Mining personal frequent routes via road corner detection

Abstract : Frequent route is an important individual outdoor behavior pattern that many trajectory-based applications rely on. In this paper, we propose a novel framework for extracting frequent routes from personal GPS trajectories. The key idea of our design is to accurately detect road corners and utilize these new metaphors to tackle the problem of frequent route extraction. Concretely, our framework contains three phases: 1) characteristic point (CP) extraction; 2) corner detection; and 3) trajectory mapping. In the first phase, we present a linear fitting-based algorithm to extract CPs. In the second phase, we develop a multiple density level DBSCAN (density-based spatial clustering of applications with noise) algorithm to locate road corners by clustering CPs. In the third phase, we convert each trajectory into an ordered sequence of road corners and obtain all routes that have been traversed by an individual for at least F (frequency threshold) times. We evaluate the framework using real-world trajectory datasets of individuals for one year and the experimental results demonstrate that our framework outperforms the baseline approach by 7.8% on average in terms of precision and 21.9% in terms of recall
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Contributor : Médiathèque Télécom Sudparis & Institut Mines-Télécom Business School <>
Submitted on : Tuesday, April 5, 2016 - 2:30:35 PM
Last modification on : Sunday, October 20, 2019 - 9:48:01 AM



Tianben Wang, Daqing Zhang, Xingshe Zhou, Xin Qi, Hongbo Ni, et al.. Mining personal frequent routes via road corner detection. IEEE Transactions on Systems, Man, and Cybernetics: Systems, IEEE, 2016, 46 (4), pp.445 - 458. ⟨10.1109/TSMC.2015.2444416⟩. ⟨hal-01298074⟩



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