An online decision-theoretic pipeline for responder dispatch
An online decision-theoretic pipeline for responder dispatch
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DOI：
10.1145/3302509.3311055
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发表时间：
2019-02
期刊：
影响因子：
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通讯作者：
Ayan Mukhopadhyay;Geoffrey Pettet;Chinmaya Samal;A. Dubey;Yevgeniy Vorobeychik
中科院分区：
文献类型：
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作者：
Ayan Mukhopadhyay;Geoffrey Pettet;Chinmaya Samal;A. Dubey;Yevgeniy Vorobeychik
派遣紧急救援人员为交通事故、火灾、求救电话和犯罪提供服务的问题困扰着全球各地的城市地区。虽然这样的问题已经得到了广泛的研究，但大多数方法都是离线的。这些方法未能捕捉到关键应急响应发生的动态变化环境，因此无法在实践中实施。任何建立有效应急反应管道的整体方法都必须着眼于它所包含的其他挑战--预测事件发生的时间和地点，并了解不断变化的环境动态。我们描述了一个以在线方式集体处理所有这些问题的系统，这意味着模型使用流数据源进行更新。我们强调了这种方法对应急响应的有效性至关重要的原因，并提出了一个算法框架，该框架可以为给定的响应者调度决策理论模型计算有希望的行动。我们认为，精心设计的启发式措施可以平衡计算时间和所获得解的质量之间的权衡，并强调为什么这样的方法比传统方法更可扩展和更容易处理。我们还提出了一种在线事件预测机制，以及一种基于递归神经网络的方法来学习和预测影响应答器调度的环境特征。我们将我们的方法与现有的最先进的调度策略和现场现有的调度策略进行了比较，结果表明，我们的方法在显著减少计算时间的同时，缩短了响应者的响应时间。
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 of responders with a drastic reduction in computational time.