Feature Engineering for Map Matching of Low-Sampling-Rate GPS Trajectories in Road Network
This work addresses map matching for low-sampling-rate GPS trajectories, which is an incremental contribution to spatial data processing.
The authors tackled the problem of map matching low-sampling-rate GPS trajectories to recover routes in a road network by constructing features in a spatial database using Conditional Random Fields, with preliminary results from real-world taxi data.
Map matching of GPS trajectories from a sequence of noisy observations serves the purpose of recovering the original routes in a road network. In this work in progress, we attempt to share our experience of feature construction in a spatial database by reporting our ongoing experiment of feature extrac-tion in Conditional Random Fields (CRFs) for map matching. Our preliminary results are obtained from real-world taxi GPS trajectories.