| Autor: |
Yoshimasa Koike, Hiroyuki Toda, Kyosuke Nishida |
| Rok vydání: |
2015 |
| Předmět: |
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| Zdroj: |
Proceedings of the 8th ACM SIGSPATIAL International Workshop on Computational Transportation Science. |
| DOI: |
10.1145/2834882.2834884 |
| Popis: |
We tackle the problem of extracting stay regions from a geospatial trajectory where a user has stayed longer than a certain time threshold. There are four major difficulties with this problem: (1) stay regions are not only point-type ones such as at a bus-stop but large and arbitrary-shaped ones such as at a shopping mall; (2) trajectories contain spatial outliers; (3) there are missing points in trajectories; and (4) trajectories should be analyzed in an online mode. Previous algorithms cannot overcome these difficulties simultaneously. Density-based batch algorithms have advantages over the previous algorithms in discovering of arbitrary-shaped clusters from spatial data containing outliers; however, they do not consider temporal durations and thus have not been used for extracting stay regions. We extended a density-based algorithm so that it would work in a duration-based manner online and have robustness to missing points in stay regions while keeping its advantages. Experiments on real trajectories of 13 users conducting their daily activities for three weeks demonstrated that our algorithm statistically significantly outperformed five state-of-the-art algorithms in terms of F1 score and works well without trajectory preprocessing consisting of filtering, interpolating, and smoothing. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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