IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v314y2022ics0306261922003439.html
   My bibliography  Save this article

Real vehicle fuel consumption in logistic corridors

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261922003439
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2022.118921?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. Lorentz Jäntschi & Sorana D. Bolboacă, 2018. "Computation of Probability Associated with Anderson–Darling Statistic," Mathematics, MDPI, vol. 6(6), pages 1-17, May.
    4. Koç, Çağrı & Bektaş, Tolga & Jabali, Ola & Laporte, Gilbert, 2016. "A comparison of three idling options in long-haul truck scheduling," Transportation Research Part B: Methodological, Elsevier, vol. 93(PA), pages 631-647.
    5. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.
    3. Karina Braga Marsola & Andréa Leda Ramos de Oliveira & Matheus Yasuo Ribeiro Utino & Paulo Mann & Thayane Caroline Oliveira da Conceição, 2025. "The Impact of Exogenous Variables on Soybean Freight: A Machine Learning Analysis," Sustainability, MDPI, vol. 17(3), pages 1-24, January.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wu, Jishi & Feng, Tao & Jia, Peng & Li, Gen, 2024. "Spatial allocation of heavy commercial vehicles parking areas through geo-fencing," Journal of Transport Geography, Elsevier, vol. 117(C).
    2. Juan Guillermo Urzúa-Morales & Juan Pedro Sepulveda-Rojas & Miguel Alfaro & Guillermo Fuertes & Rodrigo Ternero & Manuel Vargas, 2020. "Logistic Modeling of the Last Mile: Case Study Santiago, Chile," Sustainability, MDPI, vol. 12(2), pages 1-18, January.
    3. Adam, Arnaud & Finance, Olivier & Thomas, Isabelle, 2021. "Monitoring trucks to reveal Belgian geographical structures and dynamics: From GPS traces to spatial interactions," Journal of Transport Geography, Elsevier, vol. 91(C).
    4. Zvonimir Dabčević & Branimir Škugor & Jakov Topić & Joško Deur, 2022. "Synthesis of Driving Cycles Based on Low-Sampling-Rate Vehicle-Tracking Data and Markov Chain Methodology," Energies, MDPI, vol. 15(11), pages 1-21, June.
    5. Li Zhao & Kun Li & Wu Zhao & Han-Chen Ke & Zhen Wang, 2022. "A Sticky Sampling and Markov State Transition Matrix Based Driving Cycle Construction Method for EV," Energies, MDPI, vol. 15(3), pages 1-19, January.
    6. Tianming Gao & Vasilii Erokhin & Aleksandr Arskiy, 2019. "Dynamic Optimization of Fuel and Logistics Costs as a Tool in Pursuing Economic Sustainability of a Farm," Sustainability, MDPI, vol. 11(19), pages 1-16, October.
    7. Jaller, Miguel PhD & Xiao, Runhua Ivan, 2023. "The Effects of Truck Idling and Searching for Parking on Disadvantaged Communities," Institute of Transportation Studies, Working Paper Series qt9w28d01h, Institute of Transportation Studies, UC Davis.
    8. Robert Parham, 2023. "The Difference-of-Log-Normals Distribution: Properties, Estimation, and Growth," Papers 2302.02486, arXiv.org.
    9. Goel, Pooja & Kumar, Aalok, 2024. "Investigating how consumption values and place identity shape long-haul truck drivers’ intention to visit roadside rest areas," Transportation Research Part A: Policy and Practice, Elsevier, vol. 190(C).
    10. Zhuowu Zhang & Emrah Demir & Robert Mason & Carla Cairano-Gilfedder, 2023. "Understanding freight drivers' behavior and the impact on vehicles' fuel consumption and CO2e emissions," Operational Research, Springer, vol. 23(4), pages 1-35, December.
    11. Krasniuk, Sarah & Lemke, Michael K. & Hassoun, Aya & Hege, Adam & Crizzle, Alexander M., 2024. "Improving truck stop environments to support long-haul truck driver safety and health: A scoping review," Transportation Research Part A: Policy and Practice, Elsevier, vol. 185(C).
    12. Lorentz Jäntschi, 2020. "Detecting Extreme Values with Order Statistics in Samples from Continuous Distributions," Mathematics, MDPI, vol. 8(2), pages 1-21, February.
    13. Juan Garcia-Pajoy & Nelson Paz Ruiz & Mario Chong & Ana Luna, 2023. "Utilising PLS-SEM and Km 2 Methodology in Urban Logistics Analysis: A Case Study on Popayan, Colombia," Sustainability, MDPI, vol. 15(17), pages 1-19, August.
    14. Vital, Filipe & Ioannou, Petros, 2021. "Scheduling and shortest path for trucks with working hours and parking availability constraints," Transportation Research Part B: Methodological, Elsevier, vol. 148(C), pages 1-37.
    15. Ibrahim Savadogo & Adrien Beziat, 2021. "Evaluating the potential environmental impacts of a large scale shift to off-hour deliveries," Post-Print halshs-03045859, HAL.
    16. Basso, Franco & Núñez, Matías & Paredes-Belmar, German & Pezoa, Raúl & Varas, Mauricio, 2024. "Estimation of stops of last-mile delivery vehicles: An application in the food industry in the city of Santiago de Chile," Journal of Transport Geography, Elsevier, vol. 116(C).
    17. Jakov Topić & Branimir Škugor & Joško Deur, 2021. "Synthesis and Feature Selection-Supported Validation of Multidimensional Driving Cycles," Sustainability, MDPI, vol. 13(9), pages 1-21, April.
    18. He, Zhangyuan & Zhao, Pengjun & Xiao, Zuopeng & Huang, Xin & Li, Zhaoxiang & Kang, Tingting, 2024. "Exploring the distance decay in port hinterlands under port regionalization using truck GPS data," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 181(C).
    19. Goel, Asvin, 2018. "Legal aspects in road transport optimization in Europe," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 114(C), pages 144-162.
    20. Vital, Filipe & Ioannou, Petros, 2022. "Optimizing Fuel Consumption and Pollutant Emissions in Truck Routing with Parking Availability Prediction and Working Hours Constraints," Institute of Transportation Studies, Working Paper Series qt8rw99523, Institute of Transportation Studies, UC Davis.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:314:y:2022:i:c:s0306261922003439. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.