[1]于泉,刘洋,郭骁伟.基于路口相关性的交通流量修复研究[J].深圳大学学报理工版,2019,36(No.3(221-346)):304-309.[doi:10.3724/SP.J.1249.2019.03304]
 YU Quan,LIU Yang,and GUO Xiaowei.Restoration of traffic flow data based on intersection correlation[J].Journal of Shenzhen University Science and Engineering,2019,36(No.3(221-346)):304-309.[doi:10.3724/SP.J.1249.2019.03304]
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基于路口相关性的交通流量修复研究()
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《深圳大学学报理工版》[ISSN:1000-2618/CN:44-1401/N]

卷:
第36卷
期数:
2019年No.3(221-346)
页码:
304-309
栏目:
【交通物流】
出版日期:
2019-05-20

文章信息/Info

Title:
Restoration of traffic flow data based on intersection correlation
文章编号:
201903012
作者:
于泉1刘洋1郭骁伟2
1) 北京工业大学北京市交通工程重点实验室,北京 100124
2) 中国公路工程咨询集团有限公司,北京 100124
Author(s):
YU Quan1 LIU Yang1 and GUO Xiaowei2
1) Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing 100124, P.R.China
2) China Highway Engineering Consulting Corporation, Beijing 100124, P.R.China
关键词:
交通运输工程绿波协调路口相关性主成分分析径向基函数神经网络交通流量修复
Keywords:
transport engineering green wave coordinate intersection correlation principal component analysis (PCA) radical basis function neural network traffic flow restoration
分类号:
U491;TP308
DOI:
10.3724/SP.J.1249.2019.03304
文献标志码:
A
摘要:
为实现对交通流异常数据的有效修复,根据流量-占有率函数模型,利用主成分分析法对绿波协调控制交叉口群中各路口的占有率参数进行相关性分析,间接得到各路口流量相关性的大小,构建相关路口集;根据相关路口集的历史数据,分别利用流量-占有率模型、径向基函数神经网络模型以及基于两种方法的组合模型,对目标路口的缺失交通流数据进行修复;通过实例分析验证模型性能.结果表明,与单独流量-占有率模型或径向基函数神经网络模型相比,组合模型可更精确地修复交通数据,在实际数据验证中表现出更好的适应性.
Abstract:
In order to restore the fault data of traffic flow effectively, according to the flow-occupancy function model, we use the principal component analysis (PCA) method to analyze the correlation coefficient of occupancy of each intersection in signalized intersections under green wave coordinated control, and obtain the correlation of traffic flow data at each intersection indirectly. Finally, we construct the relevant intersection set. According to the historical data of the relevant intersection set, we restore the missing traffic flow data at the target intersection by using the traffic-occupancy model, the radical basis function (RBF) neural network model, and the combination model based on the two models, respectively. The performances of the three models are verified on the basis of case analysis. The results show that the combined model can be used to recover traffic data more accurately than the other two models and shows better adaptability in actual data validation.

参考文献/References:

[1] 王英会. 高速公路交通流异常数据识别及修复方法研究[D]. 北京:北京交通大学,2015.
WANG Yinghui. Research on identification and recovery method for abnormal highway traffic flow data[D]. Beijing: Beijing Jiaotong University, 2015.(in Chinese)
[2] 姜桂艳. 道路交通状态判别技术与应用[M]. 北京:人民交通出版社,2004:9-14.
JIANG Guiyan. Technologies and applications of the identification of road traffic conditions[M]. Beijing: China Communications Press, 2004: 9-14.(in Chinese)
[3] 王薇,程泽阳,刘梦依,等. 基于时空相关性的交通流故障数据修复方法[J]. 浙江大学学报工学版,2017,5(9):1727-1734.
WANG Wei, CHENG Zeyang, LIU Mengyi, et al. Repair method for traffic flow fault data based on spatial-temporal correlation[J]. Journal of Zhejiang University Engineering Science, 2017, 5(9): 1727-1734.(in Chinese)
[4] 李勇伶. 服务于城市交通控制系统的交通数据处理技术研究[D]. 西安:长安大学,2008.
LI Yongling. Research on processing methods of traffic data for traffic control system[D]. Xi’an: Chang’an University, 2008.(in Chinese)
[5] 曲腾娇. 多源数据融合的城市道路交通状态实时判别方法研究[D]. 青岛:青岛理工大学,2016.
QU Tengjiao. Research on real time identification method of urban road traffic state based on multi-source data[D]. Qingdao: Qingdao University of Technology, 2016.(in Chinese)
[6] 任福田,荣建. 新编交通工程学导论[M]. 北京: 中国建筑工业出版社,2011: 93-95.
REN Futian, RONG Jian. New introduction to traffic engineering[M]. Beijing: China Architecture & Building Industry Press, 2011: 93-95.(in Chinese)
[7] 胡兴华,朱晓宁,隆冰. 绿波协调信号交叉口群的延误计算方法研究[J]. 交通运输系统工程与信息,2015,15(5):60-66.
HU Xinghua, ZHU Xiaoning, LONG Bing. A delay calculation method of signalized intersections under green wave coordinated control[J]. Journal of Transportation Systems Engineering and Information Technology, 2015, 15(5): 60-66.(in Chinese)
[8] 董菁,张毅,张佐,等. 基于主成分分析法的城市交通路口相关性分析[J]. 西南交通大学学报, 2003,38(6): 619-622.
DONG Jing, ZHANG Yi, ZHANG Zuo, et al. Principal component analysis of dependency of urban intersections[J]. Journal of Southwest Jiaotong University, 2003, 38(6): 619-622.(in Chinese)
[9] 吴国富. 实用数据分析方法[M]. 北京:中国统计出版社,1992:44-49.
WU Guofu. Practical data analysis methods[M]. Beijing: China Statistics Press, 1992: 44-49.(in Chinese)
[10] 崔立成. 基于多断面信息的城市道路网交通流预测方法研究[D]. 大连:大连海事大学,2012.
CUI Licheng. Research on methods of traffic flow predicting of the urban road network based on the multi cross-section information[D]. Dalian: Dalian Maritime University: 2012.(in Chinese)

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备注/Memo

备注/Memo:
Received:2018-01-28;Accepted:2018-04-11
Foundation:Natural Science Foundation of Beijing(8172007)
Corresponding author:Associate professor YU Quan. E-mail: yuquan@bjut.edu.cn
Citation:YU Quan, LIU Yang, GUO Xiaowei. Restoration of traffic flow data based on intersection correlation[J]. Journal of Shenzhen University Science and Engineering, 2019, 36(3): 304-309.(in Chinese)
基金项目:北京市自然科学基金资助项目(8172007)
作者简介:于泉(1976—), 北京工业大学副教授、博士. 研究方向: 交通控制及智能交通. E-mail:yuquan@bjut.edu.cn
引文:于泉,刘洋,郭骁伟.基于路口相关性的交通流量修复研究[J]. 深圳大学学报理工版,2019,36(3):304-309.
更新日期/Last Update: 2019-04-22