车联网环境下基于路段负载链估测与优化的动态交通诱导方法研究

How to construct a systemic, real-time and route-through guidance system in connected vehicle network environment is a new problem faced by Traffic Engineering. This study is put forward from link-load-chain evaluation and optimization. We will take transport demands of vehicle group as input, generating, evaluating and optimizing of the vehicle driving route set as processes, and traffic guidance for individual vehicle as output. The purpose of this study is to coordinate the behavior of time and path choice of every individual vehicle in traffic network to balance the pressure of each period in various link sections. The specific content of the research including: First of all, we will define the link load chain and put forward its measure approaches, and then establish a mathematical model for dynamic traffic guidance problem in connected vehicles network environment. In order to find out the solution of the proposed model above, we will study on the method of solution space compression based on OD path set and the method of generating initial solution based on limited volume capacity and gradient. Then, aiming at evaluation of the solution, we will propose a method for network analysis and construct numerical simulation model of traffic flow. Related research results will play a positive role in promoting the development of real-time traffic guidance, dynamic traffic assignment and the basic theory and application technology of connected vehicle network.

车联网车-车、车-路动态信息交互的环境下如何从全局的角度对交通流进行实时、全程的引导是交通工程学科面临的新问题。本课题拟在定义路段负载链并给出其数值度量指标的基础上，以车辆出行需求为输入，行车路径集的生成、评估、优化为处理过程，以面向个体的交通诱导方案为输出，系统性地协调各车辆个体的路径选择行为，达到均衡各路段各时段的交通负荷的目的。主要研究思路：首先提出路段负载链的度量指标和车联网环境下动态交通诱导的技术流程，研究基于OD对路径集的交通诱导解空间压缩方法和基于容量与梯度约束的初始方案生成方法，进而针对方案的评估构建基于元胞传输模型的交通流数值演化模型。相关研究成果对实时交通诱导、动态交通分配、车联网基础理论和应用技术的发展将起到积极的促进作用。

车联网车-车、车-路动态信息交互的环境下如何从全局的角度对交通流进行实时、全程的引导是交通工程学科面临的新问题。本课题在定义路段负载链并给出其数值度量指标的基础上，以车辆出行需求为输入，行车路径集的生成、评估、优化为处理过程，以面向个体的交通诱导方案为输出，系统性地协调各车辆个体的路径选择行为，达到均衡各路段各时段的交通负荷的目的。主要研究思路：首先提出路段负载链的度量指标和车联网环境下动态交通诱导的技术流程，研究基于OD对路径集的交通诱导解空间压缩方法和基于容量与梯度约束的初始方案生成方法，进而针对方案的评估构建基于元胞传输模型的交通流数值演化模型。相关研究成果对实时交通诱导、动态交通分配、车联网基础理论和应用技术的发展将起到积极的促进作用。