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基于影响模型的短时交通流预测方法
Alternative TitleShort-term Traffic Flow Forecasting Method Based on Influence Model
丁栋; 朱云龙; 库涛; 王亮
Department信息服务与智能控制技术研究室
Source Publication计算机工程
ISSN1000-3428
2012
Volume38Issue:10Pages:164-167
Indexed ByCSCD
CSCD IDCSCD:4542424
Contribution Rank1
Funding Organization国家自然科学基金资助项目“面向感应网络的移动数据挖掘及复杂行为模式分析研究”(61003208),国家自然科学基金资助项目“基于细菌行为模式的复杂系统建模与优化方法研究”(61174164),国家自然科学基金资助项目“基于生物行为的RFID系统优化模型与算法研究”(61105067)
Keyword影响模型 交通流预测 隐马尔科夫过程 神经网络 智能交通 交通网络
Abstract根据复杂交通网络中多个节点之间交通流相互影响的特性,提出一种基于影响模型的短时交通流预测方法。分析交通网络中交通流预测的难点,引入随机过程中影响模型的理论对其进行建模。将每个节点的交通流处理为一个隐马尔科夫过程,整个网络由多个相互交互的隐马尔科夫过程组成,采用EM算法对模型参数进行训练。实验结果表明,该方法具有较高的预测精度,可较好地显示交通网络中多个节点之间交通流的交互规律以及动态演化规律。
Other AbstractAiming at the characteristic that traffic flow of different nodes influences each other in complex traffic network,this paper presents a novel short-term traffic flow forecasting method based on influence model.Based on the analysis of the difficulties of the traffic flow forecasting problem,this method introduces the theory of influence model to model.As the traffic flow of each node is considered as a hidden Markov process,the whole traffic network is composed of many interactive hidden Markov processes.This method uses the EM algorithm to learn parameters in the forecasting model.Experimental results show that the method not only has higher prediction accuracy,but also presents the interactive influence and dynamic evolution of the traffic flow of different nodes in traffic network. 
Language中文
Citation statistics
Cited Times:1[CSCD]   [CSCD Record]
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/9955
Collection信息服务与智能控制技术研究室
Affiliation1.中国科学院沈阳自动化研究所工业信息学实验室
2.中国科学院研究生院
Recommended Citation
GB/T 7714
丁栋,朱云龙,库涛,等. 基于影响模型的短时交通流预测方法[J]. 计算机工程,2012,38(10):164-167.
APA 丁栋,朱云龙,库涛,&王亮.(2012).基于影响模型的短时交通流预测方法.计算机工程,38(10),164-167.
MLA 丁栋,et al."基于影响模型的短时交通流预测方法".计算机工程 38.10(2012):164-167.
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