TOD: Temporal outlier detection by using quasi-functional temporal dependencies
Data & Knowledge Engineering
Top-Eye: top-k evolving trajectory outlier detection
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
iBAT: detecting anomalous taxi trajectories from GPS traces
Proceedings of the 13th international conference on Ubiquitous computing
Mining traffic incidents to forecast impact
Proceedings of the ACM SIGKDD International Workshop on Urban Computing
A “semi-lazy” approach to probabilistic path prediction in dynamic environments
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
From taxi GPS traces to social and community dynamics: A survey
ACM Computing Surveys (CSUR)
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Outlier detection in vehicle traffic data is a practical problem that has gained traction lately due to an increasing capability to track moving vehicles in city roads. In contrast to other applications, this particular domain includes a very dynamic dimension: time. Many existing algorithms have studied the problem of outlier detection at a single instant in time. This study proposes a method for detecting temporal outliers with an emphasis on historical similarity trends between data points. Outliers are calculated from drastic changes in the trends. Experiments with real world traffic data show that this approach is effective and efficient.