An information fusion map matching method based on ranking learning
LU Jiapin1, LUO Yuetong1, HUANG Zhaosong2, ZHANG Yankong1, CHEN Wei2
1.School of Computer Science and Information Technology, Hefei University of Technology, Hefei 230601, China 2.College of Computer Science and Technology, Zhejiang University, Hangzhou 310058, China
Abstract：Fusion of multi-source information can effectively improve the accuracy of map matching results. Existing map matching methods rely on mathematical models. When new types of data are introduced, it is necessary to design a new mathematical model or adjust the complex parameters of those model in order to obtain the best map matching results. To solve the problem, this paper proposes an end-to-end data-driven map matching method. Instead of establishing a specific mathematical model, our method uses a neural network to learn a scoring function of the candidate roads from the data with the matching results by the ranking learning method. During matching, the method first selects all the roads near the GPS point as candidate roads; then uses the scoring function to score and sort all the candidate roads; finally selects the road with highest score as the map matching result. The experimental results demonstrate that the proposed method can effectively use new types of data and adapt to data missing conditions so as to achieve improvement or avoid sharp drop in accuracy. On the other hand, with the accuracy close to the HMM-based method, and the run time close to the angle- and distances-feature-based method.
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2020, Vol. 47 
