Traffic congestion classification using data mining techniques

Historical traffic sensor data–including speed, counts, and occupancy–was used to estimate levels of short-term traffic congestion in Las Vegas, Nevada. Various data-mining techniques were tested, including the J48 decision tree, artificial neural networks, the support vector machine, PART, and K-nearest-neighbors. Future time slots were categorized as congested or uncongested based on the corresponding historical data. In this study, one-minute traffic data was provided by the Freeway and Arterial System of Transportation in Las Vegas, Nevada. A 10-mile northbound segment of Interstate 15 from Interstate 215 to Desert Inn Road was evaluated. Among the algorithms tested, the J48 algorithm provided the best performance, and was able to estimate congestion up to nine minutes into the future with an accuracy of approximately 80%