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

TL;DR

This paper explores feature selection in Conditional Random Fields to improve low-sampling-rate GPS map matching, balancing accuracy and computational efficiency in urban road networks.

Contribution

It introduces a feature selection approach for CRFs that enhances map matching accuracy while reducing model complexity for resource-constrained applications.

Findings

Achieved competitive map matching results on taxi datasets.

Reduced model complexity without sacrificing accuracy.

Demonstrated effectiveness at low GPS sampling rates.

Abstract

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.