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

Author

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  • 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
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    References listed on IDEAS

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    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).
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    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.

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