ATD: Statistical Modeling of Spatial Temporal Human Mobility Flows from Aggregated Mobile Phone Data

智能手机和gps应用程序的广泛使用导致运动数据产品的可用性增加。这类数据的一个例子是人类时空流动流量，它记录了个人在一段时间内从特定的原籍地到目的地的流动模式。学习正常环境下的人类流动模式可以为城市和交通规划等应用提供有价值的信息。此外，分析危机等事件对流动模式的影响有助于部署早期干预措施和应对措施。在本项目中，研究者将研究时空人类运动起点到终点（OD）流动网络的社区检测问题，重点关注不对称流动和不断演变的社区结构。该项目还将为参与研究的研究生提供培训。在这个项目中，研究者将开发一类新的贝叶斯随机图划分先验模型，该模型考虑了聚类的空间结构和邻近约束，同时允许未知空间聚类的数量。研究人员将利用该方法建立一个灵活的贝叶斯分层随机块模型，用于空间OD流量网络，以检测起源和目的地社区。此外，研究者将设计一种有效的贝叶斯算法，用于估计社区结构和不确定性措施。最后，研究者将模型扩展到动态设置，以检测时间变化的群落结构，同时考虑群落结构之间的时间依赖性。该奖项反映了美国国家科学基金会的法定使命，并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 widespread usage of smartphones and GPS-enabled applications has led to an increase in the availability of movement data products. An example of such data is the spatial temporal human mobility flow, which captures the movement patterns of individuals from specific origin locations to destination locations over time. Learning the patterns of human mobility flow in a normal context could provide valuable information for applications such as urban and traffic planning. Furthermore, analyzing the impact of events such as crises on mobility patterns can aid in the deployment of early interventions and responses. In this project, the investigator will study community detection problems of spatial temporal human movement origin-to-destination (OD) flow networks, with a focus on asymmetric flows and evolving community structures. The project will also provide training to graduate students involved in the research. In this project, the investigator will develop a new class of Bayesian random graph partition prior models that take into account spatial structures and contiguity constraints for clustering while allowing the number of spatial clusters to be unknown. The investigator will use this method to build a flexible Bayesian hierarchical stochastic block model for the spatial OD flow networks to detect origin and destination communities. Furthermore, the investigator will design an efficient Bayesian algorithm for the estimation of community structures together with uncertainty measures. Finally, the investigator will extend the model to a dynamic setting for the detection of temporally varying community structures while accounting for temporal dependence between community structures.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.