城市公共停车管理的时空模型研究及应用
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摘要
城市公共停车管理是城市交通管理中的涉及市民生活的突出问题。如何采用新的技术手段来提升停车管理的效率,以缓解“拥堵”及“停车难”是当前研究城市管理工作的热点问题。国内外针对时空数据模型方面的研究已经有很多的理论研究及成果,但在停车管理领域的时空模型研究目前还没有涉及。本论文将结合停车管理、时态GIS和时空数据模型进行研究。一方面,从停车管理这一业务领域的角度出发,研究停车管理的时空数据模型,为专项业务领域的时空数据模型研究提供理论参考。另一方面,进一步研究停车管理时空特征内容的分析方法,提出了基于马尔可夫预测分析方法的空闲停车位预测分析模型和基于灰熵理论的最优泊位选择模型,并在杭州市停车管理实践中进行应用,力图解决停车管理时空模型实际应用有效性的问题。
本文研究的主要内容如下：
1、提出停车管理的时空数据模型
基于停车管理的特点,进行停车管理时空要素的建模研究,提出停车管理的四大类时空要素主题,即停放要素主题、通道要素主题、设施要素主题、车辆要素主题,分析要素类的属性关联特征并针对要素类进行建模。
2、提出停车管理时空特征内容的分析方法
在停车管理时空数据模型的基础上,进一步研究分析停车管理的时空特征,包括车辆停放特征参数的计算与分析、停车现场管理的效率分析、停车点空间布局优化分析、停车数据的特征时段分析、停车人的停车习惯分析。
3、提出基于马尔可夫的空闲车位预测和泊车选择模型
分析了泊车流程和影响泊车选择的主要因素,在泊车行为特征分析的基础上,引入马尔可夫预测分析法,提出了空闲停车位预测分析模型,从而能够提高停车管理效率、优化停车资源配置和提高停车服务质量；引入灰熵理论,提出了最优泊位选择模型,从而在实时获取并较准确预测停车点空闲车位信息基础上能够选择性泊车
4、停车管理时空模型的实际应用检验
结合杭州市的停车管理这一实际业务应用,将所研究的停车管理时空数据模型、空闲停车位预测分析模型和泊车选择模型成果进行应用实现的检验。
研究结果表明,本文采用从业务领域角度来进行时空数据模型的研究方法具有可行性,可以有效地解决目前时空间数据模型研究成果实用性较差的问题；另一方面,本研究提出了停车管理的时空数据模型、停车管理时空特征内容的分析方法以及基于马尔可夫预测分析方法的空闲停车位预测分析模型和基于灰熵理论的最优泊位选择模型,该项研究将为停车管理时空模型的理论研究和进一步的模型扩展研究提供理论参考和应用参考。
Urban public parking management, a component of city traffic control, is a headache lingering around the daily life of city residents. How to alleviate traffic congestion by adopting new technology and improving the efficiency of parking management is a hot spot issue of city management at present. Although there are a lot of previous researches on this area both at home and abroad, none of those deals with the spatial-temporal model of parking management. This paper conducts research on two aspects:On the one hand, it studies the spatiotemporal data model of parking management from the perspective of the business area of parking management, by which the theoretical reference for practice is established. On the other hand, it explores the analytical method for the spatiotemporal characteristics of parking management, and puts forward the predictive analysis model for the spare space available in parking lots based on Markov forecasting procedure and the optimal model of parking choice based on grey entropy theory. All these research findings are to be applied to the practical parking management in Hangzhou city, and hopefully resolve the problems related to the implementing efficiency of spatiotemporal data model of parking management.
The main content of this paper is as follows:
1) The Spatiotemporal Data Model of Parking Management
Based on the characteristics of parking management, the research on modeling of spatiotemporal factors of parking management is conducted with 4 kinds of spatiotemporal factor subjects, namely the factor subjects of parking, passageways, facilities and vehicles. The related characteristic properties of factors are analyzed, and the model of the factors set up.
2) The Analytical Method for Spatiotemporal Characteristics of Parking Management
In accordance with the spatiotemporal data model of parking management, a further analysis of spatiotemporal characteristics of parking management is carried out. It involves the calculation and analysis of the characteristic parameter of parking vehicles, the analysis of management efficiency of parking lots, optimization of the planning of parking spaces, distinct time period of parking data and the parking habit of the people.
3) The Prediction Model of Spare Parking Space Based on Markov Forecasting Procedure and Optimal Model of Parking Choice
Parking process and main factors affecting parking choice are analyzed. The method of Markov forecasting analysis is introduced on the basis of feature analysis of parking habits. The model of predictive analysis of spare parking space is proposed so as to enhance the efficiency of parking management, optimize the resource allocation of parking spaces, and improve the quality of parking service. Optimal model of parking choice is presented with the introduction of grey entropy theory, which enables drivers to park vehicles selectively by acquiring accurate predictive information about spare spaces available in parking lot.
4) The Examination of Spatiotemporal Data Model of Parking Management by Practice
All the research findings of this paper, i.e. the spatiotemporal data model of parking management, the prediction model of spare parking space and the optimal model of parking choice, are put into practice in the municipal parking management of Hangzhou, accepting application tests.
