利用轨迹大数据进行城市道路交叉口识别及结构提取

唐炉亮; 牛乐; 杨雪; 张霞; 李清泉; 萧世伦

doi:10.11947/j.AGCS.2017.20160614

测绘学报 >

2017 , Vol. 46 >Issue 6: 770 - 779

DOI: https://doi.org/10.11947/j.AGCS.2017.20160614

地图学与地理信息

利用轨迹大数据进行城市道路交叉口识别及结构提取

唐炉亮 ,
牛乐 ,
杨雪 ,
张霞 ,
李清泉 ,
萧世伦

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1. 武汉大学测绘遥感信息工程国家重点实验室, 湖北武汉 430079;
2. 深圳大学土木工程学院空间信息智能感知与服务深圳市重点实验室, 广东深圳 518060;
3. 田纳西大学地理系, 美国田纳西州诺克斯维尔市 37996-0925

唐炉亮(1973—),男,博士,教授,研究方向为GIS-T、时空GIS、轨迹大数据挖掘等。E-mail:tll@whu.edu.cn

收稿日期: 2016-12-02

修回日期: 2017-04-27

网络出版日期: 2017-06-28

基金资助

国家自然科学基金（41671442；41571430；41271442）

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Urban Intersection Recognition and Construction Based on Big Trace Data

TANG Luliang ,
NIU Le ,
YANG Xue ,
ZHANG Xia ,
LI Qingquan ,
XIAO Shilun

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1. State Key Laboratory of Information 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, USAAbstract

Received date: 2016-12-02

Revised date: 2017-04-27

Online published: 2017-06-28

Supported by

The National Natural Science Foundation of China (Nos.41671442;41571430;41271442)

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摘要

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。

关键词： 城市交通路网; 交叉口自动识别; 交叉口结构; 相似度聚类; 轨迹大数据

本文引用格式

唐炉亮 , 牛乐 , 杨雪 , 张霞 , 李清泉 , 萧世伦 . 利用轨迹大数据进行城市道路交叉口识别及结构提取[J]. 测绘学报, 2017 , 46(6) : 770 -779 . DOI: 10.11947/j.AGCS.2017.20160614

Abstract

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.

Key words： urban traffic network; automatic intersection recognition; intersection structure; similarity clustering; big trace data

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