AI-Driven Safety Evaluation in Public Transport: A Case Study from Belgrade’s Closed Transit Systems

Saša Zdravković, Filip Dobrić, Zoran Injac (), Violeta Lukić-Vujadinović, Milinko Veličković, Branka Bursać Vranješ and Srđan Marinković
Additional contact information
Saša Zdravković: Road Traffic Safety Agency of the Republic of Serbia, 11070 Belgrade, Serbia
Filip Dobrić: Ministry of Internal Affairs, Government of Republic of Serbia, 21000 Novi Sad, Serbia
Zoran Injac: Faculty for Traffic Engineering, Pan European University Apeiron, 78102 Banja Luka, Bosnia and Herzegovina
Violeta Lukić-Vujadinović: Department for Industrial Engineering, Faculty of Engineering Management and Economics, University Business Academy Novi Sad, 21000 Novi Sad, Serbia
Milinko Veličković: Megatrend University, 11000 Belgrade, Serbia
Branka Bursać Vranješ: Institute Bit doo, 22304 Novi Banovci, Serbia
Srđan Marinković: University for Business Engineering and Management PIM, Despota Stefana Lazarevića, 78000 Banja Luka, Bosnia and Herzegovina

Sustainability, 2025, vol. 17, issue 18, 1-36

Abstract: Ensuring traffic safety within urban public transport systems is essential for achieving sustainable urban development, particularly in densely populated metropolitan areas. This study investigates the integration of artificial intelligence (AI) technologies to enhance safety performance in closed public transport environments, with a focus on the city of Belgrade as a representative case. The research aims to evaluate how AI-enabled systems can contribute to the early detection and reduction of traffic incidents, thereby supporting broader goals of sustainable mobility, infrastructure resilience, and urban livability. A hybrid methodological framework was developed, combining computer vision, supervised machine learning, and time series analytics to construct a real-time risk detection platform. The system leverages multi-source data—including video surveillance, onboard vehicle sensors, and historical accident logs—to identify and predict high-risk behaviors such as harsh braking, speeding, and route adherences across various public transport modes (buses, trams, trolleybuses). The AI models were empirically assessed in partnership with the Public Transport Company of Belgrade (JKP GSP Beograd), revealing that the most accurate models improved incident detection speed by over 20% and offered enhanced spatial identification of network-level safety vulnerabilities. Additionally, routes with optimized AI-driven driving behavior demonstrated fuel savings of up to 12% and a potential reduction in emissions by approximately 8%, suggesting promising environmental co-benefits. The study’s findings align with multiple United Nations Sustainable Development Goals, particularly SDG 11 (Sustainable Cities and Communities) and SDG 9 (Industry, Innovation, and Infrastructure). Moreover, the research addresses ethical, legal, and governance implications surrounding the use of AI in public infrastructure, emphasizing the importance of privacy, transparency, and inclusivity. The paper concludes with strategic policy recommendations for cities seeking to deploy intelligent safety solutions as part of their digital and green transitions in urban mobility planning.

Keywords: traffic; AI; public transport; safety; evaluation; sustainable transportation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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