DRFMM: a map-matching algorithm based on distributed random forest multi-classification

• Published in: IOP conference series. Earth and environmental science Vol. 189; no. 5; pp. 52014 - 52019
• Main Authors: Zhou, GuangLin, Chen, Feng
• Format: Journal Article
• Language: English
• Published: Bristol IOP Publishing 06.11.2018
• Subjects: Algorithms; Classification; Electronic maps; Global positioning systems; GPS; Matching; Measuring instruments; Navigation systems; Roads & highways; Trajectories; Transportation networks
• ISSN: 1755-1307; 1755-1315; 1755-1315
• DOI: 10.1088/1755-1315/189/5/052014

In the vehicle navigation system, the vehicle movement trajectory displayed on the electronic map reflects the results of real-time positioning by the GPS measuring device. Map matching is the process of matching a series of GPS coordinates to an electronic map to find the true path of the trajectory. In this study, a distributed random forest map-matching algorithm (DRFMM) is proposed. The point-line matching method is used as the basic feature. The random forest algorithm in the Spark platform is used to train the historical data. The road network data is meshed, and a multi-classification model is trained offline for each grid in the road network to predict the FCD data online. The experimental results show that the DRFMM algorithm proposed in this paper has improved the accuracy of point-line matching by 10%. The multi-classification method keeps the matching accuracy with the increase of data volume. At the same time, with multi-threading and distributed platform, DRFMM's matching speed is nearly 6 times faster than stand-alone matching algorithm.