Show simple item record

dc.contributor.advisorShamma, Jeff S.
dc.contributor.authorAlrashed, Mohammed
dc.date.accessioned2020-11-16T11:21:09Z
dc.date.available2020-11-16T11:21:09Z
dc.date.issued2020-11
dc.identifier.citationAlrashed, M. (2020). Control Theoretic Approaches to Computational Modeling and Risk Mitigation for Large Crowd Management. KAUST Research Repository. https://doi.org/10.25781/KAUST-R448L
dc.identifier.doi10.25781/KAUST-R448L
dc.identifier.urihttp://hdl.handle.net/10754/665965
dc.description.abstractWe develop a computational framework for risk mitigation in high population density events. With increased global population, the frequency of high population density events is naturally increased. Therefore, risk-free crowd management plans are critical for efficient mobility, convenient daily life, resource management and most importantly mitigation of any inadvertent incidents and accidents such as stampedes. The status-quo for crowd management plans is the use of human experience/expert advice. However, most often such dependency on human experience is insufficient, flawed and results in inconvenience and tragic events. Motivated by these issues, we propose an agent-based mathematical model describing realistic human motion and simulating large dense crowds in a wide variety of events as a potential simulation testbed to trial crowd management plans. The developed model incorporates stylized mindset characteristics as an internal drive for physical behavior such as walking, running, and pushing. Furthermore, the model is combined with a visualisation of crowd movement. We develop analytic tools to quantify crowd dynamic features. The analytic tools will enable verification and validation to empirical evidence and surveillance video feed in both local and holistic representations of the crowd. This work addresses research problems in computational modeling of crowd dynamics, specifically: understanding and modeling the impact of a collective mindset on crowd dynamics versus mixtures of heterogeneous mindsets, the effect of social contagion of behaviors and decisions within the crowd, the competitive and aggressive pushing behaviors, and torso and steering dynamics.
dc.language.isoen
dc.subjectAgent based models
dc.subjectMultiagent systems
dc.subjectcomputational models
dc.subjectSteering dynamics
dc.subjectSocio-Behavioural contagion
dc.subjectPedestrian crowd simulation
dc.titleControl Theoretic Approaches to Computational Modeling and Risk Mitigation for Large Crowd Management
dc.typeDissertation
dc.contributor.departmentComputer, Electrical and Mathematical Science and Engineering (CEMSE) Division
dc.rights.embargodate2021-11-16
thesis.degree.grantorKing Abdullah University of Science and Technology
dc.contributor.committeememberShamma, Jeff S.
dc.contributor.committeememberFeron, Eric
dc.contributor.committeememberKnio, Omar
dc.contributor.committeememberBamieh, Bassam
thesis.degree.disciplineElectrical Engineering
thesis.degree.nameDoctor of Philosophy
dc.rights.accessrightsAt the time of archiving, the student author of this dissertation opted to temporarily restrict access to it. The full text of this dissertation will become available to the public after the expiration of the embargo on 2021-11-16.
kaust.request.doiyes


Files in this item

Thumbnail
Name:
Thesis.pdf
Size:
3.581Mb
Format:
PDF
Description:
Final Thesis

This item appears in the following Collection(s)

Show simple item record