Optimization and simulation of an ambulance location problem

Mota, Idalia Flores De La; Pérez, Esther Segura; Garduño, Alexander Vindel

doi:10.1109/wsc.2017.8248188

Cited by 2 publications

(1 citation statement)

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“…Likewise, in certain parts of the world firefighters are part of emergency medical service, since they also provide ambulances. In this way, predictions of traffic accidents [33], ambulance response time and resource allocation are also included [11,15,18]. Furthermore, we can find works related to the predictions of rare events such as earthquakes [32] and hurricanes [24], which would allow firefighters to identify a specific location for damage assessment and develop better strategies when succouring the population.…”

Section: Related Workmentioning

confidence: 99%

How to Build an Optimal and Operational Knowledge Base to Predict Firefighters’ Interventions

Guyeux

Makhoul

Bahi

2022

Lecture Notes in Networks and Systems

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It has recently been shown that pre-emergency transport, whether performed by firefighters or private ambulances, has a predictive character due to the fact that rescue is directly related to human activity, which is itself predictable. XGBoost has emerged as the best tool to predict the number of interventions by type, but how to design an optimal and operational knowledge base has not been discussed so far. We propose to explain how to make such a base with a content that is both relevant and can be continuously updated, making possible the industrialization of the process, and thus a better operational response of the concerned services. We show that three feature selection tools custom-built for XGBoost are mature enough to allow the optimization of such a database, and a good accuracy in predictions. We also show what these tools can bring in terms of business knowledge, and discuss the organizational and efficiency consequences that such an optimized predictive model could bring.

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Section: Related Workmentioning

confidence: 99%

How to Build an Optimal and Operational Knowledge Base to Predict Firefighters’ Interventions

Guyeux

Makhoul

Bahi

2022

Lecture Notes in Networks and Systems

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The usefulness of NLP techniques for predicting peaks in firefighter interventions due to rare events

Cerna

Guyeux

Laiymani

2022

Neural Comput & Applic

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In some countries such as France, the number of operations assisted by firefighters has shown an almost linear increase over the years, contrary to their resource capacity. For this reason, predicting the number of interventions has become a necessity. Initially, time series models were developed with several types of qualitative and quantitative features, including the alert level of the bulletins, to predict the operational load. We realized that interventions related to human activities are quite predictable. However, the recognition of interventions due to rare events such as storms or floods needs more than quantitative meteorological data to be identified, since there are almost always zero cases. Thus, this work proposes the application of natural language processing techniques, namely long short-term memory, convolutional neural networks, FlauBERT, and CamemBERT to extract features from the texts of weather bulletins in order to recognize periods with peak interventions, where the intense workload of firefighters is caused by rare events. Four categories identified as Emergency Person Rescue, Total Person Rescue, interventions related to Heating, and Storm/Flood were our targets for the multilabel classification models developed. The results showed a remarkable accuracy of 80%, 86%, 92%, and 86% for Emergency Rescue People, Total Rescue People, Heating, and Storm/Flood, respectively.

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Optimization and simulation of an ambulance location problem

Cited by 2 publications

References 0 publications

How to Build an Optimal and Operational Knowledge Base to Predict Firefighters’ Interventions

How to Build an Optimal and Operational Knowledge Base to Predict Firefighters’ Interventions

The usefulness of NLP techniques for predicting peaks in firefighter interventions due to rare events

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