Rene Windhouwer, Gerdien A. Klunder, & F.M. Sanders. (2005). Decision support system emergency planning, creating evacuation strategies in the event of flooding. In B. C. B. Van de Walle (Ed.), Proceedings of ISCRAM 2005 – 2nd International Conference on Information Systems for Crisis Response and Management (pp. 171–180). Brussels: Royal Flemish Academy of Belgium.
Abstract: The Decision Support System (DSS) Emergency Planning is designed for use in the event of sea or river flooding. It makes accessible all the information related to the decision whether to evacuate an area. An important factor in this decision is the time required for the evacuation. The model used by the DSS Emergency Planning system to estimate the time required employs a strategy that prevents congestion on the road network in the area at risk. The use of the DSS Emergency Planning system during the proactive and prevention phases enables disaster containment organisations to prepare better for a flood situation. Moreover, all relevant information is saved and is therefore available for the post-disaster evaluation. The DSS Emergency Planning system can play a significant role in ensuring that the evacuation of an area at risk goes according to plan. In the future the DSS Emergency Planning system can also be used to evacuate people in the event of a nuclear, natural fire or extreme weather disaster.
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Shengcheng Yuan, Ma Ma, H. Zhang, & Yi Liu. (2013). An urban traffic evacuation model with decision-making capability. 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. 317–321). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: Traffic evacuation is one of the most challenging problems in a mega city due to crowded road conditions. This study focuses on developing a traffic evacuation model with decision-making capability. The model basically consists of two modules. The first one is a decision-making support module which runs very fast and provides short-forecast. The second one is a simulation module, which is used for simulating real evacuation process and for overall performance evaluation with vehicle tracking model. The first module can be considered as a “local” module as only partial information, such as traffic information in certain junctions is available. The second module can be considered as a global module which provides traffic directions for junction, and effective using of road-nets. With integration of two modules, overall system optimization may be achieved. Simulation cases are given for model validation and results are satisfied.
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Shengcheng Yuan, Yi Liu, Gangqiao Wang, Hongshen Sun, & H. Zhang. (2014). A dynamic-data-driven driving variability modeling and simulation for emergency evacuation. 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. 70–74). University Park, PA: The Pennsylvania State University.
Abstract: This paper presents a dynamic data driven approach of describing driving variability in microscopic traffic simulations for both normal and emergency situations. A four-layer DGIT (Decision, Games, Individual and Transform) framework provides the capability of describing the driving variability among different scenarios, vehicles, time and models. A four-step CCAR (Capture, Calibration, Analysis and Refactor) procedure captures the driving behaviors from mass real-time data to calibrate and analyze the driving variability. Combining the DGIT framework and the CCAR procedure, the system can carry out adaptive simulation in both normal and emergency situations, so that be able to provide more accurate prediction of traffic scenarios and help for decision-making support. A preliminary experiment is performed on a major urban road, and the results verified the feasibility and capability of providing prediction and decision-making support.
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