The Benefit of Artificial Intelligence Models in Predicting Road Traffic Accidents: A Case Study of Federal Road Safety Corps, Kano State
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1.1 Background of the Study
Road traffic accidents are a leading cause of injury and death globally, and Nigeria is no exception, with a high incidence of road crashes (Yusuf et al., 2025). In response to this issue, the Federal Road Safety Corps (FRSC) has been working to reduce road accidents through the use of modern technologies, including AI. Predictive models powered by AI can analyze historical traffic data, weather conditions, road infrastructure, and driver behavior to predict accident-prone areas and times, enabling proactive interventions.
In Kano State, one of Nigeria's largest states, the FRSC has started exploring AI-driven predictive models to forecast traffic accidents and improve road safety. This case study assesses the effectiveness of AI in predicting road traffic accidents and its potential to enhance accident prevention strategies in Kano State.
1.2 Statement of the Problem
Road traffic accidents in Kano State remain a significant concern for the FRSC. While efforts to manage and reduce accidents have been ongoing, the lack of predictive tools to anticipate and prevent accidents has hindered the effectiveness of these measures. AI has the potential to revolutionize road safety by providing real-time predictions of accident hotspots and times, yet its full potential has not been explored in Kano State. There is a need to assess the effectiveness of AI-based predictive models in enhancing road safety and reducing traffic accidents.
1.3 Objectives of the Study
1. To evaluate the effectiveness of AI models in predicting road traffic accidents in Kano State.
2. To assess the impact of AI-driven predictions on accident prevention strategies and road safety improvements.
3. To identify the challenges and opportunities in implementing AI-based predictive models for road safety in Kano State.
1.4 Research Questions
1. How effective are AI models in predicting road traffic accidents in Kano State?
2. What impact do AI-driven predictions have on accident prevention and road safety initiatives in Kano State?
3. What challenges does the FRSC face in implementing AI-based predictive models for road safety?
1.5 Research Hypothesis
1. AI models significantly improve the prediction of traffic accidents and reduce the incidence of road traffic accidents in Kano State.
2. The use of AI models for accident prediction leads to more effective accident prevention strategies and improved road safety in Kano State.
3. The implementation of AI-based predictive models faces challenges such as data quality issues, lack of technical expertise, and infrastructure constraints.
1.6 Significance of the Study
This study will provide valuable insights into how AI can enhance road safety in Kano State and other Nigerian cities. By assessing the impact of AI on accident prediction and prevention, the study could inform the development of more effective traffic safety policies and strategies, ultimately reducing traffic-related fatalities and injuries.
1.7 Scope and Limitations of the Study
The study focuses on the use of AI models in predicting road traffic accidents in Kano State by the Federal Road Safety Corps. It will not cover other road safety initiatives or cities outside of Kano. Limitations include challenges in obtaining sufficient historical traffic data and the potential difficulty in measuring the direct impact of AI tools on accident rates.
1.8 Operational Definition of Terms
1. Predictive Models: AI algorithms used to analyze data and predict future events, such as traffic accidents.
2. Road Safety: Efforts and policies aimed at reducing accidents and fatalities on the road.
3. Accident Prevention Strategies: Measures taken to reduce the likelihood of traffic accidents, including education, enforcement, and infrastructure improvement.
4. Artificial Intelligence Models: Machine learning and AI technologies used to analyze large datasets and make predictions.
5. Traffic Hotspots: Locations with a higher probability of accidents occurring based on historical data and patterns.
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Chapter One: Introduction
