Graduation Semester and Year
Summer 2024
Document Type
Thesis
Degree Name
Master of Science in Computer Science
Department
Computer Science and Engineering
First Advisor
Dr. Chengkai Li
Second Advisor
Dr. Michelle Hummel
Abstract
Flood events present substantial challenges for coastal communities, severely impacting public safety, transportation infrastructure, and overall livability. Tropical storms, hurricanes, and sea level rise can cause extensive damage to homes and critical systems, requiring costly and prolonged recovery efforts. Coastal transportation networks are particularly vulnerable to flooding, leading to road closures, increased congestion, restricted access to essential services, and long-term economic disruptions. Understanding the effects of flood events on mobility patterns is crucial for urban planning and effective disaster management.
This thesis utilizes motif analysis to examine transportation network disruptions and access patterns in Harrison County, Mississippi, during Hurricane Ida in 2021. By identifying and quantifying small-scale recurring patterns within the transportation network, this study provides a detailed view of structural and functional changes before, during, and after the hurricane. Focusing on connected 4-node motifs, the analysis uses crowdsourced traffic data to detect significant deviations from typical patterns, offering insights into the network's resilience and vulnerability during extreme weather events.
The results demonstrate notable disruptions in the transportation network due to flooding, with significant implications for emergency response and resource allocation. The findings underscore the importance of understanding the dynamic evolution of traffic networks to enhance disaster preparedness and response strategies. This research contributes to urban planning and disaster management by providing a detailed understanding of transportation network responses to natural disasters and proposing measures to improve infrastructure resilience.
Keywords
Flood disruptions, Mobility data, Big data, Transportation network dynamics, Motif extraction, Graph theory, Network structure analysis, Node significance, Degree centrality, Travel patterns
Disciplines
Civil Engineering | Other Computer Engineering
License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.
Recommended Citation
Concessao, Joswin Valerian, "Resilience in the Wake of Storms: Unveiling Spatiotemporal Mobility Dynamics of Gulf Coast Communities Through Crowd-Sourced Data" (2024). Computer Science and Engineering Theses. 366.
https://mavmatrix.uta.edu/cse_theses/366