The prediction of traffic congestion and incident on urban road networks using Naive Bayes classifier
This study proposes a Naive Bayes (NB) classifier model for predicting congestion and incident in urban road networks. NB is a machine learning classification model based on Bayes' theorem with strong (probabilistic) independence assumptions between predictor variables. This study considers congestion or incident as a target variable and applies a NB model to classify its state (i.e. occur vs. not occur) Predictor variables or features considered in this study include network environment variables (time of day, day of week, and weather) and traffic condition variables (speed on bottlenecks) This study develops a data driven approach for building and validating NB models. The models are trained and tested using actual traffic, incident, and weather data collected from Brisbane, Australia in 2014. The validation results show that the proposed models can successfully predict congestion and incident occurrence with a desired level of accuracy.