Automatic traffic incident detection based on nFOIL

Traffic incidents inevitably cause traffic delay and deteriorate road safety conditions. Incidents are increasing alongside the fast economic growth. Due to the rampant growth of traffic incidents, developing efficient and effective automated incident detection (AID) techniques has prompted a growing worldwide interest. In this paper, the great efforts on developing a new approach to this problem based on nFOIL, a novel inductive logic programming (ILP), are done. By way of illustration, a simulated traffic data generated from Ayer Rajah Expressway (AYE) highway in Singapore and a real traffic data collected in I-880 freeway in California are used to assess the detection performance of this approach, and performance metrics includes detection rate, false alarm rate, mean time to detection, classification rate and the area under Receiver Operating Characteristic (ROC) curve (AUC). For comparison, we conducted the experiments on neural networks and support vector machine. The experimental results showed that nFOIL is sensitive to the skewed distribution of positive and negative examples in the dataset, and we make use of two different techniques, resampling and ensemble learning, to cope with highly skewed data in the context of ILP classification problems and investigated the effect of them typicality on the performance of AID model. It is concluded that ILP based AID approach are feasible, and have a favorable performance compared to neural networks and support vector machines.