Andreas Lotter, Florian Brauner, Alexander Gabriel, Frank Fiedrich, & Stefan Martini. (2017). New Decision-Support Framework for Strengthening Disaster Resilience in Cross-Border Areas. In eds Aurélie Montarnal Matthieu Lauras Chihab Hanachi F. B. Tina Comes (Ed.), Proceedings of the 14th International Conference on Information Systems for Crisis Response And Management (pp. 412–419). Albi, France: Iscram.
Abstract: The improvement of disaster resilience in cross-border areas causes special challenges. Involved countries use different structures in their civil protection systems and have to work together facing more difficult conditions than in local incidents. Furthermore, in the past involved countries mainly worked individually and focused on the concerned areas in their territories regardless transnational activities. The project INCA will develop a resilience framework to support decision-makers. The framework will focus on information management, the implementation of volunteers and the needs of citizens who are receiving medical care. Therefore, a case study region on the German-French border was defined and a scenario-based approach will be used to investigate resilience opportunities through disaster collaboration. The tested scenario is a transnational long-lasting power-outage in the German-French region.
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Jane Barnett, William Wong, David Westley, Rick Adderley, & Michelle Smith. (2011). Startle points: A proposed framework for identifying situational cues, and developing realistic emergency training scenarios. 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: Real-world crises are not prescriptive and may contain unexpected events, described here as startle points. Including these events in emergency training simulator scenarios is crucial in order to prepare for startle points that may arise in the real world. Startle points occur when individuals who assess and monitor emergency scenarios, are suddenly faced with an unexpected event, and are unsure how to proceed. This paper offers a non-empirical framework that explores how cues generated by startle points affect decision making. Future research will use the framework to explore how experts and novices experience, and then adapt to startle points, as a function of decision mode, situation awareness, and emotional arousal. The resulting data can then be used to identify cues surrounding startle points and as a consequence, create dynamic scenarios for online training simulators so that individuals can prepare and adapt to them, and transfer acquired skills to real-world emergencies.
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Josey Chacko, Loren P Rees, & Christopher W. Zobel. (2014). Improving resource allocation for disaster operations management in a multi-hazard context. 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. 85–89). University Park, PA: The Pennsylvania State University.
Abstract: The initial impact of a disaster can lead to a variety of associated hazards. By taking a multi-hazard viewpoint with respect to disaster response and recovery, there is an opportunity to allocate limited resources more effectively, particularly in the context of long-term planning for community sustainability. This working paper introduces an approach for extending quantitative resource allocation models to consider multiple interrelated hazards. The discussion is motivated by a literature review of existing models and then focuses on changes necessary to take the multiplicity of hazards into consideration in the context of decision support systems for disaster operations management.
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Han Che, & Shuming Liu. (2013). Monitoring data identification for a water distribution system based on data self-recognition approach. 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. 166–170). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: Detecting the occurrence of hydraulic accidents or contamination events in the shortest time has always been a significant but difficult task. The simple and efficient way is to identify the sudden changes or outliers hidden in the vast amounts of monitoring data produced minute by minute, which is unpractical for human. A new method, which employs a data self-recognition approach to achieve that automatically, has been proposed in this paper. The autoregressive moving average (ARMA) model was employed in this research to construct the self-recognition model. 56 months monitoring data from Changping water distribution network in Beijing, which was firstly cut into different time-slice series, was used to establish the ARMA model. This provided a prediction confidence interval in order to identify the outliers in the test data series. The results showed a good performance in outlier identification and the accuracy ranges from 90% to 95%.Thus, the ARMA model showed great potential in dealing with monitoring data and achieving the expected performance of data self-recognition technology.
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Cornelia Caragea, Adrian Silvescu, & Andrea Tapia. (2016). Identifying Informative Messages in Disasters using Convolutional Neural Networks. In A. Tapia, P. Antunes, V.A. Bañuls, K. Moore, & J. Porto (Eds.), ISCRAM 2016 Conference Proceedings ? 13th International Conference on Information Systems for Crisis Response and Management. Rio de Janeiro, Brasil: Federal University of Rio de Janeiro.
Abstract: Social media is a vital source of information during any major event, especially natural disasters. Data produced through social networking sites is seen as ubiquitous, rapid and accessible, and it is believed to empower average citizens to become more situationally aware during disasters and coordinate to help themselves. However, with the exponential increase in the volume of social media data, so comes the increase in data that are irrelevant to a disaster, thus, diminishing peoples? ability to find the information that they need in order to organize relief efforts, find help, and potentially save lives. In this paper, we present an approach to identifying informative messages in social media streams during disaster events. Our approach is based on Convolutional Neural Networks and shows significant improvement in performance over models that use the ?bag of words? and n-grams as features on several datasets of messages from flooding events.
