Improving Fuzzy-Logic based Map-Matching Method with Trajectory Stay-Point Detection
| Autor: | Minoo Jafarlou, Omid Mahdi Ebadati, E., hassan naderi |
|---|---|
| Rok vydání: | 2022 |
| Předmět: |
Computational Geometry (cs.CG)
FOS: Computer and information sciences Computer Science - Machine Learning Computer Science - Logic in Computer Science Artificial Intelligence (cs.AI) Computer Science - Artificial Intelligence Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition Computer Science - Computational Geometry Machine Learning (cs.LG) Logic in Computer Science (cs.LO) |
| Zdroj: | Web of Science |
| DOI: | 10.48550/arxiv.2208.02881 |
| Popis: | 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. Comment: 10 Pages, 20 Figures |
| Databáze: | OpenAIRE |
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