Machine learning classification of recurring and non-recurring congestion using threshold-based traffic sensor alerts

This study presents a machine learning framework for classifying recurring and non-recurring traffic congestion using threshold-based alert logs from Florida’s Traffic Sensor Subsystem (TSS), addressing the research gap for methods that use only alert logs and remain computationally feasible for large-scale real-time deployment. One year of statewide TSS alerts (July 2022–June 2023) on Florida’s limited-access highways were collected, with this dataset containing 692,420 alerts from 1,873 sensors. Alerts were labeled as non-recurring congestion by associating them with Florida Highway Patrol computer-aided dispatch incident records using a 60-minute temporal window and 1-mile spatial buffer. Using only alert timestamps and coordinates, a set of engineered features was constructed to characterize alert frequency, timing irregularity, and behavioral deviations. These features were used to train an XGBoost classifier that differentiates between recurring and non-recurring congestion without requiring external data sources such as crash reports or weather feeds. The model achieved an accuracy of 0.646 and a precision of 0.229, improving the existing baseline TSS precision of 0.127 by 80.3% and reducing false alerts reported to traffic management center operators by 39.4% for the statewide test dataset. Comparable performance was observed when the model was applied to a separate test dataset for Florida’s Turnpike Enterprise roadways only. Temporal analysis showed that the model’s true positive rate was highest between 9:00 AM and 10:00 PM on weekdays, offering practical guidance for traffic management center operations. This approach has great potential to be utilized by traffic operators in a real-time setting.