From Microscopic to Macroscopic Traffic Flow: A Queuing Theoretic Approach

从微观到宏观交通流：排队理论方法

基本信息

批准号：
1636377
负责人：
Ketan Savla
金额：
$ 30.47万
依托单位：
University of Southern California
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2016
资助国家：
美国
起止时间：
2016-09-01 至 2021-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1636377&HistoricalAwards=false
关键词：
Microscopic Macroscopic Traffic Flow Queuing

项目摘要

Emerging capabilities in driverless cars and inter-vehicle communication are outpacing the analysis and design capabilities of traditional simulation-based modeling tools. This project will focus on methods linking vehicle-scale characteristics to system-level performance metrics. For example, features such as autonomous car-following and lane-changing will be related to macroscopic traffic properties such as peak capacity and average travel times. Systematic analysis and design of urban traffic systems may save lives, prevent injuries, ease traffic congestion, and improve the environment. More generally, the project will create a rigorous statistical mechanics theory applicable to other multi-particle/agent systems, such as crowd motion and formations of mobile robots. The research will be tightly integrated with the education component through software projects in the graduate and undergraduate courses developed by the PI. Existing programs at the University of Southern California will be utilized to integrate inclusive teaching practices into educational activities in order to address retention of women, underrepresented and minority students. This project seeks to overcome the shortcomings of existing analytical and simulation-based traffic models. Available analytical techniques either apply only to closed systems, i.e., to a fixed number of vehicles whose motion is possibly coordinated by a leader, or they assume that the flow dynamics are quasi-static. Simulation-based methodologies are not suitable for developing fundamental insights. This project will address these shortcomings through two novel research thrusts. The first thrust is formulation of a rigorous horizontal traffic queuing theory under various car following and lane-changing models. The distinguishing feature of this theory is the ability to capture microscopic congestion effects, in contrast to the limited resolution inherent in approaches that use point queue models. The analysis begins by identifying fundamental relationships between throughput and average travel times associated with traffic flow for specified parameters of the underlying road infrastructure, communication network between the vehicles, and mechanical constraints of individual vehicles. The second research thrust is use of the fluid limit to derive macroscopic traffic flow models for a given set of microscopic interaction rules. A key tool here is the notion of measure-valued state descriptors, taken from the processor sharing queue literature, which provides a unified setting for the analysis of traffic systems with time-varying dimension in a common state space.

无人驾驶汽车和车辆间通信的新兴功能正在超越传统基于仿真的建模工具的分析和设计能力。该项目将重点关注将车辆规模特征与系统级性能指标联系起来的方法。例如，自动跟车和变道等功能将与峰值容量和平均出行时间等宏观交通属性相关。城市交通系统的系统分析和设计可以挽救生命、防止伤害、缓解交通拥堵并改善环境。更一般地说，该项目将创建适用于其他多粒子/代理系统的严格统计力学理论，例如人群运动和移动机器人的编队。该研究将通过 PI 开发的研究生和本科生课程中的软件项目与教育部分紧密结合。南加州大学现有的项目将用于将包容性教学实践融入教育活动中，以解决女性、代表性不足和少数族裔学生的保留问题。该项目旨在克服现有基于分析和模拟的交通模型的缺点。现有的分析技术要么仅适用于封闭系统，即其运动可能由领导者协调的固定数量的车辆，要么假设流动动力学是准静态的。基于模拟的方法不适合发展基本见解。该项目将通过两项新颖的研究重点来解决这些缺点。第一个重点是在各种汽车跟随和换道模型下制定严格的水平交通排队理论。该理论的显着特征是能够捕获微观拥塞效应，这与使用点队列模型的方法固有的有限分辨率形成鲜明对比。分析首先确定吞吐量和平均行程时间之间的基本关系，这些时间与基础道路基础设施、车辆之间的通信网络以及单个车辆的机械约束的指定参数的交通流相关。第二个研究重点是利用流体限制为一组给定的微观相互作用规则导出宏观交通流模型。这里的一个关键工具是测量值状态描述符的概念，取自处理器共享队列文献，它为分析公共状态空间中具有时变维度的流量系统提供了统一的设置。