Modeling and improving urban human mobility in disaster scenarios
Date
2020
Authors
Zou, Qiling, author
Chen, Suren, advisor
Heyliger, Paul, committee member
van de Lindt, John W., committee member
Chong, Edwin K. P., committee member
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Abstract
Natural and human-made disasters, such as earthquake, tsunami, fire, and terrorist attack, can disrupt the normal daily mobility patterns, posing severe risks to human lives and resulting in tremendous economic losses. Recent disaster events show that insufficient consideration of human mobility behavior may lead to erroneous, ineffective, and costly disaster mitigation and recovery decisions for critical infrastructure, and then the same tragedies may reoccur when facing future disasters. The objective of this dissertation is to develop advanced modeling and decision-making methodologies to investigate the urban human mobility in disaster scenarios. It is expected that the proposed methodologies in this dissertation will help stakeholders and researchers gain a better understanding of emergency human behavior, evaluate the performance of disrupted infrastructure, and devise effective safety management and resilience enhancement strategies. Focusing on the two important mobility modes (i.e., walking and driving) in urban environment, this dissertation (1) develops agent-based crowd simulation models to evaluate the crowd dynamics in complex subway station environment and investigate the interplay among emotion contagion, information diffusion, decision-making process, and egress behavior under a toxic gas incident; (2) develops functionality modeling, interdependency characterization, and decision models to assess and enhance the resilience of transportation networks subject to hazards.
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Subject
human mobility
interdependencies
infrastructure resilience
crowd model