Early Detection of Traffic Congestion Before Breakdown: A Spatiotemporal Warning Framework for Intelligent Transportation Systems

Proactive traffic management requires more than
identifying congestion after it has formed. This study develops
a multi-horizon early-warning framework for detecting freeway
congestion before confirmed traffic breakdown using real-world
detector data from the Caltrans Performance Measurement
System. The PeMS08 benchmark contains 17,856 five-minute
observations from 170 detectors with traffic flow, occupancy,
and speed measurements. A sensor-adaptive congestion definition
combines speed below 75% of detector-specific free-flow speed,
elevated occupancy, and at least 15 minutes of persistence.
A 30-minute clear period is additionally required before each
onset. Chronological train, validation, and test partitions are
used to prevent temporal leakage. Four classifiers are evaluated
for warning horizons of 5, 10, 15, 20, and 30 minutes. Extra-
Trees is selected using validation precision-recall area under the
curve (PR-AUC). On the held-out test period, it achieves ROCAUC/
PR-AUC values of 0.930/0.825 at 5 minutes and 0.841/0.627
at 10 minutes. Performance decreases with longer lead time,
while useful predictive information remains detectable at 15–30
minutes. Interpretation and ablation analyses show that speed
dynamics, occupancy buildup, and neighboring-detector conditions
provide the dominant warning information. The results
support a 10-minute horizon as a practical compromise between
actionable lead time and predictive reliability for proactive
intelligent transportation systems.