CAREER: Multi-Scale Models of Urban Congestion Dynamics to Support Advanced Congestion Management Strategies

职业：支持先进拥堵管理策略的城市拥堵动态多尺度模型

• 批准号: 1749200
• 负责人: Vikash Gayah
• 金额: $ 50万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-05-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1749200&HistoricalAwards=false
• 关键词: CAREER; Multi; Scale; Models; Urban

Reducing urban traffic congestion improves overall transportation system efficiency and reliability in support of improving the nation's economy, the environment and the societal welfare. The objectives of this Faculty Early Career Development Program (CAREER) award are to: 1) investigate relationships that exist between congestion dynamics at the local (e.g., individual intersections or roadways) and regional (e.g., city-wide) spatial scales; and, 2) use this knowledge to identify strategies that can effectively combat urban traffic congestion at individual intersections, along corridors and across entire regions simultaneously. The relationships between local and regional congestion dynamics will be integrated into a novel multi-scale congestion modeling framework for optimizing and refining contemporary traffic control policies as well as supporting the development of advanced congestion management strategies that will become available as connected and automated vehicles are integrated into urban environments. The proposed research will be integrated with a set of educational activities to advance our knowledge in traffic management and promote national prosperity and welfare. Specific education activities include collaboration with local transportation agencies who will provide data to verify the identified relationships and support real-world project-based learning activities and demonstrate a holistic, multi-scale view of traffic congestion. These activities will be shared through the creation of an electronic repository of undergraduate course materials on traffic operations. The PI will also develop interactive, online simulations to demonstrate the relationships between local and regional congestion dynamics and integrate these into "Expanding Youth Horizons" STEM outreach workshops targeted to local female middle school students. This research will integrate novel models of regional congestion dynamics that relate network productivity and accumulation--the network Macroscopic Fundamental Diagram (MFD)--with existing link-based modeling paradigms. These two disparate approaches will be combined by identifying and quantifying interdependencies between local and regional network properties and congestion dynamics along each of these spatial scales. Theories and methodologies will be developed to unveil network characteristics and local congestion patterns that are conducive to the existence of reproducible and predictable regional congestion models for realistic urban traffic networks. This includes the identification of the building blocks necessary to characterize regional traffic network performance for networks made up of hierarchical streets and that serve heterogeneous traffic streams. Insights obtained for these heterogeneous network structures will also serve as the basis to study and design more sophisticated systems, particularly multimodal traffic networks with multiple unique and interacting vehicle classes. Integration of this regional MFD approach with existing link-based modeling paradigms will provide a framework that facilitates automated traffic control policies to coordinate regional and local traffic performance.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

减少城市交通拥堵可以提高整个交通系统的效率和可靠性，从而支持改善国家的经济、环境和社会福利。这个教师早期职业发展计划（Career）奖的目标是：1)调查当地（例如，个别十字路口或道路）和区域（例如，全市）空间尺度上的拥堵动态之间存在的关系；并且，2)利用这些知识来确定策略，可以有效地同时解决单个十字路口、走廊和整个地区的城市交通拥堵问题。局部和区域拥堵动态之间的关系将被整合到一个新的多尺度拥堵建模框架中，以优化和完善当代交通控制政策，并支持先进的拥堵管理策略的发展，这些策略将随着联网和自动化车辆融入城市环境而变得可用。建议的研究将与一系列教育活动相结合，以提高我们对交通管理的认识，促进国家的繁荣和福祉。具体的教育活动包括与当地交通机构合作，他们将提供数据来验证已确定的关系，并支持基于实际项目的学习活动，并展示交通拥堵的整体、多尺度视图。这些活动将通过建立一个关于交通作业的本科课程材料电子资料库来分享。PI还将开发交互式在线模拟，以展示本地和区域拥堵动态之间的关系，并将其整合到针对本地女中学生的“扩展青年视野”STEM外展研讨会中。本研究将整合与网络生产力和积累相关的区域拥堵动态的新模型——网络宏观基本图（MFD）——与现有的基于链路的建模范例。这两种不同的方法将通过识别和量化本地和区域网络属性之间的相互依赖关系以及沿着每个空间尺度的拥堵动态来结合起来。将开发理论和方法来揭示网络特征和局部拥堵模式，这有利于为现实的城市交通网络建立可复制和可预测的区域拥堵模型。这包括识别必要的构建模块，以表征由分层街道组成的网络的区域交通网络性能，并为异构交通流服务。从这些异构网络结构中获得的见解也将作为研究和设计更复杂系统的基础，特别是具有多种独特和相互作用的车辆类别的多式联运交通网络。将这种区域性MFD方法与现有的基于链接的建模范例相结合，将提供一个框架，促进自动化交通控制策略，以协调区域和本地交通性能。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Model free perimeter metering control for urban networks using deep reinforcement learning

使用深度强化学习对城市网络进行无模型周界计量控制

• 发表时间: 2021
• 期刊: 100th Annual Meeting of the Transportation Research Board
• 作者: Zhou, Dongqin;Gayah, Vikash V.
• 通讯作者: Gayah, Vikash V.

Resilience of Urban Street Network Configurations under Low Demands

低需求下城市街道网络配置的弹性

• DOI: 10.1177/0361198120933269
• 发表时间: 2020
• 期刊: Transportation Research Record: Journal of the Transportation Research Board
• 作者: Yu, Zhengyao;Gayah, Vikash V.
• 通讯作者: Gayah, Vikash V.

Opposing Hysteresis Patterns in Flow and Outflow Macroscopic Fundamental Diagrams and Their Implications

• DOI: 10.1177/03611981231155421
• 发表时间: 2023-03
• 期刊: Transportation Research Record
• 影响因子: 1.7
• 作者: Guanhao Xu;Pengxiang Zhang;V. Gayah;Xianbiao Hu

Quantifying the Impacts of Right-Turn-on-Red, Exclusive Turn Lanes and Pedestrian Movements on the Efficiency of Urban Transportation Networks

• DOI: 10.1016/j.ijtst.2024.02.007
• 发表时间: 2024-02
• 期刊: International Journal of Transportation Science and Technology
• 作者: Hao Liu;Zecheng Xiong;V. Gayah

Evaluating the Effectiveness and Transferability of a Data-Driven Two-Region Perimeter Control Method Using Microsimulation

使用微观模拟评估数据驱动的两区域周界控制方法的有效性和可转移性

• DOI: 10.1177/03611981241230313
• 发表时间: 2024
• 期刊: Transportation Research Record: Journal of the Transportation Research Board
• 作者: Zhou, Dongqin;Gayah, Vikash V.
• 通讯作者: Gayah, Vikash V.