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

智能手机和全球定位系统应用程序的广泛使用导致移动数据产品的可用性增加。这类数据的一个例子是时空人类流动流，它捕捉个人随时间从特定起点位置到目的地位置的移动模式。在正常情况下学习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和应对措施。在这个项目中，研究人员将研究时空人类运动的起点到目的地（OD）流网络的社区检测问题，重点是不对称的流量和不断变化的社区结构。该项目还将为参与研究的研究生提供培训。在这个项目中，研究人员将开发一类新的贝叶斯随机图分区先验模型，该模型考虑了空间结构和聚类的邻接约束，同时允许空间聚类的数量未知。研究者将使用此方法建立一个灵活的贝叶斯分层随机块模型的空间OD流网络检测的起源和目的地社区。此外，研究人员将设计一个有效的贝叶斯算法估计的社区结构连同不确定性措施。最后，调查员将扩展该模型的动态设置，以检测随时间变化的社区结构，同时占社区结构之间的时间依赖性。该奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

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.