Publication Date
2020
Document Type
Thesis
Committee Members
Derek Doran, Ph.D. (Advisor); Adam Nolan, Ph.D. (Committee Member); Michael Raymer, Ph.D. (Committee Member)
Degree Name
Master of Science in Computer Engineering (MSCE)
Abstract
Recent advances in location acquisition services have resulted in vast amounts of trajectory data; providing valuable insight into human mobility. The field of trajectory data mining has exploded as a result, with literature detailing algorithms for (pre)processing, map matching, pattern mining, and the like. Unfortunately, obtaining trajectory data for the design and evaluation of such algorithms is problematic due to privacy, ethical, dataset size, researcher access, and sampling frequency concerns. Synthetic trajectories provide a solution to such a problem as they are cheap to produce and are derived from a fully controllable generation procedure. Citing deficiencies in modern synthetic trajectory procedures, we propose a data-driven, seasonally-aware and simulation-based procedure that incorporates macro- and micro-level patterns from reference trajectories. The procedure is implemented as an alpha-release package; allowing an analyst to produce synthetic trajectories via the use of a modular coding framework and analysis tools.
Page Count
152
Department or Program
Department of Computer Science and Engineering
Year Degree Awarded
2020
Copyright
Copyright 2020, all rights reserved. My ETD will be available under the "Fair Use" terms of copyright law.
