基于交通信息的城市网络路径诱导策略研究

发布时间：2018-03-12 08:22

本文选题：路径诱导策略　切入点：元胞自动机　出处：《合肥工业大学》2017年硕士论文　论文类型：学位论文

【摘要】：随着国民经济的高速发展以及城市化水平的提高,居民的出行需求日益增长,机动车保有量迅速提升,城市交通拥堵已经成为制约城市发展的瓶颈。基于交通流基本特性,采用路径诱导控制策略能够有效的提高系统性能,在一定程度上能够缓解交通拥堵。近年来,选取城市交通网络为研究对象,设计不同的路径诱导策略模型,探究不同诱导策略对城市交通系统的影响受到越来越多学者们的关注。本文利用元胞自动机模型构建类曼哈顿城市交通系统,在前人模型的基础上提出基于平均速度信息反馈的路径诱导策略和基于最小费用最大流路径诱导策略,通过计算机仿真对比分析,探究城市交通网络路径诱导策略系统的交通流特征和规律。论文的主要工作如下:1.基于前人的研究,提出基于平均速度信息反馈的全局路径诱导策略,并通过元胞自动机模型对该路径诱导策略进行仿真。仿真结果显示:全局路径诱导策略极大的提高了系统的最大流量和临界密度,说明新的路径诱导策略确实能提升系统的性能。2.在全局路径诱导策略研究中,通过比较车辆分布图的变化,发现新的路径诱导策略下车辆会出现“绕路”现象:车辆会避开中心拥堵区域,而在外围绕远路到达目的地。3.现实情况中并非所有的驾驶员均会按照推送的诱导信息行驶,将接受推送信息并按照推送信息行驶的车辆定义为动态车,对不同动态车比例条件下路径诱导策略研究,发现系统存在最优动态车比例。4.在对最优动态车比例研究过程中发现:路网规模的改变并不会影响系统的最优动态车比例;诱导周期时长与系统最优动态车比例呈现负相关的关系。5.提出了基于最小费用最大流的路径诱导策略。利用Gurobi数学规划优化器求解最小费用最大流问题确定路段流量,根据流量确定交叉口转向概率,通过元胞自动机模型仿真对比发现,该路径诱导策略能够提升系统最大平均车辆到达率。6.在均质网络中求解的最大流与实际仿真的到的系统最大流率差距很小,且只要网络流率不超过系统最大流率则系统最终都会出现车辆数保持不变的状态。
[Abstract]:With the rapid development of national economy and the improvement of urbanization level, the travel demand of residents is increasing day by day, and the number of motor vehicles is increasing rapidly. Urban traffic congestion has become the bottleneck restricting the development of the city. Based on the basic characteristics of traffic flow, Path guidance control strategy can effectively improve the performance of the system and alleviate traffic congestion to a certain extent. In recent years, different path guidance strategy models are designed based on urban traffic network. More and more scholars pay attention to the influence of different inductive strategies on urban transportation system. In this paper, we use cellular automata model to construct Manhattan-like urban transportation system. Based on the previous models, a path guidance strategy based on average velocity information feedback and a path guidance strategy based on minimum cost and maximum flow are proposed, and compared with each other by computer simulation. The main work of this paper is as follows: 1. Based on previous studies, a global path guidance strategy based on average speed information feedback is proposed. The simulation results show that the global path guidance strategy greatly improves the maximum flow and critical density of the system. The results show that the new route guidance strategy can improve the performance of the system. 2. In the global path guidance strategy research, by comparing the changes of the vehicle distribution map, It is found that under the new route guidance strategy, the vehicle will have a "detour" phenomenon: the vehicle will avoid the congestion area in the center, but not all drivers will travel according to the induced information in reality. The vehicle that receives the push information and drives according to the push information is defined as the dynamic vehicle, and the path guidance strategy under different dynamic vehicle ratio is studied. It is found that the optimal dynamic vehicle ratio exists in the system. 4. In the process of studying the optimal dynamic vehicle ratio, it is found that the change of the scale of the road network will not affect the optimal dynamic vehicle proportion of the system; There is a negative correlation between the induced cycle time and the optimal dynamic vehicle proportion. 5. A path guidance strategy based on the minimum cost and maximum flow is proposed. The Gurobi mathematical programming optimizer is used to solve the minimum cost maximum flow problem to determine the road flow. The turning probability of intersection is determined according to the flow, and the simulation results of cellular automata model show that, The path guidance strategy can improve the maximum average vehicle arrival rate of the system .6.There is a small gap between the maximum flow rate solved in the homogeneous network and the simulated maximum flow rate of the system. And as long as the network flow rate does not exceed the maximum flow rate, the system will eventually have the same number of vehicles.
【学位授予单位】：合肥工业大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：U495

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