As we witness a global change occurring with the advent of e-scooters (electric scooters), it is clear that adequate measures need to be taken for the implementation of this means of transport. Bearing in mind that the user should be the focus of the proposed measures, the aim of this paper encompasses the analysis of e-scooter users’ travel patterns, as well as standard scooter users’ willingness to switch to e-scooters for different hypothetical scenarios, and the determination of factors that have an influence on the users’ willingness to switch. The scenarios include the availability of various infrastructure capacities for the city of Belgrade, namely, a separate infrastructure for e-scooters, or sharing the existing infrastructure with cyclists, pedestrians and motor vehicles. Standard methods of descriptive statistics, the McNemar–Bowker test of paired samples, and multinomial logistic regression were used in this paper. The results showed that there was a statistically significant difference in users’ willingness to use an e-scooter, depending on the available infrastructure. The results of multinomial logistic regression showed that the mode of transport used before and during the COVID-19 pandemic, as well as the average distance traveled, had a statistically significant impact on the users’ willingness to switch to e-scooters if they were allowed to use the cycling infrastructure. This paper also identifies additional factors that have a positive (environmental benefits, congestion avoidance) and negative (safety issues, lack of infrastructure, etc.) impact on users’ willingness to switch, which can be of use to decision-makers as a basic guideline for the adequate implementation of e-scooters in transport systems.

The development of Convolutional Neural Networks (CNN) has expanded with the accelerated progress of IT, as well as with the needs of the autonomous vehicle (AV) implementation. The specifics and requirements of AV towards the infrastructure primarily relate to the condition and quality of traffic signs. For the independent participation of these vehicles in traffic, an impeccable traffic sign condition is required, which is often not the case in practice. Damaged, faded, obscured, or vandalized traffic signs can usually be seen in the road network, which can impede the movement of AV in traffic. In the existing literature, little or very little attention is focused on the problem of identifying and classifying damaged and especially vandalized traffic signs. In this paper, the mentioned problem is addressed, and the CNN model is proposed. This model has been tested on a specially designed novel and challenging database containing 6,000 real-time images of traffic signs in the road network of the Republic of Serbia. This model is invariant to different lighting and weather (nighttime and fog) conditions. In this case study, the model reached an overall accuracy of 99.17%, whereby all vandalized and damaged traffic signs are accurately identified and classified.

In this paper, the needs, attitudes, and perceptions of older (50+) cyclists were examined with the aim of determining the level of comfort, safety, and the way of using different types of cycling infrastructure. Considering that by 2050, 1 in 6 people will be over the age of 65, and that this category of users (particularly cyclists) still receives insufficient attention, the authors believed that in this way, a significant contribution can be made to the existing literature. Data from 389 50+ cyclists were collected through a survey, including Canada, the United States (USA), and Serbia, and analyzed using visual preference testing (VPT), ANOVA, and Kruskal–Wallis test. The countries were chosen to include certain similarities (traffic characteristics), as well as differences (cultural characteristics), in order to enable an adequate exchange of knowledge, good practice, and experience. The results indicate the existence of differences between these countries, especially regarding the perception of safety and the way of using certain infrastructure in Serbia (e.g., major urban collectors and shared space). Based on the obtained results, a set of general guidelines was proposed for countries with similar traffic and cultural characteristics on how to treat and provide sustainable infrastructure for older cyclists.

This study presents the first comprehensive spatial accessibility assessment of essential urban services in Belgrade, Republic of Serbia, conducted entirely with open-source tools and data. The analysis focused on six facility categories: primary healthcare centers, public pharmacies, primary and secondary schools, libraries, and green markets. Spatial accessibility was modelled using OpenRouteService (ORS) isochrones for walking travel times of 5, 10, and 15 min, combined with population data from the Global Human Settlement Layer (GHSL). Results indicate that 79% of residents live within a 15-min walk of a healthcare facility, 74% of a pharmacy, 89% of an elementary school, 52% of a high school, 60% of a library, and 62% of a green market. Central administrative units such as Vračar, Zvezdara, and Stari Grad demonstrated nearly complete service coverage, while peripheral areas, including Resnik, Jajinci, and Višnjica, exhibited substantial accessibility deficits, often coinciding with lower-income zones. The developed workflow provides a transparent, replicable approach for identifying underserved neighborhoods and prioritizing investments in public infrastructure. This research emphasizes the role of spatial accessibility analysis in advancing Sustainable Development Goals (SDGs), contributing to the creation of more inclusive, walkable, and sustainable urban environments, while on the other hand, it offers practical insights for improving urban equity, guiding policy formulation, and supporting necessary planning decisions. Subsequent research will focus on alternative facilities, other cities such as Novi Sad and Niš, and the disparity between urban and rural populations.

This cross-sectional study evaluates the potential spatial accessibility of primary percutaneous coronary intervention (pPCI) facilities in the Republic of Serbia (RS) for the year 2030. Cardiovascular diseases, specifically acute myocardial infarction (AMI), are major contributors to mortality, requiring immediate intervention to reestablish blood circulation to the heart. This research employs travel time isochrone analysis to assess the percentage of the population residing within three specific time intervals (30, 60, and 90 min) from a pPCI facility. We project the percentage of the population residing within a 30 min travel time interval to be 50% in 2030. Additionally, the percentage of the population residing within the 90 min travel time interval from a pPCI facility, i.e., known as the “golden hour” travel time distance, is around 96%, with some weekly variations that equate to 1%. We utilized additional spatial analysis to identify population clusters that reside beyond the 90 min travel time from a pPCI facility. These results point to specific regions where either additional pPCI facilities or better road connections would most effectively reduce treatment delays. Additionally, the study highlighted the optimal location for a novel pPCI facility, which is the city of Vršac. Our findings underline the need for careful planning in the health system, where location and transport data can directly guide measures to improve access and lower cardiovascular disease (CVD) mortality.