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

arXiv: https://arxiv.org/abs/2208.02881 · HTML: https://mlsanity.com/paper/2208.02881/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 34 references that are in the map.
- First posted on arXiv 4 Aug 2022.

## Built on by (most cited first, 2 of 2)

- [Reducing Labeling Costs in Sentiment Analysis via Semi-Supervised Learning](https://mlsanity.com/paper/2410.11355/) (2024)
- [Two Online Map Matching Algorithms Based on Analytic Hierarchy Process and Fuzzy Logic](https://mlsanity.com/paper/2402.11866/) (2024)

## Abstract (arXiv)

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.