In the data mining of road networks, trajectory clustering of moving objects is of particular interest for its practical importance in many applications. Most of the existing approaches to this problem are based on distance measurement, and suffer from several performance limitations including inaccurate clustering, expensive computation, and incompetency to handle high dimensional trajectory data. This paper investigates the complex network theory and explores its application to trajectory clustering in road networks to address these issues. Specifically, we model a road network as a dual graph, which facilitates an effective transformation of the clustering problem from sub-trajectories in the road network to nodes in the complex network. Based on this model, we design a label-based trajectory clustering algorithm, referred to as LBTC, to capture and characterize the essence of similarity between nodes. For the evaluation of clustering performance, we establish a clustering criterion based on the classical Davies-Bouldin Index (DB), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC) to maximize inter-cluster separation and intra-cluster homogeneity. The clustering accuracy and performance superiority of the proposed algorithm are illustrated by extensive simulations on both synthetic and real-world dataset in comparison with existing algorithms.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Label-Based Trajectory Clustering in Complex Road Networks


    Beteiligte:
    Niu, Xinzheng (Autor:in) / Chen, Ting (Autor:in) / Wu, Chase Q. (Autor:in) / Niu, Jiajun (Autor:in) / Li, Yuran (Autor:in)


    Erscheinungsdatum :

    01.10.2020


    Format / Umfang :

    3155292 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Clustering-Based Trajectory Prediction of Vehicles Interacting with Vulnerable Road Users

    Sonka, Adrian / Henze, Roman / Thal, Silvia | SAE Technical Papers | 2021


    Taxi Trajectory Clustering Based on Network Clustering Method

    Wang, Rui / Tang, Jinjun / Zhou, Jun et al. | TIBKAT | 2022


    A Flight Trajectory Prediction Method based on Trajectory Clustering

    Wang, Guangchao / Chen, Hui / Liu, Kun et al. | IEEE | 2019


    Taxi Trajectory Clustering Based on Network Clustering Method

    Wang, Rui / Tang, Jinjun / Zhou, Jun et al. | ASCE | 2022