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Aïdin Sumic, Emna Amdouni, Thierry Vidal, & Hedi Karray. (2022). Towards Flexibility Sharing in Multi-agent Dynamic Planning: The Case of the Health Crisis. In Rob Grace, & Hossein Baharmand (Eds.), ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management (pp. 274–284). Tarbes, France.
Abstract: Planning problems in a crisis context are a highly uncertain environment where health facilities must cooperate in providing health services to their patients. We focus on the health crisis in France due to the COVID19 pandemic. In fact, the lack of appropriate scheduling tools, resources, and communication leads hospitals to be submerged by infected patients and forced to transfer them to other hospitals. In this work we aim to provide a global solution to such planning problems to improve the current French health system. We introduce a cooperative approach called OPPIC (Operational Planning Platform for Inter-healthcare Coordination). OPPIC is based on a decentralized system, where health facilities plan is dynamic, flexible, robust to uncertainty, and respond to goals and optimization criteria. This paper proposed a first planning model to OPPIC and provided a first way of negotiation between health facilities based on their plan’s local and global flexibility.
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Guruprasad Airy, Tracy Mullen, & John Yen. (2009). Market based adaptive resource allocation for distributed rescue teams. In S. J. J. Landgren (Ed.), ISCRAM 2009 – 6th International Conference on Information Systems for Crisis Response and Management: Boundary Spanning Initiatives and New Perspectives. Gothenburg: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: The dynamic nature of real-world rescue scenarios (e.g., military, emergency first response, hurricane relief) requires constant reevaluation of resource assignments. New events can trigger additional resource requirements generating conflicts about how to reassign resources across tasks in an emerging crisis. Reallocation is further complicated as some resources are synergistic (i.e., helicopter and pilot) and many distributed rescue teams have limited information about other teams' status. We show how integrating a team-based multi-agent planning system with standard combinatorial auction methods to dynamically re-allocate resources can maximize overall rescue utility while providing for graceful managed degradation under conditions of extreme stress. The key innovation of our approach is that we explicitly provide a framework that incorporates the costs involved in dynamically switching resources from one task to another. We compare our system's performance against two other approaches.
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Antonio De Nicola, Maria Luisa Villani, Francesco Costantino, Andrea Falegnami, & Riccardo Patriarca. (2021). Knowledge Fusion for Distributed Situational Awareness driven by the WAx Conceptual Framework. In Anouck Adrot, Rob Grace, Kathleen Moore, & Christopher W. Zobel (Eds.), ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management (pp. 79–85). Blacksburg, VA (USA): Virginia Tech.
Abstract: Large crisis scenarios involve several actors, acting at the blunt-end of the process, such as rescue team directors, and at the sharp-end, such as firefighters. All of them have different perspectives on the crisis situation, which could be either coherent, alternative or complementary. This heterogeneity of perceptions hinders situational awareness, which is defined as the achievement of an overall picture on the above-mentioned crisis situation. We define knowledge fusion as the process of integrating multiple knowledge entities to produce actionable knowledge, which is consistent, accurate, and useful for the purpose of the analysis. Hence, we present a conceptual modelling approach to gather and integrate knowledge related to large crisis scenarios from locally-distributed sources that can make it actionable. The approach builds on the WAx framework for cyber-socio-technical systems and aims at classifying and coping with the different knowledge entities generated by the involved operators. The conceptual outcomes of the approach are then discussed in terms of open research challenges for knowledge fusion in crisis scenarios.
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Audun Stolpe, & Jo Hannay. (2021). On the Adaptive Delegation and Sequencing of Actions. In Anouck Adrot, Rob Grace, Kathleen Moore, & Christopher W. Zobel (Eds.), ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management (pp. 28–39). Blacksburg, VA (USA): Virginia Tech.
