Automated Inference of Linked Transit Journeys in London Using Fare-Transaction and Vehicle Location Data

Abstract

Urban public transit providers historically have planned and managed their networks and services with little knowledge of their customers’ travel patterns. Although ticket gates and bus fareboxes yield counts of passenger activity in specific stations or vehicles, the relationships between these transactions—the origins, transfers, and destinations of individual passengers—typically have been acquired only through small, costly, and infrequent rider surveys. New methods for inferring the journeys of all riders on a large public transit network have been built on recent work into the use of automated fare collection and vehicle location systems for analysis of passenger behavior. Complete daily sets of data from London's Oyster farecard and the iBus vehicle location system were used to infer boarding and alighting times and locations for individual bus passengers and to infer transfers between passenger trips of various public modes, and origin–destination matrices of linked intermodal transit journeys that include the estimated flows of passengers not using farecards were constructed. The outputs were validated against surveys and traditional origin–destination matrices. The software implementation demonstrated that the procedure is efficient enough to be performed daily, allowing transit providers to observe travel behavior on all services at all times.