Efficient traffic management in urban areas represents a key challenge for modern cities, particularly in the context of sustainable development and reducing negative environmental impacts. This paper explores the application of artificial intelligence (AI) in optimizing urban traffic through a combination of reinforcement learning (RL) and predictive analytics. The focus is on simulating the traffic network in Belgrade (Serbia, Europe), where RL algorithms, such as Deep Q-Learning and Proximal Policy Optimization, are used for dynamic traffic signal control. The model optimized traffic signal operations at intersections with high traffic volumes using real-time data from IoT sensors, computer vision-enabled cameras, third-party mobile usage data and connected vehicles. In addition, implemented predictive analytics leverage time series models (LSTM, ARIMA) and graph neural networks (GNNs) to anticipate traffic congestion and bottlenecks, enabling initiative-taking decision-making. Special attention is given to challenges such as data transmission delays, system scalability, and ethical implications, with proposed solutions including edge computing and distributed RL models. Results of the simulation demonstrate significant advantages of AI application in 370 traffic signal control devices installed in fixed timing systems and adaptive timing signal systems, including an average reduction in waiting times by 33%, resulting in a 16% decrease in greenhouse gas emissions and improved safety in intersections (measured by an average reduction in the number of traffic accidents). A limitation of this paper is that it does not offer a simulation of the system’s adaptability to temporary traffic surges during mass events or severe weather conditions. The key finding is that integrating AI into an urban traffic network that consists of fixed-timing traffic lights represents a sustainable approach to improving urban quality of life in large cities like Belgrade and achieving smart city objectives.

Serbian SMEs face mounting pressure to stay competitive, agile, and aligned with sustainability goals amid rapid digital change. This mixed-method study—12 qualitative case studies and a survey of 200 firms—examines how AI adoption supports flexible and adaptive strategic transformation. We examine how organizational context and AI readiness translate into the strategic application of AI and, in turn, sustainable development and strategic performance outcomes among Serbian SMEs. Through the AI-Driven Strategic Transformation Framework (AISTF-SME), three adoption types were identified —Traditionalists, Experimenters, and Strategic Adopters—distinguished by digital maturity, strategic integration, and sustainability orientation. While AI is primarily deployed for operational efficiency, firms with higher AI maturity and tighter strategic alignment report stronger gains in agility, innovation, and customer experience; sustainability-oriented use cases remain limited. Key barriers include shortages of technical talent, financial constraints, and insufficient institutional support. We recommend a multi-stakeholder policy approach emphasizing sector-specific AI readiness programs, better access to funding, and stronger university–industry collaboration. The findings enrich digital transformation and sustainability research and offer practical guidance for accelerating AI adoption in transitional economies.

The number of registered commercial freight vehicles is constantly increasing, increasing therefore as well the traffic load on the roads in Bosnia and Herzegovina. A significant part of freight vehicles moving along the main and regional roads are overloaded and cause significant damage to road infrastructure, affect road safety and result in an increase of emissions of harmful gases for people and the environment. The overloading rate is extremely high, in particular with 5-axle trucks representing 58.7%. The research showed that the increased overload level ranges from 10-20% of the maximum permissible weight. The importance of load limits was recognized early in the history of road development. This interrelation led directly to limitations on vehicle loads, and laws were enacted in many countries to establish the maximum allowable motor vehicle sizes and weights. Strict enforcement of motor vehicle size and weight laws is a step toward reducing motor vehicle size and weight violations, heavy truck accidents, and, even more, improving road maintenance, rehabilitation expenditures and road safety. Thus, based on the applied model the objective of this paper is to evaluate and optimize the locations of truck weigh stations on the road network of Bosnia and Herzegovina.

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.

The vehicle routing problem is a general name for a whole set of problems in which a group of vehicles located in a depot needs to visit all customers with the condition that only one vehicle visits each customer and that all vehicles return to the depot. Vehicle routing problems are a generalization of the traveling salesman problem. The purpose of present survey in the field of vehicle routing problem (VRP) is to classify different well known variants of VRP according to the type of objects with previously defined system performance was applied . These variants include a whole class of problems where a set of routes for a car fleet located in one or more depots should be determined for some geographically distant nodes or customers, at that, the purpose of routing is the optimization of the series of customers requests (number of vehicles, distance traveled, time spent in vehicles or user waiting time).

The paper gives an overview of the model of organization in the management of public urban passenger transport systems (JGTP), which are applied in world cities as well as in some cities in the region. These are complex systems with components of economic, technical, technological and organizational nature, as well as elements of planning, arrangement and organization of space and impact on the environment and its protection, at the same time, as a public service, it is a specific network of various organizational and spatial entities. Systems are open transport systems with stochastic state change.