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Data Analysis and Knowledge Discovery  2019, Vol. 3 Issue (11): 24-34    DOI: 10.11925/infotech.2096-3467.2019.0109
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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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[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.

Key wordsAutomatic Intersection Identification      GPS Trajectory      GeoHash Coding      K Nearest Neighbor Algorithm     
Received: 24 January 2019      Published: 18 December 2019
ZTFLH:  P209  
Corresponding Authors: Hongwei Feng     E-mail:

Cite this article:

Yuan Gao,Dong Wang,Hongwei Feng,Yuanlei Shi,Zhizhou Duan. Identifying Urban Intersections with GPS Trajectories. Data Analysis and Knowledge Discovery, 2019, 3(11): 24-34.

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字符串长度 误差(km) 字符串长度 误差(km) 字符串长度 误差(km)
1 ±2500 4 ±20 7 ±0.076
2 ±630 5 ±2.4 8 ±0.019
3 ±78 6 ±0.61 9 ±0.002
纬度 经度 海拔(英尺) 转化日期 日期 时间
39.9066 116.3855 49 240 097 586516198 2009-10-11 14:04:30
39.9065 116.3856 49 240 097 586516203 2009-10-11 14:04:35
P R F1-Measure
基于转向角度的方法 0.73 0.81 0.77.
本文提出方法 0.87 0.76 0.82
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