Map-matching algorithms aim at establishing a vehicle location on a road segment based on positioning data from a variety of sensors: GPS receivers, WiFi or cellular radios. They are integral part of various Intelligent Transportation Systems (ITS) including fleet management, vehicle tracking, navigation services, traffic monitoring and congestion detection. Our work was motivated by an idea of developing an algorithm that can be both utilized for tracking individual vehicles and for monitoring traffic in real-time. We propose a new incremental map-matching algorithm that constructs of a sequence of Hidden-Markov Models (HMMs). Starting from an initial HMM, the next models are developed by alternating operations: expansion and contraction. In the later, the map-matched trace is output. We discuss results of initial experiments conducted for 20 GPS traces, which to test algorithm robustness, were modified by introduction of noise and/or downsampled.
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