Vehicle classification is a crucial task owing to vehicles' diverse and intricate features, such as edges, colors, shadows, corners, and textures. The accurate classification of vehicles enables their detection and identification on roads and facilitates the development of an electronic tollcollection system for smart cities. Furthermore, vehicle classification is useful for traffic signal control strategy. However, achieving accurate vehicle classification poses significant challenges due to the limited processing time for real-time applications, image resolution, illumination variations in the video, and other interferences. This study proposes a method for automated automobile detection, recognition, and classification using statistics derived from approximately 11,000 images. We employ SURF-based detection and different classifiers to categorize vehicles into three groups. The Traffic Management System (TMS) is crucial for studying mobility and smart cities. Our study achieves a high automobile classification rate of 91% with the medium Gaussian Support Vector Machine (SVM) classifier. The paper's main objective is to analyze five object classifiers for vehicle recognition: Decision Tree, Discriminant Analysis, SVM, K-Nearest Neighbor Classifier (KNN), and Ensemble Classifier. In the discussion section, we present the limitations of our work and provide insights into future research directions.

This research represents the results of field work conducted in the period from March 29 to May 29. 2020, to determine and record the location of illegal waste disposal sites in the municipalities of Istočno Novo Sarajevo and Istočna Ilidža. The location of illegal waste disposal sites was analyzed based on two groups of factors: space exposure and space sensitivity. The analysis included following exposure factors: distance from urban settlements, distance from roads and population density and sensitivity factors: land cover, hydrogeological characteristics of the substrate, distance from springs and watercourses and land slope. In addition to the location of illegal waste disposal sites, it was analyzed the potential vulnerability of space based on both groups of factors. Final map of spatial vulnerability was created using multi-factor analysis. This work emphasizes the possibility of using easily accessible devices for recording the locations of illegal waste disposal sites, as well as the importance of geographic information systems in the analysis and monitoring of the state of the environment. Based on the example presented in the work, the possibility of applying a similar model on the territory of other municipalities is given, with the aim of preventing the negative consequences of pollution on human health and the environment.

In order to find the optimal solution for the drainage of rainwater from roads in urban areas, as well as for the evaluation and ranking of conceptual solutions, appropriate mathematical models and software packages were used in this research. For relevant rain episodes, i.e. rainfall of appropriate duration and intensity, runoff coefficients and flows were taken into account and analyzed according to the rational method, all for the purpose of obtaining data on the amount of rainwater entering the sewage system. Through this research, very good correlations and regressions were established between the cross slope of the road and the parameters of rainwater drainage from the road, as well as the correlation and regression relationships of the cross slope of the road and the efficiency of the drain. Likewise, the dependences of the drainage parameters, the efficiency of the drains and the cross slope of the road were determined, expressed through mathematical functions.