Modeling, Predicting and Capturing Human Mobility

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Serval ID
serval:BIB_918245145AD0
Type
PhD thesis: a PhD thesis.
Collection
Publications
Institution
Title
Modeling, Predicting and Capturing Human Mobility
Author(s)
Kulkarni Vaibhav
Director(s)
Garbinato Benoît
Institution details
Université de Lausanne, Faculté des hautes études commerciales
Publication state
Accepted
Issued date
11/10/2019
Language
english
Abstract
Realistic models of human mobility are critical for modern day applications, specifically for recommendation systems, resource planning and process optimization domains. Given the rapid proliferation of mobile devices equipped with Internet connectivity and GPS functionality today, aggregating large sums of individual geolocation data is feasible. The thesis focuses on methodologies to facilitate data-driven mobility modeling by drawing parallels between the inherent nature of mobility trajectories, statistical physics and information theory. On the applied side, the thesis contributions lie in leveraging the formulated mobility models to construct prediction workflows by adopting a privacy-by-design perspective. This enables end users to derive utility from location-based services while preserving their location privacy. Finally, the thesis presents several approaches to generate large-scale synthetic mobility datasets by applying machine learning approaches to facilitate experimental reproducibility.
Keywords
Human mobility modeling, point of interest retrieval, next-place prediction, synthetic geospatial data, mobility meta-attributes
Open Access
Yes
Funding(s)
Swiss National Science Foundation / 000-157160
Create date
16/10/2019 16:11
Last modification date
18/01/2020 7:09
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