|Table of Contents|

Information acquisition method of three-dimensional intersection spatial structure based on vehicle GPS trajectory(PDF)

《交通运输工程学报》[ISSN:1671-1637/CN:61-1369/U]

Issue:
2019年05期
Page:
170-179
Research Field:
交通信息工程及控制
Publishing date:

Info

Title:
Information acquisition method of three-dimensional intersection spatial structure based on vehicle GPS trajectory
Author(s):
TANG Lu-liang1;  YU Zhi-wei1;  REN Chang1;  YANG Xue2;  ZHANG Ya-tao1
(1. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University,Wuhan 430072, Hubei, China; 2. School of Geography and Information Engineering, China University of Geosciences(Wuhan), Wuhan 430074, Hubei, China)
Keywords:
traffic information;  spatial structure of three-dimensional intersection;  trajectory clustering;  random forest selection algorithm;  GPS trajectory data;  Davies-Bouldin index
PACS:
U491.2
DOI:
-
Abstract:
In order to identify different driving rules at the three-dimensional intersections, the features of vehicle trajectory data were analyzed by using random forest feature selection algorithm, and features were clustered according to the importance scores. The clustered results were measured by Davies-Bouldin index to obtain each driving rule cluster under the optimal clustering result, and Delaunay triangle network was constructed based on the cluster range. The skeleton line extraction and common sequence combination method were used to obtain the geometric structure and topological connectivity relationship of three-dimensional intersection. Finally, the spatial structure information of three-dimensional intersection was obtained. Taking the taxi trajectory data of Wuhan in 2016 as data source, the spatial structure information acquisition experiment of three-dimensional intersection in Wuhan was conduct. Analysis result shows that the top four items of vehicle GPS trajectory feature importance scores are the angle of ending point, the angle of starting point, the difference of starting and ending point angles, and the mean angle of middle points. The clustering result using the characteristics combination of terminal angle and starting angle is optimal. The recognition precision rates of the spatial structure information acquisition method in the directions of straight, left and right turning are 85.7%, 85.4%, and 87.5%, respectively,and the comprehensive precision rate is 86.2%. The information recall rates in the directions of straight, left and right turning are 91.5%, 87.2%, and 85.9%, respectively, and the comprehensive recall rate is 88.2%. The higher precision rates and recall rates indicate that the proposed method can accurately identify the spatial structure information and extract the geometric and topological connectivity relationship of driving rules at three-dimensional intersection. 2 tabs, 14 figs, 30 refs.

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Last Update: 2019-11-13