IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i12p9231-d1165929.html
   My bibliography  Save this article

Enhancing Intersection Performance for Tram and Connected Vehicles through a Collaborative Optimization

Author

Listed:
  • Ali Louati

    (Department of Information Systems, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia
    SMART Laboratory, Higher Institute of Management of Tunis, University of Tunis, Bardo 2000, Tunisia)

  • Elham Kariri

    (Department of Information Systems, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia)

Abstract

This article tackles a pervasive problem in connected transportation networks: the issue of conflicting right-of-way between trams and Connected Vehicles (CV) at intersections. Trams are typically granted a semi-exclusive right-of-way, leading to a clash with CV. To resolve this challenge, the study introduces a Transit Signal Priority (TSP) system and a guidance framework that seeks to minimize unintended delays for trams while minimizing the negative impact on CV, passenger comfort, energy consumption, and overall travel time. The proposed framework employs a collaborative optimization system and an improved genetic algorithm to adjust both the signal phase duration and the operating path. The study is based on data collected from a simulated intersection that includes the signal phase sequence and duration. The findings demonstrate that the proposed framework was able to reduce the transit time for trams by 45.8% and the overall transit time for trams 481 and CVs by 17.1% compared to the conventional method. Additionally, the system was able to reduce energy consumption by 34.7% and the non-comfort index by 25.8%. Overall, this research contributes to the development of a more efficient and sustainable transportation system for the future.

Suggested Citation

  • Ali Louati & Elham Kariri, 2023. "Enhancing Intersection Performance for Tram and Connected Vehicles through a Collaborative Optimization," Sustainability, MDPI, vol. 15(12), pages 1-17, June.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:12:p:9231-:d:1165929
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/12/9231/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/12/9231/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Li, Da & Zhang, Zhaosheng & Zhou, Litao & Liu, Peng & Wang, Zhenpo & Deng, Junjun, 2022. "Multi-time-step and multi-parameter prediction for real-world proton exchange membrane fuel cell vehicles (PEMFCVs) toward fault prognosis and energy consumption prediction," Applied Energy, Elsevier, vol. 325(C).
    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. Ali Louati & Hassen Louati & Elham Kariri & Wafa Neifar & Mohammed A. Farahat & Heba M. El-Hoseny & Mohamed K. Hassan & Mutaz H. H. Khairi, 2024. "Sustainable Urban Mobility for Road Information Discovery-Based Cloud Collaboration and Gaussian Processes," Sustainability, MDPI, vol. 16(4), pages 1-16, February.
    2. Ali Louati & Hassen Louati & Elham Kariri & Wafa Neifar & Mohamed K. Hassan & Mutaz H. H. Khairi & Mohammed A. Farahat & Heba M. El-Hoseny, 2024. "Sustainable Smart Cities through Multi-Agent Reinforcement Learning-Based Cooperative Autonomous Vehicles," Sustainability, MDPI, vol. 16(5), pages 1-18, February.
    3. Wenhui Zhang & Yajing Song & Ge Zhou & Ziwen Song & Cong Xi, 2023. "Multiobjective-Based Decision-Making for the Optimization of an Urban Passenger Traffic System Structure," Sustainability, MDPI, vol. 15(18), pages 1-20, September.

    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. Shi, Jihao & Zhang, Xinqi & Zhang, Haoran & Wang, Qiliang & Yan, Jinyue & Xiao, Linda, 2024. "Automated detection and diagnosis of leak fault considering volatility by graph deep probability learning," Applied Energy, Elsevier, vol. 361(C).

    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:15:y:2023:i:12:p:9231-:d:1165929. 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.