There is an immense amount of location data being collected today from smartphone users by various service providers. Due to bandwidth and battery-life considerations, smartphone locations are generally sampled at sparse intervals using energy-efficient, but inaccurate, alternatives to the power-hungry Global Positioning System (GPS). If sparse sequences of coarse location data obtained from mobile users can be accurately map-matched to travel paths on the road network, then this data can be effectively used for several traffic-related applications. Unlike most other map-matching methods in the literature, we, in this paper, focus on the problem of map-matching sparse and noisy non-GPS smartphone location data. We adopt the widely-followed Hidden Markov Model (HMM) approach and propose new probabilistic models for the observation and transition probabilities tailored towards the type of data being considered. Our map-matching method has been evaluated using ground-truth labelled non-GPS location data collected from real drives. Tests show that the accuracy of the proposed method is about 12% more than that of a comparable HMM-based method from the literature. Our results also show that the runtime and latency of the proposed method can be kept within reasonable bounds using simple techniques.


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    Title :

    Probabilistic Map Matching of Sparse and Noisy Smartphone Location Data




    Publication date :

    2015-09-01


    Size :

    249953 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English