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Shideh Dashti, Leysia Palen, Mehdi P. Heris, Kenneth M. Anderson, T. Jennings Anderson, & Scott Anderson. (2014). Supporting disaster reconnaissance with social media data: A design-oriented case study of the 2013 Colorado floods. 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. 632–641). University Park, PA: The Pennsylvania State University.
Abstract: Engineering reconnaissance following an extreme event is critical in identifying the causes of infrastructure failure and minimizing such consequences in similar future events. Typically, however, much of the data about infrastructure performance and the progression of geological phenomena are lost during the event or soon after as efforts move to the recovery phase. A better methodology for reliable and rapid collection of perishable hazards data will enhance scientific inquiry and accelerate the building of disaster-resilient cities. In this paper, we explore ways to support post-event reconnaissance through the strategic collection and reuse of social media data and other remote sources of information, in response to the September 2013 flooding in Colorado. We show how tweets, particularly with postings of visual data and references to location, may be used to directly support geotechnical experts by helping to digitally survey the affected region and to navigate optimal paths through the physical space in preparation for direct observation.
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Gilbert J. Huber, Roberto F. Júnior, Paulo V. R. Carvalho, & José O. Gomes. (2015). Applying resilience approach to C2 Center during FIFA`s 2014 World Cup in Rio de Janeiro. In L. Palen, M. Buscher, T. Comes, & A. Hughes (Eds.), ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management. Kristiansand, Norway: University of Agder (UiA).
Abstract: Rio de Janeiro?s Integrated Command and Control Center (CICC-RJ ) has already seen duty in several large scale events which happened in town, some planned, others not. CICC-RJ is part of Rio de Janeiro state?s response to the Brazilian national government?s mandate to improve the state?s ability to anticipate and respond coherently to public safety events in the region. Its infrastructure is intended to enable and promote local agencies? ability to anticipate, plan, monitor, and respond to public safety events by sharing operational intelligence and acting in concert. The aim of this paper is to explore some of the CICC-RJ issues where fragility and resilience were at play during the operational management of the 2014 World Cup in Rio de Janeiro, as the CICC-RJ seeks to enhance its capabilities to promote resilience in preparation for the 2016 Olympics in Brazil.
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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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Grégoire Burel, Lara S. G. Piccolo, Kenny Meesters, & Harith Alani. (2017). DoRES -- A Three-tier Ontology for Modelling Crises in the Digital Age. In eds Aurélie Montarnal Matthieu Lauras Chihab Hanachi F. B. Tina Comes (Ed.), Proceedings of the 14th International Conference on Information Systems for Crisis Response And Management (pp. 834–845). Albi, France: Iscram.
Abstract: During emergency crises it is imperative to collect, organise, analyse and share critical information between individuals and humanitarian organisations. Although dierent models and platforms have been created for helping these particular issues, existing work tend to focus on only one or two of the previous matters. We propose the DoRES ontology for representing information sources, consolidating it into reports and then, representing event situation based on reports. Our approach is guided by the analysis of 1) the structure of a widely used situation awareness platform; 2) stakeholder interviews, and; 3) the structure of existing crisis datasets. Based on this, we extract 102 dierent competency questions that are then used for specifying and implementing the new three-tiers crisis model. We show that the model can successfully be used for mapping the 102 dierent competency questions to the classes, properties and relations of the implemented ontology.
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Flávio E. A. Horita, & João Porto De Albuquerque. (2013). An approach to support decision-making in disaster management based on volunteer geographic information (VGI) and spatial decision support systems (SDSS). 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. 301–306). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: The damage caused by recent events in Japan in 2011 and USA in 2012 highlighted the need to adopt measures to increase the resilience of communities against extreme events and disasters. In addition to the conventional and official information that is necessary for adaptation to disasters, recently, common citizens residents in the affected areas also began contributing with voluntary qualified and updated information. In this context, this work-in-progress presents an approach that uses voluntary information – Also known by VGI (Volunteered Geographic Information) – As a data source for Spatial Decision Support Systems (SDSS) in order to assist the decision-making in disaster management. Our approach consists of a framework that integrates voluntary and conventional data, a SDSS and processes and methods for decision-making. As a result, it is expected that this approach will assist official organizations in disaster management by providing mechanisms and information.
