Sustainable urban mobility plans (SUMPs) have become increasingly popular in cities with environmental problems to reduce pollution, often caused by urban transportation. Therefore, this study aims to provide a practical framework for the selection decisions of final measures and policies to be carried out to achieve SUMP workspace goals using a fuzzy multi-criteria decision-making (MCDM) methodology. Alternatives are created with the strategic “pillar”, which was first adopted by the Podgorica city council. With the measurements in this pillar, the main criteria and the recommended measures for these measures create sub-criteria. Secondly, a Fuzzy Full Consistency Method (F-FUCOM) was used to determine the weights of the main and sub-criteria. The Fuzzy Combined Compromise Solution (F-CoCoSo) method was then applied to rank the alternatives. “The implementation and assurance of the SUMP” and “establishing a system for regular data collection, monitoring and evaluation of selected mobility indicators” were decided as the most important main and sub-criteria with weights of 0.286 and 0.1079, respectively. The findings suggest that the comprehensive planning for sustainable urban mobility alternative is first and the valorization of cycling potential alternative is second. A comprehensive sensitivity analysis confirms the validity, robustness, and effectiveness of the proposed framework. The applied methodology has the potential to assist decision makers in the process of developing SUMPs.

In recent years, there has been a remarkable development in the technology and legislation related to electric and autonomous vehicles (i.e., EVs/AVs). This technological advancement requires the deployment of the most up-to-date supporting infrastructure to achieve safe operation. Further infrastructure is needed for Level 5 vehicles, namely the introduction of super-fast wireless 5G technology. To achieve harmony between the rapid technological advancement of EVs/AVs and environmental preservation, enacting legislation related to their sustainable use is vital. Thus, this manuscript provides a review of the technological development of EVs/AVs, with a special focus on carbon footprints and the implementation of additive manufacturing using recycled materials. While EVs have a 12.13% increased carbon footprint compared to conventional vehicles, AVs with basic and advanced intelligence features have an increased carbon footprint of 41.43% and 99.65%, respectively. This article emphasizes that the integration of 3D-printed components has the potential to offset this impact with a substantial 60% reduction. As a result, custom-made solutions involving 3D printing are explored, leading to greater speed, customization, and cost-effectiveness for EVs/AVs. This article also lists the advantages and disadvantages of the existing legislation in Spain, the United Kingdom, and the western Balkans, demonstrating various approaches to promoting electric mobility and the development of autonomous vehicles. In Spain, initiatives like the MOVES program incentivize EV adoption, while the UK focuses on expanding the EV market and addressing concerns about EVs’ quiet operation. In the western Balkans, the adoption of legislation lags behind, with limited incentives and infrastructure for EVs. To boost sales, legal mechanisms are necessary to reduce costs and improve accessibility, in addition to offering subsidies for the purchase of EVs. To this end, an analysis of the incentive measures proposed for the development and use of renewable power sources for the supply of energy for EVs/AVs is presented.

Public transport systems have a vital role in achieving sustainable mobility goals, diminishing reliance on private individual transport and improving overall public health. Despite that, transport operators are often in situations that require them to cope with complex working conditions that lead to negative emotions such as anger. The current study represents a segment of the permanent global research agenda that seeks to devise and test a psychometric scale for measuring driving anger in professional drivers. The present research is one of the first attempts to examine the factorial validity and the cross-cultural measurement equivalence of the broadly utilized Driving Anger Scale (DAS) in three culturally different countries within the Western Balkans region. The respondents (N = 1054) were taxi, bus and truck drivers between 19 and 75 years of age. The results pertaining to confirmatory factor analysis showed that there were adequate fit statistics for the specified six-dimensional measurement model of the DAS. The measurement invariance testing showed that the meaning and psychometric performance of driving anger and its facets are equivalent across countries and types of professional drivers. Furthermore, the results showed that driving anger facets had positive correlations with dysfunctional ways of expressing anger and negative correlations with the form of the prosocial anger expression. In addition, the results revealed that taxi drivers displayed considerably higher levels of anger while driving and aggressive driving than truck and bus drivers. Overall, this study replicates and extends the accumulated knowledge of previous investigations, suggesting that the original DAS remains a reliable and stable instrument for measuring driving anger in day-to-day driving conditions.

Traffic accidents, by their nature, are random events; therefore, it is difficult to estimate the exact places and times of their occurrences and the true nature of their impacts. Although they are hard to precisely predict, preventative actions can be taken and their numbers (in a certain period) can be approximately predicted. In this study, we investigated the relationship between accident frequency and factors that affect accident frequency; we used accident data for events that occurred on a flat rural state road in Serbia. The analysis was conducted using five statistical models, i.e., Poisson, negative binomial, random effect negative binomial, zero-inflated Poisson, and zero-inflated negative binomial models. The results indicated that the random effect negative binomial model outperformed the other models in terms of goodness-of-fit measures; it was chosen as the accident prediction model for flat rural roads. Four explanatory variables—annual average daily traffic, segment length, number of horizontal curves, and access road density—were found to significantly affect accident frequency. The results of this research can help road authorities make decisions about interventions and investments in road networks, designing new roads, and reconstructing existing roads.

The present research describes the development and validation of a self-reported instrument that measures the determinants of pedestrians’ intention to violate traffic rules, based on the theory of planned behaviour. Moreover, the research deals with the analysis of the predictive validity of an extended theoretical framework of the theory of planned behaviour in relation to pedestrians’ intention to violate. Based on the quota sample, adult pedestrian respondents (n = 383) completed a questionnaire assessing the relevant variables. Valid and reliable scales were developed, and they measure subjective, descriptive, normative, and personal norms, cognitive and affective attitudes, perceived behavioural control, habit formation, and behavioural intention concerning pedestrians’ misdemeanour. Hierarchical regression analysis indicated that all components, except descriptive norms, were significant simultaneous predictors of pedestrians’ intention to violate. The most powerful predictor is the personal norm. Overall, the findings considerably support the concept of the extended theoretical framework of the theory of planned behaviour.

The transportation of dangerous goods (DG) presents significant risks due to their hazardous chemical properties, which, in the event of an accident, can have detrimental effects on the environment, public health, and infrastructure. Although the transport of such materials is generally prohibited, the growing demand for DG transportation over long distances necessitates compliance with stringent international regulations (e.g., ADR, RID). In urban areas, where transport routes may intersect with residential zones, incidents involving DG can lead to severe consequences, including fatalities, environmental damage, evacuation of local populations, and disruptions to traffic. To mitigate these risks, effective risk management is essential, encompassing analysis, assessment, and reduction strategies. Risk assessment for DG transport can be conducted using various quantitative and qualitative methods, with this study employing the Areal Locations of Hazardous Atmospheres (ALOHA) software and Geographic Information System (GIS) tools for both risk evaluation and visualization. The study area is located in the capital of Montenegro, specifically within the Stari Aerodrom District. This research focuses on evaluating the potential impact of DG transport incidents in this area and the consequences of hazardous material releases in confined spaces. Three specific DGs—benzene, chlorine, and methane—are considered, each presenting distinct environmental, health, and property-related risks. Chlorine is selected as the worst-case scenario, with its impact radius extending approximately 10 km from the release point. The primary objective of this study is to provide a comprehensive assessment of the risks associated with DG transportation, highlighting the importance of safety improvements and effective emergency response strategies. The findings underscore the need for enhanced safety measures during transport and the development of more robust emergency management frameworks for DG-related incidents.