Abstract: Information systems support to crisis response and management relies crucially on presenting actionable information in a manner that supports cognitive processes, and does not overwhelm them. We outline how AI Planning can be used viably to support the \emph{delegation and sequencing} of tasks. The idea is to use standard operating procedures as initial specifications of plans in terms of actors, actions and delegation rules. When expressed in the AI planning language \textit{Answer set Programming} (ASP), machine reasoning can be used in a \textit{pre-incident review} to display relevant delegation and sequencing inherent in a plan. % together with measures of goal achievement. The purpose of this is to uncover weaknesses in the initial plan and to optimize sequencing and delegation to increase the likelihood of achieving goals. Further, adaptive planning can be supported in \textit{during-incident reviews} by updating the current status, upon which ASP will then compute new alternatives. % and corresponding goal achievement measures. At this point, initial goals may no longer be viable and the explicit suggestion of prior sub-optimal goals now worth pursuing can be a game-changer under stress. The conceptual basis we lay out in terms of delegation and sequencing can be readily extended with further planning factors, such as resource requirements, role transfer and goal achievement.
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Cendrella Chahine, Thierry Vidal, Mohamad El Falou, & François Pérès. (2022). Multi-Agent Dynamic Planning Architectures for Crisis Rescue Plans. In Rob Grace, & Hossein Baharmand (Eds.), ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management (pp. 243–255). Tarbes, France.
Abstract: We are interested in rescue management in crises such as in terrorist attacks. Today, there are emergency plans that take into account all the stakeholders involved in a crisis depending on the event type, magnitude and place. Unfortunately, they do not anticipate the evolution of the crisis situation such as traffic and hospital overcrowding. In addition, decisions are taken after the information has been passed from the operational level to higher levels. This work focuses on the operational level of the emergency plan. What will happen if the actors at this level, can make certain decisions without escalating the information to higher levels? To answer this question, a multi-agent dynamic planning approach is proposed and it will be tested in two different architectures in order to see how much autonomy can be given to an agent and how they coordinate to save the victims.
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Christian Iasio, Ingrid Canovas, Elie Chevillot-Miot, & Tendry Randramialala. (2022). A New Approach to Structured Processing of Feedback for Discovering and Investigating Interconnections, Cascading Events and Disaster Chains. In Rob Grace, & Hossein Baharmand (Eds.), ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management (pp. 285–298). Tarbes, France.
Abstract: Post-disaster information processing is relevant for the continuous improvement of operations and the reductionof risks. The current methodologies for post-disaster review suffer from several limitations, which reduce their use as a way of translating narrative in data for qualitative and quantitative analysis. Learning or effective knowledge sharing need a common formalism and method. Ontologies are the reference tool for structuring information in a “coded” data structure. Using the investigation of disaster management during the 2017 hurricane season in the French West Indies within the scope of the ANR “APRIL” project, this contribution introduces a methodology and a tool for providing a graphical representation of experiences for post-disaster review and lessons learning, based on a novel approach to case-based ontology development.
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Graham Coates, Glenn I. Hawe, Duncan T. Wilson, & Roger S. Crouch. (2011). Adaptive co-ordinated emergency response to rapidly evolving large-scale unprecedented events (REScUE). In E. Portela L. S. M.A. Santos (Ed.), 8th International Conference on Information Systems for Crisis Response and Management: From Early-Warning Systems to Preparedness and Training, ISCRAM 2011. Lisbon: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: This paper presents an overview of ongoing research into the development of an integrated framework aimed at adaptive co-ordination of emergency response to dynamic, fast evolving and novel events on a large-scale. The framework consists of (i) a decision support system, supported by rapid adaptive search methods, to enable the real time development of tailored response plans including emergency responder team composition and task allocation to these teams, and (ii) an agent-based simulation of emergency response to large-scale events occurring in real geographical locations. The aim of this research is to contribute to understanding how better agent-based simulation coupled with decision support can be used to enable the effective co-ordination of emergency response, involving the collective efforts and actions of multiple agencies (ambulance services, fire brigades, police forces and emergency planning units), to rapidly evolving large-scale unprecedented events.
