Anomaly-Aware Traffic Prediction Based on Automated Conditional Information Fusion

Reliable and accurate short-term traffic prediction plays a key role in modern intelligent transportation systems (ITS) for achieving efficient traffic management and accident detection. Previous work has investigated this topic but lacks study on automated anomaly detection and conditional information fusion for ensemble methods. This works aims to improve prediction accuracy by fusing information considering different traffic conditions in ensemble methods. In addition to conditional information fusion, a day-week decomposition (DWD) method is introduced for preprocessing before anomaly detection. A k-nearest neighbours (kNN) based ensemble method is used as an example. Real-world data are used to test the proposed method with stratified ten-fold cross-validation. The results show that the proposed method with incident labels improves predictions up to 15.3% and the DWD enhanced anomaly-detection improves predictions up to 8.96 %. Conditional information fusion improves ensemble prediction methods, especially for incident traffic. The proposed method works well with enhanced detections and the procedure is fully automated. The accurate predictions lead to more robust traffic control and routing systems.