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Transport demand (a set of data about trips and passengers mobility patterns in the network) is a golden key for solving a wide range of issues of transportation and town planning problems. These are development of the road network, development and optimization of public transport routes, etc. To determine the transport demand, the complex four-stage model is traditionally used, in which traditional manual surveys are needed. Indirect methods of obtaining information represent a special perspective based on the collection, integration and analysis of large and mixed type of data which been generated by various sources of human life aspects (Urban computing [10], Big data [11, 12]). One of such methods is the analysis transactions of non-cash fare payment in public transport [9 24]. The results of the passenger trips are obtained by comparing the sequence of payment transactions. As a result of processing transactions of non-cash fare payment, a sample is drawn from the general mobility of citizens transported by public transport. The article considers the following tasks: 1. Estimation of the representativeness of the non-cash fare payment transactions sample to the general set of trips. 2. Calculation of the parameters of transport demand based on the trips set’s sample, which obtained as a result of processing transactions of non-cash fare payment. We proposed calculation of the Origin-Destination (OD) matrices, which takes into account presence of nondecrypted transactions. The obtained results allow us to use automated fare collection system (AFC) data to study transport demand: to obtain information about passenger's trip between the stopping points of network route or between transport districts, as well as passenger correspondence, which consist of one or several trips (when traveling with transfers). Keywords— transport demand, passenger flows, passenger trip, Origin-Destination matrices.

Using Automated Fare Collection System Data to Determine Transport Demand