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Information technology and efficiency in trucking

Author

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

Abstract

We develop an econometric model to estimate the impact of Electronic Vehicle Management Systems (EVMS) on the load factor (LF) of heavy trucks. This technology is supposed to improve capacity utilization. The model is estimated on the Quebec subsample of the 1999 National Roadside Survey. The LF is explained as a function of truck, trip, and carrier characteristics. We show that the use of EVMS results in an increase of 16 percentage points of LF on backhaul trips. However, we also find that there is a rebound effect on fronthaul movements, with a reduction of LF by about 7.6 percentage points. Nous estimons un modèle économétrique pour évaluer l'impact des systèmes de gestion électronique des véhicules (SGEV) sur le taux de chargement (TC) des camions lourds. Cette technologie est censée améliorer l'utilisation de la capacité. Le modèle est estimé sur le sous‐échantillon québécois des données de l'enquête nationale routière en bord de route de 1999. Le TC est expliqué en fonction des caractéristiques du camion, du voyage et de l'entreprise de transport. Nous montrons que l'utilisation de SGEV accroit le TC sur le retour d'environ 16 points de pourcentage. Par contre, nous trouvons également un effet rebond sur l'aller avec une réduction de TC d'environ 7.6 points de pourcentage.

Suggested Citation

  • Philippe Barla & Denis Bolduc & Nathalie Boucher & Jonathan Watters, 2010. "Information technology and efficiency in trucking," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 43(1), pages 254-279, February.
    • Handle: RePEc:wly:canjec:v:43:y:2010:i:1:p:254-279
    DOI: 10.1111/j.1540-5982.2009.01571.x
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. 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.
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    Cited by:

    1. 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.
    2. Cédric Gossart, 2015. "Rebound effects and ICT : a review of the literature," Post-Print hal-01258112, HAL.
    3. 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.
    4. 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.
    5. Cédric Gossart, 2015. "Rebound effects and ICT : a review of the literature," Grenoble Ecole de Management (Post-Print) hal-01258112, HAL.

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    More about this item

    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

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