BIROn - Birkbeck Institutional Research Online

    Modelling metropolitan-area ambulance mobility under blue light conditions

    Poulton, Marcus J. and Noulas, A. and Weston, David J. and Roussos, George (2018) Modelling metropolitan-area ambulance mobility under blue light conditions. IEEE Access 7 , pp. 1390-1403. ISSN 2169-3536.

    Modelling Metropolitan area Ambulance Mobility under Blue Light Conditions.pdf - Published Version of Record
    Available under License Creative Commons Attribution.

    Download (2MB) | Preview


    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 LAS navigation system and our best performing speed estimation model. This hybrid approach outperforms alternative mobility models. To the best of our knowledge, this study represents the first attempt to apply a data-driven methodology for route selection and the estimation of arrival times of ambulances travelling with blue lights and sirens on.


    Item Type: Article
    Keyword(s) / Subject(s): Ambulance mobility, emergency services, routing and navigation, Smart Cities
    School: Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences
    Research Centres and Institutes: Data Analytics, Birkbeck Institute for
    Depositing User: David Weston
    Date Deposited: 03 Jan 2019 15:31
    Last Modified: 09 Aug 2023 12:45


    Activity Overview
    6 month trend
    6 month trend

    Additional statistics are available via IRStats2.

    Archive Staff Only (login required)

    Edit/View Item Edit/View Item