基于优势出行距离的方式分担率模型及软件实现

发布时间：2018-06-19 18:18

本文选题：优势出行距离 + 方式分担率　；参考：《东南大学》2017年硕士论文

【摘要】：随着我国城镇化进程的不断加速,城市机动化水平不断提高,许多大城市开始了大规模的轨道交通建设,电动自行车也进入了快速发展期,我国城市交通的交通系统更加多元,交通结构也正在发生着深刻的变化,各交通方式的分担率水平直接决定了交通系统的整体运行效能。然而,居民的出行距离分布并不均匀,各交通方式也存在着特殊的优势出行距离,因此,急需一种考虑交通方式优势距离的宏观交通方式划分模型以指导城市交通规划工程实践。本文依托国家自然科学基金项目(51338003、51378120),立足于我国城市交通发展的现实情况和工程需求,首先以南京市为例,利用其居民出行数据,建立了各交通方式出行距离数据库,对全方式和分方式的居民出行距离分布特征进行了定性分析。在居民出行距离分布规律分析的基础上,引入优势出行距离因子,运用条件概率法对出行的物理过程进行数学抽象建模,并借助最小二乘法完成了对出行距离分布函数参数的标定。相比于电子云模型,该函数的适用性较好。基于此函数,构建了基于优势出行距离的方式分担率基础模型,改进了现有的交通方式划分宏观模型。对单次典型出行过程进行分析,从交通出行者和交通决策者两个角度计算了广义出行费用函数,建立了以交通系统总出行费用最小为目标的非线性优化模型,并利用粒子群算法对模型进行求解,从而完成了对基于优势出行距离的方式分担率模型的参数优化。最后,基于改进的交通方式划分模型,利用Visual C++和Qt混合编程,在TranStar分析平台上实现了具有计算现状、出行者视角、决策者视角下交通方式划分计算和目标分担率下交通OD矩阵划分功能的专业软件模块。本文所提供的与计算机软件相结合的实用城市交通方式划分模型,可以将不同的交通政策转化为不同的广义交通出行费用来影响模型参数,进而改变城市交通结构,为城市交通政策指定提供了量化分析的基础性工具,有助于研究成果在实践中进一步推广。
[Abstract]:With the acceleration of urbanization and the improvement of the level of urban motorization, many large cities have begun to build large scale rail transit, electric bicycles have entered a period of rapid development, and the transportation system of urban transportation in China has become more diversified. The traffic structure is also undergoing profound changes, and the level of the sharing rate of each traffic mode directly determines the overall operational efficiency of the transportation system. However, the distribution of travel distance of residents is not uniform, and there are special advantages of travel distance in each mode of transportation, so, In order to guide urban traffic planning engineering practice, a macroscopic traffic mode partition model considering the dominant distance of traffic mode is urgently needed. Based on the project of National Natural Science Foundation of China 51338003 51378120, based on the reality of urban traffic development and engineering needs in China, firstly, taking Nanjing as an example, using the travel data of its residents, the paper establishes the travel distance database of various traffic modes. The characteristics of travel distance distribution in all modes and in different modes are analyzed qualitatively. Based on the analysis of the distribution law of resident travel distance, the advantage travel distance factor is introduced, and the mathematical abstract modeling of the physical process of travel is carried out by using conditional probability method. The parameters of travel distance distribution function are calibrated by least square method. Compared with the electronic cloud model, the applicability of this function is better. Based on this function, the basic model of mode sharing rate based on superior travel distance is constructed, and the existing macroscopic model of traffic mode division is improved. Based on the analysis of a typical single trip process, the generalized travel cost function is calculated from the perspective of the traffic traveler and the traffic decision maker, and a nonlinear optimization model aiming at the minimum total travel cost of the transportation system is established. Particle swarm optimization (PSO) algorithm is used to solve the model, thus the parameter optimization of the model based on dominant travel distance is completed. Finally, based on the improved traffic mode partitioning model, using Visual C and QT mixed programming, we realized the calculation status quo and traveler's angle of view on tranStar analysis platform. The professional software module of traffic mode partition calculation and traffic OD matrix partition function under the view of decision-maker and goal sharing rate. The practical urban traffic mode partitioning model combined with computer software provided in this paper can transform different traffic policies into different generalized travel costs to influence the model parameters and then change the urban traffic structure. It provides a basic tool for quantitative analysis of urban traffic policy designation, and is helpful to further spread the research results in practice.
【学位授予单位】：东南大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：U12;TU984.191

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