  Map matching of the GPS trajectory serves the purpose of recovering the
original route on a road network from a sequence of noisy GPS observations. It
is a fundamental technique to many Location Based Services. However, map
matching of a low sampling rate on urban road network is still a challenging
task. In this paper, the characteristics of Conditional Random Fields with
regard to inducing many contextual features and feature selection are explored
for the map matching of the GPS trajectories at a low sampling rate.
Experiments on a taxi trajectory dataset show that our method may achieve
competitive results along with the success of reducing model complexity for
computation-limited applications.
