ATD: Deep Learning on Anomaly Detection for Human Dynamics and Hazard Response

该项目旨在研究数学模型，以便更深入地了解人类的风险应对。在不同粒度级别上分析人类在空间和时间上的运动模式，在运输管理、医疗保健和威胁检测等领域至关重要。在过去的十年中，智能手机和全球定位系统（GPS）设备的普及使我们能够前所未有地访问大量的位置数据，这些数据具有高精度的时间戳。利用这些数据，该项目将专注于异常检测，以识别观察到的移动数据集中的意外或显著不同的行为。这项研究的成果将证明在检测自然和人为灾害情况方面的价值，从而能够做出更有效的反应。该研究将有许多实际应用，包括大流行接触者追踪和传播建模，危险疏散计划和数字足迹跟踪。此外，该项目将为STEM领域代表性不足的群体的学生提供培训机会。该项目旨在开发一个新的深度学习框架，并在地理信息系统（GIS）平台上实施。该框架将推进大时空数据分析，特别是在异常检测方面，并量化有关灾害响应行为的人类移动动态。本研究将集中在三个主要目标：1。开发一个基于代理的机器学习框架，马尔可夫决策过程-反向强化学习-生成对抗网络（MDP-IRL-GAN），以检测个体运动动态中的异常。2.通过使用建议的机器学习框架分析个人运动动态来建模危险响应，同时识别影响危险响应决策的关键因素。3.采用新设计的多分辨率图神经网络（MA-GNN）检测群体动态时空模式的变化和异常。该奖项反映了NSF的法定使命，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。

The project aims to investigate mathematical models that can provide a deeper understanding of human risk response. The analysis of human movement patterns in space and time, at various levels of granularity, holds crucial importance in fields such as transport management, healthcare, and threat detection. Over the past decade, the proliferation of smartphones and Global Positioning System (GPS)-enabled devices has granted us unprecedented access to vast amounts of location data, timestamped with high precision. Leveraging this data, the project will focus on anomaly detection to identify unexpected or significantly different behaviors within observed mobility datasets. The outcomes of this research will prove valuable in detecting both natural and human-induced hazard situations, enabling more effective responses. The research will have numerous practical applications, including pandemic contact tracing and spread modeling, hazard evacuation planning, and digital footprint tracking. Moreover, the project will provide training opportunities for students from underrepresented groups in STEM fields.The project aims to develop a new deep learning framework implemented on a Geographic Information System (GIS) platform. This framework will advance big spatiotemporal data analytics, specifically in anomaly detection, and quantify human mobility dynamics concerning hazard response behaviors. The research will focus on three main objectives: 1. Develop an agent-based machine learning framework, Markov Decision Process - Inverse Reinforcement Learning - Generative Adversarial Network (MDP-IRL-GAN), to detect anomalies in individual movement dynamics. 2. Model hazard response by analyzing individual movement dynamics using the proposed machine learning framework, while identifying the key factors influencing decision-making in response to hazards. 3. Detect changes and anomalies in spatiotemporal patterns of group dynamics by employing a newly-designed multi-resolution graph neural network (MA-GNN). The results will contribute to the efficiency of anomaly detection, the accuracy of traffic forecasting, and a deeper comprehension of human risk response behaviors.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.