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.

Intermodal freight transport is an integral, strategic foundation of the European Union’s vision to decarbonize Europe, but in non-EU countries such as Republic of Serbia, the split in modalities remains heavily focused on road haul, while terminal infrastructure remains poorly interconnected and digital maturity remains low. This research study investigates the adoption of new digital and operational technologies along the intermodal transport chain. It is measured in the context of enhancing ecological and operational performance in the Serbian environment. Research data was collected from a sample of nine medium and large Serbian intermodal logistics companies, who operate along Pan-European Corridor X and the Rhine–Danube Corridor. Three ecological and three operational key performance indicators (KPIs) were defined to measure the level on enhancements reached through sustainability efforts. The study is an uncontrolled, firm-level pre–post comparison: three directional hypotheses were evaluated using paired-sample t-tests, exact Wilcoxon signed-rank tests, and bootstrap confidence intervals on pre- and post-modernization firm-level aggregates. In the post-modernization period, mean CO₂ intensity per TEU (twenty-foot equivalent unit) was 17.4% lower, terminal energy intensity (kWh/TEU) 14.3% lower, and terminal dwell time 22.5% lower than in the pre-modernization period (exact p ≤ 0.001; 95% bootstrap confidence intervals excluding zero). Implications were discussed for EU Green Deal alignment, Trans-European Transport Network (TEN-T) extension to the Western Balkans, and future research has been initially defined. Given the pre/post design without a control group, the reported effects are associations rather than causal estimates and should be read as preliminary, exploratory evidence.