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Tina Comes, Claudine Conrado, Michael Hiete, Michiel Kamermans, Gregor Pavlin, & Niek Wijngaards. (2010). An intelligent decision support system for decision making under uncertainty in distributed reasoning frameworks. In C. Zobel B. T. S. French (Ed.), ISCRAM 2010 – 7th International Conference on Information Systems for Crisis Response and Management: Defining Crisis Management 3.0, Proceedings. Seattle, WA: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: This paper presents an intelligent system facilitating better-informed decision making under severe uncertainty as found in emergency management. The construction of decision-relevant scenarios, being coherent and plausible descriptions of a situation and its future development, is used as a rationale for collecting, organizing, filtering and processing information for decision making. The development of scenarios is geared to assessing decision alternatives, thus avoiding time-consuming analysis and processing of irrelevant information. The scenarios are constructed in a distributed setting allowing for a flexible adaptation of reasoning (principles and processes) to the problem at hand and the information available. This approach ensures that each decision can be founded on a coherent set of scenarios, which was constructed using the best expertise available within a limited timeframe. Our theoretical framework is demonstrated in a distributed decision support system by orchestrating both automated systems and human experts into workflows tailored to each specific problem.
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Dashley Rouwendal van Schijndel, Audun Stolpe, & Jo Erskine Hannay. (2021). Toward an AI-based external scenario event controller for crisis response simulations. In Anouck Adrot, Rob Grace, Kathleen Moore, & Christopher W. Zobel (Eds.), ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management (pp. 106–117). Blacksburg, VA (USA): Virginia Tech.
Abstract: There is a need for tool support for structured planning, execution and analysis of simulation-based training for crisisresponse and management. As a central component of an architecture for such tool support, we outline the design ofan AI-based scenario event controller. The event controller is a component that uses machine reasoning to computethe next state in a scenario, given the actions performed in the corresponding simulation (execution of the scenario).Scenarios are specified in Answer Set Programming, which is a logic programming language we use for automatedplanning of training scenarios. A plan encoding in ASP adds expressivity in scenario specification and enablesmachine reasoning. For exercise managers this gives AI-based tool support for before-action and during-actionreviews to optimize learning. In line with Modelling and Simulation as as Service, our approach externalizes eventcontrol from any particular simulation platform. The scenario, and its unfolding in terms of events, is externalizedas a service. This increases interoperability and enables scenarios to be designed and modified readily and rapidlyto adapt to new training requirements.
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Hagen Engelmann, & Frank Fiedrich. (2009). DMT-EOC – A combined system for the decision support and training of EOC members. In S. J. J. Landgren (Ed.), ISCRAM 2009 – 6th International Conference on Information Systems for Crisis Response and Management: Boundary Spanning Initiatives and New Perspectives. Gothenburg: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: The first hours after a disaster are essential to minimizing the loss of life. The chance for survival in the debris of a collapsed building for example decreases considerably after 72 hours. However the available information in the first hours after a disaster is limited, uncertain and dynamically changing. A goal in the development of the Disaster Management Tool (DMT) was to support the management of this situation. Its module DMT-EOC specifically deals with problems of the members in an emergency operation centre (EOC) by providing a training environment for computer based table top exercises and assistance during earthquake disasters. The system is based on a flexible and extendible architecture that integrates different concepts and programming interfaces. It contains a simulation for training exercises and the evaluation of decisions during disaster response. A decision support implemented as a multi-agent system (MAS) combines operation research approaches and rule-base evaluation for advice giving and criticising user decisions. The user interface is based on a workflow model which mixes naturalistic with analytic decision-making. The paper gives an overview of the models behind the system components, describes their implementation, and the testing of the resulting system.
