为响应调度设计一个在线决策理论管道
An Online Decision-Theoretic Pipeline for Responder Dispatch
AI总结：
本文提出了一种在线决策理论管道，用于有效应对紧急事件，通过实时数据流更新模型，提高响应效率并减少计算时间。
摘要：
向服务交通事故、火灾、 distress 电话和犯罪等紧急事件派遣应急响应人员的问题困扰着全球各地的城市。尽管此类问题已广泛研究，但大多数方法是离线的。这些方法无法捕捉到关键紧急响应发生的动态变化环境，因此无法在实践中实施。任何全面的方法必须考虑其他挑战，包括预测事件何时何地发生以及理解环境动态变化。我们描述了一个系统，该系统以在线方式处理所有这些问题，即模型通过流数据源更新。我们强调这种做法对应急响应有效性的重要性，并提出了一种算法框架，可以为给定的决策理论模型计算有希望的行动。我们还提出了一种在线机制用于事件预测，以及基于循环神经网络的方法来学习和预测影响响应调度的环境特征。我们比较了我们的方法与现有最先进的方法和现有调度策略，结果表明我们的方法在减少响应时间的同时大幅减少了计算时间。
英文摘要：
The problem of dispatching emergency responders to service traffic accidents, fire, distress calls and crimes plagues urban areas across the globe. While such problems have been extensively looked at, most approaches are offline. Such methodologies fail to capture the dynamically changing environments under which critical emergency response occurs, and therefore, fail to be implemented in practice. Any holistic approach towards creating a pipeline for effective emergency response must also look at other challenges that it subsumes - predicting when and where incidents happen and understanding the changing environmental dynamics. We describe a system that collectively deals with all these problems in an online manner, meaning that the models get updated with streaming data sources. We highlight why such an approach is crucial to the effectiveness of emergency response, and present an algorithmic framework that can compute promising actions for a given decision-theoretic model for responder dispatch. We argue that carefully crafted heuristic measures can balance the trade-off between computational time and the quality of solutions achieved and highlight why such an approach is more scalable and tractable than traditional approaches. We also present an online mechanism for incident prediction, as well as an approach based on recurrent neural networks for learning and predicting environmental features that affect responder dispatch. We compare our methodology with prior state-of-the-art and existing dispatch strategies in the field, which show that our approach results in a reduction in response time with a drastic reduction in computational time.
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。
