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Map matching of the GPS trajectory serves the purpose of recovering theoriginal route on a road network from a sequence of noisy GPS observations. Itis a fundamental technique to many Location Based Services. However, mapmatching of a low sampling rate on urban road network is still a challengingtask. In this paper, the characteristics of Conditional Random Fields withregard to inducing many contextual features and feature selection are exploredfor the map matching of the GPS trajectories at a low sampling rate.Experiments on a taxi trajectory dataset show that our method may achievecompetitive results along with the success of reducing model complexity forcomputation-limited applications.

Feature Selection in Conditional Random Fields for Map Matching of GPS Trajectories