Feature Selection in Conditional Random Fields for Map Matching of GPS Trajectories
This work addresses a specific problem in Location Based Services for urban navigation, but it is incremental as it builds on existing methods with feature selection improvements.
The paper tackled the challenge of map matching GPS trajectories with low sampling rates on urban road networks by exploring Conditional Random Fields for feature selection, achieving competitive results while reducing model complexity for computation-limited applications.
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.