This research explores the application of archived data from Automated Data Collection Systems (ADCS)

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.

This paper contributes to the emerging literature on the application of smart card fare payment data to public transportation planning. The research objective is to identify and assess complete, multimodal journeys using Oyster smart card fare payment data in London. Three transfer combinations (bus-to-Underground, Underground-to-bus, and bus-to-bus) are considered to formulate recommendations for maximum elapsed time thresholds to identify transfers between journey stages for each passenger on the London network. Recommended elapsed time thresholds for identifying transfers are 20 min for Underground-to-bus, 35 min for bus-to-Underground, and 45 min for bus-to-bus, but a range of values that account for variability across the network are also assessed. Key findings about bus and Underground travel in London include an average of 2.3 daily public transportation journeys per passenger, 1.3 journey stages per public transportation journey, and 23% of Underground journeys involving a transfer to or from a bus. The application of complete journey data to bus network planning is used to illustrate the value of new information that would be available to network planners through the use of smart card fare payment data.

This paper explores the potential of using automated fare card data to quantify the reliability of service as experienced by passengers of rail transit systems. The distribution of individual passenger journey times can be accurately estimated for those systems requiring both entry and exit fare card validation. With the use of this information, a set of service reliability measures is developed that can be used to routinely monitor performance, gain insights into the causes of unreliability, and serve as an input into the evaluation of transit service. An estimation methodology is proposed that classifies performance into typical and nonrecurring conditions, which allows analysts to estimate the level of unreliability attributable to incidents. The proposed measures are used to characterize the reliability of one line in the London Underground under typical and incident-affected conditions with the use of data from the Oyster smartcard system for the morning peak period. A validation of the methodology with the use of incident-log data confirms that a large proportion of the unreliability experienced by passengers can be attributed to incident-related disruptions. In addition, the study revealed that the perceived reliability component of the typical Underground trip exceeds its platform wait time component and equals about half of its on-train travel time as well as its station access and egress time components, suggesting that sizable improvements in overall service quality can be attained through reliability improvements.

New automated fare collection systems being implemented on urban rail and bus transit systems offer the potential of tapping a rich source of customer usage data to improve transit planning. This is especially true of transit systems that offer smart cards as a payment option allowing long-term individual travel behaviors to be tracked and analyzed. This paper presents an initial analysis of the access and usage patterns of Chicago Transit Authority, Illinois, smart card holders during September 2004. The types of analyses that can be conducted with smart card registration and transaction data are discussed, the potential difficulties encountered in conducting such analyses are described, and recommendations are offered for improvement and expansion of the use of smart card data sets. The findings reported focus on walking access distances, frequency and consistency of daily travel patterns, and variability of smart card customer behaviors by residential area.