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.