城市道路交通状态评价和预测方法及应用研究
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摘要
城市道路交通状态的完整获取、准确／实时评价和预测是准确把握城市道路交通系统行为、科学制定交通管理决策和充分发挥交通设施潜能的基础。然而,如何定义交通状态、如何定量描述交通状态、以及如何面向交通参与者对交通状态进行不同层次、不同粒度的评价和预测,一直是城市交通研究领域的重点和难点,对这些问题的研究具有重要的理论和现实意义。
本论文以城市交通管理的实际需求为背景,深入研究了交通状态的概念体系、交通状态的完整获取、实时评价和预测方法等内容,并通过实际应用,验证了上述理论方法的有效性,形成了一个较为完整的交通状态评价和预测方法及应用体系。本论文的主要研究成果具体体现为以下几个方面：
1)形成了交通状态的概念体系及其应用框架。
系统阐述了交通状态概念定义、构成要素、特征属性以及类别(等级)划分,形成了完整的交通状态概念体系；分析了城市道路交通状态的生成机理；明确了交通状态应用体系的内涵,并构建了支持交通管理决策的交通状态应用体系框架,为论文的进一步研究奠定了基础。
2)以交通状态的全面、准确获取为目标,提出了解决交通流数据两种典型“缺失”问题的处理方法。
提出了一种基于pre-selection时空模型的交通流数据软测量方法,解决了无检测器路段交通流数据检测“缺失”的问题。该方法以有检测器路段的交通流数据为基础,依据数据序列之间的时空相关性构建时空模型,并通过预选“关键节点”的策略,减少了传统时空模型的待估参数,从而在保证精度的前提下,提高了运算效率。提出了用于描述检测器数据反映真值程度的信源可信度概念,并根据对信源可信度的不同划分方式,分别建立了基于信源可信度和知识、基于信源可信度和近似推理的数据融合模型,解决了单一检测源采集数据精度“缺失”问题。这两种融合模型结合了专家的经验和推理,不仅能够反映检测数据精度随时间的变化情况,而且实现了交通流数据规律性知识和专家经验知识的统一。
3)针对交通管理不同层次,构建了符合交通参与者认知的城市道路交通状态评价方法。
分析了交通状态评价的内涵,即可以理解为交通参与者对不同交通状态类别进行逻辑判断的过程；从交通管理者的角度,明确了交通状态评价的思路。在此基础上,面向路段和路网两个层次,设计了能够基于实时交通流数据计算的交通状态指标变量,并提出了基于主客观结合实验的路段交通状态评价方法、基于模糊聚类与模糊综合评价的路网交通状态评价方法。这两种评价方法将交通状态指标变量的客观性与人对交通状态类别认识的主观性有机结合,实现了交通状态定性分类和定量评价的统一。基于实际交通流数据的实验表明,两种方法能够准确、客观、实时地评价不同层次的交通状态。
4)提出了面向城市道路交通状态两个侧面(交通状态类别和交通状态指标变量)的短时预测方法。
将交通状态类别预测理解为模式识别问题,提出了一种基于最大熵的交通状态类别预测方法。该方法的优点在于,不仅能够有效融合影响交通状态的时间维和空间维特征,而且不需要考虑特征间的相关性。基于“分解-组合”的多模型建模策略,提出了一种自适应权重的交通状态指标变量组合预测方法。该方法可以理解为一个两阶段的预测模型,其优点在于减少了预测过程中的不确定性,适应了交通流的随机变化,从而提高了预测的精度。
5)以北京市交通管理的实际需要为背景,基于上述理论方法,开发并部署应用了北京市区域交通状态和服务水平评价系统。该系统的成功运用,验证了本论文研究成果的有效性和实用性。
The complete acquisition, accurate/real-time evaluation and prediction of urban road traffic state, is the foundation to grasp accurate behaviours of urban road traffic system, make scientific decisions of traffic management, and make full use of traffic facilities. However, how to define the traffic state, how to quantitatively describe the traffic state, and how to evaluate and forecast the traffic state in multi-hierarchy and multi-granule for traffic participants, have been the difficulty and emphasis in the field of transportation research. Moreover, the study of those problems is of great theoretical and practical significance.
This dissertation, on the background of actual traffic management, elaborates the traffic state's concept system, deeply studies the traffic state's concept system, the methods of traffic state acquisition, real-time evaluation and prediction, and then verifies their validity by practical application, which formed a relatively complete methodology system of traffic state evaluation and prediction; The main innovative work of this dissertation is specifically summarized as follows:
1) A concept system of traffic state and its application framework are formed.
