Utilizing angle-based outlier detection method with sliding window mechanism to identify real-time crash risk

Developing real-time crash risk models has been a hot research topic as it could identify crash precursors and thus triggering active traffic management strategies. Currently, crash risk identification models were mainly developed based upon supervised learning techniques, which requires large sample size of historical crash data. However, crashes are rare events in the real world, where the performance of supervised learning methods can be severely degraded to deal with the imbalanced sample. Besides, the data heterogeneity issue is another critical challenge. In this study, the unsupervised learning approach has been introduced to address unbalanced samples and data heterogeneity issues, and the experimental results has verified the effectiveness of the method. Data from the Shanghai urban expressway system were utilized for the empirical analyses. Several unsupervised learning methods were tested, among which, Angle-Based Outlier Detection (ABOD) model showed the best performance with 80.4% sensitivity and 25.4% false alarm rate (FAR). Considering the varying traffic flow distribution, dynamic ABOD with sliding window is further proposed, which improves the sensitivity by 6.3% and reduces the FAR by 8.1%. Finally, the proposed model is used to construct personalized road-level models, which achieve good performance despite the small sample size and severe sample imbalance.