基于投影寻踪动态聚类的快速路交通状态判别

邴其春; 龚勃文; 杨兆升; 林赐云; 曲鑫

doi:10.3969/j.issn.0258-2724.2015.06.027

基于投影寻踪动态聚类的快速路交通状态判别

doi: 10.3969/j.issn.0258-2724.2015.06.027

邴其春¹,
龚勃文^1,2,3, ,,
杨兆升^1,2,3,
林赐云^1,2,3,
曲鑫¹

基金项目:

国家科技支撑计划资助项目(2014BAG03B03)

国家自然科学基金青年基金资助项目(51308248,51408257)

山东省省管企业科技创新项目(20122150251-1)

详细信息

作者简介:
邴其春(1989-),男,博士研究生,研究方向为智能交通信息处理与应用,E-mail:bingqc13@mails.jlu.edu.cn

通讯作者:
龚勃文(1982-),女,讲师,博士,研究方向为智能交通关键理论与技术,E-mail:gongbowen@jlu.edu.cn

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出版历程
- 收稿日期: 2015-01-09
- 刊出日期: 2015-12-25

Traffic State Identification for Urban Expressway Based on Projection Pursuit Dynamic Cluster Model

BING Qichun¹,
GONG Bowen^{1,2,3
, ,},
YANG Zhaosheng^1,2,3,
LIN Ciyun^1,2,3,
QU Xin¹

摘要

摘要: 为了提高快速路交通运行状态的判别精度,利用地点交通参数与交通状态之间的映射关系,提出了基于投影寻踪动态聚类模型的快速路交通状态判别方法.该方法综合投影寻踪技术和动态聚类方法构造投影指标函数,采用混合蛙跳算法优化投影指标函数的投影方向获得最佳投影方向,并利用仿真数据标定了交通状态判别阈值.结合仿真数据和实测数据进行了实验验证和对比分析.实验结果表明,投影寻踪动态聚类模型能够有效提高快速路交通状态判别精度,平均判别率为97.01%,平均误判率为0.86%,平均判别精度分别比BP神经网络模型和模糊C均值聚类模型方法提高了8.9%和4.5%.
- 交通状态判别 /
- 投影寻踪动态聚类 /
- 混合蛙跳算法 /
- 城市快速路
Abstract: In order to improve the accuracy of traffic state identification for urban expressway based on the spot traffic parameters, a traffic state identification method based on projection pursuit dynamic cluster model was proposed using the mapping relationship between spot traffic parameters and traffic state. First, the projection index function was constructed by combined use of the projection pursuit technology and the dynamic cluster method, and the shuffled frog leaping algorithm was used to optimize the projection direction. Then, the traffic state identification threshold was determined using simulation data. Finally, validation and comparative analysis were carried out using both the simulated data and measured data. Experimental results indicate that the proposed model can effectively improve the accuracy of traffic state identification. The average identification rate is 97.01% and the average false identification rate is 0.86%. The average identification accuracy of proposed method is 8.9% and 4.5% higher than the BP neural network model and the fuzzy C-means clustering model, respectively.
- traffic state identification /
- projection pursuit dynamic cluster /
- shuffled frog leaping algorithm /
- urban expressway

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