Learning to Detect Adverse Traffic Events from Noisily Labeled Data

Abstract

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 network measurements. First, we show that a rather straightforward supervised learner based on the SVM model outperforms a fixed detection model used by state-of-the-art traffic incident detectors. Second, we seek further improvements of learning performance by correcting misaligned incident times in the training data. The misalignment is due to an imperfect incident logging procedure. We propose a label realignment model based on a dynamic Bayesian network to re-estimate the correct position (time) of the incident in the data. Training on the automatically realigned data consistently leads to improved detection performance in the low false positive region.