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Rafael A. Gonzalez. (2009). Crisis response simulation combining discrete-event and agent-based modeling. In S. J. J. Landgren (Ed.), ISCRAM 2009 – 6th International Conference on Information Systems for Crisis Response and Management: Boundary Spanning Initiatives and New Perspectives. Gothenburg: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: This paper presents a crisis response simulation model architecture combining a discrete-event simulation (DES) environment for a crisis scenario with an agent-based model of the response organization. In multi-agent systems (MAS) as a computational organization, agents are modeled and implemented separately from the environmental model. We follow this perspective and submit an architecture in which the environment is modeled as a discreteevent simulation, and the crisis response agents are modeled as a multi-agent system. The simultaneous integration and separation of both models allows for independent modifications of the response organization and the scenario, resulting in a testbed that allows testing different organizations to respond to the same scenario or different emergencies for the same organization. It also provides a high-level architecture suggesting the way in which DES and MAS can be combined into a single simulation in a simple way.
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Glenn I. Hawe, Duncan T. Wilson, Graham Coates, & Roger S. Crouch. (2012). STORMI: An agent-based simulation environment for evaluating responses to major incidents in the UK. In Z.Franco J. R. L. Rothkrantz (Ed.), ISCRAM 2012 Conference Proceedings – 9th International Conference on Information Systems for Crisis Response and Management. Vancouver, BC: Simon Fraser University.
Abstract: This paper describes work-in-progress regarding STORMI, an agent-based simulation environment for evaluating the response by the emergency services to hypothetical major incidents in the UK. At present, STORMI consists of two main components: a Scenario Designer and a Simulator. The Scenario Designer enables the setting up of a hypothetical multi-site mass casualty incident anywhere in the UK, along with the resources which may be considered for responding to it. This provides input to the Simulator, which through its Multiple Program Multiple Data architecture, models the agents and their environment at a higher level of detail inside incident sites than it does outside, thus focusing attention on the areas of most interest. Furthermore, the multiple programs of the Simulator execute concurrently, thus targeting multi-core processors. © 2012 ISCRAM.
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Glenn I. Hawe, Graham Coates, Duncan T. Wilson, & Roger S. Crouch. (2011). Design decisions in the development of an agent-based simulation for large-scale emergency response. In E. Portela L. S. M.A. Santos (Ed.), 8th International Conference on Information Systems for Crisis Response and Management: From Early-Warning Systems to Preparedness and Training, ISCRAM 2011. Lisbon: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: As part of ongoing research into optimizing the response to large-scale emergencies, an agent-based simulation (ABS) is being developed to evaluate different rescue plans in silico. During the development of this software, decisions regarding its design have been required in order to best satisfy the following specific application requirements: (1) the construction of a sufficiently detailed virtual environment, representing a real geographical area; (2) the programming of a wide variety of agent behaviors using a minimal amount of code; (3) the computational handling of the “large-scale” nature of the emergency; and (4) the presentation of a highly visual user interface, to encourage and facilitate use of the software by practitioners involved in the project. This paper discusses the decisions made in each of these areas, including the novel use of policy-based class design to efficiently program agents. Future developments planned for the software are also outlined.
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Corine H.G. Horsch, Nanja J. J. M. Smets, Mark A. Neerincx, & Raymond H. Cuijpers. (2013). Revealing unexpected effects of rescue robots' team-membership in a virtual environment. In J. Geldermann and T. Müller S. Fortier F. F. T. Comes (Ed.), ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management (pp. 627–631). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: In urban search and rescue (USAR) situations resources are limited and workload is high. Robots that act as team players instead of tools could help in these situations. A Virtual Reality (VR) experiment was set up to test if team performance of a human-robot team increases when the robot act as such a team player. Three robot settings were tested ranging from the robot as a tool to the robot as a team player. Unexpectedly, team performance seemed to be the best for the tool condition. Two side-effects of increasing robot's teammembership could explain this result: Mental workload increased for the humans who had to work with the team-playing robot, whereas the tendency to share information was reduced between these humans. Future research should, thus, focus on team-memberships that improve communication and reduce cognitive workload.
