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Real vehicle fuel consumption in logistic corridors

Author

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  • Huertas, José I.
  • Serrano-Guevara, Oscar
  • Díaz-Ramírez, Jenny
  • Prato, Daniel
  • Tabares, Lina

Abstract

Fleet managers state that fuel consumption accounts for 50% of the operating costs of their cargo and passenger vehicle fleets. Aiming to reduce these costs, transport companies contract telematics services to track their units and monitor fuel consumption along with other variables. The gathered information is used to alert managers on events like excessive fuel consumption, abrupt breaks, and needs for mechanical maintenance. We propose the use of this information to determine the fuel consumption of cargo vehicles at each km of the main roads of a given region and the influence of altitude, road grade, and vehicle age on it. As a case study, we studied the fuel consumption in the main logistic corridor of Colombia, which is characterized by having a highly variable topography. Toward that end, we compared the fuel consumption monitored by a telematics system on 46 vehicles of different cargo capacity with the estimated by an energy balance model and observed that they are highly correlated (R2 greater than 0.99). Then, we used the calibrated model to obtain the km-by-km fuel consumption. This information is used by authorities to obtain a close estimation of the cost of cargo transport, the greenhouse gases emissions and to identify locations with unusual high fuel consumption. Furthermore, the slope of the linear correlation (Cf) decouples the fuel consumption associated with driving style (human factors) from other influencing factors. Then, we observed that the effects of altitude on fuel consumption are negligible.

Suggested Citation

  • Huertas, José I. & Serrano-Guevara, Oscar & Díaz-Ramírez, Jenny & Prato, Daniel & Tabares, Lina, 2022. "Real vehicle fuel consumption in logistic corridors," Applied Energy, Elsevier, vol. 314(C).
    • Handle: RePEc:eee:appene:v:314:y:2022:i:c:s0306261922003439
    DOI: 10.1016/j.apenergy.2022.118921
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    References listed on IDEAS

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    1. José I. Huertas & Michael Giraldo & Luis F. Quirama & Jenny Díaz, 2018. "Driving Cycles Based on Fuel Consumption," Energies, MDPI, vol. 11(11), pages 1-13, November.
    2. Lorentz Jäntschi & Sorana D. Bolboacă, 2018. "Computation of Probability Associated with Anderson–Darling Statistic," Mathematics, MDPI, vol. 6(6), pages 1-17, May.
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    4. Laranjeiro, Patrícia F. & Merchán, Daniel & Godoy, Leonardo A. & Giannotti, Mariana & Yoshizaki, Hugo T.Y. & Winkenbach, Matthias & Cunha, Claudio B., 2019. "Using GPS data to explore speed patterns and temporal fluctuations in urban logistics: The case of São Paulo, Brazil," Journal of Transport Geography, Elsevier, vol. 76(C), pages 114-129.
    5. Nevland, Erik A. & Gingerich, Kevin & Park, Peter Y., 2020. "A data-driven systematic approach for identifying and classifying long-haul truck parking locations," Transport Policy, Elsevier, vol. 96(C), pages 48-59.
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    Cited by:

    1. Jose Alejandro Cano & Abraham Londoño-Pineda & Carolina Rodas, 2022. "Sustainable Logistics for E-Commerce: A Literature Review and Bibliometric Analysis," Sustainability, MDPI, vol. 14(19), pages 1-24, September.
    2. Oscar S. Serrano-Guevara & José I. Huertas & Luis F. Quirama & Antonio E. Mogro, 2022. "Energy Efficiency of Heavy-Duty Vehicles in Mexico," Energies, MDPI, vol. 16(1), pages 1-25, December.

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