An adaptive Markov chain algorithm applied over map-matching of vehicle trip GPS data

Markov chains have frequently been applied to match the probable routes with a set of GPS trip data that pilot vehicle is emitting over specific graph road network. This class map-matching (MM) algorithms presently demonstrates and involve statistical ad-hoc measures drive chain transitional probabilities in picking best route combinations constrained In this study, we devised an adaptive scheme modify Chain (MC) kernel window as move along samples reduce mistakes can happen by use narrower MC widths. The measure for temporarily increasing width chosen be ratio between geodesic distance current actual each pair samples. has shown hardened results significantly tolerable computational cost increase. details overall algorithm are depicted example extracted from various trips validate usefulness practice.