Skip to main content
SpringerLink
Log in

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

  • Published:
Transportation Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

(Adapted from Du and Kockelman 2012, Fig. 2)

Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  • American Association of State Highway and Transportation Officials: Freight-Rail Bottom Line Report. https://rail.transportation.org/wp-content/uploads/sites/30/2017/06/freight-rail-report-2008.pdf (2008). Accessed 20 June 2019

  • American Trucking Association: Reports, Trends and Statistics. http://www.trucking.org/News_and_Information_Reports_Driver_Shortage.aspx (2015). Accessed 20 June 2019

  • Barth, M., Scora, G., Younglove, T.: Modal emissions model for heavy-duty diesel vehicles. Transp. Res. Rec. 1880, 10–20 (2004)

    Article  Google Scholar 

  • Ben-Akiva, M., Lerman, S.R.: Discrete Choice Analysis: Theory and Application to Travel Demand. MIT Press, Cambridge (1985)

    Google Scholar 

  • Bergenhem, C., Shladover, S., Coelingh, E., Englund, C., Tsugawa, S.: Overview of platooning systems. In: Proceedings of the 19th ITS World Congress, 22–26 Oct, Vienna, Austria (2012)

  • Bureau of Transportation Statistics: National Transportation Statistics. https://www.rita.dot.gov/bts/sites/rita.dot.gov.bts/files/NTS_Entire_2017Q2.pdf (2017). Accessed 10 Apr 2016

  • Bureau of Transportation Statistics, U.S. Department of Transportation: 2017 Commodity Flow Survey Standard Classification of Transported Goods (SCTG). CFS-1200. https://www.census.gov/econ/cfs/2017/CFS-1200_17.pdf (2017). Accessed 10 April 2016

  • Chen, D., Kockelman, K., Hanna, P.: Operations of a shared, autonomous, electric vehicle fleet: implications of vehicle and charging infrastructure decisions. Transp. Res. Part A Policy Practice 94, 243–254 (2016)

    Article  Google Scholar 

  • Clements, L., Kockelman, K.: Economic effects of autonomous vehicles. Transportation Research Record No. 2602 (2017)

  • Du, X., Kockelman, K.: Tracking transportation and industrial production across a nation: applications of RUBMRIO model for US trade patterns. Transp. Res. Rec. 2269, 99–109 (2012)

    Article  Google Scholar 

  • De la Barra, T., Pérez, B., Vera, N.: TRANUS-J: putting large models into small computers. Environ. Plan. 11, 87–101 (1984)

    Article  Google Scholar 

  • Echenique, M.H., Flowerdew, A.D.J., Hunt, J.D., Mayo, T.R., Skidmore, I.J., Simmonds, D.C.: The MEPLAN models of Bilbao, Leeds and Dortmund. Transp. Rev. 10, 309–322 (1990)

    Article  Google Scholar 

  • Fagnant, D., Kockelman, K.: The travel and environmental implications of shared autonomous vehicles, using agent-based model scenarios. Transp. Res. Part C Emerg. Technol. 40, 1–13 (2014)

    Article  Google Scholar 

  • Franke, U., Bottiger, F., Zomotor, Z., Seeberger, D.: Truck platooning in mixed traffic. In: Proceedings of the Symposium on Intelligent Vehicles’ 95, pp. 1–6. IEEE (1995)

  • Fullenbaum, R., Grillo, C.: Freight Analysis Framework Inter-regional Commodity Flow Forecast Study: Final Forecast Results Report (No. FHWA-HOP-16-043). https://ops.fhwa.dot.gov/publications/fhwahop16043/ (2016). Accessed 20 June 2019

  • Guzman, A., Vassallo, J.: Methodology for assessing regional economic impacts of charges for heavy-goods vehicles in Spain: an integrated approach through random utility-based multiregional input-output and road transport network models. Transp. Res. Rec. 2378, 129–139 (2013)

    Article  Google Scholar 

  • Harding, J., Powell, G., Yoon, R., Fikentscher, J., Doyle, C., Sade, D., Lukuc, M., Simons, J., Wang, J.: Vehicle-to-Vehicle Communications: Readiness of V2V Technology for Application (No. DOT HS 812 014). National Highway Traffic Safety Administration, United States (2014)

  • Hooper, A., Murray, D.: An Analysis of the Operational Costs of Trucking: 2017 Update. https://atri-online.org/wp-content/uploads/2017/10/ATRI-Operational-Costs-of-Trucking-2017-10-2017.pdf (2017). Accessed 20 June 2019

  • Hooper, A., Murray, D.: An Analysis of the Operational Costs of Trucking: 2018 Update. http://atri-online.org/wp-content/uploads/2018/10/ATRI-Operational-Costs-of-Trucking-2018.pdf (2018). Accessed 20 June 2019

  • Hunt, J.D., Abraham, J.E.: Design and application of the PECAS land use modeling system. In: Presented at the Computers in Urban Planning and Urban Management Conference, Sendai, Japan (2003)

  • Huang, T., Kockelman, K.: The introduction of dynamic features in a random-utility-based multiregional input-output model of trade, production, and location choice. J. Transp. Res. Forum 47(1), 23–42 (2010)

    Google Scholar 

  • International Transport Forum: Urban Mobility System Upgrade: How Shared Self-driving Cars Could Change City traffic. https://www.itf-oecd.org/sites/default/files/docs/15cpb_self-drivingcars.pdf (2015)

