A Simplified Theory of Urban Congestion

城市拥堵的简化理论

• 批准号: 1562536
• 负责人: Jorge Laval
• 金额: $ 30.51万
• 依托单位: Georgia Tech Research Corporation
• 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: 2016-08-01 至 2020-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1562536&HistoricalAwards=false
• 关键词: Simplified; Theory; Urban; Congestion

This award explores the premise that traffic congestion levels on large cities may be well described with a theory that requires only three aggregate network parameters: average red and green times for traffic lights and average block size. This conjecture has been validated by the research team for a single city where data are available; verifying it with additional empirical data is therefore one of the main motivations for the proposed research. If successful, this research will have profound implications in our discipline and for the society at large. Current planning models might produce suboptimal results, because they rely on detailed origin-destination tables that may be impossible to estimate reliably, and traffic dynamics that must be simplified such that they do not adequately capture the characteristics of congestion. The proposed theory overcomes these deficiencies by employing a macroscopic approach whose results are consistent with microscopic traffic dynamics. The proposed theory enables large-scale field implementations of congestion-saving control strategies for big metropolitan areas. These strategies are robust, and independent of both origin-destination tables and of the details of the road network. This project will enhance the educational and research infrastructure by making available large amounts of empirical data and software tools for their analysis and for simulation. This project will seek to validate and expand the hypothesis that the Macroscopic Fundamental Diagram of an urban network, which gives average travel time as a function of average density, depends mainly on the proposed three parameters. To test this hypothesis, the research will employ empirical data from different cities around the world. The analytical framework from prior NSF-funded research will be expanded to understand the conditions where this theory is applicable. This will shed light on the effects in macroscopic network performance of traffic signal control, route choice models and congestion pricing. Stochastic processes techniques will be employed to generate bounds to approximate network capacity reductions due to short blocks, which has proved difficult in the past. The project will develop the concept of macroscopic dynamic traffic assignment by formulating the assignment problem and its solution through a partition of the network into a collection of subnetworks, each described by a Macroscopic Fundamental Diagram. Closed form solutions will be sought for simple configurations, which will be the building blocks to develop numerical algorithms to tackle general networks. This will allow the computation of congestion control strategies, traffic state estimation and forecast over large urban networks and in real-time, all of which is intractable with current methods.

该奖项探讨了一个前提，即大城市的交通拥堵水平可以用一个理论很好地描述，该理论只需要三个聚合网络参数：交通灯的平均红灯和绿色时间以及平均街区大小。研究小组已经在一个有数据的城市验证了这一猜想;因此，用额外的经验数据来验证它是拟议研究的主要动机之一。如果成功，这项研究将对我们的学科和整个社会产生深远的影响。目前的规划模型可能会产生次优的结果，因为它们依赖于详细的起点-目的地表，可能是不可能可靠地估计，和交通动态，必须简化，使他们没有充分捕捉拥堵的特点。所提出的理论克服了这些缺陷，采用宏观的方法，其结果是一致的微观交通动力学。所提出的理论，使大规模的现场实施的节能控制策略的大都市地区。这些策略是鲁棒的，并且独立于起点-目的地表和道路网络的细节。该项目将通过提供大量经验数据和软件工具进行分析和模拟，加强教育和研究基础设施。该项目将试图验证和扩展假设，即城市网络的宏观基本图，它给出了平均出行时间作为平均密度的函数，主要取决于拟议的三个参数。为了验证这一假设，研究将采用来自世界各地不同城市的经验数据。从以前的NSF资助的研究的分析框架将被扩展，以了解该理论适用的条件。这将有助于揭示交通信号控制、路径选择模型和拥挤定价对宏观网络性能的影响。 随机过程技术将被用来生成边界，以近似网络容量减少，由于短块，这在过去已被证明是困难的。该项目将发展宏观动态交通分配的概念，通过将网络划分为一系列子网络来制定分配问题及其解决方案，每个子网络由宏观基本图描述。将为简单配置寻求封闭形式的解决方案，这将是开发解决一般网络的数值算法的基石。这将允许在大型城市网络上实时计算拥塞控制策略、交通状态估计和预测，所有这些都是当前方法难以处理的。