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Benjamin Heuer, Jan Zibuschka, Heiko Roßnagel, & Johannes Maucher. (2012). Empirical analysis of passenger trajectories within an urban transport hub. 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: In this contribution we present an analysis of passenger trajectories in an urban transportation hub. We collected an extensive amount of empirical data consisting of both gate and individual stalking observation in the central station of Cologne. Three different data mining algorithms are used to analyze this data, producing both data that may be used as input for simulation frameworks, and, as an aside, visualizations of passenger movements that could be of high interest to transport and emergency managers. © 2012 ISCRAM.
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Kathleen A. Moore, Andrea H. Tapia, & Christopher Griffin. (2013). Research in progress: Understanding how emergency managers evaluate crowdsourced data: A trust game-based 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. 272–277). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: The use, or barriers to use, of crowdsourced data by emergency managers has been a significant topic of scholarly discussion during the past several years. The single strongest barrier to use has been identified as one of data quality (Tapia, et. al, 2011). We argue that within this environment the Emergency Manager (EM) acts as a decision-maker and evaluator of crowdsourced data. The final judgement on whether to incorporate crowdsourced data into a Crisis response lies with the EM. In this paper we make a brief argument for the role of EM as trustworthy data analyst and then propose a model for capturing the trust-analytical behavior through game theory (Griffin, et. al, 2012). Lastly, we propose a simple computer game, which uses our model through which we will capture EM trust-analytical behavior though a future empirical data collection effort.
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Michael K. Lindell. (2011). Evacuation modelling: Algorithms, assumptions, and data. 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: Survey researchers need to, Find out what assumptions evacuation modelers are making and collect empirical data to replace incorrect assumptions;, Obtain data on the costs of evacuation to households, businesses, and local government; and, Extend their analyses to address the logistics of evacuation and the process of re-entry. Evacuation modelers need to, Incorporate available empirical data on household evacuation behavior, and, Generate estimates of the uncertainties in their analyses. Cognitive scientists need to, Conduct experiments on hurricane tracking and evacuation decision making to better understand these processes, and, Develop training programs, information displays, and performance aids to assist local officials who have little or no previous experience in hurricane evacuation decision making.
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