Date of Award
Doctor of Philosophy (PhD)
Electrical Engineering and Computer Science
Ivica Kostanic, Ph.D.
Carlos E. Otero, Ph.D.
Josko Zec, Ph.D.
Ersoy Subasi, Ph.D.
This dissertation investigates human mobility patterns using crowd-sourced cellular network data from different Metropolitan Statistical Areas (MSAs) in the United States, spanning the Houston, New York-Newark, NJ City, and 13 other significant MSAs. By focusing on prominent spatial mobility parameters highlighted in existing literature, the study unveils consistent findings regarding the predictability of human mobility across diverse time scales and geographic regions. The research underscores the significance of selecting appropriate sampling thresholds based on the mobility parameters being examined, the size of the dataset, and available computational resources. Through a meticulous analysis, it emerges that while values such as mean and standard deviation may fluctuate based on sampling thresholds, the distribution patterns of mobility parameters remain notably consistent. Diving deeper, the dissertation classifies MSAs into two primary groups based on observed travel patterns: inland and coastal MSAs, revealing distinct weekly travel trends for each group. These comprehensive insights not only contribute to a foundational understanding of human mobility across MSAs but also highlight the potential for influencing urban planning and business decisions when combined with supplementary data sources.
Matloub, Zaid, "Characterization of Human Mobility from Cellular Data" (2023). Theses and Dissertations. 1397.