This study was carried out to examine the severity level of crashes by analyzing traffic accidents. The study’s goal is to identify the major contributing factors to traffic accidents in connection to driver behavior and socioeconomic characteristics. In order to find the most probable causes in accordance with the major target variable, which is the level of severity of the crash, the study set out to identify the main attributes induced by the decision tree method (DT). The local people received a semi-structured questionnaire interview with closed-ended questions. The survey asked questions about drivers’ attitude and behavior, as well as other contributing factors such as time of accidents and road type. The attributes were analyzed using the machine-learning method using DT with Python programming language. This method was able to determine the relationship between severe and non-severe crashes and other significant influencing elements. The Duhok city people participated in the survey, which was conducted in the Kurdistan area of northern Iraq. The results of the study demonstrate that the number of lanes, time of the accident, and human attitudes, represented by their adherence to the speed limit, are the primary causes of accidents with victims.

This research aimed to examine the spatial distribution of road traffic accidents in Budapest, Hungary. The primary objective was to identify the factors associated with traffic accidents on the city's transportation network and to determine the locations of the most frequent accidents during peak and off-peak hours. A quantitative methodology was employed in this study, utilizing a dataset of recent accidents that occurred between 2019 and 2021, classified into peak and off-peak incidents. The data was analyzed using Python software and Quantum Geographic Information System (QGIS) tools for big data analytics. These programs enabled the creation of spatial maps of the study area and the identification of accident spots based on latitude and longitude information. A decision tree classification approach was used in the machine-learning method implemented with Python software. Additionally, the dataset file was uploaded to QGIS, which applied the heatmap (Kernel Density Estimation) algorithm to identify accident hotspots. The study findings revealed that the city center was the most common location for accidents overall, with peak and off-peak times, lanes, and days of the week investigated as potential contributing factors. The target variable was the number of accidents involving serious and minor injuries, which were found to be significantly associated with the identified accidents in this study.

Throughout the years, the increasing interest in environmentally friendly urban transportation has prompted a focus on micromobility solutions. The increasing urbanization and associated congestion, pollution, and resource depletion have spurred a focus on micromobility solutions as a potential remedy. This study aims to comprehend the preferences guiding the selection of micromobility modes among urban residents in Budapest, Hungary. Through a well-structured questionnaire and rigorous statistical analysis using the multinomial logit (MNL) model, such influential factors are examined as safety, availability, cost, and environmental friendliness. By constructing hypothetical scenarios including varied micromobility alternatives, it is observed that attributes such as availability, environmental friendliness, and safety exert positive influence on user preferences. Conversely, travel cost impacts the mode preference negatively. The interplay of attributes offers intriguing insights. For instance, the positive correlation between safety and environmental friendliness suggests that urban residents prioritize modes offering both security and sustainability. Of particular significance, electric bikes stand out as the favored choice, highlighting their effectiveness within the specific context studied. This study offers insightful perspectives on micromobility preferences and enriches the broader discourse on sustainable urban transportation.