Analytical Probabilistic Traffic Models for Large-scale Network Optimization

用于大规模网络优化的分析概率流量模型

• 批准号: 1562912
• 负责人: Carolina Osorio
• 金额: $ 33.88万
• 依托单位: Massachusetts Institute of Technology
• 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: 2016-06-01 至 2021-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1562912&HistoricalAwards=false
• 关键词: Analytical; Probabilistic; Traffic; Models; Large

Major transportation agencies in the U.S. and Europe have recognized the importance of measuring and optimizing the reliability and robustness of our networks. Evaluating reliability and robustness metrics involves the use of probabilistic network models. This project formulates, validates and uses probabilistic network models. Case studies based on actual metropolitan areas will illustrate the importance of accounting for uncertainty in large-scale transportation network analysis. The use of these methods can inform the design and operations of the considered networks, helping to mitigate congestion along with its economic, environmental and health impacts. These case studies on complex regional networks will illustrate the contributions to transportation practice of the methodologies. The findings of this project will be shared through various activities with transportation researchers, transportation stakeholders, the general public and with young engineers interested in learning about and contributing to the transportation challenges of the future.This project formulates an analytical stochastic kinematic wave model for general network topologies. It formulates a model that is suitable to address large-scale network optimization problems. First, the project formulates stochastic link models that are consistent with the kinematic wave model. Two types of models are formulated: (i) models with a complexity that is linear in the link?s space capacity, (ii) models with a complexity that is independent of the link?s space capacity. This is achieved through a combination of ideas from the fields of traffic flow theory, queueing network theory, transient queueing theory, and more generally operations research. Second, the project formulates a network decomposition approach that enables the link models to be used for large-scale network analysis. Third, this project plans a technique to approximate the joint network distribution of a given performance measure based on lower-dimensional subnetwork distributions. The case studies of this project contribute to the modeling of between-link dependency structures, as well as to their use to mitigate congestion for large-scale networks.

美国和欧洲的主要运输机构已经认识到衡量和优化我们网络的可靠性和稳健性的重要性。评估可靠性和稳健性指标涉及概率网络模型的使用。该项目制定、验证和使用概率网络模型。基于实际大都市区的案例研究将说明在大规模交通网络分析中考虑不确定性的重要性。这些方法的使用可以为所考虑的网络的设计和运营提供信息，有助于减轻拥堵及其对经济、环境和健康的影响。这些关于复杂区域网络的案例研究将说明这些方法对运输实践的贡献。该项目的研究成果将通过各种活动与交通研究人员、交通利益相关者、公众以及有兴趣了解和应对未来交通挑战的年轻工程师分享。该项目为通用网络拓扑制定了分析随机运动波模型。它制定了适合解决大规模网络优化问题的模型。首先，该项目制定了与运动波模型一致的随机链接模型。制定了两种类型的模型：(i) 复杂度与链路空间容量成线性关系的模型，(ii) 复杂度与链路空间容量无关的模型。这是通过结合交通流理论、排队网络理论、瞬态排队理论和更普遍的运筹学领域的思想来实现的。其次，该项目制定了一种网络分解方法，使链路模型能够用于大规模网络分析。第三，该项目计划一种基于低维子网络分布来近似给定性能度量的联合网络分布的技术。该项目的案例研究有助于链路间依赖结构的建模，以及它们用于缓解大规模网络的拥塞。