基于修正优化速度函数的多前车跟驰模型研究

发布时间：2018-05-27 19:09

  本文选题：交通流理论 + 优化速度　； 参考：《哈尔滨工业大学》2016年硕士论文

【摘要】：车辆跟驰理论既是微观交通流理论最基本的仿真模型,也是理解宏观交通流形成的理论基石,而且具有指导交通组织和管理、缓解交通拥堵的现实意义。随着智能交通系统和智能车载设备的发展,驾驶员在驾驶过程中获取的道路、交通和环境信息越来越丰富和准确,必然对驾驶员的驾驶行为产生一定的影响,而车辆行驶中的跟驰行为,是驾驶员主要的驾驶行为之一,因此研究多前车影响下的车辆跟驰行为具有重要的意义。首先,搭载高精度车载GPS设备获取了大量的车辆跟驰数据。以加速度变化为依据将车辆跟驰分为启动加速、稳定跟驰以及减速停车三个状态,并分别分析了每个状态下车辆速度和加速度变化规律。分析了车头间距-速度的数量关系,结果表明指数曲线能够较好地描述车头间距随速度的变化趋势;分析了加速度随后车速度和相对速度的变化趋势,结果表明加速度和相对速度呈现出较强的线性正相关关系。然后构建了基于修正优化速度函数的多前车跟驰模型。基于实测数据对原优化速度函数进行了修正,基于多前车驾驶特性的考虑对原多前车跟驰模型进行了修正,构建了基于修正优化速度函数的多前车跟驰模型。应用物理和数学方法对模型进行了稳定性求解,得到模型的稳定方程和稳定条件。应用实测数据对修正的优化速度函数和修正的多车跟驰模型进行了参数标定,确定速度-车头间距的数量关系以及驾驶员对期望速度差和前后车速度车的敏感系数。最后通过理论推导和数值仿真分析了修正的多前车跟驰模型对交通现象的解释以及模型稳定性,并与原多前车跟驰模型进行了对比。理论推导结果表明修正的优化速度函数推导的交通流参数关系与实测数据拟合精度更高。数值仿真结果表明修正的多前车跟驰模型能够较好地描述车辆在启动加速和减速停车时的速度和加速度变化趋势,且在遇到人为干扰以及多驾驶特性造成的扰动时能够以更小的波动幅度和更快的速度达到均衡状态。
[Abstract]:The car following theory is not only the most basic simulation model of the microscopic traffic flow theory, but also the theoretical foundation for understanding the formation of the macro traffic flow, and has the practical significance of guiding the traffic organization and management and alleviating traffic congestion. With the development of intelligent transportation system and intelligent vehicle equipment, the driver gets the road and traffic during the driving process. And the environment information is more and more abundant and accurate, and the driving behavior of the driver is bound to have a certain influence, and the following behavior in the vehicle is one of the main driving behavior of the driver. Therefore, it is of great significance to study the car following behavior under the influence of the multi front vehicle. First, carrying a high precision vehicle GPS equipment has obtained a large amount. The vehicle heel data, based on the acceleration change, divides the car following into three states of starting acceleration, steady and slow stopping and decelerating and stopping, and analyses the law of vehicle speed and acceleration in each state, and analyzes the number relation of the distance velocity of the head. The result shows that the index curve can describe the distance of the head better. The trend of velocity change is analyzed. The trend of the acceleration and relative velocity is analyzed. The result shows that the acceleration and relative velocity have a strong linear correlation. Then a multi front car following model based on the modified optimization velocity function is constructed. Based on the measured data, the original optimization speed function is modified, based on the more. In the consideration of the driving characteristics of the front car, the model of the multiple front car following the original multi front car is corrected, and a multi front car following model based on the modified optimization velocity function is constructed. The stability of the model is solved by the physical and mathematical methods, the stability equation and the stability condition of the model are obtained. The modified optimization speed function and the correction are used by the measured data. The parameters of the multi car following model are calibrated to determine the relationship between the speed and the head distance and the driver's sensitivity to the expected speed difference and the front and back speed car. Finally, the theory and numerical simulation are used to explain the traffic phenomenon and the model stability. The model is compared. The theoretical derivation shows that the traffic flow parameters derived from the modified optimization speed function are more accurate than the measured data. The numerical simulation results show that the modified multi front car following model can describe the speed and acceleration trend of the vehicle in the acceleration and deceleration. Disturbance caused by human interference and multiple driving characteristics can achieve a smaller state of fluctuation and faster speed.
【学位授予单位】：哈尔滨工业大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：U491
，

本文编号：1943412