Improving Resilience of Adaptive Capacity in Traffic Networks with Intelligent Systems and Advancd Methods

利用智能系统和先进方法提高交通网络适应能力的弹性

• 批准号: RGPIN-2015-05299
• 负责人: Khan, Ata
• 金额: $ 1.82万
• 依托单位: Carleton University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=680785
• 关键词: Improving; Resilience; Adaptive; Capacity; Traffic

This research program is aimed at enhancing the sustainability of urban transportation by improving "resilience of adaptive capacity" in traffic networks in order to prevent extreme congestion under nature-induced or other disruptions. Research products consisting of intelligent technology, predictive models, and traffic management decision aids are needed for this purpose. Resilience is defined as the ability to resist the loss of traffic-serving capability owing to inherent characteristics (i.e. the inherent resilience) and by dynamically activating capacity-enhancing measures (called the dynamic resilience).****The research program consists of three modules. The first module investigates vulnerabilities in traffic networks. These represent susceptibility of links or entire routes to potential disruptions. The second module is devoted to characterizing the inherent resilience of vulnerable parts of the traffic network. In this case, no recovery actions are assumed. In the third module, recovery actions activate adaptive capacity in a dynamic manner. Taking the inherent and dynamic resilience together, the sustainability of the adaptive capacity can be assessed by determining the amount of service that can be maintained with the level of disruption that occurs, as well as how long it takes to return to the pre-event state of service with the help of sustained adaptive capacity.****For testing measures to enhance the resilience of adaptive capacity, corridor level and area-wide traffic networks will be structured as virtual test beds with infrastructure and intelligent system components looped-in. Data required for all parts of this research will be sourced from the Cities of Ottawa and Toronto, and the Ministry of Transportation of Ontario. In addition to using commercially available microsimulation and macroscopic analysis software, there will be the need to develop algorithms for the implementation of new models.****The motivation for carrying out this research program is to advance technology and methods for achieving resilience in urban traffic networks. Results will enhance the scientific basis for (1) development of policies on the supply-side of planning and managing urban traffic networks for enhancing resilience of adaptive capacity so as to prevent extreme congestion under disruptions, (2) overcoming key deficiencies of the current transportation planning and control process, and (3) decision-making on transportation infrastructure that integrates intelligent systems and advanced control methods.****A number of interest groups will benefit from the availability of results of this research. These include: transportation policy analysts and planners, civic leaders, commuters, civic groups, business owners, and environmental protection groups. The objective of training HQP will be advanced due to the challenging and innovative nature of research tasks.******

该研究计划旨在通过提高交通网络的“适应能力的弹性”来提高城市交通的可持续性，以防止自然或其他中断引起的极端拥堵。为此目的，需要由智能技术、预测模型和交通管理决策辅助工具组成的研究产品。复原力的定义是，通过动态启动能力增强措施，抵御由于固有特性（即固有复原力）造成的交通服务能力损失的能力（称为动态复原力）。该研究计划包括三个模块。第一个模块调查交通网络中的漏洞。这些表示链路或整个路线对潜在中断的敏感性。第二个模块专门描述交通网络脆弱部分的固有复原力。在这种情况下，不采取任何恢复操作。在第三个模块中，恢复行动以动态方式激活自适应能力。将固有的和动态的复原力结合起来，可以通过确定在发生中断的情况下可以维持的服务量，以及在持续的适应能力的帮助下恢复到事件发生前的服务状态需要多长时间，来评估适应能力的可持续性。为了测试提高适应能力的弹性的措施，走廊级和区域范围的交通网络将被构建为虚拟测试床，基础设施和智能系统组件将被循环使用。本研究所有部分所需的数据将来自渥太华和多伦多市以及安大略交通部。除了使用商业上可获得的微观模拟和宏观分析软件之外，还需要开发用于实施新模型的算法。开展这项研究计划的动机是推进实现城市交通网络弹性的技术和方法。研究结果将加强以下方面的科学依据：（1）制定规划和管理城市交通网络的供给侧政策，以提高适应能力的弹性，从而防止中断情况下的极端拥堵，（2）克服当前交通规划和控制过程的关键缺陷，（3）集成智能系统和先进控制方法的交通基础设施决策。*一些利益集团将受益于这项研究的结果。其中包括：交通政策分析师和规划师、公民领袖、通勤者、公民团体、企业主和环境保护团体。由于研究任务的挑战性和创新性，HQP的培训目标将提前。