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Fahem Kebair, & Frédéric Serin. (2008). Towards an intelligent system for risk prevention and emergency management. In B. V. de W. F. Fiedrich (Ed.), Proceedings of ISCRAM 2008 – 5th International Conference on Information Systems for Crisis Response and Management (pp. 526–535). Washington, DC: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: Making a decision in a changeable and dynamic environment is an arduous task owing to the lack of information, their uncertainties and the unawareness of planners about the future evolution of incidents. The use of a decision support system is an efficient solution for this issue. Such a system can help emergency planners and responders to detect possible emergencies, as well as to suggest and evaluate possible courses of action to deal with the emergency. We are interested in our work to the modelling of a monitoring preventive and emergency management system, wherein we stress the generic aspect. In this paper we propose an agent-based architecture of this system and we describe a first step of our approach which is the modeling of information and their representation using a multiagent system.
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Kenneth Johnson, Javier Cámara, Roopak Sinha, Samaneh Madanian, & Dave Parry. (2021). Towards Self-Adaptive Disaster Management Systems. In Anouck Adrot, Rob Grace, Kathleen Moore, & Christopher W. Zobel (Eds.), ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management (pp. 49–61). Blacksburg, VA (USA): Virginia Tech.
Abstract: Disasters often occur without warning and despite extensive preparation, disaster managers must take action to respond to changes critical resource allocations to support existing health-care facilities and emergency triages. A key challenge is to devise sound and verifiable resourcing plans within an evolving disaster scenario. Our main contribution is the development of a conceptual self-adaptive system featuring a monitor-analyse-plan-execute (MAPE) feedback loop to continually adapt resourcing within the disaster-affected region in response to changing usage and requirements. We illustrate the system's use on a case study based on Auckland city (New Zealand). Uncertainty arising from partial knowledge of infrastructure conditions and outcomes of human participant's actions are modelled and automatically analysed using formal verification techniques. The analysis inform plans for routing resources to where they are needed in the region. Our approach is shown to readily support multiple model and verification techniques applicable to a range of disaster scenarios.
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Koki Asami, Shono Fujita, Kei Hiroi, & Michinori Hatayama. (2022). Data Augmentation with Synthesized Damaged Roof Images Generated by GAN. In Rob Grace, & Hossein Baharmand (Eds.), ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management (pp. 256–265). Tarbes, France.
Abstract: The lack of availability of large and diverse labeled datasets is one of the most critical issues in the use of machine learning in disaster prevention. Natural disasters are rare occurrences, which makes it difficult to collect sufficient disaster data for training machine learning models. The imbalance between disaster and non-disaster data affects the performance of machine learning algorithms. This study proposes a generative adversarial network (GAN)- based data augmentation, which generates realistic synthesized disaster data to expand the disaster dataset. The effect of the proposed augmentation was validated in the roof damage rate classification task, which improved the recall score by 11.4% on average for classes with small raw data and a high ratio of conventional augmentations such as rotation of image, and the overall recall score improved by 3.9%.
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Van Tuan Le, Serge Stinckwich, Noury Bouraqadi, & Arnaud Doniec. (2012). Role-based dynamic coalitions of multi-tasked rescue robots. In Z.Franco J. R. L. Rothkrantz (Ed.), ISCRAM 2012 Conference Proceedings – 9th International Conference on Information Systems for Crisis Response and Management. Vancouver, BC: Simon Fraser University.
Abstract: Organizations allow structuring and coordinating the activities of robots that take part in a multi-robot system (MRS). Within a given organization, each robot is assigned to a role that governs its behavior and its interactions with the other members of the MRS. In this paper; we investigate in a class of problems where role allocation must be done dynamically. This applies, for example in the context of rescue robotic applications where neither the number of robots nor characteristics are known a priori. Furthermore, tasks to be performed are not necessarily all known or at least a portion of the information remains to be discovered (e.g. locations of victims). Finally, some robots may temporarily leave the MRS (for battery recharging) or permanently due to failure or breakage. We propose a solution that can dynamically allocate roles to robots and revise the allocation. This revision takes place in case of failure of agents or in case of discovery of a new task. This allocation allows agents to participate in several tasks. © 2012 ISCRAM.
