交通运输系统工程与信息

交通运输系统工程与信息 ›› 2013, Vol. 13 ›› Issue (5): 30-36.

• 综合交通运输体系论坛 • 上一篇    下一篇

城市干线道路交通拥挤态势的监测

弓晋丽*1, 彭贤武2   

  1. 1.长沙理工大学 交通运输工程学院, 长沙410114; 2. 三一重工股份有限公司，长沙410100
  • 收稿日期:2013-03-28 修回日期:2013-06-10 出版日期:2013-10-25 发布日期:2013-11-08
  • 作者简介:弓晋丽（1983-），女，山西省文水县人，讲师，工学博士.
  • 基金资助:

    863计划项目（2007AA12Z242）；国家自然科学基金项目（50738004）；公路工程教育部重点实验室开放基金资助项目（kfj120106）.

Monitoring the Evolution of Traffic on Main City Roads

GONG Jin-li1, PENG Xian-wu2   

  1. 1.School of Transportation Engineering, Changsha University of Science & Technology, Changsha 410114, China;2. Sany Heavy Industry Co., Ltd, Changsha 410100, China
  • Received:2013-03-28 Revised:2013-06-10 Online:2013-10-25 Published:2013-11-08

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被引次数

2

摘要：

以城市干线道路交通拥挤态势监测为目的，设计基于定点检测数据的异常监测系统.获取交通流基础数据（流量、速度、占有率）后，使用模糊C-均值聚类算法将定量数据转化为交通定性状态（拥挤或畅通）.以此为基础，系统使用时间序列分形分析法确定交通拥挤态势指数；以序列模式相似性度量法和凝聚分层聚类法进行常规运行模式的辨识；并使用基于距离的异常模式变点识别法实时监测交通拥挤态势是否偏离常规运行模式，以此确定系统是否存在异常.以上海南北高架东侧11天的定点检测数据为例进行实证分析，监测得到了9月30日异常模式集中分布在中午12:10-13:20、13:40-14:30和下午17:10-17:15时间段内.

关键词: 交通工程, 拥挤态势, 时间序列, 异常监测, 定点检测数据

Abstract:

In order to monitor the evolution of traffic on main city roads, a fixedpoint data monitoring system is devised. Firstly, after qualitative traffic data (flow, speed, and occupancy) are acquired, they are then translated into the traffic qualitative state (congested or uncongested) by Fuzzy Cmeans Clustering algorithm. Secondly, the Congestion Evolution Index is determined using Rescaled Range Analysis of data mining. Finally, by taking a sequence pattern similarity measurement and applying condensed hierarchical clustering methods, the routine pattern is distinguished. Consequently, realtime outlier detection is realized by a distancebased outlier detection algorithm. This algorithm was successfully applied based on 11 days of fixedpoint data on the eastern segment of the Shanghai NorthSouth expressway, it is concluded that the outliers distributed in 12:10-13:20, 13:40-14:30 and 17:10-17:15 on September 30.

Key words: traffic engineering, traffic state evolution, time series, outlier detection, fixedpoint data

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