Abstract
We propose a spatial-temporal indexing method for moving objects based on a prediction technique using motion patterns extracted from practical data, such as trajectories of pedestrians. To build an efficient index structure, we conducted an experiment to analyze practical moving objects, such as people walking in a hall. As a result, we found that any moving objects can be classified into just three types of motion characteristics: 1) staying, 2) straight-moving, 3) random walking. Indexing systems can predict highly accurate future positions of each object based on our found characteristics; moreover, the indexing system can build efficient MBRs in the spatial-temporal data structure. To show the advantage of our prediction method over previous works, we conducted an experiment to evaluate the performance of each prediction method.
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Yanagisawa, Y. (2008). Predictive Indexing for Position Data of Moving Objects in the Real World. In: Gervasi, O., Murgante, B., Laganà, A., Taniar, D., Mun, Y., Gavrilova, M.L. (eds) Computational Science and Its Applications – ICCSA 2008. ICCSA 2008. Lecture Notes in Computer Science, vol 5072. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69839-5_46
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DOI: https://doi.org/10.1007/978-3-540-69839-5_46
Publisher Name: Springer, Berlin, Heidelberg
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