Antone Evans Jr., Yingyuan Yang, & Sunshin Lee. (2021). Towards Predicting COVID-19 Trends: Feature Engineering on Social Media Responses. 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. 792–807). Blacksburg, VA (USA): Virginia Tech.
Abstract: During the course of this pandemic, the use of social media and virtual networks has been at an all-time high. Individuals have used social media to express their thoughts on matters related to this pandemic. It is difficult to predict current trends based on historic case data because trends are more connected to social activities which can lead to the spread of coronavirus. So, it's important for us to derive meaningful information from social media as it is widely used. Therefore, we grouped tweets by common keywords, found correlations between keywords and daily COVID-19 statistics and built predictive modeling. The features correlation analysis was very effective, so trends were predicted very well. A RMSE score of 0.0425504, MAE of 0.03295105 and RSQ of 0.5237014 in relation to daily deaths. In addition, we found a RMSE score of 0.07346836, MAE of 0.0491152 and RSQ 0.374529 in relation to daily cases.
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Audrey Fertier, Aurélie Montarnal, Anne-Marie Barthe-Delanoë, Sébastien Truptil, & Frédérick Bénaben. (2016). Adoption of Big Data in Crisis Management. 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: Most agree that the innate complexity and uncertainty of a crisis compel the stakeholders to coordinate in a hurry, despite their heterogeneity or the volume of data to process. Supporting their coordination is now possible, thanks to a mediation system combined with big data management tools. The GéNéPi1 project explores this possibility and proposes to improve the generation of collaborative processes offered by the MISE2?s solution. The idea is to increase the number of usable data sources. To do that, in a fixed time-frame, the situation models have to be instantly generated upon sets of raw data. This new methodology holds the key to a new big data era: an age where global understanding reigns.
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Audrey Fertier, Aurélie Montarnal, Sébastien Truptil, Anne-Marie Barthe-Delanoë, & Frédérick Bénaben. (2017). A situation model to support collaboration and decision-making inside crisis cells, in real time. 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. 1020–1028). Albi, France: Iscram.
Abstract: Natural and man-made hazards have many unexpected consequences that concern as many heterogeneous services. The GéNéPi project offers to support officials in addressing those events: its purpose is to support the collaboration in the field and the decision-making in the crisis cells. To succeed, the GéNéPi system needs to be aware of the ongoing crisis developments. For now, its best chance is to benefit from the ever growing number of available data sources. One of its goals is, therefore, to learn how to manage numerous, heterogeneous, more or less reliable data, in order to interpret them, in time, for the officials. The result consists on a situation model in the shape of a common operational picture. This paper describes every stage of modelling from the raw data selection, to the use of the situation model itself.
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Hendrik Stange, Sylvia Steenhoek, Sebastian Bothe, & François Schnitzler. (2015). Insight-driven Crisis Information ? Preparing for the Unexpected using Big Data. 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: National information and situation centers are faced with rising information needs and the question of how to prepare for unexpected situations. One promising development is the access to vastly growing data produced by distributed sensors and a socially networked society. Current emergency information systems are limited in the amount of complex data they can process and interpret in real-time and provide only partially integrated prediction and alarming capabilities. In this paper we present a novel approach to build a new type of automated and scalable information systems that intelligently make use of massive streams of structured and unstructured data and incorporate human feedback for automated incident detection and learning. Big data technologies, uncertainty handling and privacy-by-design are employed to match end-user system requirements. We share first experiences analyzing data from the centennial flood in Germany 2013.
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Nilani Algiriyage, Raj Prasanna, Emma E H Doyle, Kristin Stock, & David Johnston. (2020). Traffic Flow Estimation based on Deep Learning for Emergency Traffic Management using CCTV Images. In Amanda Hughes, Fiona McNeill, & Christopher W. Zobel (Eds.), ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management (pp. 100–109). Blacksburg, VA (USA): Virginia Tech.
