There is a rapid increase in the deployment, acquisition and analysis of automated fare collection (AFC) systems, enabling a profound change in the ability to analyze high-volume data that relate to observed passenger travel behavior and recurrent patterns. The analysis of such passively collected data offers direct access to a continuous flow of observed passenger behavior at a large scale, saving expensive data collection efforts. For a review of the spectrum of applications – from strategic demand estimation to operational service performance measurements.

The FairAccess project leverages on the availability of Access-kort data for the vast majority of trips performed in Stockholm County. The overarching goal of this project is to develop means to analyse empirically the impacts of policy/planning measures based on disaggregate passively collected smart card data. This involves a series of analysis and modelling challenges. We develop and apply a series algorithms to infer of tap-out locations, infer vehicles and travel times, and infer transfers to that journeys can be composed. Tap-in records have been matched with corresponding inferred tap-out locations and time stamps for about 80% of all records. Thereafter, we construct time-dependent origin-destination matrices for which segmentations can be performed with respect to geographical and user product features.

We demonstrate the approach and algorithms developed by performing a before-after analysis of the fare scheme change from zone-based to flat fares. We analyse changes in travel patterns and derive price elasticities for distinctive market segments. The introduced fare policy delivered the desirable result of an increased ridership through improved convenience of the single-use products. Nevertheless, the significance of the service convenience component was underestimated, which resulted in the price adjustments being not in line with the mobility effects.

The planning and development of the Stockholm public transport system must rely on the best empirical foundations available to support evidence-based decision-making and make the right priorities. To this end, the development and analysis performed in the FairAccess project lay a necessary foundation for further methodological developments and analyses such as on-board crowding evaluation, demand forecasting and identifying user groups.