Publication | Findings |
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The Role of Simulation and Modeling in Disaster Management. | Levels of maturity of table-top exercise: Problem formulation, other steps in execution, performance metrics and measurement of readiness will differ for each.Discovery: Before expectations exist for behavior, a tabletop exercise conducted more nearly like a role-playing game is very useful for exposing candidate responses and raising awareness of issues.Evaluation/Validation: After some candidate expectations for appropriate response have been exposed, tabletop exercises can be useful to evaluate proposals, compare alternatives and expose remaining gaps and issues.Training: When sufficient portions of a Disaster Response Plan have been identified, articulated and validated by such simulation, it is reasonable to train the workforce to be proficient in the elected expectation for behavior.Monitoring and Adaptation: Finally, at ultimate maturity, refined by training data and simulation runs, connected |
The use of emergency operations centres in local government emergency management. | EOC ActivationTraining |
The workpad user interface and methodology: Developing smart and effective mobile applications for emergency operators | User requirementsResults of former projectsFor user interface: (1) grasping the users' mental attentions onto the system as little as possible, (2) reducing the resource consumptions (computational power, bandwidth, and battery) |
Towards a Holistic Framework for the Evaluation of Emergency Plans in Indoor Environments. | PeopleInBuildingPeopleWhoReachEmergencyPeopleWhoReachEmergency |
Towards a Lightweight Approach for On-site Interaction Evaluation of Safety-critical Mobile Systems | Effectivity. Pragmatic and hedonic quality. Improvement potential. |
Towards the development of a decision support system for multi-agency decision-making during cross-border emergencies. | logistic tools; threat assessment and ‘what if’ considerations; incident intelligence; learning tool sets; post evaluations |
Traffic evacuation simulation based on multi-level driving decision model. | Clearance time, 2) total evacuation rates and mean evacuation rates per shelter, 3) panic population, 4) number of jammed intersections |
Training and learning for crisis management using a virtual simulation/gaming environment. | Four decisions important for designing gaming :Time settingEnvironmental settingLevel of detailKnowledge, experience, and sophistication of players |
Using Monte Carlo simulation to refine emergency logistics response models: a case study. | Response times for certain activitiesEx. Clearance activity, vehicle speed, aid distribution in prone area, … |
Using Real-Time Decision Tools to Improve Distributed Decision-Making Capabilities in High-Magnitude Crisis Situations. | 15 explanatory variables (from that, 6 were quantitative and 9 represents background information for each responder e.g. interaction between levels and departments). Response variable is the quarantine strategy (QS) in no intervention (QS1) to moderate (QS3) to extreme (QS5) intervention (quarantine) responses. |
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