Cities with a growing population and complexity face urban crises related to road safety and mobility. Governments and collaborative stakeholders are introducing car-free day programs to address these challenges. In this study, a scientific criterion to identify suitable streets for such programs was established through a three-round Delphi study. The study involved twenty panelists from academia, city governments, international and local organizations, and freelance consultants. The panelists identified forty-six criteria clustered into six categories through open-ended and close-ended rounds, desk research, and insights from a car-free day workshop. The panelists reached a consensus on all categories and forty items. According to the results, the panelists reached a “good agreement” on three categories, environment, health & safety, land use & roadside, and mobility & accessibility; a “moderate agreement” on the three categories, namely, categories, geometry & functionality, and policy & governance; and a “weak” agreement on the one object, namely, social & economic. The relative importance index indicated that the two categories, “mobility and accessibility” and “environment, health and safety”, were considered the most important. The study suggests that these findings can potentially influence urban planning and transportation policies.

One of the significant sources of air pollution and greenhouse gas emissions is the road transportation sector. These emissions are worsened by driving behaviors and network conditions. It is common knowledge that experienced and inexperienced drivers behave differently when operating vehicles. Given the same vehicle in a different timeframe, the drivers’ reactions to similar situations vary, which has a significant influence on the emissions and fuel consumption as their use of acceleration and speed differ. Because the driving patterns of automated vehicles are programmable and provide a platform for smooth driving situations, it is predicted that deploying them might potentially reduce fuel consumption, particularly in urban areas with given traffic situations. This study’s goal is to examine how different degrees of automated vehicles behave when it comes to emissions and how accelerations affect that behavior. Furthermore, the total aggregated emissions on the synthesized urban network are evaluated and compared to legacy vehicles. The emission measuring model is based on the Handbook Emission Factors for Road Transport (HBEFA)3 and is utilized with the Simulation of Urban Mobility (SUMO) microscopic simulation software. The results demonstrate that acceleration value is strongly correlated with individual vehicle emissions. Although the ability of automated vehicles (AVs) to swiftly achieve higher acceleration values has an adverse effect on emissions reduction, it was compensated by the rate of accelerations, which decreases as the automation level increases. According to the simulation results, automated vehicles can reduce carbon monoxide (CO) emissions by 38.56%, carbon dioxide (CO ₂) emissions by 17.09%, hydrocarbons (HC) emissions by 36.3%, particulate matter (PM _x) emissions by 28.12%, nitrogen oxides (NO _x) emissions by 19.78% in the most optimistic scenario (that is, when all vehicles are replaced by the upper bound automated vehicles) in the network level.

Keeping the basic principles of sustainable development, it must be highlighted that decisions about transport safety projects must be made following expert preparation, using reliable, professional methods. A prerequisite for the cost–benefit analysis of investments is to constantly monitor the efficiency of accident forecasting models and to update these continuously. This paper presents an accident forecasting model for urban areas, which handles both the properties of the public road infrastructure and spatial dependency relations. As the aim was to model the urban environment, we focused on the road public transportation modes (bus and trolley) and the vulnerable road users (bicyclist) using shared infrastructure elements. The road accident data from 2016 to 2018 on the whole road network of Budapest, Hungary, is analyzed, focusing on road links (i.e., road segments between junctions) by applying spatial econometric statistical models. As a result of this article, we have developed a model that can be used by decision-makers as well, which is suitable for estimating the expected value of accidents, and thus for the development of the optimal sequence of appropriate road safety interventions.