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This document has been produced by ORH for Essex County Fire & Rescue Service on 25 January 2016. This document can be reproduced by Essex County Fire & Rescue Service, subject to it being used accurately and not in a misleading context. When the document is reproduced in whole or in part within another publication or service, the full title, date and accreditation to ORH must be included.

This document is intended to be printed double-sided. As a result, some of the pages in the document are intentionally left blank.

Disclaimer

The information in this report is presented in good faith using the information available to ORH at the time of preparation. It is provided on the basis that the authors of the report are not liable to any person or organisation for any damage or loss which may occur in relation to taking, or not taking, action in respect of any information or advice within the document.

ORH is the trading name of Operational Research in Health Limited, a company registered in England with company number 2676859.

Contents

1  Introduction ...................................................................................... 2 

2  Model Overview ................................................................................. 3 

3  Analysis and Validation ..................................................................... 4 

Data Analysis ...................................................................................................... 4 

Model Validation .................................................................................................. 5 

4  Optimisation and Simulation ............................................................. 6 

Introduction ........................................................................................................ 6 

Optimisation ....................................................................................................... 6 

Simulation .......................................................................................................... 7 

5  Summary........................................................................................... 9 

 

 

1 INTRODUCTION

1.1 This paper gives an overview of ORH’s modelling methodology for the emergency services, with particular reference to the Fire Service.

1.2 ORH has been working with the emergency services in the UK and overseas, using these modelling techniques, for 30 years, and in that time has undertaken about 600 studies for over 100 clients. ORH has worked with 20 Fire and Rescue Services using this modelling approach, typically supporting their IRMP process.

1.3 An overview of the modelling approach used by ORH is provided in Section 2. The analysis and validation processes are expanded upon in Section 3. A more detailed description of the optimisation and simulation models used by ORH is provided in Section 4 and a summary of the process is given in Section 5.

 

Figure 2-1: Modelling Approach

Validation Ensuring the model accurately reflects the current situation

Optimisation Identifying the “best” solutions 

given known constraints

Simulation Predicting future service behaviour and answering 

“what if” questions

Sensitivity Modelling to check that 

identified solutions are robust and future‐proof

 

2 MODEL OVERVIEW

2.1 ORH provides a bespoke modelling service based on proven Operational Research (OR) techniques. ORH models have been designed to help understand the complex relationships between demand, performance, resources and efficiency, for services involving emergency response and public access to facilities.

2.2 The modelling process involves validation (accurately representing the current situation), optimisation (identifying the ‘best’ solutions), simulation (asking ‘what if?’ questions) and sensitivity modelling (testing that solutions are robust). An overview of ORH’s modelling approach is given in Figure 2-1 opposite.

2.3 ORH modelling can help clients appraise and refine plans for operational change before implementation, thereby reducing risk and increasing the chance of success.

2.4 A suite of ORH models has been developed in-house over the last two decades. Two main types of model are used for FRS consultancy work:

Simulation (‘FireSim’);

Optimisation (‘OGRE’ – Optimising by Genetic Resource Evolution’).

2.5 Simulation modelling can test the impact of changes to individual factors, such as demand and resource levels, and to a combination of factors. ORH modelling support provides strategic and tactical advice, ensuring that planning decisions are cost-effective, robust and sustainable.

2.6 With specific objectives supplied by the client, ORH’s optimisation modelling can find the ‘best solution’ given the criteria and constraints agreed. These optimal solutions can then be tested out with full simulation modelling.

2.7 The overall process which is based on a comprehensive analysis of the current use of resources to meet demand and provide risk cover is illustrated in Figure 2-2 overleaf.

2.8 Key to the simulation and optimisation modelling is the development of a travel time matrix. The matrix incorporates differences in times due to vehicle type, response type (use of lights and sirens) and time of day.

2.9 Travel times between nodes on the road network are key inputs to the models. These times are assigned initially based on road types that differentiate achievable speeds in ‘average’ traffic conditions. ORH uses sophisticated HERE travel time data and RouteFinder routing software for analysing travel times. This provides a comprehensive and customisable resource for ensuring that journey times are carefully calibrated to reflect those being achieved by hour and by day (see Annex A for more details).

2.10 A comprehensive analysis is undertaken in order to set up the models and to ensure that they are validated (ie, reflect exactly the cover that is currently being provided). Once validated the models can be used to examine a wide range of resource planning and risk cover issues.

 

Figure 2-2: Overview of Analysis and Modelling Process

Model Parameters

Incident Distribution

Travel Times

Deployments

Attendance Performance

Appliance Utilisation

Reports by Sub-Areas

Maps of Coverage

FireSim

Model Parameters

Incident Distribution

Travel Times

Optimisation Criteria

Minimise Response

Times

Optimal Locations

Local/Region Optimisation

Maximise Resource Efficiency

OGRE

Demand: Locations Frequencies Incident Types

Performance: Distribution/Average 1st, 2nd, 3rd, etc. Incident Types

Resources: Utilisation Availability Crew/Vehicle Types

Job Cycle: Control Activation Crew Turnout Time at Scene, etc.

