区域交通信息集成与运输需求预测研究
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摘要
交通运输对区域经济的形成发展、地区生产力的合理布局具有十分重大的作用,它既将国民经济所有部门联系起来,将生产领域和消费领域联系起来,是发展工农业生产和经济活动的重要保障,对区域交通正确规划与决策是保证经济环境可持续发展的必要条件。本文对区域交通规划管理中的几个关键问题展开研究,主要研究内容包括：
(1)区域交通多源信息的整合与集成及概念体系的构建研究。针对区域交通信息化中的多源异构数据整合与集成问题,分别从数据应用层和语义层两个角度考虑信息的集成,针对数据层和应用层面的整合,提出建立基于数据仓库的交通信息集成平台,解决交通信息化中需要数据挖掘和信息增值的高层需要；针对方法层和语义层面的集成,提出将中间件技术应用于区域交通系统数据资源的整合中,采用XML技术利用数据的抽取转换和装载技术实现行业复杂多源异构数据的整合,实现区域交通系统内各应用系统中异构数据的整合功能,提供相互间的数据接口,建立通用的数据标准和规范。针对能有效解决交通信息系统间语义集成的映射问题,完成基于领域本体的交通信息集成与发布,并提出了交通领域本体的语义整合机制。
(2)区域交通需求关联因素分析研究。对交通需求关联因素和因果关系进行研究,往往要求事先设定这些属性的范围,为了减少人为因素的主观影响,提高分析的客观性,在构建区域交通需求影响因素指标体系的基础上,提出采用基于自组织数据挖掘的GMDH方法,并借鉴Granger因果关系检验模型提出基于GMDH的因果关系检验模型。结合作者在实际项目中采用的关联因素分析模型和灰色关联度模型,以云南省为例研究了云南省交通需求关联因素分析问题,不仅为地区的交通规划与决策提供重要参考,得到的结论同时也是后面研究内容的基础。
(3)区域交通需求中长期预测研究。由于交通运输需求的产生受到多种因素影响,在不同的通道中,其规律存在较大差异,因此需要将区域交通需求预测的研究对象根据不同时间粒度分别进行中长期预测研究和波动性研究。本文对区域交通需求中长期预测的研究首先采用传统预测方法多元线性回归作为参考,针对灰色GM(1,1)参数估计方法造成的精度不足采用改进的AGM(1,1)模型,同时灰色模型残差分布是一种Markov链,提出改进的灰色Markov修正模型,经过实例验证改进的灰色Markov模型可以根据历史规律和时序的内在联系,给出对未来波动趋势的预测,既考虑了从时间序列中挖掘数据的演变规律,又通过状态转移概率矩阵的变换提取数据的随机响应,将时间序列数据固有的两种性质有机结合起来,更加客观地反应需求的波动性的可信性。
(4)区域交通运输需求波动性研究。由于运输需求存在的波动性和周期性,而不是单纯上升或下降的趋势,为减少建模输入过程中人为主观的干预并增加预测精度,提出了基于GMDH思想的多项式神经网络PNN方法,解决了传统时间序列方法无法考虑交通系统内多种影响因素的缺点,根据多输入的影响因素自组织生成多项式神经网络,通过训练和测试,可以得到具有较高预测精度的运行结果。
Transportation play an important part in the formation and development of regional economy and proper redistribution of production capability, which connect all sectors of national economy and link the production and consumption together. Transportation is the important insurance for development of production of industry and agriculture and economic activity. It is required for the sustainable development of regional economic environment that transportation governor make right transportation planning and decision-making. In this dissertation we studied several key issues in the regional transportation planning and management, and main contents included as follows.
