Prioritized Allocation of Emergency Responders based on a Continuous-Time Incident Prediction Model
Prioritized Allocation of Emergency Responders based on a Continuous-Time Incident Prediction Model
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发表时间：
2017-05
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通讯作者：
Ayan Mukhopadhyay;Yevgeniy Vorobeychik;A. Dubey;Gautam Biswas
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作者：
Ayan Mukhopadhyay;Yevgeniy Vorobeychik;A. Dubey;Gautam Biswas
在人口稠密的城市地区，有效的应急反应是一个主要问题。已经提出了许多技术来分配紧急响应人员，以优化响应时间、覆盖范围和事件预防。有效的反应反过来又取决于对在空间和时间上发生的事件的有效预测，这一问题也得到了相当多的事先注意。我们制定了一个非线性数学程序最大化预期事件覆盖，并提出了一个新的算法框架来解决这一问题。为了帮助优化问题，我们提出了一种新的事件预测机制。事件预测的现有技术通常不考虑对最优调度至关重要的事件优先级，空间建模要么独立考虑每个离散区域，要么学习同质模型。我们通过学习事件到达时间和严重程度的联合分布来弥合这些差距，并使用分层聚类方法捕获空间异质性。此外，我们对联合到达和严重程度分布的分解使我们能够独立学习连续时间到达模型，并随后使用多项逻辑回归来捕获严重程度，条件是事件时间。我们使用来自美国纳什维尔周围城市地区的真实交通事故和响应数据来评估所提出的方法，表明它明显优于现有技术以及目前使用的真实调度方法。
Efficient emergency response is a major concern in densely populated urban areas. Numerous techniques have been proposed to allocate emergency responders to optimize response times, coverage, and incident prevention. Effective response depends, in turn, on effective prediction of incidents occurring in space and time, a problem which has also received considerable prior attention. We formulate a non-linear mathematical program maximizing expected incident coverage, and propose a novel algorithmic framework for solving this problem. In order to aid the optimization problem, we propose a novel incident prediction mechanism. Prior art in incident prediction does not generally consider incident priorities which are crucial in optimal dispatch, and spatial modeling either considers each discretized area independently, or learns a homogeneous model. We bridge these gaps by learning a joint distribution of both incident arrival time and severity, with spatial heterogeneity captured using a hierarchical clustering approach. Moreover, our decomposition of the joint arrival and severity distributions allows us to independently learn the continuous-time arrival model, and subsequently use a multinomial logistic regression to capture severity, conditional on incident time. We use real traffic accident and response data from the urban area around Nashville, USA, to evaluate the proposed approach, showing that it significantly outperforms prior art as well as the real dispatch method currently in use.