Understanding the Impacts of COVID-19 Pandemic on Human Mobility, Transportation Network Redesign and System Resilience

NSF的这项拨款将研究2019年冠状病毒病（COVID-19）大流行对现有运输和物流系统和运营的影响，运输系统重新设计的需求，以及它们在未来中断情况下的恢复能力。该研究将整合数学优化、数据分析和流行病学中的模型和技术，以研究移动性如何与不同交通网络中的病毒传播相互作用。该项目将提供一个跨学科的框架，用于分析交通网络及其弹性，具有高度的建模灵活性和政策适应性。研究结果将被纳入开源工具和在线计算/模拟平台，以便决策者能够方便地部署本项目开发的模型，重新设计具体的交通系统，并保证其复原力。研究活动与教育和外联活动相结合，将从这项研究中获得的知识扩展到更广泛的受众，包括开发新的跨学科课程，组织讲习班，共享用于研究真实世界流动性和感染数据的自动数据分析工具，并吸引本科生和研究生，特别是女性和代表性不足的少数群体，这项研究将借鉴广泛的方法和途径，包括网络优化，图论，统计学习，随机/鲁棒优化和仿真。该研究的开发、验证和校准将在三个关键方面推动交通和关键基础设施分析的前沿：（1）将不同交通系统的供需和流动性需求与感染状况和封锁政策联系起来并可视化;（2）开发交通网络重新设计的短期和长期解决方案，以限制病毒传播，同时满足旅行需求;（3）分析新系统的弹性、运作效率及可靠性。对于第一个推力，研究将集中在学习新的供需模式及其时空分布的预测模型。对于第二个目标，我们将使用整数规划，随机和鲁棒优化模型，通过考虑社交距离和其他疾病缓解策略来改进解决方案。 对于第三个推力，我们将使用基于代理的模拟和不确定性量化方法来研究系统弹性以及各种中断（例如，该奖项反映了NSF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 NSF grant will study the impacts of the coronavirus disease of 2019 (COVID-19) pandemic on existing transportation and logistics systems and operations, the needs for transportation-system redesign, and their resilience under future disruptions. The research will integrate models and techniques in mathematical optimization, data analytics and epidemiology, to study how mobility interacts with virus spread in different transportation networks. The project will deliver an interdisciplinary framework for analyzing transportation networks and their resilience with high modeling flexibility and policy adaptability. The results will be built into open-source tools and online computation/simulation platforms, so that policymakers can conveniently deploy models developed in this project to redesign specific transportation systems, with guaranteed resilience. The research activities are integrated with educational and outreach activities, to extend the knowledge obtained from this research to a broader audience, including developing new interdisciplinary courses, organizing workshops, sharing automated data analysis tools for studying real-world mobility and infection data, and engaging undergraduate and graduate students, particularly female and underrepresented minority groups, in the research and education.This research will draw from a broad range of methodologies and approaches, including network optimization, graph theories, statistical learning, stochastic/robust optimization and simulation. The development, validation, and calibration of the research will push the frontiers of transportation and critical infrastructure analysis in three key thrusts: (1) linking and visualizing supply-demand and mobility needs in different transit systems with infection status and lockdown policies; (2) developing short-term and long-term solutions of transportation network redesign to limit virus spread while satisfying travel needs; (3) analyzing the resilience, operational efficiency and reliability of the new systems. For the first thrust, the research will focus on predictive models for learning new supply-demand patterns and their spatiotemporal distributions. For the second thrust, we will use integer programing, stochastic and robust optimization models to improve solutions by also taking into account social-distancing and other disease-mitigation strategies. For the third thrust, we will use agent-based simulation and uncertainty quantification methods to study system resilience as well as cost and operational efficiency under various disruptions (e.g., demand surge, supply shortage, or link breakdown).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.