Geometrical Characteristics Extraction and Accuracy Analysis of Road Network Based on Vehicle Trajectory Data

  • Key Laboratory of Geographical Information Science, East China Normal University, Shanghai 200062, China

Received date: 2012-04-24

  Revised date: 2012-04-24

  Online published: 2012-04-24


Electronic map data is of enormous importance for analyses of travel, urban congestion, etc. It is growing an important basic data of transportation research and widely employed in vehicle navigation system and intelligent transportation system. Of electronic map data, road network data is a fundamental part and often updated. As a result of a lot of expense of manpower and resources of traditional method, more and more research efforts have been made on extracting geometrical characteristics of road network from other data resources like LIDAR data. The wide use of mobile positioning devices makes it possible to obtain a large volume of vehicle trajectory data within a short period. These data are complete records of vehicles' movement and able to reflect geometrical characteristics of road network as well. By this means, vehicle trajectory data can be employed to figure out changes happening to the spatial extent of road networks as long as enough data are available, and then we can update the road network database. Since trajectory data integrate both spatial and temporal information and their volume is often huge, it is not straightforward to realize the aforementioned target. In view of this, the paper proposes a thinning-algorithm-based method to construct road network. Vehicle trajectory data are vector data, are first converted to raster data, then thinning method can be used to extract geometrical characteristics of road network. With this method, center lines of road networks can be extracted, network topology be maintained, and meanwhile redundant information be removed. A case study in Lujiazui, Shanghai with taxi trajectory data is conducted and the results are evaluated.

Cite this article

JIANG Yijuan, LI Xiang, LI Xiaojie, SUN Jing . Geometrical Characteristics Extraction and Accuracy Analysis of Road Network Based on Vehicle Trajectory Data[J]. Journal of Geo-information Science, 2012 , 14(2) : 165 -170 . DOI: 10.3724/SP.J.1047.2012.00165


[1] 刘利娜, 吴锡生. 提高手写体数字细化效果的改进算法[J]. 计算机工程与应用, 2010, 46(3): 137-139.
[2] Stefanelli R, Rosenfeld A. Some parallel thinning algorithms for digital pictures[J]. Journal of the ACM, 1971, 18(2): 255-264.
[3] Pavlidis T, Ali F. Computer recognition of handwritten numerals by ploygonal approximations[J]. IEEE Transactions, System, Man and Cybernetics, 1975, 5(6): 610-614.
[4] Zhang T Y, Suen C Y. A fast parallel algorithm for thinning digital patterns[J]. Communications of the ACM, 1984, 27: 236-239.
[5] 吴选忠. Zhang快速并行细化算法的扩展[J]. 福建工程学院学报, 2006, 4(1): 89-92.
[6] 杨容浩, 余代俊. 扫描地图自动矢量化若干细节问题的解决方法[J]. 四川测绘, 2008, 31(3): 127-130.
[7] 张学东, 张仁秋, 关云虎. 一种快速的手写体汉字细化算法[J]. 计算机应用与软件, 2009, 26(11): 17-19.
[8] 张英琦, 张庆林. 数学形态学应用于二值图像的细化[J]. 焦作工学院学报, 1997, 16(4): 8-43.
[9] 尹铁源.机械手性能标定直线运动轨迹的特征检测技术. 沈阳工业大学, 2008, 1-79.
[10] 帕夫利迪斯(著), 吴成柯(译).计算机图形显示和图像处理的算法[M]. 北京: 科学出版社, 1985,1-403.
[11] 施启乐. 反求系统中图像获取与处理若干问题的研究. 华中科技大学, 2004, 1-55.
[12] 崔凤奎, 王晓强, 张丰收. 二值图像细化算法的比较与改进[J]. 洛阳工学院学报, 1997, 18(4): 48-52.
[13] 张昊, 徐刚. 基于四邻域的二值图像细化算法[J]. 信息技术与信息化,2004, (6): 24-27.
[14] 刘明艳. 边缘的检测与细化研究. 曲阜师范大学, 2007, 1-55.
[15] 李兰友. Visual C#图像处理程序设计实例[M]. 北京: 国防工业出版社, 2003,1-305.