DescriptionRecently, much attention has been devoted to the quality of public transport and more specifically to designating individuals and areas that suffer from public transport deficiencies. Various studies conducted in the domain of transport-related social exclusion have used geographical information systems (GIS) to unravel the linkage between social disadvantage, public transport needs and public transport provision. However, much of the empirical work to date is static because it describes what is accessible by public transit from a particular origin at a single point in time yet does not consider the temporal variability in accessibility levels at multiple origins. In answer to this limitation, this study puts forward a methodology for identifying public transit gaps by drawing on the latest accomplishments in the field of modelling time-continuous, schedule-based public transport. Therefore, General Transit Feed Specification (GTFS) datasets for bus, tram, metro and train as well as street network data were integrated in a GIS in order to compute a single multimodal network that accounts for all components of a public transport trip: ingress, the waiting time, the actual travel time through the transit network and egress. An automated Python script enables to determine the temporal variability in accessibility through this network, which occurs as a consequence of fluctuations in operating frequencies across the diurnal cycle, and between weekdays and weekends. These accessibility levels to key destinations at regular time intervals were compared to the public transport needs to highlight spatial mismatches between provision and need. This method was tested for Flanders (Belgium) and proved to be effective in pinpointing areas that need considerable attention in public transport planning due to an overall high need for and underprovision of public transportation. Additionally, the high degree of time variability suggested the inadequacy of applying a single time of day for transit-based accessibility research.
|Event title||Spatial Statistics 2015: Emerging patterns|