Based on the analysis of urban road traffic system, the traffic state's definition, elements, attributes, and categories are illuminated profoundly, which compose a more complete concept system of urban road traffic state. By analyzing the traffic state's formation and the demands of actual traffic management, an application framework based on the evaluation and prediction method of traffic state is designed to support the traffic management decision-making, which provides a foundation to further research.
2) The solutions of two typical data "missing" problem are proposed to acquire the complete, accurate traffic state.
A soft-sensing method based on pre-selection space-time model is proposed to solve the detection "missing" data. With the existing detection data, the model firstly makes use of the spatial and temporal correlation among the traffic data sequences to construct the space-time model; and then reduces its estimation parameters through the pre-selection strategy, which can improve its effectiveness and efficiency. To solve the accuracy "missing" data, the concept of source-credibility is introduced to describe the degree of the detection data approaching the true value; and then two fusion models based on source-credibility and knowledge, source-credibility and approximate reasoning are proposed respectively according to the division of source-credibility. The two fusion models not only consider the time-dependent source-credibility, but also integrate the experts'experience and reasoning respectively, which made the regularity of the traffic data and the knowledge of experts unified.
3) Two real-time traffic state evaluation methods coinciding with traffic participants'cognition are established to satisfy the demands of actual traffic management.
The connotation of the traffic state evaluation, which can be considered as a logical process of judging the traffic state's category by traffic participants, is analyzed firstly; and then the way to evaluate the traffic state from the point of traffic managers is clarified; following that, the evaluation indexes derived from the real-time traffic data are designed, and the real-time traffic state evaluation method based on subjective and objective integrated experiments for road section, the real-time evaluation method based on fuzzy clustering and fuzzy comprehensive evaluation model for road network are established respectively. The two methods integrate the subjectivity of the evaluation indexes and the objectivity of the traffic participants, which unifies the qualitative classification and the quantitative evaluation of traffic state.
4) A traffic state categories prediction model and a traffic state index prediction model are put forward to obtain the two-side of future traffic state.
By considering the category prediction as a pattern recognition problem, a category prediction model based on maximum entropy is established, which can integrate the spatial and temporal influencing features of traffic state without considering their relationship. According to the multi-model modelling ideology, a self-adaptive weight combination model is proposed to forecast the traffic state's index. The model is established on the traffic state category prediction, which can be regarded as a two-stage prediction model. The advantage of the model is it could reduce the uncertainty, adapt to the random changes of traffic flow, and improve the prediction accuracy.
5) According to the actual traffic management demands of Beijing city, and based on the above theoretical methods, the Beijing Regional Traffic State and LOS Evaluation System is developed and applied. The successful application of the system verifies our achievements are of validity and practicality.
本论文以城市交通管理的实际需求为背景,深入研究了交通状态的概念体系、交通状态的完整获取、实时评价和预测方法等内容,并通过实际应用,验证了上述理论方法的有效性,形成了一个较为完整的交通状态评价和预测方法及应用体系。本论文的主要研究成果具体体现为以下几个方面：
1)形成了交通状态的概念体系及其应用框架。
系统阐述了交通状态概念定义、构成要素、特征属性以及类别(等级)划分,形成了完整的交通状态概念体系；分析了城市道路交通状态的生成机理；明确了交通状态应用体系的内涵,并构建了支持交通管理决策的交通状态应用体系框架,为论文的进一步研究奠定了基础。
2)以交通状态的全面、准确获取为目标,提出了解决交通流数据两种典型“缺失”问题的处理方法。
提出了一种基于pre-selection时空模型的交通流数据软测量方法,解决了无检测器路段交通流数据检测“缺失”的问题。该方法以有检测器路段的交通流数据为基础,依据数据序列之间的时空相关性构建时空模型,并通过预选“关键节点”的策略,减少了传统时空模型的待估参数,从而在保证精度的前提下,提高了运算效率。提出了用于描述检测器数据反映真值程度的信源可信度概念,并根据对信源可信度的不同划分方式,分别建立了基于信源可信度和知识、基于信源可信度和近似推理的数据融合模型,解决了单一检测源采集数据精度“缺失”问题。这两种融合模型结合了专家的经验和推理,不仅能够反映检测数据精度随时间的变化情况,而且实现了交通流数据规律性知识和专家经验知识的统一。
3)针对交通管理不同层次,构建了符合交通参与者认知的城市道路交通状态评价方法。
分析了交通状态评价的内涵,即可以理解为交通参与者对不同交通状态类别进行逻辑判断的过程；从交通管理者的角度,明确了交通状态评价的思路。在此基础上,面向路段和路网两个层次,设计了能够基于实时交通流数据计算的交通状态指标变量,并提出了基于主客观结合实验的路段交通状态评价方法、基于模糊聚类与模糊综合评价的路网交通状态评价方法。这两种评价方法将交通状态指标变量的客观性与人对交通状态类别认识的主观性有机结合,实现了交通状态定性分类和定量评价的统一。基于实际交通流数据的实验表明,两种方法能够准确、客观、实时地评价不同层次的交通状态。
4)提出了面向城市道路交通状态两个侧面(交通状态类别和交通状态指标变量)的短时预测方法。
将交通状态类别预测理解为模式识别问题,提出了一种基于最大熵的交通状态类别预测方法。该方法的优点在于,不仅能够有效融合影响交通状态的时间维和空间维特征,而且不需要考虑特征间的相关性。基于“分解-组合”的多模型建模策略,提出了一种自适应权重的交通状态指标变量组合预测方法。该方法可以理解为一个两阶段的预测模型,其优点在于减少了预测过程中的不确定性,适应了交通流的随机变化,从而提高了预测的精度。
5)以北京市交通管理的实际需要为背景,基于上述理论方法,开发并部署应用了北京市区域交通状态和服务水平评价系统。该系统的成功运用,验证了本论文研究成果的有效性和实用性。
