Predicting Traffic Incidents in Road Networks Using Vehicle Detector Data

Prediction models can help to identify traffic incidents in roadways, reducing incident detection and response time. Advances in machine learning techniques to support the safety of roadways is a relatively underexplored area. This research seeks to advance machine learning to predict incidents based on detector data by comparing and analyzing the performance of different learning models. This study first discusses the severe class imbalance affecting the dataset. Then, using to-fold cross validation, the performance of the algorithms J48 Decision Tree, Naïve Bayes, and AdaBoost were compared. In the explored problem, a false positive indicates an incident where there is no incident, and this is highly undesirable. AdaBoost reached an ideal 0% false positive rate, followed by Naïve Bayes and J48. However, these values were achieved with a cost of high false negative rates for all classifiers. AdaBoost was also successful in handling the low correlation between fit and test datasets, which was not observed for the other two.