Simulation Optimisation

Analysis

Analysis

 

3 ANALYSIS AND VALIDATION

Data Analysis

3.1 Data Analysis is required in order to gain an understanding of the FRS’s operational regime, including the demand placed on the Service, the risks covered, the performance achieved and the utilisation of resources.

3.2 A comprehensive, quantitative understanding of the Service profile also provides a baseline position for simulation and optimisation modelling, so that the impacts of any changes can be compared to the current position.

3.3 For FRS modelling, data is extracted on workload for at least the last five years, potentially more depending on the size of the Service. The workload data is sourced from the CAD and therefore includes every appliance mobilisation during the sample period. Depending on the scope of the analysis, around 50 data fields may be collected for each mobilisation including: geographical/address information, all time components, vehicle properties, incident classification, etc.

3.4 In addition to the workload data from the CAD, other information sources include data regarding appliance unavailability (in terms of OTR data for wholetime and retained appliances), station and appliance locations, mobilisation protocols, geographic boundaries (eg, station grounds and command areas).

3.5 A summary of the data sources used in the analysis of data for the fire services is provided in Figure 3-1 overleaf.

3.6 Following data checking and cleaning, typical analysis outputs relate to demand, performance, resource use and key job cycle components (eg, travel times from scene to base station). Internal service data can be combined with external data, such as population and socio-demographic data, to provide a rich source for analysis of risk cover.

3.7 ORH uses a wide range of analytical, statistical and GIS tools to clean, analyse and interpret clients’ data. Analysis findings are typically used to develop an understanding of how the service is delivered and to identify opportunities for improving demand management, maximising performance and using resources more effectively and efficiently to cover the risk profile. Opportunities for change can then be appraised through simulation and optimisation modelling.

3.8 The analysis includes, but is not restricted to, the following aspects, all of which are considered at different temporal intervals, by incident type, by appliance type and by responder number:

Demand (incident frequencies, station workload, appliance workload and level of response).

Job cycle times (control activation times, crew turnout, time at scene).

Attendance performance (first appliance, second appliance).

 

Figure 3-1: Data Sources Used in Analysis

IRS Vehicle

Data Utilisation

Geographic Demand

Minute by minute analysis

Appliance Workload

Incident Profile

Response Parameters

HERE Other Data

National Fire Studies

Bench-marking

Validated Travel Times

Modelled Road

Speeds

Policy Info

Other Data

Station Diary

Operational Response

Estates Information

Deployment Changes

Other FRS Data Sources

Core FRS Databases

Data held by ORH

 

Resource use (appliance utilisation).

Model Validation

3.9 Model validation is the process whereby the model is calibrated against known performance. Once this process is completed satisfactorily, there can be confidence that model outputs will accurately reflect changes in model inputs (eg, changes in station locations or appliance deployments).

3.10 There are a number of stages involved in preparing a validated model, and these require a detailed level of understanding around the manner in which the Service functions (gained through data analysis and consultation), and sophisticated operational research techniques. The ORH consultancy team assigned to this study are experienced in successfully validating and running models in UK FRSs.

3.11 In order to represent fluctuations in demand, performance and appliance availability that occur across the day, modelling periods are developed to ensure that the validation approach is robust. These are agreed with the Service and can be structured so as to take account of existing or proposed shift systems. An aggregated 24/7 model is also produced, and is used to present appropriate summaries of the results.

3.12 Travel times between nodes on the road network are a key input to the model. These times are assigned initially based on road types that differentiate achievable speeds in ‘average’ traffic conditions. ORH uses sophisticated HERE travel time data and RouteFinder routing software for analysing travel times.

3.13 An appropriate travel time network is developed based on the existing station locations, historical incident locations, census data and the underlying road network. The number of nodes within the matrix is representative of the local and Service-wide demand.

3.14 A careful calibration process is then undertaken that gives appliance travel times broken down for different periods of the day, and for distinguishing speeds achieved by different incident types, based on an analysis of travel times currently achieved by appliances.

3.15 Model validation aims to ensure that the model is accurately reflecting performance across the entire spectrum of responses, not just focusing on a particular point or the average time. In addition, first and second appliance attendances are validated individually and in a combined manner. Finally, the validation process also matches modelled and actual utilisation of appliances.

3.16 During the model validation process, and in all future applications for the model, all responses to all incidents are simulated, even though the Service may only be concerned (in performance terms) with first or second attendance to a particular incident type. This ensures that appliance availability and utilisation levels are accurate.

