ATD: New Approaches for Analyzing Spatiotemporal Data for Anomalies

近年来，人口密集的城市地区越来越多地成为恐怖主义的目标，因为它可能在拥挤的环境中造成大量伤亡，并造成高度影响的破坏，特别是在室外环境中。该项目开发了新的方法来补充和验证现有方法产生的结果，这些方法基于视频监控和通过手机使用来估计人口密度，以监测城市地区的异常和威胁水平。它将使需要规划和分配资源的机构和地方政府受益，以便及时确保威胁级别较高的地点的安全。使用共享交通数据(例如，共享单车、出租车)来监测异常情况和威胁，增加了对城市地区的覆盖范围，并可捕获其他数据来源未涵盖的“盲点”。为此，可以大大减少一座城市发生恐怖袭击的可能性。这项为期三年的项目的目标是(I)开发一种新的数据驱动的混合微分方程组(DE)对象(例如，人、自行车等)建模方法。(Ii)利用DE模型和实时观测数据开发实时异常检测算法，以识别城市环境中异常的人群密度和交通。该项目使用的主要时空数据是来自美国三个城市的公开可用的共享单车数据。由于在对人类动态进行建模时，位置之间的相互依赖关系，时空数据被转换为时间演变的图表作为数据表示。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

High population density urban areas have increasingly been the targets of terrorism in recent years due to the possibility of inflicting a large number of casualties in a crowded environment and causing high impact disruption especially in the outdoor environment. The project develops new approaches to complement and validate results produced by existing approaches based on video surveillance and human population density estimation via cellphone usage to monitor anomalies and threat level in urban area. It will benefit agencies and local governments that require the planning and allocation of resources to secure locations with higher levels of threat in a timely manner. The use of the data on shared transportation (e.g., bikesharing, taxi) to monitor anomalies and threats provides additional coverage of an urban area and could capture "blindspots" not covered by the other data sources. Towards this end, the success of a terrorist attack in a city can be significantly reduced. The goals of the three-year project are to (i) develop a new data-driven hybrid differential equations (DE) modeling approach for object (e.g., human, bike, etc.) density and flow estimation to model human dynamics, and (ii) develop a real-time anomaly detection algorithm utilizing the DE model and real-time observed data to identify anomalous crowd density and traffic in an urban environment. The main spatiotemporal data used in this project is the publicly available bikesharing data from three US cities. Due to the interdependency of locations when modeling human dynamics, spatiotemporal data are transformed into time-evolving graphs as the data representation.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.