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Sustainable Traffic Management for Smart Cities Using Internet-of-Things-Oriented Intelligent Transportation Systems (ITS): Challenges and Recommendations

Author

Listed:
  • Auwal Alhassan Musa

    (Civil Engineering Department, Mewar University, Chittorgarh 312901, India)

  • Salim Idris Malami

    (School of Energy, Geo-Science, Infrastructure and Society, Institute for Sustainable Built Environment, Heriot-Watt University, Edinburgh EH14 4AS, UK)

  • Fayez Alanazi

    (Civil Engineering Department, College of Engineering, Jouf University, Sakaka 72388, Saudi Arabia)

  • Wassef Ounaies

    (Civil Engineering Department, College of Engineering, Jouf University, Sakaka 72388, Saudi Arabia)

  • Mohammed Alshammari

    (Civil Engineering Department, College of Engineering, Jouf University, Sakaka 72388, Saudi Arabia)

  • Sadi Ibrahim Haruna

    (School of Civil Engineering, Tianjin University, Tianjin 300350, China)

Abstract

The emergence of smart cities has addressed many critical challenges associated with conventional urbanization worldwide. However, sustainable traffic management in smart cities has received less attention from researchers due to its complex and heterogeneous nature, which directly affects smart cities’ transportation systems. The study aimed at addressing traffic-related issues in smart cities by focusing on establishing a sustainable framework based on the Internet of Things (IoT) and Intelligent Transportation System (ITS) applications. To sustain the management of traffic in smart cities, which is composed of a hybridized stream of human-driven vehicles (HDV) and connected automated vehicles (CAV), a dual approach was employed by considering traffic as either modeling- and analysis-based, or/and the decision-making issues of previous research works. Moreover, the two techniques utilized real-time traffic data, and collected vehicle and road users’ information using AI sensors and ITS-based devices. These data can be processed and transmitted using machine learning algorithms and cloud computing for traffic management, traffic decision-making policies, and documentation for future use. The proposed framework suggests that deploying such systems in smart cities’ transportation could play a significant role in predicting traffic outcomes, traffic forecasting, traffic decongestion, minimizing road users’ lost hours, suggesting alternative routes, and simplifying urban transportation activities for urban dwellers. Also, the proposed integrated framework adopted can address issues related to pollution in smart cities by promoting public transportation and advocating low-carbon emission zones. By implementing these solutions, smart cities can achieve sustainable traffic management and reduce their carbon footprint, making them livable and environmentally friendly.

Suggested Citation

  • Auwal Alhassan Musa & Salim Idris Malami & Fayez Alanazi & Wassef Ounaies & Mohammed Alshammari & Sadi Ibrahim Haruna, 2023. "Sustainable Traffic Management for Smart Cities Using Internet-of-Things-Oriented Intelligent Transportation Systems (ITS): Challenges and Recommendations," Sustainability, MDPI, vol. 15(13), pages 1-15, June.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:13:p:9859-:d:1175793
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    References listed on IDEAS

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    Cited by:

    1. Junhee Kang & Sehyun Tak & Sungjin Park, 2023. "Analyzing the Impact of C-ITS Services on Driving Behavior: A Case Study of the Daejeon–Sejong C-ITS Pilot Project in South Korea," Sustainability, MDPI, vol. 15(16), pages 1-21, August.
    2. Husnain Mushtaq & Xiaoheng Deng & Mubashir Ali & Babur Hayat & Hafiz Husnain Raza Sherazi, 2023. "DFA-SAT: Dynamic Feature Abstraction with Self-Attention-Based 3D Object Detection for Autonomous Driving," Sustainability, MDPI, vol. 15(18), pages 1-21, September.
    3. Antoine Kazadi Kayisu & Miroslava Mikusova & Pitshou Ntambu Bokoro & Kyandoghere Kyamakya, 2024. "Exploring Smart Mobility Potential in Kinshasa (DR-Congo) as a Contribution to Mastering Traffic Congestion and Improving Road Safety: A Comprehensive Feasibility Assessment," Sustainability, MDPI, vol. 16(21), pages 1-53, October.
    4. Katsiaryna Bahamazava, 2024. "Urban Mobility: AI, ODE-Based Modeling, and Scenario Planning," Papers 2410.19915, arXiv.org.

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