基于多断面信息的城市道路网交通流预测方法研究
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摘要
随着社会经济的快速发展,城市汽车保有量不断增加给城市道路交通系统造成极大的考验,交通事故、交通拥堵、环境和能源等问题不断加剧,极大地降低人们出行的便利性。智能交通系统(ITS)是解决城市道路交通问题的有效手段之一,其中交通流预测是ITS的基础性研究内容,为交通控制和诱导提供基础理论支持和数据支持。通过向出行者提供实时有效的交通信息,诱导出行者的出行行为,尽可能充分利用交通基础设施,解决或缓解交通拥堵等问题。深入分析和掌握道路交通流变化规律,提高交通流预测的实时性、可靠性和自适应性成为目前关注和研究的热点。
本课题以城市道路网断面交通流为研究对象,对各断面交通流时间空间特性和时空预测理论进行深入研究和探索,主要研究内容和成果包括：
城市道路网交通流不仅随时间变化,同时也受空间因素的影响。在分析交通流空间变化特性基础之上,重点研究城市交通流空间相互作用的影响因素,如距离、交通状态等,结合城市地理学相关理论,对交通流空间相互作用的延迟特性进行深入分析,给出基于时滞的交通流互相关性算法,通过实测数据验证计算法的正确性。
交通流时空预测模型充分考虑到交通流时间和空间变化特性,如果将所有断面作为整体进行分析和预测,将极大地增加模型的计算复杂度。对比各种聚类分析方法。本文给出基于交通流互相关性的路网断面分组算法,该算法基于平均相关性、主成分分析和k-means,无需初始化聚类参数,如聚类数、聚类中心等,具有较强的灵活性和自适应性。
本文系统研究单点和多点交通流预测理论,基于过程神经网络模型的预测方法,提出在线自适应复合交通流时空预测(OAHST)模型。OAHST模型基于并行化思想,对单一预测模型进行扩展。OAHST是一个多输入单输出模型,将断面分为主断面和辅助断面,分别估算各断面交通流变化规律进行交通流预测,根据跟断面间的关联关系,对主断面的预测结果进行修正。
在初始交通流统计样本较为丰富的情况下,建立在线自适应RBF网络预测模型。构建神经网络的难点在于网络结构的确定,即隐节点基本信息,适当的隐节点数可以降低神经网络的计算复杂度,提高学习训练的收敛速度。论文深入研究神经网络序贯学习算法,对其进行改进和优化,提出两阶段混合自适应学习算法(TSMALA算法)。TSMALA基于隐节点贡献度大小,动态增加和删除隐节点,对RBF网络结构进行自适应调整,高效快速地对外界变化做出响应。
在初始交通流统计样本较小的情况下,建立在线自适应最小二乘支持向量机LS-SVM预测模型。最小二乘支持向量机(LS-SVM)是对支持向量机(SVM)的优化,将所有样本作为支持向量,增加预测过程的计算复杂度,使LS-SVM的解缺乏稀疏性,测速度相对缓慢。本文重点分析支持向量,给出支持向量增加、删除、替换的训练算法,通过动态更新支持向量,严格限制支持向量和训练样本的数量,使预测模型能够对外界的变化及时快速地做出响应。
根据实际工作需求,在论文研究成果基础之上研发基于时空预测技术的城市道路交通流诱导系统作为实际案例研究。根据业务流程设计相关系统功能的算法流程,通过案例演示应用系统的主要功能模块和操作界面等。
本文深入分析交通流时间和空间变化特性,基于过程神经网络模型的预测原理,提出并行化交通流时空复合预测(OAHST)模型,OAHST模型单独考虑预测模块输入维,建立交通流数据滑动窗口,依据当前输入样本动态确定预测模型的输入。针对初始样本的大小,分别给出基于最小二乘支持向量机和径向基神经网络的交通流预测模型,在一定程度上提高模型的自适应程度。在理论研究的基础之上开发基于时空复合预测模型的城市道路交通诱导系统。通过理论与实践相结合的方法,实现课题的研究目标,为解决城市道路交通问题和智能交通系统的发展进行有益的探索。
With the development of social economy, the gross quantity of urban cars climbs rapidly, bringing great pressure for the urban road transport system. Traffic problems, such as, traffic accidents, traffic jam, environment, energy and so on, are getting more and more series. All of these greatly reduce the conveniences of people traveling. The Intelligent Transport System (ITS) is a effective method to solve urban road traffic problems. One of the basic works is traffic flow prediction, which provides theoretical and data support for ITS, gives real-time effective traffic information for travelers and induces their travel behaviors. So it is great helpful to make full use of transportation infrastructures and solve or relieve traffic problems. In recent years, it is a hot spot to deeply analyze and get the changing law, improve the real time, reliability and self-adaptability of traffic flow prediction.
