Impact of automation on long haul trucking operator-hours in the United States

Aniruddh Mohan and Parth Vaishnav ()
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Aniruddh Mohan: Carnegie Mellon University
Parth Vaishnav: Carnegie Mellon University

Palgrave Communications, 2022, vol. 9, issue 1, 1-10

Abstract: Abstract Automated long haul trucking is being developed for commercial deployment in the United States. One possible mode of deployment for this technology is a “transfer-hub” model where the operationally less complex highway driving is automated, while human drivers drive the more complex urban segment of the route. We study the possible net impacts on tractor-trailer operator-hours from this mode of deployment. Using data from the 2017 Commodity Flow Survey, we gather information on trucking shipments and the operator-hours required to fulfill those shipments. We find that up to 94% of long haul trucking operator-hours may be impacted as the technology improves to operate in all weather conditions. If the technology is however restricted to the southern states where the majority of companies are currently testing automated trucking, we find that only 10% of operator-hours are impacted. We conduct interviews with industry stakeholders including tractor-trailer operators on the feasibility of such a system of deployment. We find that an increase in short haul operation is unlikely to compensate for the loss in long haul operator-hours, despite public claims to this effect by the developers of the technology. Policymakers should consider the impact of different scenarios of deployment on the long haul trucking workforce.

Date: 2022
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DOI: 10.1057/s41599-022-01103-w

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