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Alternative TitleResearch on Mobile Reality Mining and the Application in Intelligent Transportation
Thesis Advisor朱云龙
Keyword感应网络 影响模型 行为分析 交通流预测
Call NumberTP311.13/D58/2012
Degree Discipline机械电子工程
Degree Name硕士
Degree Grantor中国科学院沈阳自动化研究所
Place of Conferral沈阳
Abstract各种感应技术的发展使人们处在一个感应网络(sense networks)环境中,随时随地都可能会遗留下一些数字信息,而这些信息当中可能蕴含了大量有价值的人类行为模式。采用移动数据挖掘的手段充分利用这些信息,观测人类行为模式的内在规律,建立认知结构,更好地服务于人类社会是当前信息科学领域的重大研究问题。本论文以挖掘移动数据所蕴含的规律为研究目标,以城市交通为研究背景,探索移动行为建模和行为分析的新方法,具有重要的理论意义和实际应用价值。 本文给出了感应网络的研究框架,从内容体系、方法体系和应用体系三个方面对感应网络做了总结,指出了当前研究存在的问题,使人们对感应网络有一个系统、全面的认识。 影响模型是对多个相互影响的随机过程进行建模的理论,为了进行移动行为建模和移动行为分析,本文在阐述影响模型基础理论的基础上,构建了感应网络下个体和群体的影响模型,总结了影响模型建模的一般过程以及适用问题的一般特征。 准确的交通流预测是实现智能交通的关键,本文描述了交通流问题,分析了交通流的特性,对多节点之间交通流相互影响的特性进行了定性和定量的分析,证明了交节点之间交通流是相互影响的,从而提出了交通流预测方法的要求。 在以上研究的基础上,提出了基于影响模型的短时交通流预测方法,构建了城市交通网络的影响模型,用实际的交通流数据对模型参数进行了训练,实验表明本文所提出的方法不仅具有更高的预测精度,而且可以显示交通网络中交通流的相互交互规律和动态演化规律。
Other AbstractThe human are living in a sense network environment due to the development of a variety of sensing technologies. A number of digital information which contains lots of valuable human behavior patterns may be left behind anywhere and at anytime. How to take advantage of the information has been a major research field in information science with the purpose of observing inherent laws of human behavior, establishing the cognitive structure and serving the human society better. This paper explores new methods for behavior modeling and behavior analysis to mine the laws hidden in mobile data in the background of city transportation, thus not only has important theoretical value, but also has major operation significance. This paper presents a research framework of sense network from three aspects of contents, methods and application and points out the problems of the current research. The research framework here can give us a comprehensive understand of sense network. Influence model is the theory for several interactive stochastic processes. After explaining the basic theory of influence model, this paper constructs the influence model for singles and swarms in sense network which can be used to behavior modeling and behavior analysis, then summarize the general steps for modeling and the characteristics of problems which can be solved by this theory. Forecasting traffic flow in a high accuracy is the key to intelligent transportation. This paper describes the traffic flow forecasting problem, analyzes the characteristics of traffic flow with an emphasis on the qualitative and quantitative proving of influence between nodes in transportation network, and gives the requirements of the methods for traffic flow forecasting. According to 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. 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 proposed in this paper 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.
Contribution Rank1
Document Type学位论文
Recommended Citation
GB/T 7714
丁栋. 感应网络下的移动现实挖掘及其在智能交通中的应用[D]. 沈阳. 中国科学院沈阳自动化研究所,2012.
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