This thesis takes urban road transport network as study objects, explores the spatial-temporal characteristics of the traffic flow and spatial-temporal prediction theory deeply. Its main study contents and results are given as follows.
The urban road traffic flow changes over time, which is affected by space factors. Based on the analysis of the traffic flow spatial variations, some influence factors on the traffic flow spatial interaction, such as distance, traffic states and so on, are analyzed emphatically. Meanwhile, delay characteristics of traffic flow spatial interaction is analyzed in detail referring to the urban geography theories. A computing formula of traffic flow spatial correlation based on delay characteristics is given. The validity of this formula is verified through quantitative analysis.
After comparing some clustering methods, a new clustering algorithm of road cross-sections based on the spatial correlation of road traffic flow is given
Spatial-temporal prediction considers spatial-temporal variation characteristics of the traffic flow, which makes multiple cross-sections as its study objects. So, if all of cross-sections are analyzed and predicted, it will greatly increase the computational complexity. After comparing some clustering methods, a new clustering algorithm of road cross-sections based on the spatial correlation of the road traffic flow is given, which introduces average correlation, principal component analysis and k-means. It doesn't need to initialize clustering parameters, such as clustering number, clustering centers and so on, so it is highly flexible and self-adaptive.
The thesis explores traffic flow prediction of single and multiple cross-sections. Referring to the idea of process neural network prediction model, the Online Adaptive Hybrid Spatial-temporal Short-term prediction model (OAHST model) is proposed. OAHST model is a single-input-multiple-output model, which divides the road cross-sections into main cross-section and assisting cross-sections. It first predicts the traffic flow variation of each cross-section, and finally the predicting result of the main cross-section is revised according to the correlativity between cross-section traffic flow.
Under the condition rich initial traffic flow samples, an online self-adaptive RBF network prediction algorithm is constructed. In the process of establishing the neural network, Determining the network structure is a difficult point, that is basic information of hidden nodes of neural network. Appropriate number of hidden nodes can make computational complexity model lower, improve the training convergence speed. After deeply exploring the sequential learning algorithm of the neural network and Radial Basis Function neural network (RBFNN), the Two-Stage Mixed self-adaptive Learning Algorithm of the RBFNN (TSMALA) is proposed, which dynamically determines the network structure. In TSMALA, RBFNN dynamically adds and deletes hidden nodes according to the importance of hidden nodes. This can make the prediction model respond to external changes quickly and efficiently.
Under the condition of less initial samples, the online self-adaptive least squares support vector machine prediction algorithm is established. Least squares support vector machine (LS-SVM) is the optimization of support vector machine (SVM). It makes all samples as support vectors, which lead to increases the computational complexity of the model, make the results of the model lack sparsity and slow down its testing speed. Based on these and exploring support vectors, the online self-adaptive LS-SVM prediction model (OALS-SVM) is proposed, which dynamically adds, deletes support vectors, strictly limits the number of support vectors, and makes the prediction model respond to external changes quickly and efficiently.
According to the real requirements, urban road traffic flow guiding system is developed as the case study based on the researching results. At last, the main algorithms and several operation interfaces are presented.
