Utilizing automatically collected data to infer travel behavior : a case study of the East London Line extension

Abstract

Utilizing automatically collected data sources, this research strengthens the understanding of changes in user travel behavior caused by the introduction of the extended East London Line (ELL) into London's public transportation network. A recently developed method for inferring all Oyster users' origins and destinations on the public transportation system, and linking trip segments into full journeys, enables analysts to study the influence of a major capital investment on the larger public transportation network in great detail over a span of time and geography not available with traditional survey methods. Expanding an Oyster-based origin-destination matrix to represent all users provides estimates of overall ridership and passengers' travel patterns. Careful analysis of the usage of the rail line and other public transportation services in its vicinity provides a new method to infer the passenger demand generated by the new service. Through the creation of a large user panel (made up of over 54,000 Oyster users with active cards in April 2010 and who travelled on the ELL in October 2011), this thesis studies changes in journey frequency, travel time, journey distance, public transportation mode share, and access distance by comparing journeys made before and after the introduction of the extended ELL.