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

Aug 5, 2026

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This study explores machine learning for predicting road traffic accident likelihood in Katsina State, Nigeria, aiming to reduce fatalities and property loss. Accident datasets from the Federal Road Safety Corps database are cleaned and analyzed using variables such as accident counts, locations, time of day, weather conditions, and involved vehicle types. Models including Decision Trees, Random Forest, and K-Nearest Neighbors are trained, with feature selection and correlation analysis identifying key contributing factors. Random Forest achieves 85% predictive value.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages11

Reading Time28 min

AccessFree to download

Abstract

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IJSGS ISSN: 2488-9229 FEDERAL UNIVERSITY GUSAU-NIGERIA INTERNATIONAL JOURNAL OF SCIENCE FOR GLOBAL SUSTAINABILITY Predictive Analysis of Road Traffic Accidents in Katsina State, Nigeria Using Machine Learning Algorithms: A Study on Factors and Mitigation Strategies Umar Iliyasu, Muhammad Muntasir Yakudu and A.A. Abdulwasiu Department of Computer Science, Faculty of Computing, Federal University Dutsin-Ma, Katsina State, Nigeria. Corresponding Author’[s E-mail:](s E-mail: umariliyasut@gmail.com)[ ](s E-mail: umariliyasut@gmail.com)[umariliyasut@gmail.com](s E-mail: umariliyasut@gmail.com) Received on: April, 2023 Revised and Accepted on: May, 2023 Published on: July, 2023 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. Keywords: Machine Learning, Road traffic accident, K-Nearest Neighbour, Random Forest and Decision Tree. 1.0 INTRODUCTION The study on predicting road traffic accidents in Katsina State using machine learning aims to explore the use of machine learning algorithms in predicting the likelihood of road traffic accidents in Katsina State, Nigeria. Katsina State is located in the north-western region of Nigeria and has a high incidence of road traffic accidents.The study is motivated by the need to reduce the high rate of road traffic accidents in Katsina State, which has resulted in the loss of lives and properties. Machine learning algorithms have been shown to be effective in predicting various phenomena, including traffic accidents. The study will involve collecting and analyzing data on road traffic accidents in Katsina State, including the number of accidents, the location, the time of the day, weather conditions, and the type of vehicles involved. Machine learning algorithms such as Decision trees, Random Forest, and K-Nearest Neighbour will be trained using the data to predict the likelihood of road traffic accidents (Zheng et al., 2019) . The findings ofthe study will provide insights into the factors that contribute to road traffic accidents in Katsina State and how machine learning algorithms can be used to predict and prevent accidents. This information can be used by stakeholders, including the government, law enforcement agencies, and road safety organizations, to develop and implement effective strategies for reducing road traffic accidents in Katsina State. The study on predicting road traffic accidents in Katsina State using machine learning aims to explore the use of machine learning algorithms in predicting the likelihood of road traffic accidents in Katsina State. The study will also explore the performance of different machine learning algori

FAQs

What does the study aim to achieve in Katsina State?

The study predicts the likelihood of road traffic accidents in Katsina State using machine learning to support strategies that reduce accidents and associated losses of lives and property.

Which data sources and variables are used for the machine learning models?

The study uses data from the Federal Road Safety Corps database and includes accident counts, location, time of day, weather conditions, and vehicle types, along with data cleaning and feature selection to improve data quality.

How do the machine learning algorithms perform?

Random Forest delivers the strongest predictive value at 85%, while K-Nearest Neighbors and Decision Tree yield 17% and 42% respectively.

Aug 5, 2026

Upload by Finn