POWRE: Scaling the Two-Fluid Model of Town Traffic to Characterize Traffic Flow Through Signalized Intersections

POWRE：扩展城镇交通的二流体模型来表征通过信号交叉口的交通流

• 批准号: 9973788
• 负责人: Elizabeth Jones
• 金额: $ 7.49万
• 依托单位: University of Nebraska-Lincoln
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1999
• 资助国家: 美国
• 起止时间: 1999-09-01 至 2001-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9973788&HistoricalAwards=false
• 关键词: POWRE; Scaling; Two; Fluid; Model

9973788JonesIntelligent transportation systems (ITS) represent a fundamental shift in how transportation systems are designed and operated. Benefits from ITS projects accrue at different geographic scales, yet no one model of traffic flow quality is able to assess these benefits at various scales. A model that is capable of quantifying the quality of traffic flow across scales of transportation systems will be beneficial in assessing the benefits and operations of ITS projects in terms of both localized and system-wide effects. Such a scalable model has the potential to greatly enhance our understanding of the processes governing traffic flow. This project, which is supported under the POWRE Program, investigates the two-fluid model of town traffic, which is one model that appears to have scalable properties. The scalability of this model will be investigated through a program of data collection and analysis that utilizes machine vision processing. The research is expected to provide a novel model for traffic flow through signalized intersections. The analytical work related to the scalability of the two-fluid model and the experienced gained with machine vision processing will expand the research capabilities of the PI and will allow her to do future work related to the scalability of other traffic flow models.***

智能交通系统(ITS)代表着交通系统设计和运营方式的根本性转变。ITS项目的收益在不同的地理尺度上累积，但没有一个交通流量质量模型能够在不同的尺度上评估这些收益。一个能够量化不同交通系统规模的交通流量质量的模型将有助于从局部和系统范围的影响来评估智能交通系统项目的效益和运作。这种可扩展的模型有可能极大地增强我们对控制交通流的过程的理解。该项目由POWRE计划支持，研究城镇交通的双流体模型，这是一个似乎具有可伸缩特性的模型。这个模型的可扩展性将通过一个利用机器视觉处理的数据收集和分析程序进行调查。该研究可望为信号交叉口的交通流研究提供一种新的模型。与双流体模型的可扩展性相关的分析工作以及在机器视觉处理方面获得的经验将扩展PI的研究能力，并将使她能够进行与其他交通流模型的可扩展性相关的未来工作。