 

 

4 OPTIMISATION AND SIMULATION

Introduction

4.1 The models used by ORH are divided into two categories:

Optimisation: OGRE (Optimisation by Genetic Resource Evolution) can be applied to answering questions around the locations of resources in any Emergency Service.

Simulation: individual simulation models have been developed for each of the Emergency Services – AmbSim, PolSim and FireSim.

4.2 Two ORH models are therefore used to examine options for change studies for FRSs: OGRE (an optimisation model used to identify and evaluate location options) and FireSim (a simulation model used to assess the impacts of change). Full descriptions of the models are given below.

4.3 Once FireSim is validated, it is possible to populate OGRE with demand frequencies, geographical incident distributions, the road network and a sub-set of the validated parameters. As the modelling inputs have been validated in FireSim, OGRE can then be used with confidence to identify potential changes in appliance deployments. Options put forward by OGRE are always evaluated for their impact on emergency response times through a full simulation run using FireSim.

4.4 The combination of the optimisation model (to identify and develop potential solutions) and the simulation model (to fully evaluate options through a complete simulation of operational activity), coupled with input from the Service, can therefore develop options for change which meet the Service’s requirements.

Optimisation

4.5 OGRE is a powerful optimisation model that can optimise the deployment of emergency service resources. It uses a sophisticated genetic algorithm to assess millions of options in minutes, quickly identifying optimum solutions. The model is run by experienced modelling consultants and the optimisation criteria are carefully agreed with the client to ensure that solutions meet individual client’s needs.

4.6 OGRE is a flexible model, ideally suited to identifying the scope for operational efficiencies, improving service delivery and optimising the location of resources. Further information about OGRE is provided in Annex B. Options generated by OGRE are fully evaluated in the appropriate simulation model to check that optimal solutions deliver service improvements.

4.7 For undertaking any optimisation modelling it is necessary to carefully consider the criteria which will be used. OGRE provides the flexibility to look for optimal solutions which meet any given optimisation objective.

 

 

4.8 The optimisation criteria to be used in this modelling are discussed with the Service, generally focused on a particular sub-set of incidents and applying specified weighting between 1st and 2nd pump attendances. When solutions are tested in FireSim, all incidents are included in the modelling so as to capture all pumping appliance activity and accurately monitor performance standards.

4.9 The broad aim of optimisation modelling is to develop options around the optimum distribution of operational resources, including the number of appliances at each location.

4.10 The range of the optimisation could include the following aspects:

Undertaking a Service-wide optimisation to inform the Service’s estates strategy.

Identifying optimal ‘greenfield’ deployments.

Evaluating the locations of existing stations in comparison to optimal sites.

Developing an optimal strategy for the deployment of pumping appliances during periods of reduced availability (eg, pandemic flu).

Producing site-search maps to define optimal locations and informing the Service’s property searches.

Simulation

4.11 FireSim is a sophisticated simulation models that simulates operational service delivery – the model is run in-house by experienced modelling consultants. All of ORH’s simulation models are spatially dependent discrete event simulations developed specifically for emergency service operations. Once validated, the models can provide evidence-based answers to a wide range of ‘what if’ questions. The impact of changes to a number of operational factors, such as station locations and appliance deployments, duty systems and resource use, service demand and response regimes, can be fully assessed. Further information around FireSim is given in Annex C.

4.12 In the simulation model, incidents are generated at specific locations and vehicles are assigned to respond based upon rules covering incident types, crew skills and operational protocols. The full job cycle for each incident is simulated using times to mobilise, reach the incident, be at scene, and return to station. The simulation models report operational performance in terms of attendance times, vehicle workload and capacity for non-incident workload (eg, community safety work).

4.13 A key element of the simulation modelling is that it fully allows for the actual geographical distributions of demand and resources and incorporates travel times between locations (eg, station, scene, standby points). This element is not reflected properly in alternative probabilistic or algorithmic approaches.

4.14 The presentation of results from FireSim is fully customisable and outputs can be shown in tabular and mapped formats as appropriate. The exact

 

 

format is discussed with the Service and it is possible to produce results encompassing, but not limited to, the following:

Attendance performance against a specified set of response standards for first and second appliance.

Attendance performance and average times by area, as specified by the Service (eg, service delivery areas, boroughs and station grounds).

The geographical coverage given by a particular option in terms of a map showing areas which either meet or fall outside of specified attendance standards.

The workload and utilisation of pumping appliances.

4.15 These outputs are discussed with the Service in draft form and then finalised once discussed.

 

 

5 SUMMARY

5.1 ORH’s modelling approach has been developed over at least two decades across several hundred emergency service clients. The application of this approach to Fire and Rescue Services in the UK has been refined over the last ten years and has been used in 20 FRSs.

5.2 The validation stage ensures that the base model accurately reflects the way in which cover is currently being provided. The optimisation model allows an FRS to find the ‘best’ way of organising cover, and the simulation model tests the detailed impacts of operating in a changed manner. Sensitivity modelling ensures that any preferred conclusions are tested for variation in any of the input factors.

