  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.
