IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v17y2025i4p1678-d1593525.html
   My bibliography  Save this article

Predicting Fuel Consumption by Artificial Neural Network (ANN) Based on the Regular City Bus Lines

Author

Listed:
  • Augustyn Lorenc

    (Faculty of Mechanical Engineering, Cracow University of Technology, 31-864 Cracow, Poland)

Abstract

This article discusses the application of an ANN model for forecasting the fuel consumption of vehicles on the regular city bus lines. In the context of rising fuel costs and their impact on transportation companies, the developed system supports the optimization of fuel consumption standards and fleet management. The model accounts for prediction factors such as route length [km], number of bus stops, probability of traffic jams [from 1—low to 3—high], ambient temperature [°C], from external database, technical state of the vehicle [from 1—good to 5—bad], type of petrol [1—ON; 2—E95], filling of the vehicle/number of passengers [from 1—empty to 5—full]. Based on this these data, the presented model was developed. The system analyzes input, generates reports, and identifies potential issues, including excessive fuel consumption or fuel theft. Its modular design allows for further development and adaptation to user needs. Implementing this solution enhances operational efficiency, reduces costs, and optimizes transportation management.

Suggested Citation

  • Augustyn Lorenc, 2025. "Predicting Fuel Consumption by Artificial Neural Network (ANN) Based on the Regular City Bus Lines," Sustainability, MDPI, vol. 17(4), pages 1-24, February.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:4:p:1678-:d:1593525
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/17/4/1678/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/17/4/1678/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Samuel Oghale Oweh & Peter Alenoghena Aigba & Olusegun David Samuel & Joseph Oyekale & Fidelis Ibiang Abam & Ibham Veza & Christopher Chintua Enweremadu & Oguzhan Der & Ali Ercetin & Ramazan Sener, 2024. "Improving Productivity at a Marble Processing Plant Through Energy and Exergy Analysis," Sustainability, MDPI, vol. 16(24), pages 1-30, December.
    2. Dragan Adamović & Savka Adamović & Zoran Čepić & Slobodan Morača & Aleksandra Mihailović & Ivan Mijailović & Milena Stošić, 2024. "Possibilities of Improving the Emission Characteristics of Passenger Cars by Controlling the Concentration Levels of Combustion-Generated BTEX Components," Sustainability, MDPI, vol. 16(24), pages 1-24, December.
    Full references (including those not matched with items on IDEAS)

    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. Jun Liu & Hengxu Shen & Yuexing Li & Tianci Yin, 2025. "Impact of Industrial Symbiotic Agglomeration on Energy Efficiency: Analysis Using Time-Varying Difference-in-Difference Model," Sustainability, MDPI, vol. 17(5), pages 1-18, February.

    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:gam:jsusta:v:17:y:2025:i:4:p:1678-:d:1593525. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.