In most of the literature on emergency medical service (EMS) system design and analysis, arrivals of EMS calls are assumed to follow Poisson process. However, it is not uncommon for real-world EMS systems to experience batch arrivals of EMS requests, where a single call involves more than one patient. Properly capturing such batch arrivals is needed to enhance the quality of analyses, thereby improving the fidelity of a resulting system design. This paper proposes a spatio-temporal demand model that incorporates batch arrivals of EMS calls. Specifically, we construct a spatio-temporal compound Poisson process which consists of a call arrival model and call size model. We build our call arrival model by combining two models available in the existing EMS demand modeling literature—artificial neural network and spatio-temporal Gaussian mixture model. For the call size model, we develop a k-inflated mixture regression model. This model reflects the characteristics of EMS call arrivals that most calls involve one patient while some calls involve multiple patients. The utility of the proposed EMS demand model is illustrated by a probabilistic ambulance location model, where we show ignoring batch arrivals leads to overestimation of ambulance availability.

在有关紧急医疗服务（EMS）系统设计和分析的大多数文献中，假定EMS呼叫的到达遵循Poisson过程。但是，现实世界中的EMS系统经历EMS请求的批量到达并不少见，其中单个呼叫涉及多个患者。需要适当地捕获这种批次到达以提高分析质量，从而提高所得系统设计的保真度。本文提出了一个时空需求模型，该模型结合了EMS呼叫的批量到达。具体来说，我们构造了一个时空复合泊松过程，该过程由一个呼叫到达模型和一个呼叫大小模型组成。我们通过结合现有EMS需求建模文献中可用的两个模型（人工神经网络和时空高斯混合模型）来构建呼叫到达模型。k膨胀混合回归模型。该模型反映了EMS呼叫到达的特征，即大多数呼叫涉及一名患者，而某些呼叫涉及多名患者。所建议的EMS需求模型的实用性由概率性的救护车位置模型说明，在该模型中，我们显示忽略批量到达会导致对救护车可用性的高估。