Results show that the research method for modeling spatiotemporal data from the angle of business application is feasible. It can effectively resolve the problem of poor applicability of the present achievements on spatiotemporal data model. Meanwhile the paper puts forward the spatiotemporal data model of parking management, the analytical method for spatiotemporal characteristics of parking management, the prediction model of spare parking space based on Markov Forecasting Procedure and the optimal model of parking choice on grey entropy theory. These approaches offer a theoretical and applicable reference for the further researches on spatiotemporal model of parking management and model expansion.
本文研究的主要内容如下：
1、提出停车管理的时空数据模型
基于停车管理的特点,进行停车管理时空要素的建模研究,提出停车管理的四大类时空要素主题,即停放要素主题、通道要素主题、设施要素主题、车辆要素主题,分析要素类的属性关联特征并针对要素类进行建模。
2、提出停车管理时空特征内容的分析方法
在停车管理时空数据模型的基础上,进一步研究分析停车管理的时空特征,包括车辆停放特征参数的计算与分析、停车现场管理的效率分析、停车点空间布局优化分析、停车数据的特征时段分析、停车人的停车习惯分析。
3、提出基于马尔可夫的空闲车位预测和泊车选择模型
分析了泊车流程和影响泊车选择的主要因素,在泊车行为特征分析的基础上,引入马尔可夫预测分析法,提出了空闲停车位预测分析模型,从而能够提高停车管理效率、优化停车资源配置和提高停车服务质量；引入灰熵理论,提出了最优泊位选择模型,从而在实时获取并较准确预测停车点空闲车位信息基础上能够选择性泊车
4、停车管理时空模型的实际应用检验
结合杭州市的停车管理这一实际业务应用,将所研究的停车管理时空数据模型、空闲停车位预测分析模型和泊车选择模型成果进行应用实现的检验。
研究结果表明,本文采用从业务领域角度来进行时空数据模型的研究方法具有可行性,可以有效地解决目前时空间数据模型研究成果实用性较差的问题；另一方面,本研究提出了停车管理的时空数据模型、停车管理时空特征内容的分析方法以及基于马尔可夫预测分析方法的空闲停车位预测分析模型和基于灰熵理论的最优泊位选择模型,该项研究将为停车管理时空模型的理论研究和进一步的模型扩展研究提供理论参考和应用参考。
Urban public parking management, a component of city traffic control, is a headache lingering around the daily life of city residents. How to alleviate traffic congestion by adopting new technology and improving the efficiency of parking management is a hot spot issue of city management at present. Although there are a lot of previous researches on this area both at home and abroad, none of those deals with the spatial-temporal model of parking management. This paper conducts research on two aspects:On the one hand, it studies the spatiotemporal data model of parking management from the perspective of the business area of parking management, by which the theoretical reference for practice is established. On the other hand, it explores the analytical method for the spatiotemporal characteristics of parking management, and puts forward the predictive analysis model for the spare space available in parking lots based on Markov forecasting procedure and the optimal model of parking choice based on grey entropy theory. All these research findings are to be applied to the practical parking management in Hangzhou city, and hopefully resolve the problems related to the implementing efficiency of spatiotemporal data model of parking management.
The main content of this paper is as follows:
1) The Spatiotemporal Data Model of Parking Management
Based on the characteristics of parking management, the research on modeling of spatiotemporal factors of parking management is conducted with 4 kinds of spatiotemporal factor subjects, namely the factor subjects of parking, passageways, facilities and vehicles. The related characteristic properties of factors are analyzed, and the model of the factors set up.
2) The Analytical Method for Spatiotemporal Characteristics of Parking Management
In accordance with the spatiotemporal data model of parking management, a further analysis of spatiotemporal characteristics of parking management is carried out. It involves the calculation and analysis of the characteristic parameter of parking vehicles, the analysis of management efficiency of parking lots, optimization of the planning of parking spaces, distinct time period of parking data and the parking habit of the people.
3) The Prediction Model of Spare Parking Space Based on Markov Forecasting Procedure and Optimal Model of Parking Choice
Parking process and main factors affecting parking choice are analyzed. The method of Markov forecasting analysis is introduced on the basis of feature analysis of parking habits. The model of predictive analysis of spare parking space is proposed so as to enhance the efficiency of parking management, optimize the resource allocation of parking spaces, and improve the quality of parking service. Optimal model of parking choice is presented with the introduction of grey entropy theory, which enables drivers to park vehicles selectively by acquiring accurate predictive information about spare spaces available in parking lot.
4) The Examination of Spatiotemporal Data Model of Parking Management by Practice
All the research findings of this paper, i.e. the spatiotemporal data model of parking management, the prediction model of spare parking space and the optimal model of parking choice, are put into practice in the municipal parking management of Hangzhou, accepting application tests.
Results show that the research method for modeling spatiotemporal data from the angle of business application is feasible. It can effectively resolve the problem of poor applicability of the present achievements on spatiotemporal data model. Meanwhile the paper puts forward the spatiotemporal data model of parking management, the analytical method for spatiotemporal characteristics of parking management, the prediction model of spare parking space based on Markov Forecasting Procedure and the optimal model of parking choice on grey entropy theory. These approaches offer a theoretical and applicable reference for the further researches on spatiotemporal model of parking management and model expansion.
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