Predictive Analysis of Road Traffic Accidents in Katsina State, Nigeria Using Machine Learning Algorithms: A Study on Factors and Mitigation Strategies

Abstract

This study aims to explore the use of machine learning algorithms in predicting the likelihood of road traffic accidents in Katsina State, Nigeria, with the goal of reducing the high rate of road traffic accidents and associated loss of lives and properties. The study collects and analyzes data on road traffic accidents in Katsina State, including the number of accidents, location, time of day, and the type of vehicles involved. Machine learning algorithms such as Decision Trees, Random Forest, and K-Nearest Neighbors, are trained using the data to predict the likelihood of road traffic accidents. The study also identifies the factors that contribute to road traffic accidents in Katsina State by using techniques such as feature selection and correlation analysis, to identify the most important variables. The findings can be used by stakeholders, including the government, law enforcement agencies, and road safety organizations, to develop and implement effective strategies to reducing road traffic accidents in Katsina State. The study utilizes data collected from the Federal Road Safety Corps database in Katsina, Nigeria, and employs data cleaning and feature selection techniques to improve data quality. The Random Forest algorithm achieved the predictive value as 85% while K-Nearest Neighbors (KNN) and Decision Tree algorithms yielded 17% and 42% respectively.