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.

Speeding behavior is recognized as one of the three main causes of risk on the roads and the most frequent ordinary violation among truck drivers. The study reported in this article focused on the ...

Objective: Driving anger has been found to be a motivational determinant of risky and aggressive driving. The study reported in this article focused on research questions concerning the conceptualization of driving anger and relationships between driving anger and aggressive driving. Methods: In a cross-sectional survey study, 1,020 regular Serbian drivers completed a web-based online questionnaire. Exploratory Factor Analysis and Confirmatory Factor Analysis were used to assess the factor structure and psychometric properties of the Driving Anger Scale. Results: The results showed that the adapted DAS was a valid and reliable instrument divided into five subscales such as discourteous behavior, illegal behavior, slow driving, hostile gestures and traffic obstructions. The robustness of the five-factor model obtained from the EFA was confirmed by the CFA. The study provided evidence supporting the predictive validity of the adapted DAS and revealed that the dimensions of driving anger are differently associated with prosocial and aggressive driving behaviors. Conclusions: The study supports the adapted DAS validity and produces evidence that driving anger is associated with driving outcomes.

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.