In the mobile era, data capturing individuals’ locations have become unprecedentedly available. Data from Location-Based Social Networks is one example of large-scale user-location data. Such data provide a valuable source for understanding patterns governing human mobility, and thus enable a wide range of research. However, mining and utilizing raw user-location data is a challenging task. This is mainly due to the sparsity of data (at the user level), the imbalance of data with power-law users and locations check-ins degree (at the global level), and more importantly the lack of a uniform low-dimensional feature space describing users. Three latent feature models are proposed in this dissertation. Each proposed model takes as an input a collection of user-location check-ins, and outputs a new representation space for users and locations respectively. To avoid invading users privacy, the proposed models are designed to learn from anonymized location data where only IDs - not geophysical positioning or category - of locations are utilized. To enrich the inferred mobility patterns, the proposed models incorporate metadata, often associated with user-location data, into the inference process. In this dissertation, two types of metadata are utilized to enrich the inferred patterns, timestamps and social ties. Time adds context to the inferred patterns, while social ties amplifies incomplete user-location check-ins. The first proposed model incorporates timestamps by learning from collections of users’ locations sharing the same discretized time. The second proposed model also incorporates time into the learning model, yet takes a further step by considering time at different scales (hour of a day, day of a week, month, and so on). This change in modeling time allows for capturing meaningful patterns over different times scales. The last proposed model incorporates social ties into the learning process to compensate for inactive users who contribute a large volume of incomplete user-location check-ins. To assess the quality of the new representation spaces for each model, evaluation is done using an external application, social link prediction, in addition to case studies and analysis of inferred patterns. Each proposed model is compared to baseline models, where results show significant improvements.

A mobile ad hoc network is a collection of wireless nodes, all of which may be mobile, that dynamically create a wireless network amongst them without using any infrastructure. Ad hoc wireless networks come into being solely by peer-to-peer interactions among their constituent mobile nodes, and it is only such interactions that are used to provide the necessary control and administrative functions supporting such networks. Mobile hosts are no longer just end systems; each node must be able to function as a router as well to relay packets generated by other nodes. As the nodes move in and out of range with respect to other nodes, including those that are operating as routers, the resulting topology changes must somehow be communicated to all other nodes as appropriate. In accommodating the communication needs of the user applications, the limited bandwidth of wireless channels and their generally hostile transmission characteristics impose additional constraints on how much administrative and control information may be exchanged, and how often. Ensuring effective routing is one of the greatest challenges for ad hoc networking. As a practice, ad hoc routing protocols make routing decisions based on individual node mobility even for applications such as disaster recovery, battlefield combat, conference room interactions, and collaborative computing etc. that are shown to follow a pattern. In this thesis we propose an algorithm that performs routing based on underlying mobility patterns. A mobility pattern aware routing algorithm is shown to have several distinct advantages such as: a more precise view of the entire network topology as the nodes move; a more precise view of the location of the individual nodes; ability to predict with reasonably accuracy the future locations of nodes; ability to switch over to an alternate route before a link is disrupted due to node movements. / Master of Science

Traditional network design problems only consider the long-term stationary travel patterns (e.g., fixed OD demand) and short-term variations of human mobility are ignored. This study aims to integrate human mobility characteristics and travel patterns into network design problems using a space-time network structure. Emerging technologies such as location-based social network platforms provide a unique opportunity for understanding human mobility patterns that can lead to advanced modeling techniques. To reach our goal, at first multimodal network design problems are investigated by considering safety and flow interactions between different modes of transport. We develop a network reconstruction method to expand a single-modal transportation network to a multi-modal network where flow interactions between different modes can be quantified. Then, in our second task, we investigate the trajectory of moving objects to see how they can reveal detailed information about human travel characteristics and presence probability with high-resolution detail. A time geography-based methodology is proposed to not only estimate an individual’s space-time trajectory based on his/her limited space-time sample points but also to quantify the accuracy of this estimation in a robust manner. A series of measures including activity bandwidth and normalized activity bandwidth are proposed to quantify the accuracy of trajectory estimation, and cutoff points are suggested for screening data records for mobility analysis. Finally, a space-time network-based modeling framework is proposed to integrate human mobility into network design problems. We construct a probabilistic network structure to quantify human’s presence probability at different locations and time. Then, a Mixed Integer Nonlinear Programming (MINLP) model is proposed to maximize the spatial and temporal coverage of individual targets. To achieve near optimal solutions for large-scale problems, greedy heuristic, Lagrangian relaxation and simulated annealing algorithms are implemented to solve the problem. The proposed algorithms are implemented on hypothetical and real world numerical examples to demonstrate the performance and effectiveness of the methodology on different network sizes and promising results have been obtained.

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