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.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Review and Perspective for Distance-Based Clustering of Vehicle Trajectories


    Beteiligte:


    Erscheinungsdatum :

    01.11.2016


    Format / Umfang :

    2061837 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Clustering of Vehicle Trajectories

    Atev, Stefan / Miller, Grant / Papanikolopoulos, Nikolaos P | IEEE | 2010


    Vehicle Motion Trajectories Clustering via Embedding Transitive Relations

    Hoseini, Fazeleh / Rahrovani, Sadegh / Chehreghani, Morteza Haghir | IEEE | 2021


    Clustering of Driving Encounter Scenarios Using Connected Vehicle Trajectories

    Wang, Wenshuo / Ramesh, Aditya / Zhu, Jiacheng et al. | IEEE | 2020


    Clustering Vehicle Maneuver Trajectories Using Mixtures of Hidden Markov Models

    Martinsson, John / Mohammadiha, Nasser / Schliep, Alexander | IEEE | 2018