Ⅰ. Integration of multi-source regional transport information and construction research of conceptual system. Aiming at the problem of heterogeneity in integration of multi-sources data in area transportation informatization, this paper discussed the integration from two angles that is from data application layer and from semantic layer, and proposed the construction of transportation information platform based on data warehouse according to integration in data layer and application layer to meet the high-level needs of data mining and information value-added. Middleware technology can be applied in regional transportation system's data integration, and XML technology is adopted to realize complex multi-source heterogeneous data integration within the department and different application information systems by making data extraction, transformation and loading, as well as to provide mutual data interface to establish the common data standards and specifications. To solve mapping problem effectively in traffic information systems'semantic integration domain ontology is raised and published and semantic integration mechanism of transportation domain ontology was proposed.
Ⅱ. Study on analysis of regional transport demand association factors. To study the association factors and causality of transport demand, a pre-set range of properties are often required. In order to reduce the subjective effects of human factors and to improve the objectivity of the analysis, the GMDH method based on self-organizing data mining was proposed on the building of regional transport indicator system and using the Granger causality test model for reference to raise a GMDH-based causality model. Combined with association factor analysis model and grey correlation used in the actual project in Yunnan Province that not only for the region's transportation planning and decision-making but also providing an important reference for the follow-up.
Ⅲ. The mid long term forecasts on traffic demand. As transportation demand generated by a variety of factors and in different measure its operation law is quite different, so it is necessary to forecast demand by conducting in different time granularity that are long-term prediction and the volatility analysis. The mid long term regional transport demand forecast study begin with traditional methods of multiple linear regression as a reference as classic GM (1,1) model's flaw in parameter estimation accuracy and improved AGM (1,1) model was proposed, while grey model residuals is a Markov chain, an improved grey-Markov correction model is verified through improved grey Markov model based on historical patterns and internal relations of time series, given the forecast of future volatility of the trend, not only considered from the time series data mining the evolution of the law, but also through the state transition probability matrix of the random response transform to extract the data, the time series data is inherently combine the two properties, a more objective response to fluctuations in demand of credibility.
Ⅳ. Volatility study on regional transport demand. Since the transport demand does not increase or decrease monotonically but the volatility and periodicity are always existed. To reduce subjective intervention in the input process of modeling and improve prediction accuracy, polynomial neural network PNN based on GMDH thinking is proposed, the problem of the traditional time series analysis methods not considering the inner influencing factors can be solved, the shortcomings of a variety of factors, according to multi-input of the influencing factors polynomial neural network was generated by self-organized through training and testing and the result with higher accuracy can be get.
(1)区域交通多源信息的整合与集成及概念体系的构建研究。针对区域交通信息化中的多源异构数据整合与集成问题,分别从数据应用层和语义层两个角度考虑信息的集成,针对数据层和应用层面的整合,提出建立基于数据仓库的交通信息集成平台,解决交通信息化中需要数据挖掘和信息增值的高层需要；针对方法层和语义层面的集成,提出将中间件技术应用于区域交通系统数据资源的整合中,采用XML技术利用数据的抽取转换和装载技术实现行业复杂多源异构数据的整合,实现区域交通系统内各应用系统中异构数据的整合功能,提供相互间的数据接口,建立通用的数据标准和规范。针对能有效解决交通信息系统间语义集成的映射问题,完成基于领域本体的交通信息集成与发布,并提出了交通领域本体的语义整合机制。
(2)区域交通需求关联因素分析研究。对交通需求关联因素和因果关系进行研究,往往要求事先设定这些属性的范围,为了减少人为因素的主观影响,提高分析的客观性,在构建区域交通需求影响因素指标体系的基础上,提出采用基于自组织数据挖掘的GMDH方法,并借鉴Granger因果关系检验模型提出基于GMDH的因果关系检验模型。结合作者在实际项目中采用的关联因素分析模型和灰色关联度模型,以云南省为例研究了云南省交通需求关联因素分析问题,不仅为地区的交通规划与决策提供重要参考,得到的结论同时也是后面研究内容的基础。
(3)区域交通需求中长期预测研究。由于交通运输需求的产生受到多种因素影响,在不同的通道中,其规律存在较大差异,因此需要将区域交通需求预测的研究对象根据不同时间粒度分别进行中长期预测研究和波动性研究。本文对区域交通需求中长期预测的研究首先采用传统预测方法多元线性回归作为参考,针对灰色GM(1,1)参数估计方法造成的精度不足采用改进的AGM(1,1)模型,同时灰色模型残差分布是一种Markov链,提出改进的灰色Markov修正模型,经过实例验证改进的灰色Markov模型可以根据历史规律和时序的内在联系,给出对未来波动趋势的预测,既考虑了从时间序列中挖掘数据的演变规律,又通过状态转移概率矩阵的变换提取数据的随机响应,将时间序列数据固有的两种性质有机结合起来,更加客观地反应需求的波动性的可信性。
