ERI: An Integrative Risk Quantification and Management Framework to Enhance the Resiliency of Surface Transportation Systems Under Disruptive Precipitation

该奖项全部或部分由《2021年美国救援计划法案》（公法117-2）资助。洪水事件是影响美国最常见的自然灾害之一。与更严重的事件相比，经常性的有害降水和不太严重的洪水事件对城市环境的影响频率要高得多。更频繁的事件给公共交通使用者、行人和司机带来更高的安全风险，并对交通基础设施的使用构成挑战。通过关注不太严重的洪水事件和强降水，该工程研究启动（ERI）项目提供了一个可扩展的框架，该框架将全面捕捉城市特征、基础设施属性、人类出行行为和破坏性事件模式对交通弹性的影响。所研究的框架将提高美国地面运输系统的安全性和效率，从而产生强大的社会和经济影响。本项目的目标是科学地了解破坏性降水事件下地面交通网络的风险，并进行有效的风险沟通。此外，研究的框架将提供一个教育工具，教授定量决策和风险分析，并协助计算高效的交通运营政策学习。以新泽西州霍博肯的洪水易发地区为重点，这种多元素建模方法将促进一个实用的决策平台，支持人类与建筑环境之间的互动。该项目利用了弹性规划、风险分析和沟通、调查设计、交通工程、图论和网络建模以及强化学习等方面的知识，以改变现有交通弹性建模工作中使用的简化假设。该项目将评估和验证洪水对当地和整个系统的流动性和可达性的影响。在量化破坏性降水对交通和可达性的影响，并预测道路洪水易感性的基础上，通过情景生成来评估和模拟驾驶员行为和破坏性模式。目标是提供一个框架，通过优先排序和风险沟通来改进业务决策。由此产生的框架将包括导致易受洪水影响地区的降水中断的所有因素，以及由此产生的对旅行网络造成进一步阻碍和安全危害的旅行模式。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。

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).Flood events are one of the most common natural disasters affecting the US. Recurring nuisance precipitation and less severe flood events affect urban environments with much higher frequency when compared to more severe events. More frequent events pose higher safety risks to public transit users, pedestrians, and drivers and cause challenges to accessing transportation infrastructure. By focusing on the less severe flood events and heavy precipitation, this Engineering Research Initiation (ERI) project provides a scalable framework that will holistically capture the impacts of a city's characteristics, infrastructures' attributes, human travel behaviors, and disruptive events' patterns on transportation resilience. The researched framework will enhance the safety and efficiency of the US surface transportation system, resulting in strong societal and economic impacts. The goal of this project is to scientifically understand surface transportation network’s risks under disruptive precipitation events and effectively communicate such risks. Further, the researched framework will provide an educational tool to teach quantitative decision-making and risk analysis and assist with computationally efficient transportation operation policy learning. Focusing on the flood-prone area of Hoboken, New Jersey, this multi-element modeling approach will promote a pragmatic decision-making platform supporting the interactions between humans and the built environment. The project harnesses knowledge from resiliency planning, risk analysis and communication, survey design, transportation engineering, graph theory and network modeling, and reinforcement learning to transform the simplified assumptions utilized in the existing transportation resiliency modeling efforts. The project will estimate and validate the local and system-wide impacts of flooding on mobility and accessibility. Upon quantifying the effects of disruptive precipitation on mobility and accessibility, and predicting road flood susceptibility, driver behavior and disruptive patterns will be evaluated and simulated during and after disruptions through scenario generation. The objective is to provide a framework that improves operational decision-making through prioritization and risk communication. The resulting framework will encompass all elements leading to disruption by precipitation in flood-prone areas and consequential travel patterns causing further hindrance and safety hazards to travel networks.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.