Improving Fuzzy-Logic based Map-Matching Method with Trajectory Stay-Point Detection

TL;DR

This paper enhances fuzzy-logic map-matching by detecting stay-points with DBSCAN clustering, reducing data size and processing time while maintaining accuracy in GPS trajectory matching.

Contribution

It introduces a stay-point detection method using DBSCAN to improve fuzzy-logic map-matching efficiency and accuracy.

Findings

27.39% reduction in data size

8.9% decrease in processing time

Maintains same accuracy as previous methods

Abstract

The requirement to trace and process moving objects in the contemporary era gradually increases since numerous applications quickly demand precise moving object locations. The Map-matching method is employed as a preprocessing technique, which matches a moving object point on a corresponding road. However, most of the GPS trajectory datasets include stay-points irregularity, which makes map-matching algorithms mismatch trajectories to irrelevant streets. Therefore, determining the stay-point region in GPS trajectory datasets results in better accurate matching and more rapid approaches. In this work, we cluster stay-points in a trajectory dataset with DBSCAN and eliminate redundant data to improve the efficiency of the map-matching algorithm by lowering processing time. We reckoned our proposed method's performance and exactness with a ground truth dataset compared to a fuzzy-logic based map-matching algorithm. Fortunately, our approach yields 27.39% data size reduction and 8.9% processing time reduction with the same accurate results as the previous fuzzy-logic based map-matching approach.