Abstract

Given the aging demographics and rapid urbanisation, cities need to be equipped to respond to emergency (eg. 999
calls) more quickly. By 2050, over 25% of the UK’s population will be over 65. This has implications on the
overall health services as well as the NHS Trust to cope with anticipated rise in ambulance call outs amidst
worsening urban congestion. Ambulance services are required to reach 75% of emergency calls within 8 minutes.
For this reason, there is a growing need to develop new and innovative applications for an even more intelligent
use of the existing transport system that will support in real-time emergency vehicles to reach life threatening
emergency cases quicker.
this paper will discuss the methodology and the preliminary results of the modelling framework implementation
of a “Life First Emergency Traffic Control” or “LiFE” system, a ITS implementation seeking to identify the best
solution to reduce the time to respond to emergency calls, whilst operating a resilient service with a cost and fuelefficient
fleet. Results of the application of a microsimulation model to replicate the behaviour of ambulances in
urban area and how different reactions of general traffic can impact on the travel time of an ambulance are
presented. The proposed microsimulation modelling framework has been developed with the final aim to
understand and evaluate the impacts and the best scenarios to improve ambulance (or any Emergency vehicle)
response time and gains in cost-saving, whilst assessing mitigation strategies to reduce other impacts such as
residual congestion.
The work is part of an Innovate UK collaborative funded project, namely Life First Emergency Traffic Control
(LiFE) with the aim to develop an innovative application for an intelligent transport system that operates in realtime
to enable ambulances to reach life threatening emergency cases quicker by integrating ambulance route finder
applications with traffic management systems.


Original document

The different versions of the original document can be found in:

https://zenodo.org/record/1435629 under the license http://creativecommons.org/licenses/by-nc-nd/4.0/legalcode
http://dx.doi.org/10.5281/zenodo.1435628 under the license http://creativecommons.org/licenses/by-nc-nd/4.0/legalcode


DOIS: 10.5281/zenodo.1435628 10.5281/zenodo.1435629

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Published on 01/01/2018

Volume 2018, 2018
DOI: 10.5281/zenodo.1435628
Licence: Other

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