Review and Perspective for Distance-Based Clustering of Vehicle Trajectories
In this paper, we tackle the issue of clustering trajectories of geolocalized observations based on the distance between trajectories. We first provide a comprehensive review of the different distances used in the literature to compare trajectories. Then, based on the limitations of these methods, w...
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| Veröffentlicht in: | IEEE transactions on intelligent transportation systems 2016-11, Vol.17 (11), p.3306-3317 |
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| creator | Besse, Philippe C. Guillouet, Brendan Loubes, Jean-Michel Royer, Francois |
| description | In this paper, we tackle the issue of clustering trajectories of geolocalized observations based on the distance between trajectories. We first provide a comprehensive review of the different distances used in the literature to compare trajectories. Then, based on the limitations of these methods, we introduce a new distance: symmetrized segment-path distance (SSPD). We compare this new distance to the others according to their corresponding clustering results obtained using both the hierarchical clustering and affinity propagation methods. We finally present a python package: trajectory distance, which contains the methods for calculating the SSPD distance, and the other distances reviewed in this paper. |
| doi_str_mv | 10.1109/TITS.2016.2547641 |
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We first provide a comprehensive review of the different distances used in the literature to compare trajectories. Then, based on the limitations of these methods, we introduce a new distance: symmetrized segment-path distance (SSPD). We compare this new distance to the others according to their corresponding clustering results obtained using both the hierarchical clustering and affinity propagation methods. 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| subjects | Applications Clustering Clustering methods Indexes Measurement Roads Shape Statistics Trajectories Trajectory Trajectory clustering Vehicle spacing Vehicles |
| title | Review and Perspective for Distance-Based Clustering of Vehicle Trajectories |
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