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Jan Wendland, Christian Ehnis, Rodney J. Clarke, & Deborah Bunker. (2018). Sydney Siege, December 2014: A Visualisation of a Semantic Social Media Sentiment Analysis. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 493–506). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: Sentiment Analyses are widely used approaches to understand and identify emotions, feelings, and opinion on social media platforms. Most sentiment analysis systems measure the presumed emotional polarity of texts. While this is sufficient for some applications, these approaches are very limiting when it comes to understanding how social media users actually use language resources to make sense of extreme events. In this paper, a Sentiment Analysis based on the Appraisal System from the theory of communication called Systemic Functional Linguistics is applied to understand the sentiment of event-driven social media communication. A prototype was developed to analyze Twitter data using the Appraisal System. This prototype was applied to tweets collected during and after the Sydney Siege 2014, a hostage situation in a busy café in Sydney. Because the Appraisal System is a theorised functional communication method, the results of this analysis are more nuanced than is possible with traditional polarity based sentiment analysis.
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Murray E. Jennex. (2012). Social media – Truly viable for crisis response? 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: On September 8, 2011 the Great San Diego/Southwest Blackout occurred. Approximately 5 million people were affected by this blackout. This paper explores the availability of social media following such a crisis event. Contrary to expectations, the cell phone system did not have the expected availability and as a result, users had a difficult time using social media to status/contact family and friends. This paper presents a survey exploring the use and availability of social media during the Great San Diego/Southwest Blackout event. © 2012 ISCRAM.
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Kaisa Riikka Ylinen, & Juha Pekka Kilpinen. (2018). Calibrating Ensemble Forecasts to Produce More Reliable Probabilistic Extreme Weather Forecasts. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 1089–1097). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: Accurate predictions of severe weather events are extremely important for society, economy, and environment. Due to the fact that weather forecasts are inherently uncertain, it is required to give information about forecast uncertainty to all users providing weather forecasts in probabilistic terms utilizing ensemble forecasts. Since ensemble forecasts tend to be under dispersive and biased, they need to be calibrated with statistical methods. This paper presents a method for the calibration of temperature forecasts using Gaussian regression, and the calibration of wind gust forecasts with a box-cox t-distribution method. Statistical calibration was made for the operational European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble prediction system (ENS) forecasts for lead times from 3 to 360 hours. The verification results showed that calibration improved both temperature and wind gust ensemble forecasts. The probabilistic temperature forecasts were better after calibration over whole lead time scale, but the probabilistic wind gust forecasts up to 240 hours.
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Leon J. M. Rothkrantz, & Siska Fitrianie. (2015). Bayesian Classification of Disaster Events on the Basis of Icon Messages of Observers. In L. Palen, M. Buscher, T. Comes, & A. Hughes (Eds.), ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management. Kristiansand, Norway: University of Agder (UiA).
Abstract: During major disaster events, human operators in a crisis center will be overloaded with under-stress a flood of phone calls. As an increasing number of people in and around big cities do not master the native language, the need for automated systems that automatically process the context and content of information about disaster situations from the communicated messages becomes apparent. To support language-independent communication and to reduce the ambiguity and multitude semantics, we developed an icon-based reporting observation system. Contrast to previous approaches of such a system, we link icon messages to disaster events without using Natural Language Processing. We developed a dedicated set of icons related to the context and characteristic features of disaster events. The developed system is able to compute the probability of the appearance of possible disaster events using Bayesian reasoning. In this paper, we present the reporting system, the developed icons, the Bayesian model, and the results of two experiments.
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Marta Poblet Balcell, Stan Karanasios, & Vanessa Cooper. (2018). Look after Your Neighbours: Social Media and Vulnerable Groups during Extreme Weather Events. In Kristin Stock, & Deborah Bunker (Eds.), Proceedings of ISCRAM Asia Pacific 2018: Innovating for Resilience – 1st International Conference on Information Systems for Crisis Response and Management Asia Pacific. (pp. 408–415). Albany, Auckland, New Zealand: Massey Univeristy.
Abstract: Emergency management organisations across the world routinely use social media to reach out populations for preparedness and response to extreme weather events. In this paper we present a preliminary analysis of social media strategies towards vulnerable populations in the State of Victoria (Australia). Using the notion of vulnerability in an emergency management context (e.g. older persons, socially/geographically isolated persons, people with disabilities, refugee/recent migrant communities) we explore whether and how organisations address vulnerable groups with targeted messages. Our initial findings suggest that organisations do not tend to interact directly with these groups. Rather, reliance on 'information brokers' (intermediary organisations and individuals with an expected duty of care) seems to be a preferred strategy.
