Accurate identification of transportation mode distribution is essential for effective urban planning. Recent advancements in machine learning have spurred research on automated Transportation Mode Detection (TMD). While existing TMD methods predominantly employ standard flat classification methods, this paper introduces HiClass4MD, a novel hierarchical approach. By leveraging the misclassification errors from standard flat classifier, HiClass4MD learns the class hierarchy for transportation modes. Although hierarchical met-rics initially indicated performance improvements when applied to real-world GPS trajectories dataset, a subsequent evaluation using conventional metrics revealed inconsistent results. While decision trees benefited marginally, other classifiers exhibited no significant gains or even degraded. This study highlights the complexity of applying hierarchical classification to TMD and underscores the need for further investigation into the factors influencing its effectiveness.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    HiClass4MD: a Hierarchical Classifier for Transportation Mode Detection


    Beteiligte:


    Erscheinungsdatum :

    24.09.2024


    Format / Umfang :

    464592 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Hierarchical Pattern Classifier

    Yates, Gigi L. / Eberlein, Susan J. | NTRS | 1992


    Road intersection detection and classification using hierarchical SVM classifier

    Rebai, Karima / Achour, Nouara / Azouaoui, Ouahiba | Tema Archiv | 2014


    Hierarchical classifier design for airborne SAR images of ships

    Gagnon, Langis / Klepko, Robert | SPIE | 1998


    Motion detection system for transportation mode analysis

    CORDOVA BRAD / SAHOO SANUJIT | Europäisches Patentamt | 2018

    Freier Zugriff