The thesis deeply explores spatial-temporal variation characteristics of traffic flow, referring to the idea of the process neural network prediction model, a novel spatial-temporal hybrid prediction model (OAHST model) is proposed. OAHST model considers the input dimensions individually, establishes the moving data window of traffic flow, and dynamically determines the inputs of the prediction model according to the input samples. Based on different size of the Initial samples, two prediction algorithms of the single cross-section is proposed, which are based on LS-SVM and RBFNN respectively. These greatly improve the self-adaptability and flexibility of the model. By the way of combining theory with practice, the aims of the research are achieved. It is a useful exploration process for solving urban road traffic problems and developing the ITS.
本课题以城市道路网断面交通流为研究对象,对各断面交通流时间空间特性和时空预测理论进行深入研究和探索,主要研究内容和成果包括：
城市道路网交通流不仅随时间变化,同时也受空间因素的影响。在分析交通流空间变化特性基础之上,重点研究城市交通流空间相互作用的影响因素,如距离、交通状态等,结合城市地理学相关理论,对交通流空间相互作用的延迟特性进行深入分析,给出基于时滞的交通流互相关性算法,通过实测数据验证计算法的正确性。
交通流时空预测模型充分考虑到交通流时间和空间变化特性,如果将所有断面作为整体进行分析和预测,将极大地增加模型的计算复杂度。对比各种聚类分析方法。本文给出基于交通流互相关性的路网断面分组算法,该算法基于平均相关性、主成分分析和k-means,无需初始化聚类参数,如聚类数、聚类中心等,具有较强的灵活性和自适应性。
本文系统研究单点和多点交通流预测理论,基于过程神经网络模型的预测方法,提出在线自适应复合交通流时空预测(OAHST)模型。OAHST模型基于并行化思想,对单一预测模型进行扩展。OAHST是一个多输入单输出模型,将断面分为主断面和辅助断面,分别估算各断面交通流变化规律进行交通流预测,根据跟断面间的关联关系,对主断面的预测结果进行修正。
在初始交通流统计样本较为丰富的情况下,建立在线自适应RBF网络预测模型。构建神经网络的难点在于网络结构的确定,即隐节点基本信息,适当的隐节点数可以降低神经网络的计算复杂度,提高学习训练的收敛速度。论文深入研究神经网络序贯学习算法,对其进行改进和优化,提出两阶段混合自适应学习算法(TSMALA算法)。TSMALA基于隐节点贡献度大小,动态增加和删除隐节点,对RBF网络结构进行自适应调整,高效快速地对外界变化做出响应。
在初始交通流统计样本较小的情况下,建立在线自适应最小二乘支持向量机LS-SVM预测模型。最小二乘支持向量机(LS-SVM)是对支持向量机(SVM)的优化,将所有样本作为支持向量,增加预测过程的计算复杂度,使LS-SVM的解缺乏稀疏性,测速度相对缓慢。本文重点分析支持向量,给出支持向量增加、删除、替换的训练算法,通过动态更新支持向量,严格限制支持向量和训练样本的数量,使预测模型能够对外界的变化及时快速地做出响应。
根据实际工作需求,在论文研究成果基础之上研发基于时空预测技术的城市道路交通流诱导系统作为实际案例研究。根据业务流程设计相关系统功能的算法流程,通过案例演示应用系统的主要功能模块和操作界面等。
本文深入分析交通流时间和空间变化特性,基于过程神经网络模型的预测原理,提出并行化交通流时空复合预测(OAHST)模型,OAHST模型单独考虑预测模块输入维,建立交通流数据滑动窗口,依据当前输入样本动态确定预测模型的输入。针对初始样本的大小,分别给出基于最小二乘支持向量机和径向基神经网络的交通流预测模型,在一定程度上提高模型的自适应程度。在理论研究的基础之上开发基于时空复合预测模型的城市道路交通诱导系统。通过理论与实践相结合的方法,实现课题的研究目标,为解决城市道路交通问题和智能交通系统的发展进行有益的探索。
With the development of social economy, the gross quantity of urban cars climbs rapidly, bringing great pressure for the urban road transport system. Traffic problems, such as, traffic accidents, traffic jam, environment, energy and so on, are getting more and more series. All of these greatly reduce the conveniences of people traveling. The Intelligent Transport System (ITS) is a effective method to solve urban road traffic problems. One of the basic works is traffic flow prediction, which provides theoretical and data support for ITS, gives real-time effective traffic information for travelers and induces their travel behaviors. So it is great helpful to make full use of transportation infrastructures and solve or relieve traffic problems. In recent years, it is a hot spot to deeply analyze and get the changing law, improve the real time, reliability and self-adaptability of traffic flow prediction.
