Resampling Methods for Solving Class Imbalance Problem in Traffic Incident Detection

Abstract

Traffic data is highly skewed with rare traffic incidents in the real word while most of the existing automatic incident detection (AID) algorithms suffer from limitations due to their inability to detect incidents under imbalanced traffic data condition. This paper developed feasible AID algorithms based on resampling methods to process imbalanced traffic data. In order to obtain the optimal sampling method for incident detection, we compare the detection performance of different AID algorithms based on various resampling methods. The detection performance is evaluated by the common criteria including classification rate, detection rate, false alarm rate, mean time to detection and an integrated performance index. The I-880 dataset is finally used in experiments to verify the proposed algorithms. The experimental results indicate that the proposed AID algorithm based on resampling can achieve better performance through handling imbalanced traffic data problem. Moreover, the under-sampling is competitive than over-sampling for traffic incident detection.