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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

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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.

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Acknowledgements

The authors thank the Texas Department of Transportation (TxDOT) for financially supporting this research (Under Research Project 0-6838, “Bringing Smart Transport to Texans: Ensuring the Benefits of a Connected and Autonomous Transport System in Texas”) and Caliper Corporation for providing TransCAD 7.0 software. The authors acknowledge the Texas Advanced Computing Center (TACC) at The University of Texas at Austin for providing computing and data storage resources. The authors are also grateful to the U.S. DOT for the Freight Analysis Framework (FAF4) data and to Scott Schauer-West for his editing and administrative support.

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  1. Department of Civil, Architectural and Environmental Engineering, The University of Texas at Austin, Austin, USA

    Yantao Huang & Kara M. Kockelman

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Correspondence to Kara M. Kockelman.

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Huang, Y., Kockelman, K.M. What will autonomous trucking do to U.S. trade flows? Application of the random-utility-based multi-regional input–output model. Transportation 47, 2529–2556 (2020). https://doi.org/10.1007/s11116-019-10027-5

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