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

计量
- 文章访问数: 686
- HTML全文浏览量: 123
- PDF下载量: 379
- 被引次数: 0
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

HTML全文

参考文献(17)

姜桂艳. 道路交通状态判别技术[M]. 北京:人民交通出版社,2004: 66-67.

BHASKAR A, TSUBOTA T, KIEU L M, et al. Urban traffic state estimation: fusing point and zone based data[J]. Transportation Record Part C: Emerging Technologies, 2014, 48: 120-142.

XIA Jingxin, CHEN Mei. A nested clustering technique for freeway operating condition classification[J]. Journal of Computed-Aided Civil and Infrastructure Engineering, 2007, 22(6): 430-437.

姜桂艳,冮龙晖,王江锋. 城市快速路交通拥挤识别方法[J]. 交通运输工程学报,2006,6(3): 87-91. JIANG Guiyan, GANG Longhui, WANG Jiangfeng. Traffic congestion identification method of urban expressway[J]. Journal of Traffic and Transportation Engineering, 2006, 6(3): 87-91.

殷俊杰,丁宏飞,薄雾,等. 基于模糊聚类的城市快速路交通流状态划分[J]. 重庆交通大学学报:自然科学版,2013,32(4): 652-655. YIN Junjie, DING Hongfei, BO Wu, et al. Classification of urban expressway traffic flow situation based on fuzzy clustering[J]. Journal of Chongqing Jiaotong University: Natural Science, 2013, 32(4): 652-655.

巫威眺,靳文舟,林培群. 基于BP神经网络的道路交通状态判别方法研究[J]. 交通信息与安全,2011,29(4): 71-74. WU Weitiao, JIN Wenzhou, LIN Peiqun. The method of traffic state identification based on BP neural network[J]. Journal of Transportation Information and Safety, 2011, 29(4): 71-74.

张亮亮,贾元华,牛忠海,等. 交通状态划分的参数权重聚类方法研究[J]. 交通运输系统工程与信息,2014,14(6): 147-151. ZHANG Liangliang, JIA Yuanhua, NIU Zhonghai, et al. Traffic state classification based on parameter weighting and clustering method[J]. Journal of Transportation Systems Engineering and Information Technology, 2014, 14(6): 147-151.

汪丽娜,陈晓宏,李粤安,等. 混合蛙跳算法和投影寻踪模型的洪水分类研究[J]. 水电能源科学,2009,27(2): 62-64. WANG Lina, CHEN Xiaohong, LI Yuean, et al. Study on flood classification based on shuffled frog leaping algorithm and projection pursuit model[J]. Water Resources and Power, 2009, 27(2): 62-64.

倪长健,王顺久,崔鹏. 投影寻踪动态聚类模型及其在地下水分类中的应用[J]. 四川大学学报:工程科学版,2006,38(6): 29-33. NI Changjian, WANG Shunjiu, CUI Peng. Projection pursuit dynamic cluster model and its application in groundwater classification[J]. Journal of Sichuan University: Engineering Science Edition, 2006, 38(6): 29-33.

王顺久,倪长健. 投影寻踪动态聚类模型及其应用[J]. 哈尔滨工业大学学报,2009,41(1): 178-180. WANG Shunjiu, NI Changjian. A projection pursuit dynamic cluster model and its application[J]. Journal of Harbin Institute of Technology, 2009, 41(1): 178-180.

倪长健,崔鹏. 投影寻踪动态聚类模型[J]. 系统工程学报,2007,22(6): 634-638. NI Changjian, CUI Peng. Projection pursuit dynamic cluster model[J]. Journal of System Engineering, 2007, 22(6): 634-638.

任若恩,王惠文. 多元统计数据分析:理论、方法、实例[M]. 北京:国防工业出版社,1999: 76-80.

付强,赵小勇. 投影寻踪模型原理及其应用[M]. 北京:科学出版社,2006: 144-145.

EUSUFF M, LANSEY K, PASHA F. Shuffled frog-leaping algorithm: a memetic meta-heuristic for discrete optimization[J]. Engineering Optimization, 2006, 38(2): 129-154.

马平莉. 混合蛙跳算法研究[D]. 西安:西安电子科技大学,2013.

姜桂艳,李继伟,张春勤. 城市主干道拥挤路段基于地点交通参数的行程速度估计[J]. 吉林大学学报:工学版,2010,40(5): 1023-1028. JIANG Guiyan, LI Jiwei, ZHANG Chunqin. Travel speed estimation for congested arterial road segment based on spot traffic parameters[J]. Journal of Jilin University: Engineering and Technology Edition, 2010,40(5): 1023-1028.

冮龙晖. 城市道路交通状态判别及拥挤扩散范围估计方法研究[D]. 长春:吉林大学,2007.

施引文献

附加材料(0)

访问统计