Clustering and Outlier Detection for Trajectory Stream Data

As mobile devices continue becoming a larger part of our lives, the development of location acquisition technologies to track moving objects have focused the minds of researchers on issues ranging from longitude and latitude coordinates, speed, direction, and timestamping, as part of parameters needed to calculate the positional information and locations of objects, in terms of time and position in the form of trajectory streams. Recently, recent advances have facilitated various urban applications such as smart transportation and mobile delivery services.

Unlike other books on spatial databases, mobile computing, data mining, or computing with spatial trajectories, this book is focused on smart transportation applications.

This book is a good reference for advanced undergraduates, graduate students, researchers, and system developers working on transportation systems.
Contents: East China Normal University Scientific ReportsPrefaceAbout the AuthorsAcknowledgementsIntroductionClustering Analysis upon Trajectory StreamsOutlier Detection upon Trajectory StreamsOnline Analyzing upon Distributed Trajectory StreamsCloned Vehicle DetectionRoad Map Updating Using Trajectory AnalysisSummary and Future TrendsReferencesIndex
Readership: Advanced undergraduates, graduate students, researchers, and application developers specializing in trajectory stream data clustering and distribution.Trajectory Clustering;Trajectory Outlier Detection;Concept Drift;Sliding Window;Feature-group Based;Distributed Streams00