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

Heejoo Son, Jinhyeok Jang, Jihan Park, Akos Balog, Patrick Ballantyne, Heeseo Rain Kwon, Alex Singleton and Jinuk Hwang ()
Additional contact information
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
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

Sustainability, 2025, vol. 17, issue 5, 1-35

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.

Keywords: smart transportation planning; transportation technologies; systematic literature review (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/17/5/2245/pdf (application/pdf)
https://www.mdpi.com/2071-1050/17/5/2245/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:17:y:2025:i:5:p:2245-:d:1605501

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().