Graduation Semester and Year
2021
Language
English
Document Type
Thesis
Degree Name
Master of Science in Industrial Engineering
Department
Industrial and Manufacturing Systems Engineering
First Advisor
Caroline Krejci
Abstract
Agent-based modeling is frequently used to produce geospatial models of transportation systems. However, reducing the computational requirements of these models can require a degree of abstraction that can compromise the fidelity of the modeled environment. The purpose of the agent-based model presented in this thesis is to explore the potential of a volunteer-based crowd-shipping system for rescuing surplus meals from restaurants and delivering them to homeless shelters in Arlington, Texas. Each iteration of the model’s development has sought to improve model realism by incorporating empirical data to strengthen underlying assumptions. This thesis describes the most recent iteration, in which a method is presented for selecting eligible volunteers crowd-shippers based on total trip duration, derived from real-time traffic data. Preliminary experimental results illustrate the impact of adding trip duration constraints and increasing the size of the modeled region on model behavior, as well as illuminating the need for further analysis.
Keywords
Crowd-shipping platforms, Agent-based modeling, Network effects
Disciplines
Engineering | Operations Research, Systems Engineering and Industrial Engineering
License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.
Recommended Citation
Marusak, Amy A., "One Step at a Time: Improving the Fidelity of Geospatial Agent-Based Models Using Empirical Data" (2021). Industrial, Manufacturing, and Systems Theses. 20.
https://mavmatrix.uta.edu/industrialmanusys_theses/20
Comments
Degree granted by The University of Texas at Arlington