Fast Hidden Markov Model Map-Matching for Sparse and Noisy Trajectories

The problem of map-matching sparse and noisy GPS trajectories to road networks has gained increasing importance in recent years. A common state-of-the-art solution to this problem relies on a Hidden Markov Model (HMM) to identify the most plausible road sequence for a given trajectory. While this approach has been shown to work well on sparse and noisy data, the algorithm has a high computational complexity and becomes slow when working with large trajectories and extended search radii. We propose an optimization to the original approach which significantly reduces the number of state transitions that need to be evaluated in order to identify the correct solution. In experiments with publicly available benchmark data, the proposed optimization yields nearly identical map-matching results as the original algorithm, but reduces the algorithm runtime by up to 45%. We demonstrate that the effects of our optimization become more pronounced when dealing with larger problem spaces and indicate how our approach can be combined with other recent optimizations to further reduce the overall algorithm runtime.