以从业者为中心的方法，使用众包数据进行早期事件检测，用于紧急服务
Practitioner-Centric Approach for Early Incident Detection Using Crowdsourced Data for Emergency Services
摘要：
应急响应高度依赖于事件报告的时间。不幸的是，接收事件报告的传统方法（例如，在美国拨打911）有时间延迟。Waze等众包平台为及早识别事件提供了机会。然而，从众包数据流中检测事件是困难的，这是由于与这些数据相关联的噪声和不确定性的挑战。此外，简单地优化检测精度可能会损害推理的时空定位，从而使这种方法对于现实世界的部署不可行。本文提出了一种新的问题制定和解决方案的方法，以应急响应管理为例研究，使用众包数据为中心的事件检测。所提出的方法CROME（众包多目标事件检测）量化了事件分类的性能度量（例如，F1分数）和示范从业人员的要求（例如，1公里用于事件检测的半径）。首先，我们展示了如何将众包报告、地面实况历史数据和其他相关决定因素（如交通和天气）一起用于卷积神经网络（CNN）架构，以早期检测紧急事件。然后，我们使用基于Pareto优化的方法来优化CNN的输出，并结合以神经网络为中心的参数来平衡检测精度和时空定位。最后，我们使用来自Waze的众包数据和来自美国田纳西州纳什维尔的交通事故报告证明了这种方法的适用性。我们的实验表明，所提出的方法优于现有的方法在事件检测，同时优化现实世界的部署和可用性的需求。
英文摘要：
Emergency response is highly dependent on the time of incident reporting. Unfortunately, the traditional approach to receiving incident reports (e.g., calling 911 in the USA) has time delays. Crowdsourcing platforms such as Waze provide an opportunity for early identification of incidents. However, detecting incidents from crowdsourced data streams is difficult due to the challenges of noise and uncertainty associated with such data. Further, simply optimizing over detection accuracy can compromise spatial-temporal localization of the inference, thereby making such approaches infeasible for real-world deployment. This paper presents a novel problem formulation and solution approach for practitioner-centered incident detection using crowdsourced data by using emergency response management as a case-study. The proposed approach CROME (Crowdsourced Multi-objective Event Detection) quantifies the relationship between the performance metrics of incident classification (e.g., F1 score) and the requirements of model practitioners (e.g., 1 km. radius for incident detection). First, we show how crowdsourced reports, ground-truth historical data, and other relevant determinants such as traffic and weather can be used together in a Convolutional Neural Network (CNN) architecture for early detection of emergency incidents. Then, we use a Pareto optimization-based approach to optimize the output of the CNN in tandem with practitioner-centric parameters to balance detection accuracy and spatial-temporal localization. Finally, we demonstrate the applicability of this approach using crowdsourced data from Waze and traffic accident reports from Nashville, TN, USA. Our experiments demonstrate that the proposed approach outperforms existing approaches in incident detection while simultaneously optimizing the needs for real-world deployment and usability.
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。
