ATD: A Statistical Geo-Enabled Dynamic Human Network Analysis

近年来，全球定位系统（GPS）等新的跟踪和传感器技术被应用于移动物体上，以高时空分辨率收集其跟踪位置。这些大量跟踪数据的可用性为许多依赖人类运动知识的应用领域带来了巨大的机会。然而，数据的规模和复杂的时空动态性质也给统计建模和计算带来了挑战。迫切需要开发计算效率高的定量模型来处理大量时空人类轨迹数据。该项目结合理论方法和计算方法，开发新颖的统计模型，以及高效的算法，以满足对大量人类轨迹数据的高效分析工具日益增长的需求。该项目在多个跨学科领域具有广泛的影响。其结果可以应用于广泛的实际和重要的问题，包括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。越来越多地使用GPS和其他位置感知设备，导致高时空分辨率的可用人类轨迹数据越来越多。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。该项目将集中于以下研究重点。首先，将在个体层面开发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。利用该方法对轨迹数据进行去噪和压缩，并在每个分段中使用不同的均匀运动状态的分段表示。此外，还建立了一个时空点过程模型，用于从分割的轨迹中识别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，开发了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，以描述个体在空间和时间上的运动之间的相互作用。个人和人口轨迹模型都经过精心设计，以允许可扩展的并行和在线推理算法进行近乎实时的高效计算。最后，将所开发的方法应用于利用GPS收集的人类运动数据来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.