Many large transit systems use automatic fare collection (AFC) systems. Most AFC systems were designed solely for revenue management, but they contain a wealth of customer use data that can be mined to create inputs to operations planning and demand forecasting models for transportation planning. More detailed information than could ever be collected by any travel survey is potentially available if it is assumed that the transactional data can be processed to produce the desired information. Previous work in this field focused primarily on rail transit, since boardings at fixed stations are easier to locate than boardings of buses, which move around. This paper presents a case study for the Metropolitan Transit Authority's New York City Transit, a transit system in which a rider swipes a fare card only to enter a station or board a bus. This is the first work to include trips by all transit modes in a system that records the transaction only on rider entry, which is significantly more challenging because all the alighting locations need to be inferred and the bus boarding locations need to be estimated. No location information (from automated vehicle location technology or a Global Positioning System) was available for buses. Software that processes the 7 million–plus daily transactions and that creates a data set of linked transit trips was created. The data set can then be analyzed by using geographic information system-based query software to create reports, maps, origin–destination matrices, load profiles, and new data sets. Subway journeys are assigned by using a schedule-based shortest-path algorithm.

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This paper presents a unified approach for improving travel demand models through the application and extension of supernetwork models of multi-dimensional travel choices. Proposed quite some time ago, supemetwork models solved to stochastic user equilibrium can provide a simultaneous solution to trip generation, distribution, mode choice, and assignment that is consistent with disaggregate models and predicts their aggregate effects. The extension to incorporate the time dimension through the use of dynamic equilibrium assignment methods is proposed as an enhancement that is necessary in order to produce realistic models. A variety of theoretical and practical problems are identified whose solution underlies implementation of this approach. Recommended future research includes improved algorithms for stochastic and dynamic equilibrium assignment, new methods for calibrating assignment models, and the use of Geographic Information Systems (GIS) technology for data and model management.