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Adriaan ter Mors, Jeroen M. Valk, & Cees Witteveen. (2005). An event-based task framework for disaster planning and decision support. In B. C. B. Van de Walle (Ed.), Proceedings of ISCRAM 2005 – 2nd International Conference on Information Systems for Crisis Response and Management (pp. 151–153). Brussels: Royal Flemish Academy of Belgium.
Abstract: Because of the apparent ineffectiveness of current disaster plans, we focus our research on modeling emergency response activities. If we can capture the crucial concepts of emergency response in a mathematical framework and apply this framework to construct disaster plans, then we pave the way for the development of automated decisions support systems for emergency response.
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Thomas Plagemann, Katrine S. Skjelsvik, Matija Puzar, Aslak Johannessen, Ovidiu Drugan, Vera Goebel, et al. (2008). Cross-layer overlay synchronization in sparse MANETs. In B. V. de W. F. Fiedrich (Ed.), Proceedings of ISCRAM 2008 – 5th International Conference on Information Systems for Crisis Response and Management (pp. 546–555). Washington, DC: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: Mobile Ad-Hoc Networks maintain information in the routing table about reachable nodes. In emergency and rescue operations, human groups play an important role. This is visible at the network level as independent network partitions which are for some time stable before their members change through merging or partitioning. We use the information from stable routing tables to optimize the synchronization of Mediators in a Distributed Event Notification System. In a stable partition each node has the same information, thus a single Mediator can efficiently coordinate the synchronization, while all other Mediators just receive updates. We show in our experiments that just a few seconds are needed until routing tables stabilize and all nodes have a common view of the partition. We present a heuristic to determine the proper time to synchronize. Furthermore, we show how exceptions, like disappearing coordinating Mediators and unexpected messages, can be efficiently handled.
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Rafael de Sousa Ferreira Costa, Tharcisio Cotta Fontainha, Adriana Leiras, Hugo Tsugunobu Yoshida Yoshizaki, Paulo Gonçalves, & Abdon Baptista de Paula Filho. (2017). IT infrastructure at the Rio de Janeiro City Operations Center – the case of 2016 Olympic and Paralympic Games. In eds Aurélie Montarnal Matthieu Lauras Chihab Hanachi F. B. Tina Comes (Ed.), Proceedings of the 14th International Conference on Information Systems for Crisis Response And Management (pp. 739–751). Albi, France: Iscram.
Abstract: Rio Operations Center (COR) was the agency of Rio de Janeiro Prefecture responsible for monitoring the Rio 2016 Olympic and Paralympic Games operations, due to its role in the integrated management of the city operations. This paper presents a case study considering a brief theoretical reference and data collected through direct observations, interviews, internal documents and access to the systems and software used by COR. The analysis of the COR IT infrastructure and monitoring teams' preparation for the Olympics revealed a successful development of new teams and conflict solving practice. Despite the use of different sources of information and the development of specific systems for the event, the COR preparation faced some restrictions in analytical functions, security and integration among systems. Nevertheless, regionalization for monitoring and inter-agency coordination, cross-agency instant messaging, and a team for active monitoring of social media emerged as new practices, representing opening venues for future research.
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Leon J.M. Rothkrantz, & Zhenke Yang. (2009). Crowd control by multiple cameras. 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: One of the goals of the crowd control project at Delft University of Technology is to detect and track people during a crisis event, classify their behavior and assess what is happening. The assumption is that the crisis area is observed by multiple cameras (fixed or mobile). The cameras sense the environment and extract features such as the amount of motion. These features are the input to a Bayesian network with nodes corresponding to situations such as terroristic attack, fire, and explosion. Given the probabilities of the observed features, by reasoning, the likelihood of the possible situations can be computed. A prototype was tested in a train compartment and its environment. Forty scenarios, performed by actors, were recorded. From the recordings the conditional probabilities have been computed. The scenarios are designed as scripts which proved to be a good methodology. The models, experiments and results will be presented in the paper.
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Heiko Roßnagel, & Jan Zibuschka. (2011). Using mobile social media for emergency management – A design science approach. 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: Over the last couple of years social networks have become very popular and part of our daily lives. With the emergence of powerful smartphones and cheap data rates social media can now be accessed anytime and anywhere. Obviously, it makes sense to also facilitate social media for crisis management and response. In this contribution we present a system design for emergency support based on mobile social media with an emphasis on increasing security during large public events. We follow the design science approach as we provide an artifact design along with a description of its implementation and evaluate our artifact using the simulation study methodology. As a result of this study we gained valuable insight into how the users interact with our system and obtained information on how to improve it. Overall the users were quite satisfied with the perceived usefulness and the perceived ease of use of our system.