  • Isard, W.: Methods of Regional Analysis: An Introduction to Regional Science. M.I.T. Press, Cambridge (1960)

    Google Scholar 

  • Kim, T., Ham, H., Boyce, D.: Economic impacts of transportation network changes: implementation of a combined transportation network and input–output model. Pap. Reg. Sci. 81(2), 223–246 (2002)

    Article  Google Scholar 

  • Kockelman, K.: Bringing Smart Transport to Texans: Ensuring the Benefits of a Connected and Autonomous Transport System in Texas—Final Report 0-6838. Center for Transportation Research and The University of Texas at Austin. Report No. FHWA/TX-16/0-6838-2. https://library.ctr.utexas.edu/ctr-publications/0-6838-2.pdf (2016)

  • Kockelman, K., Li, T.: Valuing the safety benefits of connected and automated vehicle technologies. Transportation Research Board 95th Annual Meeting (No. 16-1468) (2016)

  • Kockelman, K.M., Jin, L., Zhao, Y., Ruíz-Juri, N.: Tracking land use, transport, and industrial production using random-utility-based multiregional input–output models: applications for Texas trade. J. Trans. Geogr. 13(3), 275–286 (2005)

    Article  Google Scholar 

  • LaMondia, J., Fagnant, D., Qu, H., Barrett, J., Kockelman, K.: Long-distance travel mode shifts due to automated vehicles: a statewide mode-shift simulation experiment and travel survey analysis. Transp. Res. Rec. 2566, 1–10 (2016)

    Article  Google Scholar 

  • Land Transport Authority: Self-driving Vehicle Initiatives in Singapore. http://connectedautomateddriving.eu/wp-content/uploads/2017/02/3_Day2_PL10_WeeShann_CAD_final.pdf (2017). Accessed 20 June 2019

  • Leontief, W.: The Structure of American Economy, 1919–1929. Harvard University, Cambridge (1941)

    Google Scholar 

  • Liu, J., Kockelman, K.: Anticipating the emissions impacts of autonomous vehicles using the MOVES model. In: 96th Annual Meeting of the Transportation Research Board (2017)

  • Maoh, H., Kanaroglou, P., Woudsma, C.: Simulation model for assessing the impact of climate change on transportation and the economy in Canada. Transp. Res. Rec. 2067, 84–92 (2008)

    Article  Google Scholar 

  • O’Brien, C.: Self-driving Trucks Projected to Slash Trucker Jobs by Half or More. https://www.trucks.com/2017/05/31/self-driving-trucks-slash-truck-driver-jobs/ (2017). Accessed 20 June 2019

  • Perrine, K., Kockelman, K., Huang, Y.: Anticipating Long-Distance Travel Shifts Due to Self-driving Vehicles. http://www.caee.utexas.edu/prof/kockelman/public_html/TRB18AVLong-DistanceTravel.pdf (2017)

  • Railroad Performance Measures: http://www.railroadpm.org/Graphs/Terminal%20Dwell%20Graph.aspx (2011). Accessed 20 June 2019

  • Santa, J., Gómez-Skarmeta, A.F., Sánchez-Artigas, M.: Architecture and evaluation of a unified V2V and V2I communication system based on cellular networks. Comput. Commun. 31(12), 2850–2861 (2008)

    Article  Google Scholar 

  • Simmonds, D., Still, B.: DELTA/START: adding land use analysis to integrated transport models. In: World Transport Research: Selected Proceedings of the 8th World Conference on Transport ResearchWorld Conference on Transport Research Society, vol. 4 (1998)

  • Strocko, E., Sprung, M.J., Nguyen, L.X., Rick, C., Sedor, J.: Freight Facts and Figures, 2013 (No. FHWA-HOP-14-004). Federal Highway Administration (2014)

  • Shladover, S.E., Lu, X.Y., Song, B., Dickey, S., Nowakowski, C., Howell, A., Bu, F., Marco, D., Tan, H.S., Nelson, D.: Demonstration of automated heavy-duty vehicles. In: California Partners for Advanced Transit and Highways (PATH) (2006)

  • Uranga, R.: Driverless Trucks: Coming Soon to a Road Near You? Southern California News Group. http://www.mercurynews.com/2017/03/05/driverless-trucks-coming-soon-to-a-road-near-you/ (2017). Accessed 20 June 2019

  • U.S. Census Bureau: North American Industry Classification System. https://www.census.gov/cgi-bin/sssd/naics/naicsrch?chart=2017 (2017). Accessed 20 June 2019

  • Viscelli, S.: Driverless? Autonomous Trucks and the Future of the American Trucker. http://driverlessreport.org/files/driverless.pdf (2018)

  • Yankelevich, A., Rikard, R.V., Kadylak, T., Hall, M.J., Mack, E.A., Verboncoeur, J.P., Cotton, S.R.: Preparing the Workforce for Automated Vehicles. https://comartsci.msu.edu/sites/default/files/documents/MSU-TTI-Preparing-Workforce-for-AVs-and-Truck-Platooning-Reports%20.pdf (2018). Accessed 20 June 2019

  • Zhao, Y., Kockelman, K.: The random-utility-based multiregional input–output model: solution existence and uniqueness. Transp. Res. Part B Methodol. 38(9), 789–807 (2004)

    Article  Google Scholar 

Download references

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.

Author information

Authors and Affiliations

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

    Yantao Huang & Kara M. Kockelman

Authors
  1. Yantao Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kara M. Kockelman
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kara M. Kockelman.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11116-019-10027-5

Keywords

Search

Navigation