Learning to detect incidents from noisily labeled data

Many deployed traffic incident detection systems use algorithms that require significant manual tuning. We seek machine learning incident detection solutions that reduce the need for manual adjustments by taking advantage of massive databases of traffic sensor measurements. We first examine which traffic flow features are most useful for the incident detection task. Then we show that a supervised learner based on the SVM model outperforms a...