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Information technology and efficiency in trucking

Author

Listed:
  • Philippe Barla
  • Denis Bolduc
  • Nathalie Boucher
  • Jonathan Watters

Abstract

In this paper, we develop an econometric model to estimate the impacts of Electronic Vehicle Management Systems (EVMS) on the load factor (LF) of heavy trucks using data at the operational level. This technology is supposed to improve capacity utilization by reducing coordination costs between demand and supply. The model is estimated on a subsample of the 1999 National Roadside Survey, covering heavy trucks travelling in the province of Quebec. The LF is explained as a function of truck, trip and carrier characteristics. We show that the use of EVMS results in a 16 percentage points increase of LF on backhaul trips. However, we also find that the LF of equipped trucks is reduced by about 7.6 percentage points on fronthaul movements. This last effect could be explained by a rebound effect: higher expected LF on the returns lead carriers to accept shipments with lower fronthaul LF. Overall, we find that this technology has increased the tonne-kilometers transported of equipped trucks by 6.3% and their fuel efficiency by 5%.

Suggested Citation

  • Philippe Barla & Denis Bolduc & Nathalie Boucher & Jonathan Watters, 2008. "Information technology and efficiency in trucking," Working Papers of Department of Economics, Leuven ces0813, KU Leuven, Faculty of Economics and Business (FEB), Department of Economics, Leuven.
    • Handle: RePEc:ete:ceswps:ces0813
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    References listed on IDEAS

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    1. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, December.
    2. A. Greening, Lorna & Greene, David L. & Difiglio, Carmen, 2000. "Energy efficiency and consumption -- the rebound effect -- a survey," Energy Policy, Elsevier, vol. 28(6-7), pages 389-401, June.
    3. Thomas N. Hubbard, 2000. "The Demand for Monitoring Technologies: The Case of Trucking," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 115(2), pages 533-560.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Cédric Gossart, 2015. "Rebound effects and ICT : a review of the literature," Post-Print hal-01258112, HAL.
    2. Ron Yang, 2022. "(Don’t) Take Me Home: Home Preference and the Effect of Self-Driving Trucks on Interstate Trade," NBER Chapters, in: Economics of Artificial Intelligence, National Bureau of Economic Research, Inc.
    3. Cédric Gossart, 2015. "Rebound effects and ICT : a review of the literature," Grenoble Ecole de Management (Post-Print) hal-01258112, HAL.
    4. Zolfagharinia, Hossein & Haughton, Michael A., 2017. "Operational flexibility in the truckload trucking industry," Transportation Research Part B: Methodological, Elsevier, vol. 104(C), pages 437-460.
    5. Abate, Megersa & de Jong, Gerard, 2014. "The optimal shipment size and truck size choice – The allocation of trucks across hauls," Transportation Research Part A: Policy and Practice, Elsevier, vol. 59(C), pages 262-277.

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    More about this item

    Keywords

    Information and Communication Technology; Efficiency; Load factor; Trucking; Energy Efficiency;
    All these keywords.

    JEL classification:

    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • Q55 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environmental Economics: Technological Innovation
    • R40 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - General

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