Modern fare collection systems based on contactless cards and connected to centralmanagement systems increase the passenger comfort and reduce fraud but also opentransportation systems to new opportunities in the “big data” era, enabling a betterunderstanding and optimization of the infrastructure. Every magnetic or contactless cardtransaction contains information about when and where a passenger boarding happens. Theuse of this data to study transportation demand, detect passenger activity patterns andoptimize networks accordingly was so far an exercise limited to the academic domain. In thisstudy, we present an extension of ACS Atlas? fare collection systems allowing city planners,urban network operators and even users to easily understand the transportation demand of acity. The system takes the fare collection systems as input data source and processes it withsuitable data mining and visualization techniques. We will present in this paper the use of thefare collection data in order to visualize demand in combination with GIS, its evolution overtime, the characterization of geographical areas based on boarding patterns, the estimation oforigin-destinations matrices and finally the estimation of vehicle load solely based on farecollection boarding events as provided by the fare collection systems.
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