基于知识的交通拥堵疏导决策方法及系统研究
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摘要
如何应对城市现代化带来的交通拥堵问题,是交通管理者需要迫切解决的问题。本文以构筑智能化城市交通拥堵疏导决策支持系统为研究目的,着重应用数据驱动的决策支持方法,比较深入地研究了基于知识的城市交通拥堵疏导决策中的几个突出问题:
1.从智能决策分析的角度,系统地分析了城市交通拥堵的各种用于决策的属性,特别是其时空分布及发展趋势特性,提出了一种实用的交通拥堵时空分布判别算法以及发展趋势分析解释机制;接着提出了拥堵特征描述模型,从影响因素和分类的角度,研究了拥挤疏导的对策问题;最后,阐述了本文将基于知识的系统应用于该领域的目的及作用,并进行了拥堵疏导决策知识类型及内容的深入剖析。
2.提出了数据仓库在城市交通拥堵疏导决策支持系统中的应用及理论框架。探讨了基于数据仓库的城市交通拥堵疏导决策数据管理方法,按主题组织数据,以星型模型建模,提供有效的数据抽取和集成功能,经过加工的数据是面向决策的,从而为进行智能化决策提供了一个集成的公用数据平台。在此基础上,交通拥堵管理决策人员不仅能够有效整合多种异构数据源,获得对整个交通拥堵状态信息的集成视图,而且还为进一步数据挖掘提供了数据基础。
3.提出将粗糙集理论及方法应用于城市交通拥堵疏导决策分析中的知识获取问题。针对决策过程中的某些不确定性问题,着重研究了城市交通道路监测数据与交通拥堵程度之间的依赖关系,建立了进行交通状态模式识别的知识模型,给出了基于案例推理的交通报警处理系统中案例特征项权值确定算法,说明粗糙集是一种交通管理研究的理想动态工具。这一方面是对粗糙集应用领域的扩展,另一方面,也有效地解决了基于知识的城市交通拥堵疏导决策支持系统中知识获取的瓶颈问题。
4.提出利用数据仓库、范例推理和知识获取的理论及方法加以辅助决策分析,构建了基于知识的城市交通拥堵疏导决策支持系统体系结构。这种应用方案通过扩展传统的定量决策模型为知识决策模型,综合利用定性推理和定量计算的长处,一方面较好地解决了传统四库结构的决策支持系统中的数据基础和知识获取问题,降低了分析模型和算法的设计复杂性,使系统具有清晰的结构和较强的知识处理、更新能力;另一方面由于交通拥堵管理在实践中积累了丰富的经验,存在着大量的实际案例,所以范例推理很好利用了这一实际情况,避免了知识增加时知识库的完整性和一致性问题。
Aiming at constructing an intelligent decision support system with data driven decision support methods for urban traffic congestion management, several key issues are discussed in this dissertation.
At first, in order to make decision more effectively and sufficiently, the elemental characters and main causes of urban traffic congestion are analyzed. Especially, for its spatio-temporal peculiarity, a real time analytic method to model urban congestion’s spatio-temporal peculiarity and its trend is presented. And then a universal model describing the urban traffic congestion is brought forward. At same time, some essential relations between congestion types and its dispersing strategies are drawn out and a basic description of the decision problem, the decision process and some special characters of this process are presented. On the Basis of those analyzed before, the necessity of applying knowledge-based system in the urban traffic congestion dispersion decision process and what kinds of knowledge are necessary during the decision process are discussed.
Secondly, based on the analysis of the data involved in the urban traffic congestion management decision process, the data flow during making decisions to disperse urban traffic congestion and how to manage those data efficiently are studied. For the purpose of further decision and data mining, a data warehouse based data model is presented.
Later on, by importing rough set theory and method into the urban traffic congestion management decision process, the relations between the real time data drawn from the urban traffic monitoring system and the severity levels of the urban traffic congestion is studied, a knowledge-based model to deal with the uncertainty in urban traffic congestion pattern recognition is constructed and then a algorithm based on rough set theory to compute the weight of each aspect of a case which will be used while dealing with the traffic alarms through case-based reasoning method is presented.
And then, via extending the traditional quantitative decision model to knowledge based decision model, cooperating qualitative reasoning methods with quantitative computing method, integrating data warehouse technique, case based reasoning method and knowledge acquisition method into the congestion dispersing process, the idea of applying knowledge based decision support system to deal with urban traffic congestion is brought around, including the decision support system’s structure and its analytical model.
