NSF/USDOT: A Non-Continuum Model of the Flow of Traffic Via Aggregation and its Application to Trip-Time Prediction

有经验证据表明，车辆和公路层面的信息获取和利用可以用来增强交通流量。然而，为了实现为车辆和高速公路配备信息获取、处理和广播设备的潜在好处，需要有工具来模拟车辆层面的信息如何影响交通动态。该项目旨在发展新的和创新的理论，如何将交通流建模为一个有限维的聚合动态系统。聚合动力系统的概念是：在任何时刻，交通流参数都可以被描述为一组聚合参数（均值、方差、众数、中位数等），这些参数可以用来定义交通流中的代表性车辆。该项目将开发一个空间离散的交通流模型，该模型将使用诸如车辆保护和车辆动力学等物理原理来模拟车辆在一段高速公路上的行驶。该项目还将研究获得欧拉（宏观）描述拉格朗日（微观）过程的问题。该模型将用于研究随着智能交通系统（如自适应巡航控制和动态信息标志）的引入，交通动态是如何变化的，这两种系统都会改变高速公路上一些车辆的行为。这项研究的成功将有助于更好地理解交通流量和驾驶员行为，并有助于更准确地预测智能交通系统的好处。该项目的研究结果将被纳入本科和研究生课程。

There is empirical evidence that information acquisition and utilization at the vehicular and highway level can be used to enhance the flow of traffic. However, to realize the potential benefits of equipping vehicles and highways with information acquiring, processing and broadcasting devices, tools are needed that can model how information at the vehicular level affects the dynamics of traffic. This project seeks to develop new and innovative theories on how to model traffic flow as a finite dimensional aggregated dynamic system. The concept of an aggregated dynamical system is as follows: at any instant in time, traffic flow parameters can be describes as a set of aggregation parameters (mean, variance, mode, median, etc.) and that these parameters can be used as to define representative vehicles in the traffic stream. This project will develop a spatially discrete model for traffic flow that will use such physical principles as conservation of vehicles and vehicle dynamics to model vehicle travel through a section of highway. The project will also examine the issue of obtaining a Eulerian (macroscopic) description for a Lagrangian (microscopic) process. The model will be used to examine how the dynamics of traffic change with the introduction of Intelligent Transportation Systems such as adaptive cruise control and dynamic message signs, both of which alter the behavior of some vehicles on the highway. The success of this research will lead to a better understanding of traffic flow and driver behavior, and will lead to more accurate predictions of the benefits of Intelligent Transportation Systems. Results of the project will be integrated into both undergraduate and graduate course.