A Bayesian Learning Framework Powered by IoT for Enhancing Highway Safety and Reducing Accidents: A Case Study of Benin-Onitsha Express Way

The problem of frequent accidents on the Benin-Onitsha Express Way in Nigeria expressway necessitated the development of a predictive system that can enhance road safety. To address this issue, the study employed a framework that leverages real-time data collection from vehicles and road conditions to predict accident risks and deliver timely warnings to drivers. The methodology involved analyzing three years of historical accident data to identify high-risk areas and develop a cost-effective IoT model utilizing Bayesian learning techniques. The framework incorporated ESP8266-based client devices installed in vehicles, which gathered information on location, speed, and road conditions. This data was transmitted to a central server via Wi-Fi or GSM for analysis. The IoT platform ThingSpeak was utilized for data storage and visualization, facilitating real-time monitoring of vehicle locations and accident risk intensity. Results from the study demonstrated that the Bayesian model effectively identified accident-prone locations, achieving an area under the receiver operating characteristic curve (AUC-ROC) of 0.82, indicating excellent discrimination between high-risk and low-risk areas. The model achieved 75% precision and 80% recall, with a 15% false positive rate, which was deemed acceptable given the significant reduction in accidents observed. A comparative analysis revealed a decrease in reported accidents from 25 to 15 after system deployment, suggesting a positive impact on highway safety. Overall, the findings underscore the potential of integrating IoT and Bayesian learning for developing intelligent systems aimed at improving road safety and reducing accident rates on the Benin-Onitsha Express Way.