At last, the stepwise realization of the knowledge-based urban traffic congestion dispersing decision support system armed with rule based reasoning ,case based reasoning and knowledge acquisition is showed.
1.从智能决策分析的角度,系统地分析了城市交通拥堵的各种用于决策的属性,特别是其时空分布及发展趋势特性,提出了一种实用的交通拥堵时空分布判别算法以及发展趋势分析解释机制;接着提出了拥堵特征描述模型,从影响因素和分类的角度,研究了拥挤疏导的对策问题;最后,阐述了本文将基于知识的系统应用于该领域的目的及作用,并进行了拥堵疏导决策知识类型及内容的深入剖析。
2.提出了数据仓库在城市交通拥堵疏导决策支持系统中的应用及理论框架。探讨了基于数据仓库的城市交通拥堵疏导决策数据管理方法,按主题组织数据,以星型模型建模,提供有效的数据抽取和集成功能,经过加工的数据是面向决策的,从而为进行智能化决策提供了一个集成的公用数据平台。在此基础上,交通拥堵管理决策人员不仅能够有效整合多种异构数据源,获得对整个交通拥堵状态信息的集成视图,而且还为进一步数据挖掘提供了数据基础。
3.提出将粗糙集理论及方法应用于城市交通拥堵疏导决策分析中的知识获取问题。针对决策过程中的某些不确定性问题,着重研究了城市交通道路监测数据与交通拥堵程度之间的依赖关系,建立了进行交通状态模式识别的知识模型,给出了基于案例推理的交通报警处理系统中案例特征项权值确定算法,说明粗糙集是一种交通管理研究的理想动态工具。这一方面是对粗糙集应用领域的扩展,另一方面,也有效地解决了基于知识的城市交通拥堵疏导决策支持系统中知识获取的瓶颈问题。
4.提出利用数据仓库、范例推理和知识获取的理论及方法加以辅助决策分析,构建了基于知识的城市交通拥堵疏导决策支持系统体系结构。这种应用方案通过扩展传统的定量决策模型为知识决策模型,综合利用定性推理和定量计算的长处,一方面较好地解决了传统四库结构的决策支持系统中的数据基础和知识获取问题,降低了分析模型和算法的设计复杂性,使系统具有清晰的结构和较强的知识处理、更新能力;另一方面由于交通拥堵管理在实践中积累了丰富的经验,存在着大量的实际案例,所以范例推理很好利用了这一实际情况,避免了知识增加时知识库的完整性和一致性问题。
Aiming at constructing an intelligent decision support system with data driven decision support methods for urban traffic congestion management, several key issues are discussed in this dissertation.
At first, in order to make decision more effectively and sufficiently, the elemental characters and main causes of urban traffic congestion are analyzed. Especially, for its spatio-temporal peculiarity, a real time analytic method to model urban congestion’s spatio-temporal peculiarity and its trend is presented. And then a universal model describing the urban traffic congestion is brought forward. At same time, some essential relations between congestion types and its dispersing strategies are drawn out and a basic description of the decision problem, the decision process and some special characters of this process are presented. On the Basis of those analyzed before, the necessity of applying knowledge-based system in the urban traffic congestion dispersion decision process and what kinds of knowledge are necessary during the decision process are discussed.
Secondly, based on the analysis of the data involved in the urban traffic congestion management decision process, the data flow during making decisions to disperse urban traffic congestion and how to manage those data efficiently are studied. For the purpose of further decision and data mining, a data warehouse based data model is presented.
Later on, by importing rough set theory and method into the urban traffic congestion management decision process, the relations between the real time data drawn from the urban traffic monitoring system and the severity levels of the urban traffic congestion is studied, a knowledge-based model to deal with the uncertainty in urban traffic congestion pattern recognition is constructed and then a algorithm based on rough set theory to compute the weight of each aspect of a case which will be used while dealing with the traffic alarms through case-based reasoning method is presented.
And then, via extending the traditional quantitative decision model to knowledge based decision model, cooperating qualitative reasoning methods with quantitative computing method, integrating data warehouse technique, case based reasoning method and knowledge acquisition method into the congestion dispersing process, the idea of applying knowledge based decision support system to deal with urban traffic congestion is brought around, including the decision support system’s structure and its analytical model.
At last, the stepwise realization of the knowledge-based urban traffic congestion dispersing decision support system armed with rule based reasoning ,case based reasoning and knowledge acquisition is showed.
引文
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