Abstract: Emergency Traffic Management (ETM) is one of the main problems in smart urban cities. This paper focuses on selecting an appropriate object detection model for identifying and counting vehicles from closed-circuit television (CCTV) images and then estimating traffic flow as the first step in a broader project. Therefore, a case is selected at one of the busiest roads in Christchurch, New Zealand. Two experiments were conducted in this research; 1) to evaluate the accuracy and speed of three famous object detection models namely faster R-CNN, mask R-CNN and YOLOv3 for the data set, 2) to estimate the traffic flow by counting the number of vehicles in each of the four classes such as car, bus, truck and motorcycle. A simple Region of Interest (ROI) heuristic algorithm is used to classify vehicle movement direction such as \quotes{left-lane} and \quotes{right-lane}. This paper presents the early results and discusses the next steps.
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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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Rachel L. Finn, Hayley Watson, & Kush Wadhwa. (2015). Exploring big ?crisis? data in action: potential positive and negative externalities. 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: As growing emphasis is placed on engaging with big ?crisis? data, including data from social media, GPS, and satellite, adequate policies and measures must be in place in order to use this data in an ethically and legally responsible manner. The current working paper introduces the BYTE study, which is working towards identifying and understanding the various positive and negative externalities, or impacts, associated with the use of big ?crisis? data. This insight paper provides a preliminary discussion of various externalities that may be encountered in this study. By doing so, the authors highlight the need for additional research in this area to promote ethically and legally responsible crisis data practices.
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Robin Gandhi, Deepak Khazanchi, Daniel Linzell, Brian Ricks, & Chungwook Sim. (2018). The Hidden Crisis : Developing Smart Big Data pipelines to address Grand Challenges of Bridge Infrastructure health in the United States. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 1016–1021). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: The American Society of Civil Engineers (ASCE) Report Card for America's Infrastructure gave bridges a C+ (mediocre) grade in 2017. Approximately, 1 in 5 rural bridges are in critical condition, which presents serious challenges to public safety and economic growth. Fortunately, during a series of workshops on this topic organized by the authors, it has become clear that Big Data could provide a timely solution to these critical problems. In this work in progress paper we describe a conceptual framework for developing SMart big data pipelines for Aging Rural bridge Transportation Infrastructure (SMARTI). Our framework and associated research questions are organized around four ingredients: o Next-Generation Health Monitoring: Sensors; Unmanned Aerial Vehicle/System (UAV/UAS); wireless networks o Data Management: Data security and quality; intellectual property; standards and shared best practices; curation o Decision Support Systems: Analysis and modeling; data analytics; decision making; visualization, o Socio-Technological Impact: Policy; societal, economic and environmental impact; disaster and crisis management.
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Yoshiki Ogawa, Yuki Akiyama, & Ryosuke Shibasaki. (2017). Extraction of significant scenarios for earthquake damage estimation using sparse modeling. 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. 150–163). Albi, France: Iscram.
Abstract: The recent diversification and accumulation of data from GPS equipped mobile phones, building sensors, and other resources in Japan has caused a large increase in the number of earthquake disaster scenarios that can be identified. Disaster prevention planning requires us to contemplate which scenario should be focused on and the required response to various scenarios. As a means to solve this problem, the damage distribution of building collapse and fire from GPS data can be used to estimate future damage based on people flow and various hypocenter models of earthquakes. We propose a method that uses sparse modeling to extract scenarios that are important for disaster estimation and prevention. As a result, this paper makes it possible to quickly grasp the scenario distribution, which was previously impossible to do, and to extract the significant scenarios.
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Yossi Nygate, William Johnson, Mark Indelicato, Miguel Bazdresch, & Clark Hochgraf. (2018). Intelligent Wireless Infrastructure Management for Emergency Communications. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 1156–1160). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: This poster describes the research of a collaborative faculty-led research that will enable first responders to identify and visualize geo-located quality of service and coverage gaps in wireless and deployable networks during an emergency event and support the deployment additional LTE base stations within FirstNet to augment network coverage and capacity. Our crowd sourced cellular metrics system uses big data analytics to detect changes in coverage and usage patterns and recommends where to deploy additional communication assets. The approach uses machine learning methods to measure and model coverage gaps and automatically implement bandwidth prioritization on whatever communication assets are available.
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