 

Annexes

A HERE Travel Time Data and RouteFinder

B Optimisation

C Simulation

 

 

HERE Travel Time Data and RouteFinder  HERE's  comprehensive  data  build  process  ensures  the  highest  quality  data  available  for  routing  and mapping applications.   The data  is  from a variety of sources  including  local governments, utility companies, other public agencies, and commercial mapping agencies.   Aerial photos and differential GPS are used to accurately position roads  and  represent  lakes,  rivers,  railroads,  etc.,  and  proprietary  software  is  then  used  to  add  navigable information, addresses, and points of interest. HERE data has been additionally road tested to collect and verify new data, and drives are taken to confirm the accuracy of all  information  contained  in  the database.   Photographs are also  taken of all overhead  signage  to ensure that the data accurately reflects the real world.  

  HERE Streets Data contains the most navigable attributes available in a database and has over 50 layers. Important attributes of the data: 

Speed category  is based on the  legal speed  limit of a road  link, however  it also takes  into account some physical characteristics and access restrictions that may result  in a difference from the  legal speed  limit (eg, speed bumps, chicanes).   

Function class is added to the road links in the database to indicate their importance for route guidance. It allows optimisation of routing and reduces route planning time.  

Lane category is broken into 3 categories: 1 lane roads (ie, one lane in each direction of travel), 2‐3 lane roads and 4+ lane roads.   

Urban  areas  have  been  defined  in  a  separate  layer  in  HERE  to  cover metropolitan  areas,  towns  and settlements. Urban is applied to all road links that fall within the HERE ‘Built Up Area’ polygon.    

RouteFinder  is  a MapBasic  program which works  in MapInfo.  It  creates  a  street  network  from  HERE  tables. Changes can be made to the network such as closing roads, changing speeds and changing the direction of travel. RouteFinder  can  be  used  to  calculate  the  shortest  times  and  distances  along  roads  between  points. Multiple drive‐time isochrones and catchment areas can be created around many points in a table. 

© HERE All rights reserved. Based upon Crown Copyright material. 

Field Name Description

Link_ID Unique reference

StNm_Base Street name

Ref_Intrsect_ID Intersecting road links

Func_Class Type of road

Speed_Cat Road speed category

Lane_Cat Number of lanes

Dir_Of_Travel One way or both directions

AR_EmerVeh Accessibility to vehicles

Bridge Levels of roads

ControlledAccess General accessibility

Urban Urban/Rural identifier

Attribute Composite speed

There are over 90 fields associated with eachsection of road, including those listed in thetable below. Four properties of each roadsection (shown in bold) are used to determinethe potential speed achieved (or ‘attribute’),taking account of road type, speed restrictions,numberof lanes and the ‘urbanity’ of the link.

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and

or

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to

clin

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ffere

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ed

ical

facili

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can b

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d

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ind

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in c

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bin

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wit

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op

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ed

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MO

DE

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PR

OC

ES

S

The m

od

elli

ng

pro

cess

invo

lves

four

key s

tep

s:

• V

alid

ati

on –

ensu

ring

the

mo

del accura

tely

reflects

the c

urr

ent

situ

ati

on

• O

pti

mis

ati

on –

id

enti

fyin

g

the “

best

” so

luti

ons

• S

imula

tio

n –

pre

dic

ting

futu

re

serv

ice b

ehavio

ur

and

answ

eri

ng

“what

if”

quest

ions

• S

ensi

tivit

y m

od

elli

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– e

nsu

ring

that

identi

fied

so

luti

ons

are

rob

ust

and

futu

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Y B

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kes

into

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unt

co

mp

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inte

rrela

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• P

red

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the im

pact

of

co

ntr

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facto

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nd

pri

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for

chang

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• D

em

onst

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pact

of

chang

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in u

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ntr

olla

ble

facto

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• C

an b

e u

sed

to

exam

ine t

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pact

of

ind

ivid

ual fa

cto

rs, o

r o

f m

ult

iple

sim

ult

aneo

us

facto

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• P

rovid

es

a r

isk-

free e

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ent

in w

hic

h m

any d

iffe

rent

op

tio

ns

can b

e c

onsi

dere

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• P

rod

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evid

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solu

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Stu

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pp

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44

(0

)118

95

9 6

623

Emergency Service Planning

Simulation

Usi

ng

a s

imu

lati

on

m

od

el,

the im

pact

of

futu

re c

han

ges

on

se

rvic

e p

erf

orm

an

ce

an

d r

eso

urc

e

uti

lisati

on

can

be

qu

ickly

un

ders

too

d

Sim

ula

tio

n

FIND OUT MORE

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range of services at:

www.orhltd.com

If you would like to

talk to one of our

consultants please call:

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