ATD: A Statistical Geo-Enabled Dynamic Human Network Analysis

最近，全球定位系统(GPS)等新的跟踪和传感器技术被部署在移动对象上，以获取高时空分辨率的跟踪位置。这些海量跟踪数据的可获得性为许多依赖于人体运动知识的应用领域带来了巨大的机遇。然而，数据的大小和复杂的时空动态性质也给统计建模和计算带来了挑战。迫切需要开发计算高效的定量模型来处理海量的时空人体轨迹数据。该项目将理论方法和计算方法相结合，开发新的统计模型和高效的算法，以满足对海量人体轨迹数据日益增长的高效分析工具的需求。该项目涉及多个跨学科领域，影响广泛。研究结果可广泛应用于军事和国家安全行动、城市规划、交通管理、交通预测、公共卫生和社会行为研究等实际和重要的问题。GPS和其他位置感知设备的日益使用导致了越来越多的高时空分辨率的可用人体轨迹数据。对这些数据的分析为不同领域的许多重要研究问题提供了宝贵的信息。本项目将重点研究以下几个方面的工作。首先，在个体层面建立一类新的轨迹模型来描述个体在空间和时间上的运动行为。利用这种方法，轨迹数据通过在每个分段内具有不同均匀运动状态的分段表示来去噪和压缩。此外，建立了一个时空点过程模型，从分段的轨迹中识别出重要和复杂的运动模式。然后，在个体轨迹模型的基础上进行扩展，以发展一类新的种群水平轨迹模型，该模型涉及一个潜在的动态网络来描述个体在空间和时间上的运动之间的相互作用。个体和群体轨迹模型都经过精心设计，以支持可扩展的并行和在线推理算法，以实现近乎实时的高效计算。最后，利用GPS采集的人体运动数据，将所提出的方法用于解决城市规划中的问题。

Recently, new tracking and sensor technologies such as the Global Positioning System (GPS) have been deployed on mobile objects to collect their tracking position with high spatio-temporal resolution. The availability of these massive amounts of tracking data brings great opportunities to many application fields that rely on human movement knowledge. However, the size and the complex spatial temporal dynamic nature of the data also impose challenges for statistical modeling and computation. There is a pressing need to develop computationally efficient quantitative models to handle massive spatial temporal human trajectory data. This project combines theoretical methods and computational approaches to develop novel statistical models, along with efficient algorithms, to meet the increasing demand of efficient analytical tools for massive human trajectory data. The project has broad impact on multiple interdisciplinary fields. The results can be applied to a wide range of practical and important problems including military and national security operations, urban planning, transportation management, traffic forecasting, public health, and social behavioral studies.The increasing use of GPS and other location-aware devices has led to an increasing amount of available human trajectory data at high spatial temporal resolution. Analysis of such data provides invaluable information for many important research problems in different fields. This project will focus on the following research thrusts. First, a new class of trajectory models at the individual level will be developed to describe individual movement behavior in both space and time. With the use of this method, trajectory data is denoised and compressed by a segmented representation with different homogeneous movement states within each segment. In addition, a spatio-temporal point process model is developed to recognize important and complex movement patterns from the segmented trajectories. Extensions beyond the individual trajectory model are then pursued to develop a new class of population level trajectory models that involve a latent dynamic network to describe interactions among individual movements in space and time. Both individual and population trajectory models are carefully designed to allow scalable parallel and online inference algorithms for near real time efficient computations. Finally, the developed methods are used to solve a problem in urban planning with human movement data collected from GPS.