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Leveraging Advanced Technologies for (Smart) Transportation Planning: A Systematic Review

Author

Listed:
  • Heejoo Son

    (Department of Urban Planning and Engineering, Pusan National University, Busan 46241, Republic of Korea)

  • Jinhyeok Jang

    (Department of Urban Planning and Engineering, Pusan National University, Busan 46241, Republic of Korea
    Institute for Future Earth, Pusan National University, Busan 46241, Republic of Korea)

  • Jihan Park

    (Department of Urban Planning and Engineering, Pusan National University, Busan 46241, Republic of Korea)

  • Akos Balog

    (Department of Geography and Planning, University of Liverpool, Liverpool L69 7ZT, UK)

  • Patrick Ballantyne

    (Department of Geography and Planning, University of Liverpool, Liverpool L69 7ZT, UK)

  • Heeseo Rain Kwon

    (Department of Geography and Planning, University of Liverpool, Liverpool L69 7ZT, UK)

  • Alex Singleton

    (Department of Geography and Planning, University of Liverpool, Liverpool L69 7ZT, UK)

  • Jinuk Hwang

    (Department of Urban Planning and Engineering, Pusan National University, Busan 46241, Republic of Korea
    Institute for Future Earth, Pusan National University, Busan 46241, Republic of Korea)

Abstract

Transportation systems worldwide are facing numerous challenges, including congestion, environmental impacts, and safety concerns. This study used a systematic literature review to investigate how advanced technologies (e.g., IoT, AI, digital twins, and optimization methods) support smart transportation planning. Specifically, this study examines the interrelationships between transportation challenges, proposed solutions, and enabling technologies, providing insights into how these innovations support smart mobility initiatives. A systematic literature review, following PRISMA guidelines, identified 26 peer-reviewed articles published between 2013 and 2024, including studies that examined smart transportation technologies. To quantitatively assess relationships among key concepts, a Sentence BERT-based natural language processing approach was employed to compute alignment scores between transportation challenges, technological solutions, and implementation strategies. The findings highlight the fact that real-time data collection, predictive analytics, and digital twin simulations significantly enhance traffic flow, safety, and operational efficiency while mitigating environmental impacts. The analysis further reveals strong correlations between traffic congestion and public transit optimization, reinforcing the effectiveness of integrated, data-driven strategies. Additionally, IoT-based sensor networks and AI-driven decision-support systems are shown to play a critical role in sustainable urban mobility by enabling proactive congestion management, multimodal transportation planning, and emission reduction strategies. From a policy perspective, this study underscores the need for investment in urban-scale data infrastructures, the integration of digital twin modeling into long-term planning frameworks, and the alignment of optimization tools with public transit improvements to foster equitable and efficient mobility. These findings offer actionable recommendations for policymakers, engineers, and planners, guiding data-driven resource allocation and legislative strategies that support sustainable, adaptive, and technologically advanced transportation ecosystems.

Suggested Citation

  • Heejoo Son & Jinhyeok Jang & Jihan Park & Akos Balog & Patrick Ballantyne & Heeseo Rain Kwon & Alex Singleton & Jinuk Hwang, 2025. "Leveraging Advanced Technologies for (Smart) Transportation Planning: A Systematic Review," Sustainability, MDPI, vol. 17(5), pages 1-35, March.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:5:p:2245-:d:1605501
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    References listed on IDEAS

    as
    1. Chen, Chialin & Achtari, Guyves & Majkut, Kevin & Sheu, Jiuh-Biing, 2017. "Balancing equity and cost in rural transportation management with multi-objective utility analysis and data envelopment analysis: A case of Quinte West," Transportation Research Part A: Policy and Practice, Elsevier, vol. 95(C), pages 148-165.
    2. Ani Matei & Mădălina Cocoșatu, 2024. "Artificial Internet of Things, Sensor-Based Digital Twin Urban Computing Vision Algorithms, and Blockchain Cloud Networks in Sustainable Smart City Administration," Sustainability, MDPI, vol. 16(16), pages 1-20, August.
    3. Iqram Hussain, 2024. "Secure, Sustainable Smart Cities and the Internet of Things: Perspectives, Challenges, and Future Directions," Sustainability, MDPI, vol. 16(4), pages 1-3, February.
    4. 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.
    5. David Moher & Alessandro Liberati & Jennifer Tetzlaff & Douglas G Altman & The PRISMA Group, 2009. "Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement," PLOS Medicine, Public Library of Science, vol. 6(7), pages 1-6, July.
    6. Mario Henrique Callefi & Gilberto Miller Devós Ganga & Moacir Godinho Filho & Elias Ribeiro da Silva & Lauro Osiro & Vasco Reis, 2024. "A roadmap for prioritising technology-enabled capabilities in road freight transportation management," Post-Print hal-04973884, HAL.
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