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Title:

What Will Autonomous Trucking Do To U.S. Trade Flows? Application Of The Random-Utility-Based Multi-Regional Input-Output Model

Accession Number:

01697561

Record Type:

Component

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 (RUBMRIO) 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 (FAF) 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 1,000 and 1,500 miles, with truck volumes rising for other distances. Introduction of Atrucks favors longer truck trades, but rail's low price remains competitive for trade distances over 3,000 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.

Supplemental Notes:

This paper was sponsored by TRB committee AT015 Standing Committee on Freight Transportation Planning and Logistics.

Report/Paper Numbers:

19-02092

Language:

English

Corporate Authors:

Transportation Research Board

Authors:

Huang, Yantao
Kockelman, Kara M

Pagination:

4p

Publication Date:

2019

Conference:

Transportation Research Board 98th Annual Meeting

Location: Washington DC, United States
Date: 2019-1-13 to 2019-1-17
Sponsors: Transportation Research Board

Media Type:

Digital/other

Features:

References

Identifier Terms:

Freight Analysis Framework

Uncontrolled Terms:

Random utility models

Subject Areas:

Data and Information Technology; Freight Transportation; Highways; Motor Carriers; Planning and Forecasting; Railroads; Vehicles and Equipment

Source Data:

Transportation Research Board Annual Meeting 2019 Paper #19-02092

Files:

TRIS, TRB, ATRI

Created Date:

Dec 7 2018 9:31AM