Exploring Bus Rapid Transit passenger travel behaviour using big data

Over the past two decades, a growing international trend has been the implementation of Bus Rapid Transit (BRT) as a cost-effective way to enhance urban public transport (UPT) service quality and progress towards sustainable urban transport. To date, in excess of 40 cities worldwide operate BRT within their UPT networks. Despite the international prominence of BRT systems, there has been limited in-depth investigation of their spatial-temporal dynamics. Drawing on a case study BRT system, Brisbane, Australia, we apply a geo-visualisation-based method to a large smart card database to examine spatial-temporal dynamics. The conditional flow-maps (or flow-comaps) are created to visually compare the spatial trajectories of BRT trips and other bus-based trips and their variation by calendar events (i.e., a workday, a weekend, a school holiday and a public holiday). The results highlight (1) marked differences between BRT-based trips to those bus trips undertaken on the broader UPT network; (2) spatial heterogeneity in BRT trips; and (3) the potential of drawing on 'big data' to support evidence-based BRT planning. These findings render important implications with the capacity to inform future BRT strategy as it relates to service management and infrastructure expansion.