The article considers an actual problem of organizing a safe and sustainable urban transport system. We have examined the existing positive global experience in both infrastructural and managerial decisions. Then to assess possible solutions at the stage of infrastructure design, we have developed the simulation micromodels of transport network sections of the medium-sized city (Naberezhnye Chelny) with a rectangular building type. The models make it possible to determine the optimal parameters of the traffic flow, under which pollutant emissions from cars would not lead to high concentrations of pollutants. Also, the model allows to obtain the calculated values of the volume of emissions of pollutants and the parameters of the traffic flow (speed, time of passage of the section, etc.). On specific examples, the proposed method’s effectiveness is shown. Case studies of cities of different sizes and layouts are implementation examples and possible uses proposed by the models. This study has shown the rationality of the suggested solution at the stage of assessing infrastructure projects and choosing the best option for sustainable transport development. The proposed research method is universal and can be applied in any city.

This research focuses on addressing critical driving safety issues on university campuses, particularly vehicular congestion, inadequate parking, and hazards arising from the interaction between vehicles and pedestrians. These challenges are common across campuses and demand effective solutions to ensure safe and efficient mobility. To address these issues, the study developed detailed microsimulation models tailored to the Victor Levi Sasso campus of the Technological University of Panama. The primary function of these models is to evaluate the effectiveness of various safety interventions, such as speed reducers and parking reorganization, by simulating their impact on traffic flow and accident risk. The models provide calculations of traffic parameters, including speed and travel time, under different safety scenarios, allowing for a comprehensive assessment of potential improvements. The results demonstrate that the proposed measures significantly enhance safety and traffic efficiency, proving the model’s effectiveness in optimizing campus mobility. Although the model is designed to tackle specific safety concerns, it also offers broader applicability for addressing general driving safety issues on university campuses. This versatility makes it a valuable tool for campus planners and administrators seeking to create safer and more efficient traffic environments. Future research could expand the model’s application to include a wider range of safety concerns, further enhancing its utility in promoting safer campus mobility.