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

这笔赠款将开发理论基础，并实证测试一个新框架的可行性，该框架用于在路段粒度级别的极端事件期间对道路交通网络进行实时监测和中断预测。最近发生的自然灾害表明，公路运输网络的瘫痪主要是由于不寻常的交通模式造成的意外交通堵塞。这些中断对疏散、救援和恢复行动等应急管理流程产生不利影响。中断的及时预测是一个成功的应急管理系统至关重要，但尽管这一关键需求，应急管理过程中仍然缺乏实时网络监测和预测方法的运输系统。道路网络的实时监控和中断预测为开发主动应急管理系统创造了机会，从而实现高效、快速和成功的救援和恢复操作。更有效和更成功的应急管理行动的好处将传递给公众，并将提高通勤者和其他基础设施用户的生活质量、健康和福祉。最终，该项目将有助于发展可持续和有复原力的城市和社区，即使在极端事件的压力下也能正常运作。结合这一项目，设想将教育和外联工作纳入本科生和研究生课程。该补助金将为K-12，本科生和研究生，特别是少数民族和代表性不足的群体提供基于项目的学习机会。该研究项目旨在进行基础研究，以创建一种方法和框架，根据极端事件期间检测到的异常交通模式动态预测道路交通网络的中断。该项目中设计的方法提供了一种具有路段粒度级别的整体网络方法，在考虑整个网络的同时，还能够监控和预测每个路段的交通流量。具体的研究目标是：1）发现和建模时间交通流的相互依赖性，2）有效地监测交通流的实时基础上，在一个路段的粒度水平，使用瞬时交通数据，以检测不寻常的交通模式，和3）预测中断集成网络的相互依赖性和识别的不寻常的交通模式。该项目的成果将为积极的应急管理系统的发展奠定基础，这将导致更有效和成功的救援和恢复操作。该奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

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.