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

Yantao Huang () and Kara M. Kockelman ()
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Yantao Huang: The University of Texas at Austin
Kara M. Kockelman: The University of Texas at Austin

Transportation, 2020, vol. 47, issue 5, No 19, 2529-2556

Abstract: 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.

Keywords: Autonomous trucks; Spatial input–output model; Nationwide trade flow patterns; Integrated transportation-land use modeling (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (5)

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DOI: 10.1007/s11116-019-10027-5

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