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Ola Leifler. (2008). Combining technical and human-centered strategies for decision support in command and control: The ComPlan approach. In B. V. de W. F. Fiedrich (Ed.), Proceedings of ISCRAM 2008 – 5th International Conference on Information Systems for Crisis Response and Management (pp. 504–515). Washington, DC: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: ComPlan (A Combined, Collaborative Command and Control Planning tool) is an approach to providing knowledge-based decision support in the context of command and control. It combines technical research on automated planning tools with human-centered research on mission planning. At its core, ComPlan uses interconnected views of a planning situation to present and manipulate aspects of a scenario. By using domain knowledge flexibly, it presents immediate and directly visible feedback on constraint violations of a plan, facilitates mental simulation of events, and provides support for synchronization of concurrently working mission planners. The conceptual framework of ComPlan is grounded on three main principles from human-centered research on command and control: transparency, graceful regulation, and event-based feedback. As a result, ComPlan provides a model for applying a human-centered perspective on plan authoring tools for command and control, and a demonstration for how to apply that model in an integrated plan-authoring environment.
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Fiona McNeill, Andriana Gkaniatsou, & Alan Bundy. (2014). Dynamic data sharing for facilitating communication during emergency responses. In and P.C. Shih. L. Plotnick M. S. P. S.R. Hiltz (Ed.), ISCRAM 2014 Conference Proceedings – 11th International Conference on Information Systems for Crisis Response and Management (pp. 369–373). University Park, PA: The Pennsylvania State University.
Abstract: This paper describes the CHAIn system, which is designed to facilitate data sharing between disparate organisations during emergency response situations by resolving mismatches in their data. It uses structured data matching to reformulate failed queries in cases where these failed because of incompatibilities between the query (derived from the source schema) and the schema of the queried datasource (the target schema). This reformulation is done by developing matches between the source schema and the target schema. These matches are then used to reformulate the query and retrieve responses relevant to those expected by the original query. Despite the growing interest in intelligent query answering, integration of data matching into query answering is novel, and allows users to successfully query datasources even if they do not know how the data in that source is organized, which is often the case during emergency responses. We describe the proof-of-concept system we have developed and an encouraging initial evaluation.
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Stefan Moellmann, David Braun, Hagen Engelmann, & Wolfgang Raskob. (2011). Key performance indicator based calculations as a decision support for the tactical level. In E. Portela L. S. M.A. Santos (Ed.), 8th International Conference on Information Systems for Crisis Response and Management: From Early-Warning Systems to Preparedness and Training, ISCRAM 2011. Lisbon: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: For the planning of relief operations the duration and the necessary resources are key factors for a successful completion. Those factors, however, are difficult to estimate due to the large number of influencing factors in a complex crisis situation. This paper presents a software module that supports the planning by calculating the duration and the required resources for relief measures based on key performance indicators (KPI). It is part of a project called SECURITY2People aiming to develop the basics for an integrated disaster management system. The module consists of an easy to use tool to calculate the timing of a relief measure when applied to a given disaster site. In addition it contains a detailed view to display and edit the model of the selected measure which is depicted as a Gantt chart and forms the basis of the calculation. Finally, the paper describes how this module can benefit from interoperability with other modules of this project and existing systems and services.
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Nada Matta, Paul Henri Richard, Alain Hugerot, & Theo Lebert. (2022). Experience Feedback Capitalization of Covid-19 Management in Troyes city. In Rob Grace, & Hossein Baharmand (Eds.), ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management (pp. 311–319). Tarbes, France.
