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Computational geometry: algorithms and applications
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Approximate Geometric Pattern Matching Under Rigid Motions
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A Framework for Generating Network-Based Moving Objects
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ACM Transactions on Database Systems (TODS)
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Machine Learning
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VLDB '05 Proceedings of the 31st international conference on Very large data bases
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The VLDB Journal — The International Journal on Very Large Data Bases
Indexing Multidimensional Time-Series
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The VLDB Journal — The International Journal on Very Large Data Bases
Addressing the Need for Map-Matching Speed: Localizing Globalb Curve-Matching Algorithms
SSDBM '06 Proceedings of the 18th International Conference on Scientific and Statistical Database Management
Journal of Mathematical Imaging and Vision
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ESA'06 Proceedings of the 14th conference on Annual European Symposium - Volume 14
Visual analytics tools for analysis of movement data
ACM SIGKDD Explorations Newsletter - Special issue on visual analytics
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Visually driven analysis of movement data by progressive clustering
Information Visualization
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Multi-dimensional phenomenon-aware stream query processing
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Finding long and similar parts of trajectories
Computational Geometry: Theory and Applications
Pedestrian-movement prediction based on mixed Markov-chain model
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Visually exploring movement data via similarity-based analysis
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Similarity in (spatial, temporal and) spatio-temporal datasets
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U2STRA: high-performance data management of ubiquitous urban sensing trajectories on GPGPUs
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Exploring pattern-aware travel routes for trajectory search
ACM Transactions on Intelligent Systems and Technology (TIST) - Special Sections on Paraphrasing; Intelligent Systems for Socially Aware Computing; Social Computing, Behavioral-Cultural Modeling, and Prediction
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This work addresses the problem of obtaining the degree of similarity between trajectories of moving objects. Typically, a Moving Objects Database (MOD) contains sequences of (location, time) points describing the motion of individual objects, however, they also implicitly storethe velocity -- an important attribute describing the dynamics the motion. Our main goal is to extend the MOD capability with reasoning about how similar are the trajectories of objects, possibly moving along geographically different routes. We use a distance function which balances the lack of temporal-awareness of the Hausdorff distance with the generality (and complexity of calculation) of the Fréchet distance. Based on the observation that in practice the individual segments of trajectories are assumed to have constant speed, we provide efficient algorithms for: (1) optimal matching between trajectories; and (2) approximate matching between trajectories, both under translations and rotations, where the approximate algorithm guarantees a bounded error with respect to the optimal one.