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The number of emergencies have increased over the years with the growth inurbanization. This pattern has overwhelmed the emergency services with limitedresources and demands the optimization of response processes. It is partly dueto traditional `reactive' approach of emergency services to collect data aboutincidents, where a source initiates a call to the emergency number (e.g., 911in U.S.), delaying and limiting the potentially optimal response. Crowdsourcingplatforms such as Waze provides an opportunity to develop a rapid, `proactive'approach to collect data about incidents through crowd-generated observationalreports. However, the reliability of reporting sources and spatio-temporaluncertainty of the reported incidents challenge the design of such a proactiveapproach. Thus, this paper presents a novel method for emergency incidentdetection using noisy crowdsourced Waze data. We propose a principledcomputational framework based on Bayesian theory to model the uncertainty inthe reliability of crowd-generated reports and their integration across spaceand time to detect incidents. Extensive experiments using data collected fromWaze and the official reported incidents in Nashville, Tenessee in the U.S.show our method can outperform strong baselines for both F1-score and AUC. Theapplication of this work provides an extensible framework to incorporatedifferent noisy data sources for proactive incident detection to improve andoptimize emergency response operations in our communities.

Emergency Incident Detection from Crowdsourced Waze Data using Bayesian Information Fusion