Fang, Luoyang, authorYang, Liuqing, advisorJayasumana, Anura P., committee memberLuo, Jie, committee memberWang, Haonan, committee member2019-09-102019-09-102019https://hdl.handle.net/10217/197361Modern mobile network technologies and smartphones have successfully penetrated nearly every aspect of human life due to the increasing number of mobile applications and services. Massive mobile data generated by mobile networks with timestamp and location information have been frequently collected. Mobile data analytics has gained remarkable attention from various research communities and industries, since it can broadly reveal the human spatiotemporal mobility patterns from the individual level to an aggregated one. In this dissertation, two types of spatiotemporal modeling with respect to human mobility behaviors are considered, namely the individual modeling and aggregated modeling. As for individual spatiotemporal modeling, location privacy is studied in terms of user identifiability between two mobile datasets, merely based on their spatiotemporal traces from the perspective of a privacy adversary. The success of user identification then hinges upon the effective distance measures via user spatiotemporal behavior profiling. However, user identification methods depending on a single semantic distance measure almost always lead to a large portion of false matches. To improve user identification performance, we propose a scalable multi-feature ensemble matching framework that integrates multiple explored spatiotemporal models. On the other hand, the aggregated spatiotemporal modeling is investigated for network and traffic management in cellular networks. Traffic demand forecasting problem across the entire mobile network is first studied, which is considered as the aggregated behavior of network users. The success of demand forecasting relies on effective modeling of both the spatial and temporal dependencies of the per-cell demand time series. However, the main challenge of the spatial relevancy modeling in the per-cell demand forecasting is the uneven spatial distribution of cells in a network. In this work, a dependency graph is proposed to model the spatial relevancy without compromising the spatial granularity. Accordingly, the spatial and temporal models, graph convolutional and recurrent neural networks, are adopted to forecast the per-cell traffic demands. In addition to demand forecasting, a per-cell idle time window (ITW) prediction application is further studied for predictive network management based on subscribers' aggregated spatiotemporal behaviors. First, the ITW prediction is formulated into a regression problem with an ITW presence confidence index that facilitates direct ITW detection and estimation. To predict the ITW, a deep-learning-based ITW prediction model is proposed, consisting of a representation learning network and an output network. The representation learning network is aimed to learn patterns from the recent history of demand and mobility, while the output network is designed to generate the ITW predicts with the learned representation and exogenous periodic as inputs. Upon this paradigm, a temporal graph convolutional network (TGCN) implementing the representation learning network is also proposed to capture the graph-based spatiotemporal input features effectively.born digitaldoctoral dissertationsengCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.location privacynetwork managementspatiotemporalmobile big datacellular networkspredictionText