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What will autonomous trucking do to U.S. trade flows? Application of the random-utility-based multi-regional input–output model

Author

Listed:
  • Yantao Huang

    (The University of Texas at Austin)

  • Kara M. Kockelman

    (The University of Texas at Austin)

Abstract

This study anticipates changes in U.S. highway and rail trade patterns following widespread availability of self-driving or autonomous trucks (Atrucks). It uses a random-utility-based multiregional input–output model, driven by foreign export demands, to simulate changes in freight flows among 3109 U.S. counties and 117 export zones, via a nested-logit model for shipment or input origin and mode, including the shipper’s choice between autonomous trucks and conventional or human-driven trucks (Htrucks). Different value of travel time and cost scenarios are explored, to provide a sense of variation in the uncertain future of ground-based trade flows. Using the current U.S. Freight Analysis Framework (FAF4) data for travel times and costs—and assuming that Atrucks lower trucking costs by 25% (per ton-mile delivered), truck flow values in ton-miles are predicted to rise 11%, due to automation’s lowering of trucking costs, while rail flow values fall 4.8%. Rail flows are predicted to rise 6.6% for trip distances between 1000 and 1500 miles, with truck volumes rising for all other distance bands. Introduction of Atrucks favors longer truck trades, but rail’s low price remains competitive for trade distances over 3000 miles. Htrucks continue to dominate in shorter-distance freight movements, while Atrucks dominate at distances over 500 miles. Eleven and twelve commodity sectors see an increase in trucking’s domestic flows and export flows, respectively. And total ton-miles across all 13 commodity groups rise slightly by 3.1%, as automation lowers overall shipping costs.

Suggested Citation

  • Yantao Huang & Kara M. Kockelman, 2020. "What will autonomous trucking do to U.S. trade flows? Application of the random-utility-based multi-regional input–output model," Transportation, Springer, vol. 47(5), pages 2529-2556, October.
    • Handle: RePEc:kap:transp:v:47:y:2020:i:5:d:10.1007_s11116-019-10027-5
    DOI: 10.1007/s11116-019-10027-5
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    References listed on IDEAS

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    1. Tschangho John Kim & Heejoo Ham & David E. Boyce, 2002. "Economic impacts of transportation network changes: Implementation of a combined transportation network and input-output model," Review of Economic Design, Springer;Society for Economic Design, vol. 81(2), pages 223-246, April.
    2. Tschangho John Kim & Heejoo Ham & David E. Boyce, 2002. "Economic impacts of transportation network changes: Implementation of a combined transportation network and input-output model," Economics of Governance, Springer, vol. 81(2), pages 223-246, April.
    3. Heejoo Ham & Tschangho John Kim & David E. Boyce, 2002. "Economic impacts of transportation network changes: Implementation of a combined transportation network and input-output model," Papers in Regional Science, Springer;Regional Science Association International, vol. 81(2), pages 223-246.
    4. Itf, 2015. "Urban Mobility System Upgrade: How shared self-driving cars could change city traffic," International Transport Forum Policy Papers 6, OECD Publishing.
    5. Chen, T. Donna & Kockelman, Kara M. & Hanna, Josiah P., 2016. "Operations of a shared, autonomous, electric vehicle fleet: Implications of vehicle & charging infrastructure decisions," Transportation Research Part A: Policy and Practice, Elsevier, vol. 94(C), pages 243-254.
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    Cited by:

    1. Huang, Yantao & Kockelman, Kara M. & Quarles, Neil, 2020. "How will self-driving vehicles affect U.S. megaregion traffic? The case of the Texas Triangle," Research in Transportation Economics, Elsevier, vol. 84(C).
    2. Anna Pernestål & Albin Engholm & Marie Bemler & Gyözö Gidofalvi, 2020. "How Will Digitalization Change Road Freight Transport? Scenarios Tested in Sweden," Sustainability, MDPI, vol. 13(1), pages 1-18, December.
    3. Jafar Mandouri & Nuri C. Onat & Murat Kucukvar & Burak Sen, 2024. "Trilemma of life cycle carbon, employment, and costs of trucking industry's shift toward automation and electrification," Journal of Industrial Ecology, Yale University, vol. 28(4), pages 981-995, August.
    4. Weijia (Vivian) Li & Kara M. Kockelman, 2022. "How does machine learning compare to conventional econometrics for transport data sets? A test of ML versus MLE," Growth and Change, Wiley Blackwell, vol. 53(1), pages 342-376, March.
    5. Engholm, Albin & Kristoffersson, Ida & Pernestal, Anna, 2021. "Impacts of large-scale driverless truck adoption on the freight transport system," Transportation Research Part A: Policy and Practice, Elsevier, vol. 154(C), pages 227-254.

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