利用实时路况数据聚类方法检测城市交通拥堵点
Urban Traffic Congestion Detection Based on Clustering Analysis of Real-time Traffic Data
查看参考文献17篇
|
文摘
|
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(DensityBased 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。 |
|
其他语种文摘
|
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. |
|
来源
|
地球信息科学学报
,2012,14(6):775-780 【核心库】
|
|
关键词
|
交通拥堵
;
拥堵点检测
;
时空关联
;
聚类分析
;
DBSCAN算法
|
|
地址
|
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
|
|
语种
|
中文 |
|
ISSN
|
1560-8999 |
|
学科
|
测绘学 |
|
基金
|
国土资源公益性行业科研专项经费
;
国家自然科学基金项目
;
中央高校基本科研业务费专项资金
|
|
文献收藏号
|
CSCD:4716228
|
参考文献 共
17
共1页
|
|
1.
Jiang G Y. The method of traffic congestion identification and spatial and temporal dispersion range estimation.
International Asia Conference on Informatics in Control, Automation and Robotics. 1,2010:36-39
|
被引
1
次
|
|
|
|
|
2.
祝付玲.
城市道路交通拥堵评价指标体系研究,2006
|
被引
11
次
|
|
|
|
|
3.
赵有婷. 基于视频全局光流场的交通拥堵检测.
计算机应用研究,2010,27(11):4355-4362
|
被引
2
次
|
|
|
|
|
4.
Li L. A traffic congestion estimation approach from video using time-spatial imagery.
ICINIS '08 Proceedings of the 2008 First International Conference on Intelligent Networks and Intelligent Systems,2008:465-469
|
被引
1
次
|
|
|
|
|
5.
石征华. 城市快速路拥挤度判别方法研究.
交通与计算机,2006,24(5):20-23
|
被引
3
次
|
|
|
|
|
6.
Payne H.
Development and testing of incident-detection algorithm. research methodology and detailed results. FHWA-RD-06-20:19-76
|
被引
1
次
|
|
|
|
|
7.
刘伟铭.
高速公路系统控制方法,1998
|
被引
32
次
|
|
|
|
|
8.
周成虎.
地理信息系统空间分析原理,2011
|
被引
12
次
|
|
|
|
|
9.
Michalopoulos P G. Vehicle detection video through image processing:The autoscope system.
IEEE Trans on Vehicular Technology,1991,40(1):21-29
|
被引
20
次
|
|
|
|
|
10.
Yang Y. Fuzzy c-means clustering and opposition-based reinforcement learning for traffic congestion identification.
Journal of Information and Computational Science,2012,9(9):2441-2450
|
被引
2
次
|
|
|
|
|
11.
Lozano A. An algorithm for the recognition of levels of congestion in road traffic problems.
Mathematics and Computers in Simulation,2009,79(6):1926-1934
|
被引
5
次
|
|
|
|
|
12.
Erman J. Traffic classification using clustering algorithms.
Proceedings of the 2006 SIGCOMM Workshop on Mining Network Data, MineNet'06,2006
|
被引
1
次
|
|
|
|
|
13.
贾森.
基于实时信息的城市道路交通状态判别方法研究,2007
|
被引
2
次
|
|
|
|
|
14.
Ester M. A density-based algorithm for discovering clusters in large spatial databases with noise.
KDD-96 Proceedings,1996:226-231
|
被引
3
次
|
|
|
|
|
15.
吴信才. 时态GIS的基本概念、功能及实现方法.
地球科学-中国地质大学学报,2002,27(3):241-245
|
被引
31
次
|
|
|
|
|
16.
Birant D. ST-DBSCAN: An algorithm for clustering spatial-temporal data.
Data and Knowledge Engineering,2007,60(1):208-221
|
被引
101
次
|
|
|
|
|
17.
邓敏. 时空聚类分析的普适性方法.
中国科学:信息科学,2012,42(1):111-124
|
被引
17
次
|
|
|
|
|
了解气象卫星更多信息，请访问