基于宏观模型的交通状态分析

发布时间：2018-06-17 13:58

本文选题：交通问题 + 区域路网拥堵　；参考：《长沙理工大学》2015年硕士论文

【摘要】：交通是经济大发展背景下的产物,而交通问题则是当交通与经济出现矛盾时导致的结果,具有一定的滞后性。交通管理部门或研究者们往往只关注发生阻塞的局部堵点或区域,运用到的交通知识也仅限于描述车辆在道路上的运动规律的模型,但是现在交通拥堵的特征发生了变化,逐渐从点拥堵、线拥堵发展到区域路网拥堵,而人们却没有意识到区域性交通网络的内在规律,治理交通问题往往是治标不治本。而宏观交通组织优化是解决一切交通问题的根源,它从土地利用、路网结构、交通方式等宏观方面对城市交通系统进行优化,本文提出了基于宏观模型的交通状态分析,结合大数据时代背景用海量数据重新快速标定宏观模型的参数,验证宏观模型的准确性与适用性,将传统的仅仅依靠仿真软件进行单交叉口或单一路段实验推向区域化。本文以宏观模型为基础进行交通状态分析,主要取得以下两个成果:1、提出了利用出租车GPS数据推导二流模型曲线的数据处理方法,将研究区域划分成小区,用所有小区二流曲线的离散程度来判断各小区交通状态的差别,并建立了利用二流模型推算出行时间OD矩阵的方法,通过分析不同停驶时间Ts下行程时间T的变化,可以分析各小区的敏感性和脆弱性,为不同交通状况下的路径诱导提供依据。对长沙市路网的实例利用矩阵迭代法找出了某一停驶时间Ts下任意两小区之间的最短路径。2、提出了利用路口停车线的流量数据、出租车GPS数据推导MFD和GMFD的方法。通过分析长沙市中心城区13个交叉口构成的路网的GMFD,发现密度的不均匀分布是造成通行能力降低的原因之一。通过详细分析路网中每个路段、每个小路网的MFD,可知每个路段和每个小路网的临界平均车辆数(反映存储空间能力)和通行能力,通过对比当前路网平均车辆数与值可进行路网分流或路径诱导,为优化交通管理措施提供依据。本文提出的区域性交通网络分析,能够以较少数量的常规交通指标快速获得路网交通运行状态,从而提高交通部门的管理效率和效果,具有较大应用价值。
[Abstract]:Traffic is the result of the development of economy, and the traffic problem is the result of the contradiction between traffic and economy. Traffic management departments or researchers often only pay attention to the local congestion points or areas, and the traffic knowledge is only used to describe the model of the movement of vehicles on the road, but now the characteristics of traffic congestion have changed. Gradually from point congestion, line congestion to regional road network congestion, but people do not realize the inherent law of regional traffic network, traffic problems are often not a permanent cure. The optimization of macro-traffic organization is the root of solving all traffic problems. It optimizes the urban traffic system from the aspects of land use, road network structure, traffic mode, etc. In this paper, the traffic state analysis based on macroscopic model is proposed. Based on the background of the big data era, the parameters of the macro model are quickly calibrated with massive data to verify the accuracy and applicability of the macro model. The traditional simulation software is used to carry out single intersection or single section experiments to regionalize. In this paper, traffic state analysis based on macro model is carried out, and two main achievements: 1. A data processing method to deduce two-stream model curve using GPS data of taxi is put forward, and the research area is divided into small areas. The difference of traffic state in each cell is judged by the dispersion degree of the two-flow curve in all the cells. A method of calculating the travel time OD matrix by using the two-flow model is established. The variation of travel time T under different stopping times ts is analyzed. The sensitivity and vulnerability of each plot can be analyzed to provide the basis for route guidance under different traffic conditions. The method of matrix iteration is used to find out the shortest path between any two communities under a stopping time ts. The method of deriving MFD and GMFD from the traffic data of intersection parking line and taxi GPS data is put forward. By analyzing the GMFDs of 13 intersections in downtown Changsha, it is found that the uneven distribution of density is one of the reasons for the decrease of traffic capacity. By analyzing the MFDs of each section of the road network and the road network in detail, we can know the critical average number of vehicles (reflecting the storage space capacity) and the capacity of each road network. By comparing the average number of vehicles and the value of the current road network, the road network can be divided or guided, which provides the basis for the optimization of traffic management measures. The regional traffic network analysis proposed in this paper can quickly obtain the traffic running state of the road network with a small number of conventional traffic indexes, thus improving the efficiency and effect of traffic management, and has great application value.
【学位授予单位】：长沙理工大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：U491

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