The dynamic origin-destination (OD) information is crucial for traffic operations and control. This paper presents a dynamic traffic demand prediction framework based on an anomaly detection algorithm. The Principal Component Analysis (PCA) method is applied to extract main demand patterns which are used to detect the abnormal conditions. The proposed approach can select prediction methods (parametric or nonparametric) automatically based on the pattern detection results. Both simulation and field observed Automatic Number Plate Recognition (ANPR) data are used to verify the proposed approach where the Kalman filter model and the K-nearest neighbor model are chosen as the basic prediction methods. The results show that the prediction framework can effectively reduce the noise of a single prediction model particularly in the abnormal conditions and provide more accurate and reliable prediction results.

AbstractThe Yangtze River is the first of China and the third-longest river in the world. It is the most developed inland water transportation system in China. Chongqing, located in the upstream of the Three Gorges Project, is not only the gateway of the industrial and commercial center of China but also the most abundant water and intermodal inland hub and material distribution center in the southwest. Frequent water traffic accidents pose severe threats to the safety of human life and property and the water environment as well as bring adverse effects on social stability. Therefore, based on the ten years of statistical data of ship accidents in 2009-2018 from the Chongqing Maritime Safety Administration (MSA), this paper summarizes the characteristics of the spatiotemporal distribution of accidents through statistical analysis of the historical data. Moreover, it proposes accident prevention and supervision methods to provide decision support for maritime safety. This research is of great significance to the prevention and control of water traffic accidents in this region. Index TermsShip accidents, statistical analysis, water transportation, risk analysis, data visualization, maritime safety.