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

• URL: http://arxiv.org/abs/2208.02881v1
• Date: Thu, 4 Aug 2022 20:41:13 GMT
• Title: Improving Fuzzy-Logic based Map-Matching Method with Trajectory Stay-Point Detection
• Authors: Minoo Jafarlou, Omid Mahdi Ebadati E., Hassan Naderi
• Abstract summary: Most GPS trajectory datasets include stay-points irregularity, which makes map-matching algorithms mismatch trajectories to irrelevant streets. We cluster stay-points in a trajectory dataset with DBSCAN and eliminate redundant data to improve the efficiency of the map-matching algorithm. 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.
• Score: 3.093890460224435
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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