【作者】 刘梦涵；

【导师】 于雷；

【作者基本信息】 北京交通大学 ， 交通运输规划与管理， 2009， 博士

【摘要】 特大城市由于存在超大的城市规模、庞大的人口数量和复杂的网络结构,其交通拥堵现象和规律较中小城市有所不同。在传统的拥堵评价研究领域,国内外已经提出了大量城市拥堵评价的一般化模型,但缺乏针对特大城市的拥堵评价模型。本文在综述国内外现有的交通拥堵评价理论的基础上,结合对特大城市复杂的交通流特性的分析,运用统计学技术和信号处理技术,构建了面向特大城市的分层次交通拥堵评价模型。为了构建评价模型,本文首先确定了评价特大城市交通拥堵的四个维数,包括:拥堵强度、拥堵时间、拥堵空间和拥堵点段。提出了面向特大城市的路网分层次交通拥堵评价体系框架,包括:宏观、中观和微观三个层次,用于确定各层次拥堵评价模型的基本功能及相互联系。构建的模型具体包括:首先,提出了面向特大城市的宏观交通强度评价模型。将累积Logistic回归理论引入到路段拥堵强度评价,构建了基于行程速度的路段拥堵强度评价模型。在此基础上,采用分层抽样技术和综合评价法,构建了基于车辆行驶时间(VHT)加权的分层次交通拥堵指数(TCI)模型。其次,提出了面向特大城市的中观交通拥堵时空分布评价模型。在依次分析拥堵时间和空间评价因变量和自变量的相关性后,采用非线性回归技术,提出了基于行程速度的某等级道路的严重拥堵持续时间(SCD)评价模型和拥堵持续时间(CD)评价模型,并确定SCD模型采用指数函数形式,CD模型采用三次曲线函数形式;以及采用多元线性回归技术构建了严重拥堵空间范围(SCB)模型和拥堵空间范围(CB)模型,确定了采用行程速度和停车时间的二元线性SCB模型,和基于行程速度的CB单元线性模型。最后,提出了面向特大城市的微观重点拥堵点段识别模型。通过分析重点拥堵点段具备的典型拥堵特征,针对每个特征分别设计了判别算法。在该过程中,本文利用了小波变换提取行程速度数据的近似成分并用于评价周期性拥堵特征。在分析拥堵的时空分布特征基础上,提出了原发性拥堵的判别方法。这两个特征被用于重点拥堵点段的判定。为了验证模型的有效性和准确性,本文采用了统计检验和模型预测与实测数据对比两种检验方法。验证过程表明所构建的评价模型和算法适用于特大城市的交通拥堵评价。更多还原

【Abstract】 Due to the mega-scale nature, huge population size, and complicated network structures, megacities have demonstrated unique but different traffic congestion phenomena and patterns from small and/or medium-sized cities. In the field of traditional traffic congestion measurement, numerous models have been developed for cities. However, few have been developed specifically for megacities. After a comprehensive synthesis of state-of-the-art in congestion measurements and an analysis of traffic flow characteristics, the research in this dissertation developed stratified traffic congestion measurement models for megacities by applying statistical and signal processing techniquesIn order to develop the measurement models, the dissertation first defined four dimensions for measuring congestions for megacities, including congestion intensity, congestion duration, congestion breadth, as well as congested segments and intersections. A stratified traffic congestion measurement framework for megacities, including macroscopic, mesoscopic, as well as microscopic levels, was developed for determining the basic functions of congestion measurement models and their inter-relationships. The models were developed as follows:First, macroscopic models for measuring the congestion intensity were developed. By introducing the cumulative logistic regression theory into the measurement of congestion intensity for road segments, a travel-speed based measurement model for the congestion intensity was presented. Then, a weighted vehicle-hours-traveled (VHT) based traffic congestion indices (TCI) model was developed by using stratified sampling and comprehensive evaluation method. Second, mesoscopic models for measuring the congestion duration and breadth were developed. After analyzing the relationship between the independent and dependent variables in the measurements of temporal and spatial congestions, the non-linear regression technique was used to develop the travel-speed based models for the measurement of severe congestion duration (SCD) and congestion duration (CD) for a specific road class, in which the exponential and cubic functions were selected for SCD and CD models respectively. Further, multivariate linear regression techniques were adopted to develop models for the measurement of severe congestion breadth (SCB) and congestion breadth (CB), in which a bivariate linear regression of travel-speed and stop-time was used in the SCB model, and a simple linear regression of travel-speed was used in the CB model. Finally, microscopic models for the identification of key congested segments and intersections were developed. Identification algorithms were developed in light of an analysis of the typical traffic congestion characteristics of the key congested segments and intersections. In the modeling process, the wavelet transformation was used to extract the approximate components of travel speeds, which were used in the identification of periodical congestion characteristics. The method to identify the primary congestion was proposed based on an analysis of characteristics of the spatial-temporal distribution of congestions. The characteristics of both periodical and primary congestions were used to identify the key congested segments and intersections.The validity and accuracy of proposed models were tested by using the statistical test methods and by comparing the model estimation with the field observed data. The test demonstrated that the proposed models and algorithms can be readily used to evaluate traffic congestions for megacities.更多还原