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Adaptive Traffic Light Management for Mobility and Accessibility in Smart Cities

Author

Listed:
  • Malik Almaliki

    (Department of Computer Science, College of Computer Science and Engineering, Taibah University, Yanbu 46421, Saudi Arabia
    King Salman Center for Disability Research, Riyadh 11614, Saudi Arabia)

  • Amna Bamaqa

    (King Salman Center for Disability Research, Riyadh 11614, Saudi Arabia
    Computer Science and Information Department, Applied College, Taibah University, Madinah 42353, Saudi Arabia)

  • Mahmoud Badawy

    (King Salman Center for Disability Research, Riyadh 11614, Saudi Arabia
    Computer Science and Information Department, Applied College, Taibah University, Madinah 42353, Saudi Arabia
    Computers and Control Systems Engineering Department, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt)

  • Tamer Ahmed Farrag

    (King Salman Center for Disability Research, Riyadh 11614, Saudi Arabia
    Department of Electrical Engineering, College of Engineering, Taif University, Taif 21944, Saudi Arabia)

  • Hossam Magdy Balaha

    (Computers and Control Systems Engineering Department, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt
    Bioengineering Department, J.B. Speed School of Engineering, University of Louisville, Louisville, KY 40292, USA)

  • Mostafa A. Elhosseini

    (King Salman Center for Disability Research, Riyadh 11614, Saudi Arabia
    Computers and Control Systems Engineering Department, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt
    Department of Information Systems, College of Computer Science and Engineering, Taibah University, Yanbu 46421, Saudi Arabia)

Abstract

Urban road traffic congestion poses significant challenges to sustainable mobility in smart cities. Traditional traffic light systems, reliant on static or semi-fixed timers, fail to adapt to dynamic traffic conditions, exacerbating congestion and limiting inclusivity. To address these limitations, this paper proposes H-ATLM (a hybrid adaptive traffic lights management), a system utilizing the deep deterministic policy gradient (DDPG) reinforcement learning algorithm to optimize traffic light timings dynamically based on real-time data. The system integrates advanced sensing technologies, such as cameras and inductive loops, to monitor traffic conditions and adaptively adjust signal phases. Experimental results demonstrate significant improvements, including reductions in congestion (up to 50%), increases in throughput (up to 149%), and decreases in clearance times (up to 84%). These findings open the door for integrating accessibility-focused features such as adaptive signaling for accessible vehicles, dedicated lanes for paratransit services, and prioritized traffic flows for inclusive mobility.

Suggested Citation

  • Malik Almaliki & Amna Bamaqa & Mahmoud Badawy & Tamer Ahmed Farrag & Hossam Magdy Balaha & Mostafa A. Elhosseini, 2025. "Adaptive Traffic Light Management for Mobility and Accessibility in Smart Cities," Sustainability, MDPI, vol. 17(14), pages 1-31, July.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:14:p:6462-:d:1701898
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    References listed on IDEAS

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    1. Appio, Francesco Paolo & Lima, Marcos & Paroutis, Sotirios, 2019. "Understanding Smart Cities: Innovation ecosystems, technological advancements, and societal challenges," Technological Forecasting and Social Change, Elsevier, vol. 142(C), pages 1-14.
    2. Appio, Francesco Paolo & Lima, Marcos & Paroutis, Sotirios, 2019. "Understanding Smart Cities: Innovation ecosystems, technological advancements, and societal challenges," Technological Forecasting and Social Change, Elsevier, vol. 142(C), pages 1-14.
    3. Anandkumar Balasubramaniam & Anand Paul & Won-Hwa Hong & HyunCheol Seo & Jeong Hong Kim, 2017. "Comparative Analysis of Intelligent Transportation Systems for Sustainable Environment in Smart Cities," Sustainability, MDPI, vol. 9(7), pages 1-12, June.
    4. Zhi (Aaron) Cheng & Min-Seok Pang & Paul A. Pavlou, 2020. "Mitigating Traffic Congestion: The Role of Intelligent Transportation Systems," Information Systems Research, INFORMS, vol. 31(3), pages 653-674, September.
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