CAREER: Human Mobility Prediction and Intervention based on Cross-Domain Infrastructure-Human Interactions

该项目旨在模拟和支持城市规模的实时人类流动性。更好的人类流动模型可以帮助应对可持续性挑战（通过预测交通拥堵、空气质量和能源消耗），并提高城市对破坏性事件（如基础设施故障、自然灾害或流行病）的抵御能力。该项目的关键思想是，人们与交通、通信、金融和其他基础设施之间日益频繁的互动可以用来理解移动模式。然而，收集这些信息并将其集成到移动模型中仍然是一个开放的挑战。此外，人们的集体决策可能会对基础设施产生负面影响，增加等待时间，降低交通和信息基础设施的容量。通过收集和整合多个来源的出行相关行为，项目团队将推动出行建模方法的最新发展，并制定干预措施，鼓励人们做出大规模改善出行结果的选择，尤其是在危机事件期间。该团队还将开发教育材料，以培养学生成为未来的研究人员和工人，他们拥有数据收集和建模专业知识，特别是围绕人类流动问题。该项目分为两个主要部分，与机动性建模和机动性干预的目标相一致。移动性预测的第一个重点是探索多种基础设施之间相互作用的相关性和相互依赖性，特别是交通、通信和金融相互作用。这将通过基于多视图学习的先进技术来集成跨域交互，这将使用关联驱动的多任务递归神经网络架构集成到预测框架中。第二个重点是通过制定干预措施来改善破坏性事件下的流动性，从而提高城市韧性。该团队将使用一种新的动态马尔可夫决策过程公式，通过分布式深度强化学习来解决，以制定提高集体机动性的建议，例如为个人提供新的出发时间或路线，或为城市规划者提供道路封闭和运输能力分配。这些模型将利用在2019冠状病毒病大流行之前、期间和之后收集的基础设施互动和中断交通的现有数据集，并对其进行评估。总之，这项工作将导致跨领域基础设施-人类交互的一般原则、设计方法和长期研究轨迹。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 project aims to model and support human mobility in real time at an urban scale. Better models of human mobility can help meet both sustainability challenges (through predicting traffic congestion, air quality, and energy consumption) and improve urban resilience to disruptive events (such as infrastructure failures, natural disasters, or pandemics). The key idea of the project is that people's increasingly frequent interaction with transportation, communication, financial, and other infrastructure can be used to understand mobility patterns. However, collecting and integrating this information into mobility models is still an open challenge. Further, people's collective decisions can negatively impact infrastructure, increasing wait times and reducing capacity in both transit and information infrastructures. Through collecting and integrating mobility-related behavior across multiple sources, the project team will advance the state of the art around mobility modeling methods and develop interventions that encourage people to make choices that improve mobility outcomes at scale, especially during crisis events. The team will also develop educational materials to train students to be both future researchers and workers who possess data collection and modeling expertise, particularly around questions of human mobility.The project is structured as two main thrusts that align with the goals of mobility modeling and mobility interventions. The first thrust for mobility prediction will explore the correlation and interdependency of interactions across multiple types of infrastructure, notably transportation, communication, and financial interactions. This will be done through advancing techniques based on multi-view learning to integrate cross-domain interactions, which will be integrated into a prediction framework using a correlation-driven multi-task recurrent neural network architecture. The second thrust aims to improve urban resilience by developing interventions to improve mobility under disruptive events. The team will use a novel dynamic Markov decision process formulation solved with distributed deep reinforcement learning to develop recommendations that enhance collective mobility, such as new departure times or routes for individuals, or road closures and transit capacity allocations for city planners. These models will leverage and be evaluated using existing datasets of infrastructure interaction and disrupted mobility collected before, during, and after the COVID-19 pandemic. Together, this work will lead to general principles, design methodologies, and a long-term research trajectory for cross-domain infrastructure-human interaction.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.