利用轨迹大数据进行城市道路交叉口识别及结构提取

交叉口是城市交通路网生成、更新的重要组成部分。本文基于车辆时空轨迹大数据,提出了一种城市交叉口自动识别方法。该方法首先通过轨迹跟踪识别轨迹数据中包含的车辆转向点对;然后基于距离和角度的生长聚类方法进行转向点对的空间聚类,并采用基于局部点连通性的聚类方法识别交叉口;最后利用交叉口范围圆和转向点对提取城市各级别路网下的交叉口结构。以武汉市出租车轨迹大数据为例,对武汉市城区内189个交叉口进行了探测。试验结果表明,本文所提方法可以准确地从轨迹大数据中识别出城市交叉口及其结构。...

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Published inCe hui xue bao Vol. 46; no. 6; pp. 770 - 779
Main Author 唐炉亮 牛乐 杨雪 张霞 李清泉 萧世伦
Format Journal Article
LanguageChinese
English
Published Beijing Surveying and Mapping Press 01.06.2017
Subjects
Clustering
Point pairs
Roads & highways
Traffic intersections
交叉口结构
交叉口自动识别
城市交通路网
相似度聚类
轨迹大数据
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Summary:交叉口是城市交通路网生成、更新的重要组成部分。本文基于车辆时空轨迹大数据,提出了一种城市交叉口自动识别方法。该方法首先通过轨迹跟踪识别轨迹数据中包含的车辆转向点对;然后基于距离和角度的生长聚类方法进行转向点对的空间聚类,并采用基于局部点连通性的聚类方法识别交叉口;最后利用交叉口范围圆和转向点对提取城市各级别路网下的交叉口结构。以武汉市出租车轨迹大数据为例,对武汉市城区内189个交叉口进行了探测。试验结果表明,本文所提方法可以准确地从轨迹大数据中识别出城市交叉口及其结构。
Bibliography:11-2089/P
Intersection is an important part of the generation and renewal of urban traffic network.In this paper, a new method was proposed to detect urban intersections automatically from the spatiotemporal big trace data.Firstly, the turning point pairs were based on tracking the trace data collected by vehicles.Secondly, different types of turning point pairs were clustered by using spatial growing clustering method based on angle and distance differences, and the clustering methods of local connectivity was used to recognize the intersection.Finally, the intersection structure of multi-level road network was constructed with the range of the intersection and turning point pairs.Taking the taxi trajectory data in Wuhan city as an example, the experimental results showed that the method proposed in this paper can automatically detect and recognize the road intersection and its structure.
TANG Luliang1,NIU Le1 ,YANG Xue1,ZHANG Xia2,LI Qingquan1,2 ,XIAO Shilun1,3 （1. State Key Laboratory of kiformation Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China ; 2. Shenzhen Key Laboratory of Spatial Smart Sensing and Services, College of Civil Engineering, Shenzhen University, Shenzhen 518060, China; 3. Department of Geography, University of Tennessee, Knoxville, 37996-0925, USA）
urban traffic network;automatic intersection recognition;intersection structure;similarity clustering;big trace data
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1001-1595
1001-1595
DOI:10.11947/j.AGCS.2017.20160614