ATD: Pop-Flow: Spatio-Temporal Modeling of Flows in Mobility Networks for Prediction and Anomaly Detection

ATD：Pop-Flow：用于预测和异常检测的移动网络中的流时空建模

基本信息

批准号：
1925352
负责人：
Bernhard Bodmann
金额：
$ 18万
依托单位：
University of Houston
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-01 至 2023-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1925352&HistoricalAwards=false
关键词：
ATD Pop Flow Spatio Temporal

项目摘要

Human mobility can be modeled at different scales, ranging from traffic in a city to global migration patterns. Such models can describe a macroscopic, population-wide behavior or include more levels of detail up to a microscopic description in which individuals are followed. A main task in this project is to convert between the averaged mobility flows at the population level and observed individual trajectories. For example, the habits of commuters and other traffic participants in a city are extracted from records of transportation systems in order to build a model for the population-averaged flow. On the other hand, once this model is established, it permits us to classify how rare an observed pattern is in which a few individuals that were assumed to be independent behave in a very coordinated way. This project includes a similar matching between a model for global migration patterns and the behavior of a small group of migrants. Next to anomaly detection, predicting mobility is important. Once the mobility model is inferred from the observed data, it permits us to compute future events, including the detection of high-traffic areas. This helps determine an expected stress on infrastructure and find vulnerabilities, whether it is for urban traffic or global migration.This project treats the probabilistic description of human dynamics at two levels of detail: The deterministic description of the population-averaged time evolution, and the description of the random outcomes for individual trajectories. A version of the Feynman-Kac formula for perturbations of Markov semigroups is the central element that connects the two levels of the probabilistic description. The proposed techniques for model building from sample trajectories are new developments in the emergent field of graph signal processing, together with elements of statistics and dynamical systems. The behavior of dynamics at both population-averaged and individual levels is characterized by the generator. It encodes the probabilities for observing an event and allows us to predict flows or to compute likelihood ratios between observed and most likely trajectories, which permits classifying the behavior of individuals. A main problem addressed in this project is to obtain an accurate estimate for the generator of population flows for models at different scales. This estimate is then used to predict mobility or to detect anomalies in small sets of individual trajectories. Mobility patterns considered in this project include migration, the time evolution of a global population whose members move between countries, or mobility at a smaller scale, for example the inference of traffic flows from observing individuals through GPS traces or data acquired by transportation systems in a city. This project investigates under which additional, regularizing assumptions reliable estimates of the generator can be made. In that case, anomalies in mobility patterns can be detected with desired levels for likelihood ratios. The regularization includes optimal transport plans and techniques of time-frequency analysis applied to signals on graphs. The outcomes of this project will complement models for human mobility in the engineering literature by providing performance guarantees and rigorous error estimates.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.

人类流动性可以以不同的规模建模，从城市的交通到全球迁移模式。这样的模型可以描述宏观，人口范围的行为，或在遵循个人的微观描述中包含更多的细节。该项目的主要任务是在人口级别的平均移动性流量和观察到的单个轨迹之间转换。例如，从运输系统的记录中提取了通勤者和其他交通参与者的习惯，以建立人口平均流量的模型。另一方面，一旦建立了该模型，它允许我们对观察到的模式进行分类，在这种模式中，一些被认为是独立的人以非常协调的方式行为。该项目包括全球迁移模式模型与一小部分移民的行为之间的类似匹配。旁边是异常检测，预测迁移率很重要。一旦从观察到的数据推断出移动性模型，它就可以计算未来的事件，包括检测高流量区域。这有助于确定基础架构的预期压力并发现漏洞，无论是用于城市交通还是全球迁移。本项目将人类动态的概率描述在两个级别的细节上：对人口平均时间演化的确定性描述，以及对个体轨迹的随机量角的描述。 Markov Semigroups扰动的Feynman-KAC公式的版本是连接概率描述的两个级别的中心元素。从样本轨迹构建模型的技术是图形信号处理的新兴领域，以及统计和动态系统的元素。在人群平均和个体水平上的动力学行为是发电机的特征。它编码观察事件的概率，使我们能够预测流量或计算观察到的轨迹和最可能的轨迹之间的似然比，这允许对个体的行为进行分类。该项目中解决的一个主要问题是，在不同尺度上为模型的人口流的发生器获得了准确的估计。然后，该估计值用于预测迁移率或检测一小部分单个轨迹中的异常。该项目中考虑的流动性模式包括迁移，其成员在国家之间移动的全球人口的时间演变，或者以较小规模的流动性，例如从观察个人通过GPS痕迹或城市中运输系统获取的数据的交通流的推断。该项目调查了可以对发电机进行可靠的可靠估计的其他正规化假设。在这种情况下，可以使用所需水平检测出可能性比率的迁移率异常。正则化包括适用于图形信号的最佳运输计划和时间频率分析技术。该项目的结果将通过提供绩效保证和严格的错误估计来补充工程文献中人类流动性的模型。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛影响的评估评估标准来通过评估来支持的。