Real-World Data Driven Characterization of Urban Human Mobility Patterns

Human movement in urban areas is a complex phenomenon to analyze and understand. The geographical spread of human movement, described by locations between which individuals tend to travel, varies from city to city. The geographical spread of such locations within a city would also vary with time. This thesis explores how to formally analyze and understand human mobility using large sets of realworld data from ride-sharing services for more than a dozen cities and to derive succinct characterizations for large urban areas which account for both geographical and temporal changes. A wide range of machine learning problems require immense amounts of data. To overcome this issue for human mobility, we propose a framework which includes a stochastic graph model, and adversarial networks to generate synthetic human

mobility data which conforms with the geographical and temporal characteristics observed in real data.

Deriving interesting insights from large sets of data and applying those to realworld applications can be challenging. Here we also highlight how formal characterizations can be applied to applications like ride pooling and vehicle placement. We also derive performance bounds for online algorithms using city level characterizations. Finally, we provide an open-source framework to understand properties of human

movement, generate synthetic human mobility data, and apply it to different what-if scenarios for real-world applications. Using such a framework would help public

entities and researchers alike to thoroughly understand a complex and highly relevant phenomenon.

Throughout, we link our problem formulations with other domains like social network graphs and online algorithms along with extensive empirical evaluations to

increase our understanding of not just human mobility but established techniques in the linked domains.