Descrição

As access to public or shared transport systems becomes increasingly digitised, new
datasets have emerged offering opportunities to research travel behaviour in a
continuous, large-scale and non-invasive way (Blythe and Bryan 2007; Froehlich,
Neumann, and Oliver 2008; Kusakabe, Iryo, and Asakura 2010; Páez, Trépanier, and
Morency 2011; Lathia, Ahmed, and Capra 2012). The data produced by urban bike
share schemes can be regarded as a particular instance of these new datasets. In most
recent bike share schemes, data on usage are continually reported to central databases.
Researchers working within data mining (Froehlich, Neumann, and Oliver 2008; Jensen
et al. 2010; Borgnat et al. 2011; Lathia, Ahmed, and Capra 2012) and information
visualization (Wood, Slingsby, and Dykes 2011) have processed and then queried these
data to identify patterns of usage at various spatial and temporal resolutions. Some of
this work has been used by scheme operators to help overcome problems around fleet
management, and by policy makers for better understanding usage at particular docking
stations. It has nevertheless been constrained by the level of detailed information made
easily available (Wood, Slingsby, and Dykes 2011; Lathia, Ahmed, and Capra 2012). In
many studies, data were harvested from the web, where local transport authorities
publish in real-time the number of available bikes at individual docking stations
(Froehlich, Neumann, and Oliver 2008; Lathia, Ahmed, and Capra 2012). Others gained
access to journey records, including journey origin-destination (OD) and start and end
times, and identified more sophisticated usage characteristics (Jensen et al. 2010;
Borgnat et al. 2011; Wood, Slingsby, and Dykes 2011). Without access to a customer
database, however, journeys could not be linked back to individual customers:
individuals’ journey histories, and the context framing those journeys, could not be
identified. This limits the extent to which such datasets can be used to study the more
complex motivations and barriers that might affect cycle behaviours