Automated data collection systems are becoming increasingly common in urban public transport systems, both in the US and throughout the developed world. These systems, which include Automatic Vehicle Location (AVL), Automatic Passenger Counting (APC), and Automatic Fare Collection (AFC), are often designed to support specific and fairly narrow functions within the transport agency. However, it is clear that the data obtained from these systems can have wide-ranging applications within public transport, well beyond the design applications. Of particular interest in the planning of public transport is the opportunity to make use of these increasingly ubiquitous databases to develop a better picture of how public transport systems are performing and being used. In some cases, better estimates of certain performance measures and usage attributes may be made at lower cost than by using conventional data collection me thods, even though there are important limitations on the detailed attributes typically available from these systems. In other cases it is possible for the first time to estimate important performance attributes, such as those related to reliability and its impacts, which have hitherto been virtually impossible to quantify because of paucity of data. This paper describes two applications, focusing on system usage and passenger behavior, which have been developed jointly between MIT and the Chicago Transit Authority (CTA), taking advant age of CTA’s AFC and AVL systems. The specific applications are the estimation of passenger origin-destination matrices for the CTA rail system and the estimation of path choice models for CTA rail passengers. Next steps in the development of further applications for urban public transport systems are also discussed.