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Identifying Urban Intersections with GPS Trajectories |
Yuan Gao1,Dong Wang2,Hongwei Feng2(),Yuanlei Shi2,Zhizhou Duan2 |
1 School of Economics & Management, Northwest University, Xi’an 710127, China; 2 School of Information Science & Technology, Northwest University, Xi’an 710127, China; |
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Abstract [Objective] This paper proposes a new supervised learning method to identify road intersections automatically based on GPS trajectory data generated by travelers in the mixed traffic mode. [Methods] Firstly, we encoded and partitioned the original trajectory data and their active regions with the GeoHash algorithm. Then, the coded trajectory and the coding matrix of active regions were mapped into a binary fusion matrix for characteristics of road intersections. Finally, we employed the K nearest neighbor classification algorithm with sliding window to identify the intersections. [Results] The proposed method was more efficient than the Latitude and Longitude Coordination based systems. Encoding with GeoHash algorithm helped us reduce the volume of datasets by 61%. It had better performance than the turning-angle based methods, and its F1-measure score was 0.82 with the distance measure of 50 meters. [Limitations] More real life GPS data is needed to better evaluate our method’s performance. [Conclusions] The proposed method is robust to the changing of sampling frequencies and could effectively identify the urban intersections based on GPS trajectory data.
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Received: 24 January 2019
Published: 18 December 2019
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Corresponding Authors:
Hongwei Feng
E-mail: 59460688@qq.com
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