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

NSF/USDOT：通过聚合实现交通流的非连续模型及其在行程时间预测中的应用

• 批准号: 0231649
• 负责人: Swaroop Darbha
• 金额: $ 19.89万
• 依托单位: Texas A&M Engineering Experiment Station
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-01-01 至 2006-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0231649&HistoricalAwards=false
• 关键词: NSF; USDOT; Non; Continuum; Model

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.

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改善交通流量。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，需要能够模拟车辆层面的信息如何影响交通动态的工具。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。聚集动力系统的概念如下：在任何时刻，交通流参数都可以描述为一组聚集参数(均值、方差、模式、中位数等)。并且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，该模型将使用车辆守恒和车辆动力学等物理原理来模拟车辆通过一段高速公路的旅行。该项目还将研究获得拉格朗日(微观)过程的欧拉(宏观)描述的问题。该模型将用于研究随着自适应巡航控制和动态信息标志等智能交通系统的引入，交通动态将如何变化，这两者都会改变一些车辆在高速公路上的行为。这项研究的成功将有助于更好地了解交通流量和驾驶员行为，并将导致对智能交通系统好处的更准确预测。该项目的成果将纳入本科生和研究生课程。