新型非机动车微观运动模型研究

发布时间：2018-04-30 22:10

本文选题：新型非机动车流 + 交通特性　；参考：《石家庄铁道大学》2015年硕士论文

【摘要】：非机动车交通一直是我国城市交通系统的重要组成部分,近年来电动自行车的飞速发展,改变了原来以自行车为主体的非机动车交通体系,形成了电动自行车和自行车混合行驶的新型非机动车流。各地的非机动车流情况不尽相同,本文所研究的新型非机动车流是指电动自行车和自行车相混合的非机动车流。该交通流有快慢交通个体深度混合运行并相互影响的特点,然而新型非机动车流微观运动机理目前尚未得到充分重视和系统研究,以对比分析电动自行车和自行车的交通特性为切入点,在简单制定了非机动车运动状态划分准则的前提下,建立非机动车流的微观运动模型,具体包括跟驰模型和间隙插空模型,以期达到更加真实贴切的描述新型非机动车运行规律的目的。并运用面向对象的方法,对所建立的跟驰和间隙插空模型进行仿真编程,对模型的真实可行性进行了验证。总结分析了非机动车的特性,具体包含基础特性、非机动车微观行为特性以及交通流特性。参照机动车并结合非机动车的特点,简单制定了非机动车自由行使状态与相互影响状态、跟驰状态与间隙插空状态判断准则以及对跟驰状态进行了分类。建立了无车道划分概念的新型非机动车跟驰模型。以机动车的非线性跟驰模型为基础,充分考虑快慢交通个体深度混合运行的具体情况,以及高速个体在跟驰过程中总是追求其运行速度接近其期望速度,且无车道限制的具体情况,构建了包含双车跟驰、偏转跟驰和普通跟驰的非机动车跟驰模型。利用视频处理软件获取大量车辆跟驰运行轨迹微观数据,对模型进行了标定,并验证了模型的正确性。建立了基于效用选择的非机动车间隙插空运动模型。由于新型非机动车流是高低速交通个体混合运动,高速个体为接近其期望速度运行,总是在不断寻找更舒适自由的间隙,并向该间隙运动。本文认为间隙插空是驾驶人在不同间隙行驶状态下满意程度的选择结果。通过利用Logit模型,建立了基于效用选择的间隙插空模型。由于非机动车运行时无车道概念,因此将间隙距离目标车的距离、间隙本身的特性作为效益函数变量。利用视频处理软件获取大量车辆插空运行轨迹微观数据,采用极大似然估计法对构建的判断性间隙插空模型进行了标定,分析相关参数值,验证了模型的可行性。并以跟驰模型和间隙插空模型作为核心子模型,编写仿真程序,对模型的有效性进行验证,得到比较满意的结果。本文基于电动自行车大量混入非机动车流,新型非机动车流已经成为快慢交通个体深度混合运行的具体情况,对其特性进行了系统的总结分析。并根据其交通特性,建立了分类详细的跟驰模型和将间隙距离与间隙特性融为一体的间隙插空运动模型。这两个模型与经典模型相比,更能贴切反映新情况下的非机动车运行状态。研究结果可为非机动车的微观仿真研究提供相关参考,也可为非机动车交通设施优化提供理论依据。
[Abstract]:The traffic of non motor vehicles has been an important part of the urban transportation system in China. In recent years, the rapid development of electric bicycles has changed the non motor vehicle traffic system which was the main body of the bicycle, and formed a new non motor vehicle flow with the mixed driving of electric bicycles and bicycles. The non motor vehicle flow in all parts of the country is not the same, this article is not the same. The new non motor vehicle flow is a non motor flow which is mixed with electric bicycles and bicycles. The traffic flow has the characteristics of mixed operation and interaction of fast and slow traffic. However, the micro motion mechanism of the new non motor vehicle flow has not been fully paid attention to and the system has been studied so far as to compare and analyze the electric bicycles and self. The traffic characteristic of driving is the breakthrough point. On the premise of simple formulation of the criterion of non motor vehicle movement state, the micro motion model of the non motor vehicle flow is set up, including the following model and the gap space model, in order to achieve a more true and appropriate description of the new non motor vehicle running law. And the object oriented method is used. The real feasibility of the model is verified. The characteristics of the non motor vehicle are summarized and analyzed, including the basic characteristics, the characteristics of the non motor vehicle micro behavior and the traffic flow characteristics, and the non motor vehicle freedom is simply formulated with reference to the motor vehicle and the characteristics of the non motor vehicle. On the basis of the nonlinear following model of motor vehicle, a new non motor vehicle following model is established, which takes full consideration of the specific situation of the individual depth of the fast and slow traffic and the high speed of the vehicle. In the course of the fast speed, the speed individual always seeks the speed approaching its desired speed, and the no lane limit is specific. A non motor vehicle following model, including double car following, deflecting heel following and ordinary car following, is constructed. The micro data of a large number of vehicle following running tracks are obtained by video processing software, and the model is calibrated and tested. The validity of the model is proved. A non motor vehicle clearance motion model based on utility selection is established. Since the new non motor vehicle flow is a hybrid motion of high and low speed traffic, the high-speed individual is always looking for more comfortable and free space and moving to the gap. By using the Logit model, a gap interpolation model based on utility selection is established by using the utility selection model. The distance of the gap and the gap itself is regarded as the benefit function variable. Using the maximum likelihood estimation method to calibrate the determined gap interpolation model by maximum likelihood estimation, analyze the relevant parameters and verify the feasibility of the model, and use the following model and gap interpolation model as the core sub model to write the simulation program to verify the validity of the model and get the comparison. In this paper, based on a large number of non motor vehicles mixed with electric bicycles, the new non motor vehicle flow has become a concrete case of the individual depth mixed operation of fast and slow traffic. The characteristics are systematically summarized and analyzed. According to the traffic characteristics, a detailed classification model is established and the gap distance and the gap characteristics are fused into the characteristics. The two models are more suitable to reflect the non motor vehicle running state in the new situation. The results can provide some reference for the study of the micro simulation of non motor vehicles, and also provide a theoretical basis for the optimization of non motor vehicle facilities.

【学位授予单位】：石家庄铁道大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：U491.225

【相似文献】