This thesis takes urban road transport network as study objects, explores the spatial-temporal characteristics of the traffic flow and spatial-temporal prediction theory deeply. Its main study contents and results are given as follows.
The urban road traffic flow changes over time, which is affected by space factors. Based on the analysis of the traffic flow spatial variations, some influence factors on the traffic flow spatial interaction, such as distance, traffic states and so on, are analyzed emphatically. Meanwhile, delay characteristics of traffic flow spatial interaction is analyzed in detail referring to the urban geography theories. A computing formula of traffic flow spatial correlation based on delay characteristics is given. The validity of this formula is verified through quantitative analysis.
After comparing some clustering methods, a new clustering algorithm of road cross-sections based on the spatial correlation of road traffic flow is given
Spatial-temporal prediction considers spatial-temporal variation characteristics of the traffic flow, which makes multiple cross-sections as its study objects. So, if all of cross-sections are analyzed and predicted, it will greatly increase the computational complexity. After comparing some clustering methods, a new clustering algorithm of road cross-sections based on the spatial correlation of the road traffic flow is given, which introduces average correlation, principal component analysis and k-means. It doesn't need to initialize clustering parameters, such as clustering number, clustering centers and so on, so it is highly flexible and self-adaptive.
The thesis explores traffic flow prediction of single and multiple cross-sections. Referring to the idea of process neural network prediction model, the Online Adaptive Hybrid Spatial-temporal Short-term prediction model (OAHST model) is proposed. OAHST model is a single-input-multiple-output model, which divides the road cross-sections into main cross-section and assisting cross-sections. It first predicts the traffic flow variation of each cross-section, and finally the predicting result of the main cross-section is revised according to the correlativity between cross-section traffic flow.
Under the condition rich initial traffic flow samples, an online self-adaptive RBF network prediction algorithm is constructed. In the process of establishing the neural network, Determining the network structure is a difficult point, that is basic information of hidden nodes of neural network. Appropriate number of hidden nodes can make computational complexity model lower, improve the training convergence speed. After deeply exploring the sequential learning algorithm of the neural network and Radial Basis Function neural network (RBFNN), the Two-Stage Mixed self-adaptive Learning Algorithm of the RBFNN (TSMALA) is proposed, which dynamically determines the network structure. In TSMALA, RBFNN dynamically adds and deletes hidden nodes according to the importance of hidden nodes. This can make the prediction model respond to external changes quickly and efficiently.
Under the condition of less initial samples, the online self-adaptive least squares support vector machine prediction algorithm is established. Least squares support vector machine (LS-SVM) is the optimization of support vector machine (SVM). It makes all samples as support vectors, which lead to increases the computational complexity of the model, make the results of the model lack sparsity and slow down its testing speed. Based on these and exploring support vectors, the online self-adaptive LS-SVM prediction model (OALS-SVM) is proposed, which dynamically adds, deletes support vectors, strictly limits the number of support vectors, and makes the prediction model respond to external changes quickly and efficiently.
According to the real requirements, urban road traffic flow guiding system is developed as the case study based on the researching results. At last, the main algorithms and several operation interfaces are presented.
The thesis deeply explores spatial-temporal variation characteristics of traffic flow, referring to the idea of the process neural network prediction model, a novel spatial-temporal hybrid prediction model (OAHST model) is proposed. OAHST model considers the input dimensions individually, establishes the moving data window of traffic flow, and dynamically determines the inputs of the prediction model according to the input samples. Based on different size of the Initial samples, two prediction algorithms of the single cross-section is proposed, which are based on LS-SVM and RBFNN respectively. These greatly improve the self-adaptability and flexibility of the model. By the way of combining theory with practice, the aims of the research are achieved. It is a useful exploration process for solving urban road traffic problems and developing the ITS.
引文
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