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    Automated Identification of Traffic Patterns from Large Data Archives

    Automated Identification of Traffic Patterns from Large Data Archives


    The notion of traffic patterns plays a vital role in the transportation profession. Demand patterns, for example the time-series of traffic volumes experienced on highways during a.m. and p.m. peak commuter travel, are used in many applications such as performance measurement, planning and operations. However, due to limited data collection in the past for the specific purposes, there was no need for automated algorithms to identify these patterns. Now, huge data archives are increasingly available for many locations with the vast deployments of ITS systems across the nation. Taking advantage of this unique opportunity, this paper identifies and applies solutions to automatically identify traffic patterns. Clustering algorithm, logistic regression, and tree-based methods are adapted and applied. Based on application to real datasets, clustering algorithms hold high potential for use in automated traffic pattern identification.

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    Titel :

    Automated Identification of Traffic Patterns from Large Data Archives

    Beteiligte:
    Venkatanarayana, Ramkumar (Autor:in) / Smith, Brian L. (Autor:in) / Demetsky, Michael J. (Autor:in) / Zhang, Guimin (Autor:in)
    Kongress:

    Ninth International Conference on Applications of Advanced Technology in Transportation (AATT) ; 2006 ; Chicago, Illinois, United States

    Erschienen in:

    Applications of Advanced Technology in Transportation (2006) ; 822-827

    Erscheinungsdatum :

    2006-08-04

    ISBN :

    9780784407998

    DOI :

    https://doi.org/10.1061/40799(213)132

    Medientyp :

    Aufsatz (Konferenz)

    Format :

    Elektronische Ressource

    Sprache :

    Englisch

    Schlagwörter :

    Technology , Transportation management , Automatic identification systems , Data analysis , Infrastructure , Travel patterns

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