-952, September.
    10. Kelle, Peter & Song, Jinglu & Jin, Mingzhou & Schneider, Helmut & Claypool, Christopher, 2019. "Evaluation of operational and environmental sustainability tradeoffs in multimodal freight transportation planning," International Journal of Production Economics, Elsevier, vol. 209(C), pages 411-420.
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