Improving Fuzzy-Logic based Map-Matching Method with Trajectory Stay-Point Detection
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| Main Authors: | , , |
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| Format: | Preprint |
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2022
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| _version_ | 1866912069578653696 |
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| author | Jafarlou, Minoo E., Omid Mahdi Ebadati Naderi, Hassan |
| author_facet | Jafarlou, Minoo E., Omid Mahdi Ebadati Naderi, Hassan |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2208_02881 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Improving Fuzzy-Logic based Map-Matching Method with Trajectory Stay-Point Detection Jafarlou, Minoo E., Omid Mahdi Ebadati Naderi, Hassan Machine Learning Artificial Intelligence Computational Geometry Computer Vision and Pattern Recognition Logic in Computer Science 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. |
| title | Improving Fuzzy-Logic based Map-Matching Method with Trajectory Stay-Point Detection |
| topic | Machine Learning Artificial Intelligence Computational Geometry Computer Vision and Pattern Recognition Logic in Computer Science |
| url | https://arxiv.org/abs/2208.02881 |