The complete acquisition, accurate/real-time evaluation and prediction of urban road traffic state, is the foundation to grasp accurate behaviours of urban road traffic system, make scientific decisions of traffic management, and make full use of traffic facilities. However, how to define the traffic state, how to quantitatively describe the traffic state, and how to evaluate and forecast the traffic state in multi-hierarchy and multi-granule for traffic participants, have been the difficulty and emphasis in the field of transportation research. Moreover, the study of those problems is of great theoretical and practical significance.
This dissertation, on the background of actual traffic management, elaborates the traffic state's concept system, deeply studies the traffic state's concept system, the methods of traffic state acquisition, real-time evaluation and prediction, and then verifies their validity by practical application, which formed a relatively complete methodology system of traffic state evaluation and prediction; The main innovative work of this dissertation is specifically summarized as follows:
1) A concept system of traffic state and its application framework are formed.
Based on the analysis of urban road traffic system, the traffic state's definition, elements, attributes, and categories are illuminated profoundly, which compose a more complete concept system of urban road traffic state. By analyzing the traffic state's formation and the demands of actual traffic management, an application framework based on the evaluation and prediction method of traffic state is designed to support the traffic management decision-making, which provides a foundation to further research.
2) The solutions of two typical data "missing" problem are proposed to acquire the complete, accurate traffic state.
A soft-sensing method based on pre-selection space-time model is proposed to solve the detection "missing" data. With the existing detection data, the model firstly makes use of the spatial and temporal correlation among the traffic data sequences to construct the space-time model; and then reduces its estimation parameters through the pre-selection strategy, which can improve its effectiveness and efficiency. To solve the accuracy "missing" data, the concept of source-credibility is introduced to describe the degree of the detection data approaching the true value; and then two fusion models based on source-credibility and knowledge, source-credibility and approximate reasoning are proposed respectively according to the division of source-credibility. The two fusion models not only consider the time-dependent source-credibility, but also integrate the experts'experience and reasoning respectively, which made the regularity of the traffic data and the knowledge of experts unified.
3) Two real-time traffic state evaluation methods coinciding with traffic participants'cognition are established to satisfy the demands of actual traffic management.
The connotation of the traffic state evaluation, which can be considered as a logical process of judging the traffic state's category by traffic participants, is analyzed firstly; and then the way to evaluate the traffic state from the point of traffic managers is clarified; following that, the evaluation indexes derived from the real-time traffic data are designed, and the real-time traffic state evaluation method based on subjective and objective integrated experiments for road section, the real-time evaluation method based on fuzzy clustering and fuzzy comprehensive evaluation model for road network are established respectively. The two methods integrate the subjectivity of the evaluation indexes and the objectivity of the traffic participants, which unifies the qualitative classification and the quantitative evaluation of traffic state.
4) A traffic state categories prediction model and a traffic state index prediction model are put forward to obtain the two-side of future traffic state.
By considering the category prediction as a pattern recognition problem, a category prediction model based on maximum entropy is established, which can integrate the spatial and temporal influencing features of traffic state without considering their relationship. According to the multi-model modelling ideology, a self-adaptive weight combination model is proposed to forecast the traffic state's index. The model is established on the traffic state category prediction, which can be regarded as a two-stage prediction model. The advantage of the model is it could reduce the uncertainty, adapt to the random changes of traffic flow, and improve the prediction accuracy.
5) According to the actual traffic management demands of Beijing city, and based on the above theoretical methods, the Beijing Regional Traffic State and LOS Evaluation System is developed and applied. The successful application of the system verifies our achievements are of validity and practicality.
引文
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