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dc.contributor.authorSenarath, Yasas
dc.contributor.authorNannapaneni, Saideep
dc.contributor.authorPurohit, Hemant
dc.contributor.authorDubey, Abhishek
dc.date.accessioned2021-10-18T02:09:37Z
dc.date.available2021-10-18T02:09:37Z
dc.date.issued2021-06-24
dc.identifier.citationSenarath, Y., Nannapaneni, S., Purohit, H., & Dubey, A. (2020). Emergency incident detection from crowdsourced Waze data using Bayesian information fusion. Paper presented at the Proceedings - 2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2020, 187-194. doi:10.1109/WIIAT50758.2020.00029en_US
dc.identifier.isbn978-1-6654-1924-6
dc.identifier.isbn978-1-6654-3017-3
dc.identifier.urihttps://doi.org/10.1109/WIIAT50758.2020.00029
dc.identifier.urihttps://soar.wichita.edu/handle/10057/22204
dc.descriptionClick on the DOI link to access this conference paper at the publishers website (may not be free).en_US
dc.description.abstractThe number of emergencies have increased over the years with the growth in urbanization. This pattern has overwhelmed the emergency services with limited resources and demands the optimization of response processes. It is partly due to traditional `reactive' approach of emergency services to collect data about incidents, where a source initiates a call to the emergency number (e.g., 911 in U.S.), delaying and limiting the potentially optimal response. Crowdsourcing platforms such as Waze provides an opportunity to develop a rapid, `proactive' approach to collect data about incidents through crowd-generated observational reports. However, the reliability of reporting sources and spatio-temporal uncertainty of the reported incidents challenge the design of such a proactive approach. Thus, this paper presents a novel method for emergency incident detection using noisy crowdsourced Waze data. We propose a principled computational framework based on Bayesian theory to model the uncertainty in the reliability of crowd-generated reports and their integration across space and time to detect incidents. Extensive experiments using data collected from Waze and the official reported incidents in Nashville, Tenessee in the U.S. show our method can outperform strong baselines for both Fl-score and AUC. The application of this work provides an extensible framework to incorporate different noisy data sources for proactive incident detection to improve and optimize emergency response operations in our communities.en_US
dc.description.sponsorshipWe thank the U.S. National Science Foundation grants (1814958 and 1815459) and a grant from Tennessee Department of Transportation (T-DOT) for partial research support. We also thank Mr Said El Said from T-DOT, Dr. Ayan Mukhopadhyay and Dr. Sayyed Vazirizade from Vanderbilt University for data support and providing feedback.en_US
dc.language.isoen_USen_US
dc.publisherIEEEen_US
dc.relation.ispartofseries2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT);
dc.subjectEmergency informaticsen_US
dc.subjectBayesian theoryen_US
dc.subjectInformation fusionen_US
dc.subjectUser-generated contenten_US
dc.subjectUncertaintyen_US
dc.titleEmergency incident detection from crowdsourced Waze data using Bayesian information fusionen_US
dc.typeConference paperen_US
dc.rights.holderCopyright © 2020, IEEEen_US


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