Traffic Intensity Detection in Lagos State Using Bayesian Estimation Model

Traffic congestion is a significant challenge in Lagos State, Nigeria. Existing methods rely on limited data sources and simplistic models that fail to capture the complexities of traffic dynamics in a congested urban environment. This study focused on traffic intensity detection in Lagos State using a Bayesian estimation model. Data was obtained from the Kaggle website which involved observing the number of vehicles intersecting junctions at various times of the day for a week. The model captured both spatial and temporal variations, providing real-time estimations of traffic congestion levels across different road segments. Comparative analysis with existing traffic estimation methods showed superior performance in terms of accuracy and reliability. The Gaussian Naive Bayes model achieved a high accuracy of 96% and balanced f1-score of 96%, precision of 0.96, and recall of approximately 0.96. On the other hand, the multinomial Naive Bayes model achieved an accuracy of 69% with a lower f1-score of 69%, precision of 0.67, and recall of 0.69. The model's capacity to provide accurate real-time site traffic facts can significantly contribute to effective traffic control and concrete making plans initiatives.