Proceedings Article10.1109/ISPCE-CN48734.2019.8958632
Map Matching Integration Algorithm Based on Historical Trajectory Data
Xin Lai,Jianhua Chen,Jingjing Cao,Fei Xia +3 more
- 01 Oct 2019
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TL;DR: This experiment uses the truck trajectory data to test the algorithm and shows that the map matching integration algorithm effectively improves the accuracy of map matching.
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Abstract: The insufficient accuracy of GPS technology leads to the sampling trajectory data being away from the actual road. In order to improve the accuracy of GPS trajectory data for matching to map, a map matching integration algorithm based on historical trajectory data is proposed. Firstly projection distance and hidden Markov model are used respectively to compare the matching results. Then the difference road segments are found and the DBSCAN algorithm is used to cluster the historical trajectory data to adjust the segments. Our experiment uses the truck trajectory data to test the algorithm. The results show that the map matching integration algorithm effectively improves the accuracy of map matching.
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Citations
Real time map matching for low frequency GPS data based on machine learning technology
Ornicha Sinthopvaragul
- 15 Sep 2023
TL;DR: Machine learning-based map matching for low-frequency GPS data improves accuracy and efficiency.
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Mohamed M. Atia,Allaa R. Hilal,Clive Stellings,Eric Hartwell,Jason Toonstra,William Ben Miners,Otman A. Basir +6 more
TL;DR: A low-cost real-time lane-determination system that fuses micro-electromechanical systems inertial sensors, global navigation satellite system (GNSS), and commercially available road network maps that can be used for intelligent transportation systems, telematics applications, and autonomous driving is presented.
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