Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Comparison of Similarity Measures for Trajectory Clustering in Outdoor Surveillance Scenes
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
On-line trajectory clustering for anomalous events detection
Pattern Recognition Letters
Sensitivity of PCA for traffic anomaly detection
Proceedings of the 2007 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Anomaly detection using manifold embedding and its applications in transportation corridors
Intelligent Data Analysis - Knowledge Discovery from Data Streams
A dynamic hierarchical clustering method for trajectory-based unusual video event detection
IEEE Transactions on Image Processing
A sense of self for Unix processes
SP'96 Proceedings of the 1996 IEEE conference on Security and privacy
Histogram-based traffic anomaly detection
IEEE Transactions on Network and Service Management
Automatic license plate recognition
IEEE Transactions on Intelligent Transportation Systems
Data-Driven Intelligent Transportation Systems: A Survey
IEEE Transactions on Intelligent Transportation Systems
Inferring the Root Cause in Road Traffic Anomalies
ICDM '12 Proceedings of the 2012 IEEE 12th International Conference on Data Mining
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We focus on detecting anomalous events in transportation systems. In transportation systems, other than normal road situation, anomalous events happen once in a while such as traffic accidents, ambulance car passing, harsh weather conditions, etc. Identifying the anomalous traffic events is essential because the events can lead to critical conditions where immediate investigation and recovery may be necessary. We propose an anomaly detection method for transportation systems where we create a police report automatically after detecting anomalies. Unlike the traditional police report, in this case, some quantitative analysis shall be done as well to provide experts with an advanced, precise and professional description of the anomalous event. For instance, we can provide the moment, the location as well as how severe the accident occurs in the upstream and downstream routes. We present an anomaly detection approach based on view association given multiple feature views on the transportation data if the views are more or less independent from each other. For each single view, anomalies are detected based on a manifold learning and hierarchical clustering procedures and anomalies from different views are associated and detected as anomalies with high confidence. We study two well-known ITS datasets which include the data from Mobile Century project and the PeMS dataset, and we evaluate the proposed method by comparing the automatically generated report and real report from police during the related period.