This study introduces a general methodology to process sparse floating car data, reconstruct the routes followed by the drivers, and cluster them to achieve suitable choice sets of significantly different routes for calibrating behavioural models. This methodology is applied to a large set of floating car data collected in Rome in 2010. Results underlined that routes assigned to different clusters are actually very different to each other. Nevertheless, as expected according to Wardrop's principle, the clusters belonging to the same origin–destination have rather similar average route travel times, even if there is a large range between their minimum and maximum values. A focus on drivers’ behaviour highlighted their propensity to follow the same route to their usual destination, though the 12% of the drivers switched to an alternative route. However, the analysis conducted over the 1 month of observations did not reveal the existence of any systematic correlation between neither the change of route nor the change of departure time and the travel time experienced the day before.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    Experimental analyses and clustering of travel choice behaviours by floating car big data in a large urban area


    Beteiligte:

    Erschienen in:

    Erscheinungsdatum :

    2018-05-01


    Format / Umfang :

    9 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Experimental analyses and clustering of travel choice behaviours by floating car big data in a large urban area

    Fusco, Gaetano / Bracci, Agnese / Caligiuri, Tommaso et al. | IET | 2018

    Freier Zugriff


    Urban travel behavior analyses and route prediction based on floating car data

    Sun, Daniel (Jian) / Zhang, Chun / Zhang, Lihui et al. | Taylor & Francis Verlag | 2014


    Understanding passenger travel choice behaviours under train delays in urban rail transits: a data-driven approach

    Chen, Enyi / Luo, Qin / Chen, Jingjing et al. | Taylor & Francis Verlag | 2023


    Cycling and Urban Travel Choice

    Page, M. / Siu Yim Lin / Tight, M. et al. | British Library Conference Proceedings | 1999