Online Particle Smoothing with Application to Map-matching

TL;DR

This paper presents a new online smoothing method called particle stitching, which improves trajectory inference in dense urban map-matching tasks by overcoming path degeneracy.

Contribution

It introduces a novel online resampling algorithm that converts marginal samples into full posterior estimates for state-space models, specifically applied to urban map-matching.

Findings

Effective in dense urban road networks

Overcomes path degeneracy in online smoothing

Improves trajectory inference accuracy

Abstract

We introduce a novel method for online smoothing in state-space models that utilises a fixed-lag approximation to overcome the well known issue of path degeneracy. Unlike classical fixed-lag techniques that only approximate certain marginals, we introduce an online resampling algorithm, called particle stitching, that converts these marginal samples into a full posterior approximation. We demonstrate the utility of our method in the context of map-matching, the task of inferring a vehicle's trajectory given a road network and noisy GPS observations. We develop a new state-space model for the difficult task of map-matching on dense, urban road networks.