地球信息科学学报 ›› 2012, Vol. 14 ›› Issue (6): 775-780.doi: 10.3724/SP.J.1047.2012.00775

• 本期要文(可全文下载) • 上一篇    下一篇

利用实时路况数据聚类方法检测城市交通拥堵点

鲁小丫1, 宋志豪2, 徐柱2, 李木梓2, 李婷2, 孙维亚2   

  1. 1. 西南民族大学计算机科学与技术学院, 成都 610041;
    2. 西南交通大学地球科学与环境工程学院, 成都 610031
  • 收稿日期:2012-11-26 修回日期:2012-12-03 出版日期:2012-12-25 发布日期:2012-12-25
  • 通讯作者: 徐柱(1972-),男,博士,教授,博士生导师,研究方向为空间数据挖掘、空间数据综合、空间数据共享、GIS软件体系结构等。E-mail:xuzhucn@gmail.com E-mail:xuzhucn@gmail.com
  • 作者简介:鲁小丫(1975-),女,讲师,主要从事计算机应用、计算机网络安全研究。E-mail:lu_xiaoya@163.com
  • 基金资助:

    国土资源公益性行业科研专项经费(201111013);国家自然科学基金项目(40971209);中央高校基本科研业务费专项(SWJTU11CX059)。

Urban Traffic Congestion Detection Based on Clustering Analysis of Real-time Traffic Data

LU Xiaoya1, SONG Zhihao2, XU Zhu2, LI Muzi2, LI Ting2, SUN Weiya2   

  1. 1. Faculty of Computer Science and Technology, Southwest University for Nationalities, Chengdu 610041, China;
    2. Faculty of Geosciences and Environment Engineering, Southwest Jiaotong University, Chengdu 610031, China
  • Received:2012-11-26 Revised:2012-12-03 Online:2012-12-25 Published:2012-12-25

摘要:

城市交通拥堵严重制约其网络总体效率。开展检测交通拥堵点可有效识别网络瓶颈,以整治交通拥堵现象。对此,本文提出一种新的城市交通时空拥堵点检测的方法:即采用实时路况数据,通过定义时空关联,检测时空意义上长期性、规律性交通拥堵点。本文基于DBSCAN(Density-Based Spatial Clustering of Applications with Noise)算法,以成都市为试验区,实现了这种拥堵点检测方法。试验表明,该方法可快速、有效、准确地检测出城市道路严重拥堵路段,并确定其拥堵时空范围,为交通管理、交通拥堵机理分析、交通拥堵预测等提供参考。

关键词: 交通拥堵, 时空关联, 聚类分析, 拥堵点检测, DBSCAN算法

Abstract:

Traffic congestion in urban road network heavily restricts transportation efficiency. Detecting traffic congestions in the spatio-temporal sense and identifying network bottlenecks become an important task in transportation management. Up to now, many traffic congestion detection methods have been proposed, which have focused on the detection of momentary local congestions. Larger-scale, longer-time and regular congestions can't be detected using these methods. That is because congestions have different temporo-spatial scales, and a characteristic is not considered in those methods. This paper proposes a new kind of urban traffic congestion detection method that deals with spatio-temporal extension of congestion. It is based on spatio-temporal clustering analysis of real-time traffic data. By defining a proper spatio-temporal correlation, the classic DBSCAN algorithm is adapted to tackle spatio-temporal clustering. With it we can detect longer time and regular traffic congestion in the spatio-temporal sense. Experiments have been conducted using real traffic condition data of Chengdu to validate the effectiveness of the method. The experiment shows that the proposed method can detect the congestion areas and identify the spatio-temporal extent of congestions accurately. The detected congestion areas were compared with congestion report from local traffic management authority and found to be consistent with the later.

Key words: spatio-temporal correlation, spatio-temporal clustering analysis, DBSCAN algorithm, traffic congestion detection