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.
Experimental analyses and clustering of travel choice behaviours by floating car big data in a large urban area
IET Intelligent Transport Systems ; 12 , 4 ; 270-278
2018-05-01
9 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
floating car data set collection , driver information systems , travel choice behaviour clustering , behavioural sciences computing , Wardrop's principle , automobiles , Big Data , experimental analysis , behavioural models , pattern clustering , route reconstruction , large urban area , sparse floating car data processing , floating car Big Data
IET | 2018
|Urban travel behavior analyses and route prediction based on floating car data
Online Contents | 2014
|Urban travel behavior analyses and route prediction based on floating car data
Taylor & Francis Verlag | 2014
|Taylor & Francis Verlag | 2023
|Cycling and Urban Travel Choice
British Library Conference Proceedings | 1999
|