Highlights A methodology for categorizing bicycling environments is proposed. GPS based public bicycle speed data is used. A support vector machine is adopted for the proposed categorization algorithm. Technical feasibility of the proposed algorithm is demonstrated.

    Abstract A promising alternative transportation mode to address growing transportation and environmental issues is bicycle transportation, which is human-powered and emission-free. To increase the use of bicycles, it is fundamental to provide bicycle-friendly environments. The scientific assessment of a bicyclist’s perception of roadway environment, safety and comfort is of great interest. This study developed a methodology for categorizing bicycling environments defined by the bicyclist’s perceived level of safety and comfort. Second-by-second bicycle speed data were collected using global positioning systems (GPS) on public bicycles. A set of features representing the level of bicycling environments was extracted from the GPS-based bicycle speed and acceleration data. These data were used as inputs for the proposed categorization algorithm. A support vector machine (SVM), which is a well-known heuristic classifier, was adopted in this study. A promising rate of 81.6% for correct classification demonstrated the technical feasibility of the proposed algorithm. In addition, a framework for bicycle traffic monitoring based on data and outcomes derived from this study was discussed, which is a novel feature for traffic surveillance and monitoring.


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    Titel :

    Categorizing bicycling environments using GPS-based public bicycle speed data


    Beteiligte:
    Joo, Shinhye (Autor:in) / Oh, Cheol (Autor:in) / Jeong, Eunbi (Autor:in) / Lee, Gunwoo (Autor:in)


    Erscheinungsdatum :

    2015-04-10


    Format / Umfang :

    12 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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