An Online Map Matching Algorithm Based on Second-Order Hidden Markov Model

Map matching is a key preprocess of trajectory data which recently have become major source for various transport applications and location-based services. In this paper, an online map algorithm based on the second-order hidden Markov model (HMM) proposed processing in complex urban road networks such as parallel segments intersections. Several factors driver’s travel preference, network topology, level, vehicle heading are well considered. An extended Viterbi self-adaptive sliding window mechanism adopted to solve problem efficiently. To demonstrate effectiveness algorithm, case study carried out using massive taxi dataset Nanjing, China. Case results show that accuracy outperforms baseline built first-order HMM testing experiments.