The phenomenal growth of sensor-equipped smartphones and GPS-equipped vehicles have produced an unprecedented wealth of digital information, such as humans’ phone call records, mobility traces, Bluetooth proximity readings, as well as city taxi trajectories. Learning the hidden patterns from such mobile data can help understand the contexts and facts about individual, social group, community, and urban environment, and hence is an important task in both artificial intelligence and mobile computing. In this dissertation, we study how to build effective analytical and predictive models of human behavior contexts and urban dynamics, which is crucial in building context-aware ubiquitous systems and enabling smart-city applications.

In the first part, we design effective learning me...[ Read more ]

The phenomenal growth of sensor-equipped smartphones and GPS-equipped vehicles have produced an unprecedented wealth of digital information, such as humans’ phone call records, mobility traces, Bluetooth proximity readings, as well as city taxi trajectories. Learning the hidden patterns from such mobile data can help understand the contexts and facts about individual, social group, community, and urban environment, and hence is an important task in both artificial intelligence and mobile computing. In this dissertation, we study how to build effective analytical and predictive models of human behavior contexts and urban dynamics, which is crucial in building context-aware ubiquitous systems and enabling smart-city applications.

In the first part, we design effective learning methods to capture semantic behavior contexts from human mobile data, including personal mobility data and social interaction data. Our modeling methods contrast with existing approaches in that we address several typical challenges in real world mobile data jointly, including the presence of noise and data sparsity, and the absence of semantic labels, as well as enable exploratory, inference, and predictive purposes in a unified framework. For personal mobility data, we first design a Bayesian network to discover the routine behavior pattern from a single user’s time-stamped mobility traces. Based on such, we then make non-trivial extensions to address the difficult problem of representing mobility habits of multiple individuals in a unified way. To address the key challenge that multiple individuals rarely have spatial overlap or social connections in their mobility, we leverage the observation that temporal structures in habits can be highly shared across individuals. We design two methods based on novel extensions of matrix factorization and hierarchical Dirichlet processes to realize such population mobility models, and demonstrate how they help solve several challenging pervasive tasks, including routine behavior pattern discovery from sparse mobility data, individual mobility prediction under cold-start condition, and organizational rhythm discovery. We also apply similar methods to Bluetooth proximity data to discover social circles semantics for human social behavior characterization and social interaction prediction.

In the second part, we design effective methods to learn road latent cost from historical taxi trajectory data for urban sensing. Road latent cost quantifies how desirable each road is for traveling, and is a good representation of driving experience and urban dynamics. We first study how to robustly estimate the temporal dynamics of road travel time cost under temporal sparsity by exploiting temporal smoothness in a multi-task regression framework. This contrasts with existing work which either ignores such temporal dynamics or assumes it as a known function. In addition to travel time, a plenty of other hidden factors may influence the desirability of a road, but are impossible to obtain in practice. To address this, we propose to learn road latent cost from entire trajectories by modeling drivers’ routing decisions based on inverse reinforcement learning, while at the same time properly considering the heterogeneity of destinations so that trajectories with different goals can be learned jointly. In addition, observing that real trajectory data often contains a few anomalous trajectories, we design a sparse noise-oriented robust inverse reinforcement learning framework which can automatically identify and remove anomalies in cost learning. Real data experiments show that compared with past edge-centric approaches and noise-indifferent approaches, the road latent costs learned in our way are more useful and robust in facilitating typical smart-city applications, and require less data for learning.