# Modelling Metropolitan-area Ambulance Mobility under Blue Light Conditions

## TL;DR

This paper develops a data-driven model for ambulance routing in London, improving response time predictions by accurately estimating segment speeds and predicting routes, thus enhancing emergency response efficiency.

## Contribution

It introduces a novel hybrid model combining route prediction and speed estimation to optimize ambulance response times in metropolitan areas.

## Key findings

- The model achieves high route similarity scores.
- It outperforms alternative mobility models.
- It accurately predicts ambulance arrival times.

## Abstract

Actions taken immediately following a life-threatening personal health incident are critical for the survival of the sufferer. The timely arrival of specialist ambulance crew in particular often makes the difference between life and death. As a consequence, it is critical that emergency ambulance services achieve short response times. This objective sets a considerable challenge to ambulance services worldwide, especially in metropolitan areas where the density of incident occurrence and traffic congestion are high. Using London as a case study, in this paper we consider the advantages and limitations of data-driven methods for ambulance routing and navigation. Our long-term aim is to enable considerable improvements to their operational efficiency through the automated generation of more effective response strategies and tactics. A key ingredient of our approach is to use a large historical dataset of incidents and ambulance location traces to model route selection and arrival times. Working on the London road network graph modified to reflect the differences between emergency and civilian vehicle traffic, we develop a methodology for the precise estimation of expected ambulance speed at the individual road segment level. We demonstrate how a model that exploits this information achieves best predictive performance by implicitly capturing route-specific persistent patterns in changing traffic conditions. We then present a predictive method that achieves a high route similarity score while minimising journey duration error. This is achieved through the combination of a technique that correctly predicts routes selected by the current navigation system of the London Ambulance Service and our best performing speed estimation model. This hybrid approach outperforms alternative mobility models.