IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v17y2025i3p1267-d1583660.html
   My bibliography  Save this article

Developing a Sustainable Traffic Management Framework Using Machine Learning Models for Fuel Consumption Minimization at Closely Spaced Intersections

Author

Listed:
  • Ruti R. Politi

    (Department of Civil Engineering, Izmir University of Economics, Balçova 35330, Turkey
    The Graduate School of Natural and Applied Sciences, Dokuz Eylul University, Central Campus, Buca 35390, Turkey)

  • Serhan Tanyel

    (Department of Civil Engineering, Dokuz Eylul University, Central Campus, Buca 35390, Turkey)

Abstract

Closely spaced intersections can be specified as special types of intersections with short-distance characteristics that are generally located in urban areas. This study aimed to develop a sustainable transportation framework of machine learning algorithms to predict and minimize fuel consumption as a measure of environmental impact at closely spaced intersections. In the theoretical framework, this study incorporates key traffic parameters such as left-turn-lane length, cycle time, distance between intersections, left-turn movement ratio, and traffic volume fluctuations to model fuel consumption. In this context, different scenarios were modeled and compared with SIDRA Intersection (version 6.1), which is a well-known traffic analysis and intersection modeling software, by using partial least square regression (PLSR), polynomial support vector machine (PSVM), and artificial neural network (ANN) models to conduct a comparative analysis of their applicability. The results demonstrated that the ANN model best captured fuel consumption variations across different key influencing factors. Among all models, cycle time showed the highest sensitivity, highlighting its critical impact; the optimization of left-turn-lane length and cycle time is performed using Particle Swarm Optimization (PSO) to minimize the impact of left-turns on fuel consumption. These enhancements promote more efficient and environmentally friendly traffic management. The integration of the predictive and optimized PSO-ANN model establishes a foundation for optimizing intersection performance. The findings indicate that an overall improvement of 8.9% in fuel consumption is achieved by evaluating the optimized parameters under varying traffic volumes. The proposed framework supports sustainable signalized intersection management by improving fuel efficiency and reducing environmental impact.

Suggested Citation

  • Ruti R. Politi & Serhan Tanyel, 2025. "Developing a Sustainable Traffic Management Framework Using Machine Learning Models for Fuel Consumption Minimization at Closely Spaced Intersections," Sustainability, MDPI, vol. 17(3), pages 1-38, February.
  • Handle: RePEc:gam:jsusta:v:17:y:2025:i:3:p:1267-:d:1583660
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/17/3/1267/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/17/3/1267/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fayez Alanazi & Ibrahim Khalil Umar & Sadi Ibrahim Haruna & Mahmoud El-Kady & Abdelhalim Azam, 2023. "Development of Artificial Intelligence Based Safety Performance Measures for Urban Roundabouts," Sustainability, MDPI, vol. 15(14), pages 1-17, July.
    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.

      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:3:p:1267-:d:1583660. 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.