Collaborative Research: A Dynamic Disruption Prediction System for Transportation Networks at a Road-Segment Level of Granularity

合作研究：路段粒度级交通网络动态中断预测系统

• 批准号: 2026795
• 负责人: Mohammad Ilbeigi
• 金额: $ 25.21万
• 依托单位: Stevens Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2026795&HistoricalAwards=false
• 关键词: Collaborative; Research; Dynamic; Disruption; Prediction

This grant will develop theoretical foundations and empirically test the feasibility of a novel framework for real-time monitoring and disruption prediction of road transportation networks during extreme events at a road-segment level of granularity. Recent natural disasters have shown that road transportation networks mainly fail due to unexpected gridlocks resulting from unusual traffic patterns. These disruptions adversely affect emergency management processes such as evacuations, rescue, and recovery operations. Prompt prediction of disruptions is vital for a successful emergency management system, but despite this critical need, emergency management processes still lack real-time network monitoring and prediction methods for transportation systems. Real-time monitoring and disruption prediction of road networks creates opportunities to develop proactive emergency management systems that lead to efficient, fast, and successful rescue and recovery operations. The benefits of more efficient and successful emergency management operations will be passed down to the public and will result in enhanced quality of life, health, and well-being for commuters and other infrastructure users. Ultimately, this project will contribute to developing sustainable and resilient cities and communities that can function properly even under the stress of extreme events. In connection with this project, educational and outreach efforts are envisioned for integration into undergraduate and graduate courses. This grant will provide an opportunity for project-based learning for K-12, undergraduate, and graduate students, especially minorities and under-represented groups. This research project aims to conduct fundamental research to create a methodology and framework that dynamically predicts disruptions in road transportation networks based on unusual traffic patterns detected during extreme events. The method designed in this project offers a holistic network approach with a road segment level of granularity that, while it considers the entire network, is also able to monitor and predict traffic flow in each road segment. The specific research objectives are to 1) discover and model temporal traffic flow interdependencies, 2) efficiently monitor traffic flows on a real-time basis at a road segment level of granularity using instantaneous traffic data to detect unusual traffic patterns, and 3) predict disruptions by integrating network interdependencies and the identified unusual traffic patterns. The outcomes of this project will set the stage for development of proactive emergency management systems that will result in more efficient and successful rescue and recovery operations.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.

该赠款将开发理论基础，并在经验上检验一个新型框架在极端事件中，在粒度范围内的极端事件中，实时监控和破坏道路运输网络的可行性。最近的自然灾害表明，道路运输网络主要是由于异常交通模式引起的意外僵局而失败的。这些破坏会对紧急管理过程（例如撤离，救援和恢复行动）产生不利影响。迅速预测中断对于成功的应急管理系统至关重要，但是尽管需要这种迫切的需求，但紧急管理流程仍然缺乏实时网络监控和运输系统的预测方法。道路网络的实时监控和破坏预测创造了开发积极的应急管理系统的机会，从而导致有效，快速和成功的救援和恢复操作。更高效，更成功的紧急管理运营的好处将被传给公众，并将为通勤者和其他基础设施使用者增强生活质量，健康和福祉。最终，该项目将有助于发展可持续和韧性的城市和社区，即使在极端事件的压力下，这些城市和社区也可以正常运作。与该项目有关，设想教育和外展工作将其整合到本科和研究生课程中。该赠款将为K-12，本科生和研究生，尤其是少数民族和代表性不足的团体提供基于项目的学习机会。该研究项目旨在进行基本研究，以创建一种方法和框架，该方法和框架会根据极端事件中检测到的异常交通模式动态预测道路运输网络中的中断。该项目中设计的方法提供了一种整体网络方法，并具有粒度的路段水平，尽管它考虑了整个网络，但也能够监视和预测每个路段中的交通流量。具体的研究目标是1）发现和建模时间流量相互依存关系，2）使用瞬时交通数据在粒度的路段水平上实时监视交通流量，以检测异常的交通模式，3）3）通过整合网络相互依存和确定的异常流量模式来预测中断。该项目的成果将为发展积极的应急管理系统的发展奠定了基础，该系统将导致更有效，成功的救援和恢复运营。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛的影响来评估的评估。