  In order to improve offline map matching accuracy of low-sampling-rate GPS, a
map matching algorithm based on conditional random fields (CRF) and route
preference mining is proposed. In this algorithm, road offset distance and the
temporal-spatial relationship between the sampling points are used as features
of GPS trajectory in CRF model, which can utilize the advantages of integrating
the context information into features flexibly. When the sampling rate is too
low, it is difficult to guarantee the effectiveness using temporal-spatial
context modeled in CRF, and route preference of a driver is used as
replenishment to be superposed on the temporal-spatial transition features. The
experimental results show that this method can improve the accuracy of the
matching, especially in the case of low sampling rate.
