Abstract
The growing adoption of automated data collection systems in the transit industry, such as automated fare-collection (AFC) and automated vehicle location (AVL), is providing operators with extensive data about the state of the system and its usage by passengers. The paper proposes a framework for using automated data to support the various functions, both planning and real time, and demonstrates its use with two examples of recent developments: a) using AFC and AVL data from metro systems with entry and exit transactions to assign passengers to the itinerary they actually used on a particular day; and b) a predictive decision support platform with real time prediction of passenger demand in terms of station arrivals and OD flows, and its use for platform and train crowding prediction.
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