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Heiko Roßnagel, & Olaf Junker. (2010). Evaluation of a mobile emergency management system – A simulation approach. 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: Large public events such as sporting events, concerts, fairs and street festivals are quite common in metropolitan areas. Because of the high frequency of such events and the increasing number of involved parties, those being responsible for the organization and execution have to cope with increasing complexity and shortening time frames for planning and preperation. Because of the high concentration of passengers, unplanned incidents that occur during these public events can have devastating effects and can lead to crises and disasters. Emergency management systems that utilize mobile communication infrastructures can provide prompt information delivery to save human lives. In this paper we propose a system design for mobile emergency management and outline our approach of evaluating this system design using multi-agent based simulation. To make our simulation of passenger movements as realistic as possible we gathered empirical data for a large event as well as for normal rush hour traffic.
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Heiko Roßnagel, Jan Zibuschka, & Olaf Junker. (2011). On the effectiveness of mobile service notification for passenger egress during large public events. 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: In this contribution we evaluate the effectiveness of mobile services for passenger egress of a train station during a large public event using an agent-based simulation approach. For this simulation we built a virtual replica of the Cologne central train station and collected empirical data on passenger numbers and their movements during a large public event. We simulate several different scenarios and compare the results using key performance indicators, such as time for egress. Our results show that dedicated cell broadcast messages under the described circumstances can be used to decrease evacuation time significantly and that the simulation can be used to quickly investigate the relevant key performance indicators needed to asses and evaluate the effectiveness of different notification and evacuation strategies.
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Siska Fitrianie, & Leon J. M. Rothkrantz. (2015). Dynamic Routing during Disaster Events. In L. Palen, M. Buscher, T. Comes, & A. Hughes (Eds.), ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management. Kristiansand, Norway: University of Agder (UiA).
Abstract: Innovations in mobile technology allow the use of Internet and smartphones for communicating disasters and coordinating evacuations. However, given the turbulent nature of disaster situations, the people and systems at crisis center are subjected to information overload, which can obstruct timely and accurate information sharing. A dynamic and automated evacuation plan that is able to predict future disaster outcome can be used to coordinate the affected people to safety in times of crisis. In this paper, we present a dynamic version of the shortest path algorithm of Dijkstra. The algorithm is able to compute the shortest path from the user?s location (sent by the smartphone) to the safety area by taking into account possible affected areas in future. We aim at employing the computed routes on our mobile communication system for navigating affected people during emergency and disaster evacuations. Two simulation studies have validated the performance of the developed algorithm.
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Milica Stojmenovic, Cathy Dudek, Patrick Noonan, Bruce Tsuji, Devjani Sen, & Gitte Lindgaard. (2011). Identifying user requirements for a CBRNE management system: A comparison of data analysis methods. 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: The purpose of this paper was to identify an effective user-requirements data analysis method for informing the design of a Chemical, Biological, Radiological, Nuclear, and Explosive (CBRNE) management decision support system. Data were collected from a large simulation involving medical, police, hazmat/firefighters and subjected to three different kinds of analysis methods: Social Network Analysis, Content Analysis, and Observational Analysis. While all three methods yielded valuable information, the observational method was by far the best for the present purpose.
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Murray Turoff, Connie White, Linda Plotnick, & Starr Roxanne Hiltz. (2008). Dynamic emergency response management for large scale decision making in extreme events. In B. V. de W. F. Fiedrich (Ed.), Proceedings of ISCRAM 2008 – 5th International Conference on Information Systems for Crisis Response and Management (pp. 462–470). Washington, DC: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: Effective management of a large-scale extreme event requires a system that can quickly adapt to changing needs of the users. There is a critical need for fast decision-making within the time constraints of an ongoing emergency. Extreme events are volatile, change rapidly, and can have unpredictable outcomes. Large, not predetermined groups of experts and decision makers need a system to prepare for a response to a situation never experienced before and to collaborate to respond to the actual event. Extreme events easily require a hundred or more independent agencies and organizations to be involved which usually results in two or more times the number of individuals. To accomplish the above objectives we present a philosophical view of decision support for Emergency Preparedness and Management that has not previously been made explicit in this domain and describe a number of the current research efforts at NJIT that fit into this framework.
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