Map matching with Hidden Markov Model on sampled road network

This paper presents a map matching method based on an ideal Hidden Markov Model (HMM) to find a sequence of roads that corresponds to a given sequence of raw GPS points. Our method is a simplification of the more-complex HMM-based method that maintains its capabilities to cope with the noises and sparsity of the raw GPS data. We test the method with the real-world raw GPS data that is publicly available. Experiments show that despite its simplicity, the proposed method performs sufficiently well under sparse GPS points and sparse road network data.