使用贝叶斯信息融合从众包Waze数据中检测紧急事件
Emergency Incident Detection from Crowdsourced Waze Data using Bayesian Information Fusion
AI总结：
针对紧急事件检测中众包数据噪声与时空不确定性问题，提出基于贝叶斯信息融合的计算框架，利用Waze数据实现主动检测，在纳什维尔数据集上F1与AUC均优于强基线。
摘要：
随着城市化进程的推进，紧急事件的数量逐年增加。这一趋势使得资源有限的紧急服务不堪重负，并要求优化响应流程。部分原因在于紧急服务收集事件数据的传统“被动”方式，即由信息源拨打紧急号码（如美国的911）进行报告，这延迟并限制了潜在的最优响应。Waze等众包平台为开发一种快速、“主动”的事件数据收集方法提供了机会，该方法通过人群生成的观察报告来收集信息。然而，报告来源的可靠性以及所报告事件在时空上的不确定性，对设计这种主动方法构成了挑战。因此，本文提出了一种利用嘈杂的众包Waze数据进行紧急事件检测的新方法。我们基于贝叶斯理论提出了一种原则性的计算框架，用以对人群生成报告的可靠性不确定性及其在空间和时间上的整合进行建模，从而检测事件。使用从Waze收集的数据以及美国田纳西州纳什维尔市的官方报告事件进行的大量实验表明，我们的方法在F1分数和AUC方面均能超越强基线方法。本工作的应用提供了一个可扩展的框架，能够整合不同的嘈杂数据源，用于主动事件检测，以改善和优化我们社区中的紧急响应操作。
英文摘要：
The 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 F1-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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
