LAUSR

Automatic incident detection (AID) has always been one of the focus issues in the field of transportation. However, due to the contingency and randomness of traffic incident, traffic incident samples are scarce and far less than non-incident samples. Therefore, unlike other scenarios using large-scale deep networks, traffic incident detection tackle at small and imbalanced sample size. Imbalanced, small sample data sets, inappropriate and incomplete initial variable sets make the AID model insensitive to incident samples, resulting in unsatisfactory model performance (low detection rate or high false alarm rate). Therefore, a hybrid AID method (SASYNO-RF-RSKNN) is proposed using self-adaptive synthetic over-sampling, random forest and random subspace k nearest neighbor. First, the spatial-temporal and real-time characteristics of traffic stream are used for the selection of appropriate initial variables to construct a relatively complete set of initial variables. Second, the SASYNO oversampling method is used to expand the original imbalanced sample database, so that the number of minority class samples is consistent with the number of most class samples. Then, feature variables are selected from the initial variables using the RF algorithm. Finally, the RSKNN ensemble algorithm with feature variables as input is employed to detect traffic incident. In addition, six indexes are used to evaluate model performance, including accuracy (ACC), false alarm rate (FAR), detection rate (DR), precision, Matthews correlation coefficient (MCC) and F1-score. Simultaneously, we also designed horizontal and vertical contrast experiments, and the experimental results show that SASYNO-RF-RSKNN model has superior performance. It is worth mentioning that experiments are implemented on two real-world datasets. Most indexes of the proposed model are the best compared with other five excellent machine learning algorithms. On the whole, the proposed model has a dependable and high-performance for traffic incident detection.