(4)区域交通运输需求波动性研究。由于运输需求存在的波动性和周期性,而不是单纯上升或下降的趋势,为减少建模输入过程中人为主观的干预并增加预测精度,提出了基于GMDH思想的多项式神经网络PNN方法,解决了传统时间序列方法无法考虑交通系统内多种影响因素的缺点,根据多输入的影响因素自组织生成多项式神经网络,通过训练和测试,可以得到具有较高预测精度的运行结果。
Transportation play an important part in the formation and development of regional economy and proper redistribution of production capability, which connect all sectors of national economy and link the production and consumption together. Transportation is the important insurance for development of production of industry and agriculture and economic activity. It is required for the sustainable development of regional economic environment that transportation governor make right transportation planning and decision-making. In this dissertation we studied several key issues in the regional transportation planning and management, and main contents included as follows.
Ⅰ. Integration of multi-source regional transport information and construction research of conceptual system. Aiming at the problem of heterogeneity in integration of multi-sources data in area transportation informatization, this paper discussed the integration from two angles that is from data application layer and from semantic layer, and proposed the construction of transportation information platform based on data warehouse according to integration in data layer and application layer to meet the high-level needs of data mining and information value-added. Middleware technology can be applied in regional transportation system's data integration, and XML technology is adopted to realize complex multi-source heterogeneous data integration within the department and different application information systems by making data extraction, transformation and loading, as well as to provide mutual data interface to establish the common data standards and specifications. To solve mapping problem effectively in traffic information systems'semantic integration domain ontology is raised and published and semantic integration mechanism of transportation domain ontology was proposed.
Ⅱ. Study on analysis of regional transport demand association factors. To study the association factors and causality of transport demand, a pre-set range of properties are often required. In order to reduce the subjective effects of human factors and to improve the objectivity of the analysis, the GMDH method based on self-organizing data mining was proposed on the building of regional transport indicator system and using the Granger causality test model for reference to raise a GMDH-based causality model. Combined with association factor analysis model and grey correlation used in the actual project in Yunnan Province that not only for the region's transportation planning and decision-making but also providing an important reference for the follow-up.
Ⅲ. The mid long term forecasts on traffic demand. As transportation demand generated by a variety of factors and in different measure its operation law is quite different, so it is necessary to forecast demand by conducting in different time granularity that are long-term prediction and the volatility analysis. The mid long term regional transport demand forecast study begin with traditional methods of multiple linear regression as a reference as classic GM (1,1) model's flaw in parameter estimation accuracy and improved AGM (1,1) model was proposed, while grey model residuals is a Markov chain, an improved grey-Markov correction model is verified through improved grey Markov model based on historical patterns and internal relations of time series, given the forecast of future volatility of the trend, not only considered from the time series data mining the evolution of the law, but also through the state transition probability matrix of the random response transform to extract the data, the time series data is inherently combine the two properties, a more objective response to fluctuations in demand of credibility.
Ⅳ. Volatility study on regional transport demand. Since the transport demand does not increase or decrease monotonically but the volatility and periodicity are always existed. To reduce subjective intervention in the input process of modeling and improve prediction accuracy, polynomial neural network PNN based on GMDH thinking is proposed, the problem of the traditional time series analysis methods not considering the inner influencing factors can be solved, the shortcomings of a variety of factors, according to multi-input of the influencing factors polynomial neural network was generated by self-organized through training and testing and the result with higher accuracy can be get.
引文
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