Feature Engineering for Map Matching of Low-Sampling-Rate GPS Trajectories in Road Network

TL;DR

This paper explores feature engineering for map matching of low-sampling-rate GPS trajectories using Conditional Random Fields, demonstrating preliminary results on real taxi data to improve route recovery accuracy.

Contribution

It presents an ongoing experiment on feature extraction in CRFs specifically tailored for low-sampling-rate GPS map matching tasks.

Findings

Preliminary results show promise in improving map matching accuracy.

Feature construction in spatial databases enhances CRF performance.

Real-world taxi GPS data validates the approach.

Abstract

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.