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.
@cite_24: Locational analysis has grown to maturity over the last decades, from its earliest roots, to fruitfulness in a wide-ranging number of strands that join with other disciplines and applications such as environmental planning and supply chain management. This paper charts the progress of location theory in three stages: a period of early contributions, when a number of seminal geometrical and geographical problems were studied; a ‘coming of age’ with the development of defining or classical problems that have proved fundamental to much later research and a third period of new models and new applications.
@cite_27: Abstract This article traces the evolution of ambulance location and relocation models proposed over the past 30 years. The models are classified in two main categories. Deterministic models are used at the planning stage and ignore stochastic considerations regarding the availability of ambulances. Probabilistic models reflect the fact that ambulances operate as servers in a queueing system and cannot always answer a call. In addition, dynamic models have been developed to repeatedly relocate ambulances throughout the day.
@cite_1: An urban emergency service system provides mobile units vehicles to respond to requests for service which can occur at any time and any place throughout a city. This paper describes the common characteristics and operational problems of these systems and surveys the various methods, both traditional and recently developed, which may be used for allocating their units. Aspects of allocation policy discussed include 1 determining the number of units to have on duty, 2 locating the units, 3 designing their response areas or patrol areas, 4 relocating units, and 5 planning preventive-patrol patterns for police cars. Typical policy changes which may be suggested by the use of quantitative allocation models include selective queuing of low priority calls, varying the number of units on duty and their locations by time of day, dispatching units other than the closest ones to certain incidents, relocating units as unavailabilities begin to develop, and assigning police cars to overlapping patrol sectors. As a result of making such changes, it is often possible to reduce queuing and travel time delays, improve the balance of workload among units, and enhance the amount of preventive patrol where needed.
@cite_18: 
The study of computational techniques for the effective and efficient management of Emergency Medical Service (EMS) resources has a relatively long history @cite_1 . The main focus of research in this domain has been on the development of models for the placement of EMS facilities, such as ambulance stations, and on strategies for resource relocation so that specific performance metrics are maximised @cite_27 @cite_18 . The survey @cite_24 provides a chronological perspective on the development of research in this area.