Minimizing response times is crucial for emergency medical services to reduce
patients' waiting times and to increase their survival rates. Many models exist
to optimize operational tasks such as ambulance allocation and dispatching.
Including accurate demand forecasts in such models can improve operational
decision-making. Against this background, we present a novel convolutional
neural network (CNN) architecture that transforms time series data into
heatmaps to predict ambulance demand. Applying such predictions requires
incorporating external features that influence ambulance demands. We contribute
to the existing literature by providing a flexible, generic CNN architecture,
allowing for the inclusion of external features with varying dimensions.
Additionally, we provide a feature selection and hyperparameter optimization
framework utilizing Bayesian optimization. We integrate historical ambulance
demand and external information such as weather, events, holidays, and time. To
show the superiority of the developed CNN architecture over existing
approaches, we conduct a case study for Seattle's 911 call data and include
external information. We show that the developed CNN architecture outperforms
existing state-of-the-art methods and industry practice by more than 9%.

研究了应用卷积神经网络 (CNN) 把时间序列数据转换为热力图来预测救护车需求，结合历史救护车需求和外部信息如天气、事件、假日和时间等，提供了一个灵活的、通用的 CNN 架构，并使用贝叶斯优化框架进行特征选择和超参数优化。在西雅图的 911 呼叫数据上进行了实验，表明该 CNN 架构的性能比现有技术和行业实践提高了超过 9%。

卷积神经网络预测救护车需求

Ambulance Demand Prediction via Convolutional Neural Networks

Accurate and fast demand forecast is one of the hot topics in supply chain
for enabling the precise execution of the corresponding downstream processes
(inbound and outbound planning, inventory placement, network planning, etc). We
develop three alternatives to tackle the problem of forecasting the customer
sales at day/store/item level using deep learning techniques and the
Corporación Favorita data set, published as part of a Kaggle competition. Our
empirical results show how good performance can be achieved by using a simple
sequence to sequence architecture with minimal data preprocessing effort.
Additionally, we describe a training trick for making the model more time
independent and hence improving generalization over time. The proposed solution
achieves a RMSLE of around 0.54, which is competitive with other more specific
solutions to the problem proposed in the Kaggle competition.

利用深度学习技术和 Corporación Favorita 数据集，我们提出三种替代方案来解决具体的需求预测问题，设计了一种简单的序列到序列的体系结构以及在此基础上的训练技巧，达到了约 0.54 的 RMSLE 的好性能。