A MACHINE LEARNING-DRIVEN APPROACH FOR ACCIDENT PREDICTION AND TRAFFIC SAFETY ANALYSIS IN NAMIBIA
No Thumbnail Available
Date
2024-12
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Namibia University of Science and Technology
Abstract
Globally, road traffic accidents contribute a large portion of injuries, fatalities, and significant economic losses and ongoing research has projected that by 2030, car crashes would be the 5th top reason for loss of life around the world. The key cause of traffic accidents is hard to determine nowadays because of a complex mix of factors, such as road conditions, weather conditions, and the mental condition of the drivers, to list a few. Without a thorough understanding of the characteristics and causes, intelligence-led countermeasures to decrease crashes cannot be created or implemented. Therefore, if traffic accident characteristics can be better understood, it might be easier to take some mitigative action. Nowadays, the utility of machine learning methods in the field of road traffic crashes is gaining traction. The objective of this dissertation is to analyse historical data for a five-year period (2018-2023) and to understand the patterns in accident occurrences by making use of machine learning methods. Machine learning models as such Random Forest, Support Vector Machine, K-Nearest Neighbours, Association Rule Algorithm (AARA), and k-Clustering were employed on the dataset. The Apriori Association Rule algorithm explored the rules with high lift and high support, respectively. The research shows that the Random Forest model is the reliable model in predicting crash severity, reaching an accuracy of approximately 81%. Factors such as junction type, poor road sign conditions, uncontrolled traffic, weather, lighting, road surface, vehicle type, and driver behaviours were identified as the significant variables influencing road accidents. Pedestrian, rollovers, and collision are the leading crash causes of the road accidents, and they are associated with uncontrolled traffic and daylight. Additionally, the research shows notable differences in accident rates by region, month, year, day of the week, and hour of the day, underscoring the impact of geographical features, seasonal trends, and commuting habits on accident rates. The findings indicate that high traffic volumes and urban congestion are the main causes of the greatest accident rates in metropolitan areas, especially in the Khomas Region of Namibia. Further, more accidents are happening more toward the weekend as compared to weekdays and during night hours.
Description
Keywords
Citation
Ngolo, M. (2025). A machine learning-driven approach for accident prediction and traffic safety analysis in Namibia [Master’s thesis, Namibia University of Science and Technology].