Using Bikeshare Datasets to Improve Urban Cycling Experience and Research Urban Cycling Behaviour

With access to public and shared transport systems becoming increasingly digitized, transaction datasets of unprecedented size as well as temporal and spatial precision are automatically generated (Blythe and Bryan 2007; Bagchi and White 2005; Pelletier et al. 2011). Data collected through smartcard payment methods are perhaps the largest and most obvious example. Although introduced for the purpose of improving payment processes, such data provide a detailed view of demand on a transport system, the potential for service improvements to be suggested (Ferrari et al. 2014) and an opportunity for studying individual traveller behaviour (Agard et al. 2006; Morency et al. 2006; Lathia et al. 2013). A substantial benefit of such data over more traditional data collection methods is that a complete and total record of usage for every smartcard customer is automatically generated (Bagchi and White 2005). Problems associated with sampling and recall bias, which make actively collected travel surveys somewhat difficult to administer, are avoided. The two most obvious disadvantages, at least for travel behaviour research, are that those individuals using smartcard technology may not be representative of the total population using that system or navigating a city more generally; and that variables such as individual trip purpose can only be inferred since they are not recorded directly.