An HMM-based map matching method with cumulative proximity-weight formulation

Abstract

In this study, we introduce a hidden Markov model map matching method that is based on a cumulative proximity-weight formulation. This new formula is based on the line integral of a point-wise segment weight rather than the almost standard shortest distance based weight. The proposed method was tested using vehicle and map data from Seattle area. Several simulations were conducted so as to have a clear comparison of the new weight to the traditional one; and particular emphasis were given to matching of GPS data with long sampling periods and high level noise. Overall, possible improvements to MM accuracies by the new weight were identified. It was seen that the new weight could be a better option than the shortest distance based weight in the presence of low-frequency sampled and/or noisy GPS data.