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.

Food safety in supply chains remains a critical concern due to the complexity of global distribution networks. This study develops a conceptual framework to evaluate how food safety risks influence supply chain performance through predictive analytics. The framework identifies and minimizes food safety risks before they cause serious problems. The study examines the impact of food safety practices, supply chain transparency, and technological integration on adopting predictive analytics. To illustrate the complex dynamics of food safety and supply chain performance, the study presents supply chain transparency, technological integration, and food safety practices and procedures as independent variables and predictive analytics as a mediator. The results show that supply chain managers’ capacity to anticipate and control risks related to food safety can be improved by predictive analytics, leading to safer food production and distribution methods. The research recommends that businesses create scalable cloud-based predictive model solutions, combine data sources, and employ cutting-edge AI and machine learning tools. Companies should also note that strong, data-driven approaches to food safety require cooperative data sharing, regulatory compliance, training initiatives and ongoing improvement.

Concerns about public food safety are comparatively common in the Chinese food distribution industry. A dearth of expertise and scarce resources lead to frequent instances of incapacity and inadequate oversight, which negatively affect stakeholders in the circulation industry. The main challenges to food supervision are the need for more alignment between the technical specifications, comprehensiveness, and continuity of the existing food safety supervision legislation and the real circumstances facing the regulatory agencies. Despite the circulation field’s critical position in food safety regulation, its complex and variable characteristics make it challenging to implement and manage. There exist notable concerns over inadequate food safety standards and supervisory frameworks, vagueness in enforcing rules, and insufficient workforce and technical know-how in food safety supervision. The opportunities for regulating the food business with the government’s focus and attention considerably outweigh the obstacles that lie ahead. The growth of the food business needs to be viewed in the larger framework of the country’s economic development. Professional involvement and collaboration with technical departments can help regulatory bodies tackle non-compliant actions in the market circulation process in a timely way, resulting in a more evidence-based and responsive regulatory approach. Establishing a healthy equilibrium and elucidating the relationship between oversight and the food business will be crucial in the future.