Abstract: All countries have to face the COVID’19 pandemic and its heavy consequences. This sanitary crisis differs from all others in terms of the quick spread of contaminations, the high number of deaths (more than 5,5 Million globally and 123,893 in France) and the accrued number of patients hospitalized and induced in intensive care units. All sanitary procedures have proven to be inadequate. Several actors at different levels, whether international, European, national and local, as well as at the level of public and private organizations have been involved in the management of this type of crisis. These actors deal with different aspects of it, i.e., health, people protection, and economic and social situations. Existing procedures revealed a big lack in the relationships between different local and departmental actors. We did a number of interviews with strategic actors addressing the COVID’19 crisis in the City of Troyes. The objective of these interviews is to identify lessons learned from their experience feedback about relational problems and modifications needed. We present in this paper the first results of this study.
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Nada Matta, Thomas Godard, Guillaume Delatour, Ludovic Blay, Franck Pouzet, & Audrey Senator. (2021). Analyzing Social Media in Crisis Management Using Expertise Feedback Modelling. In Anouck Adrot, Rob Grace, Kathleen Moore, & Christopher W. Zobel (Eds.), ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management (pp. 17–27). Blacksburg, VA (USA): Virginia Tech.
Abstract: Currently social media are largely used in interactions, especially in crisis situations. We note a big volume of interactions around events. Observing these interactions give information even to alert the existence of an incident, event, or to understand the expansion of a problem. Crisis management actors observe social media to be aware about this type of information in order to consider them in their decisions. Specific organizations are founded in order to observe social media interactions and send their analysis to rescue and crisis management actors. In our work, an experience feedback of this type of organizations (VISOV, a crisis social media analysis association) is capitalized in order to emphasize from one side, main dimensions of this analysis and from another side, to simulate some aspects using TextMining that help to explore big volume of data.
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Nilani Algiriyage, Raj Prasanna, Kristin Stock, Emma Hudson-Doyle, David Johnston, Minura Punchihewa, et al. (2021). Towards Real-time Traffic Flow Estimation using YOLO and SORT from Surveillance Video Footage. In Anouck Adrot, Rob Grace, Kathleen Moore, & Christopher W. Zobel (Eds.), ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management (pp. 40–48). Blacksburg, VA (USA): Virginia Tech.
Abstract: Traffic emergencies and resulting delays cause a significant impact on the economy and society. Traffic flow estimation is one of the early steps in urban planning and managing traffic infrastructure. Traditionally, traffic flow rates were commonly measured using underground inductive loops, pneumatic road tubes, and temporary manual counts. However, these approaches can not be used in large areas due to high costs, road surface degradation and implementation difficulties. Recent advancement of computer vision techniques in combination with freely available closed-circuit television (CCTV) datasets has provided opportunities for vehicle detection and classification. This study addresses the problem of estimating traffic flow using low-quality video data from a surveillance camera. Therefore, we have trained the novel YOLOv4 algorithm for five object classes (car, truck, van, bike, and bus). Also, we introduce an algorithm to count the vehicles using the SORT tracker based on movement direction such as ``northbound'' and ``southbound'' to obtain the traffic flow rates. The experimental results, for a CCTV footage in Christchurch, New Zealand shows the effectiveness of the proposed approach. In future research, we expect to train on large and more diverse datasets that cover various weather and lighting conditions.
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Oduor Erick Nelson Otieno, Anna Gryszkiewicz, Nihal Siriwardanegea, & Fang Chen. (2010). Concept for intelligent integrated system for crisis management. In C. Zobel B. T. S. French (Ed.), ISCRAM 2010 – 7th International Conference on Information Systems for Crisis Response and Management: Defining Crisis Management 3.0, Proceedings. Seattle, WA: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: In this document, we describe the need for providing a uniform common picture that is missing in several crisis management decision support tools. Through research, we have reviewed some existing crisis management support systems in use and noted key user requirements that these tools are missing. A significant point of this research is to stress the importance of developing a decision support system that would improve the way an ideal support system would collect, analyze and disseminate necessary information to a crisis management decision maker. We also note the importance of ensuring that such a tool presents information to its user over a user friendly interface. The structure thus developed should be a standalone application that could be incorporated into existing platforms (Rinkineva, 2004) such as cell phones, PDAs and laptops.
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