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Place crowd safety, crowd
science? Case
studies and application
Keith Still and Marina Papalexi
Department of Business, Manchester Metropolitan University –All Saints
Campus, Manchester, UK
Yiyi Fan
Department of Management, Lancaster University, Lancaster, UK, and
David Bamford
Department of Business, Manchester Metropolitan University –All Saints
Campus, Manchester, UK
Abstract
Purpose –This paper aims to explore the development and application of place crowd safety management
tools for areas of public assembly and major events, from a practitioner perspective.
Design/methodology/approach –The crowd safety risk assessment model is known as design,
information, management-ingress, circulation, egress (DIM-ICE) (Still, 2009) is implemented to optimise crowd
safety and potentially throughput. Three contrasting case studies represent examples of some of the world’s
largest and most challenging crowd safety projects.
Findings –The paper provides some insight into how the DIM-ICE model can be used to aid strategic
planning at major events, assess potential crowd risks and to avoid potential crowd safety issues.
Practical implications –It provides further clarity to what effective place management practice is.
Evidence-based on the case studies demonstrates that the application of the DIM-ICE model is useful for
recognising potential place crowd safety issues and identifyingareas for require improvement.
Originality/value –Crowd science is an emerging field of research, which is primarily motivated by place
crowd safety issues in congested places; the application and reporting of an evidence-based model (i.e. DIM-
ICE model) add to this. The paper addresses a research gap related to the implementation of analytic tools in
characterising place crowd dynamics.
Keywords Place crowd safety, Crowd science development
Paper type Case study
1. Introduction
Managing crowded places is challenging. Over 100 years ago, in 1902, 25 people died and
517 were injured when the West Stand at Ibrox Park, Glasgow, UK collapsed during an
international football match (Still, 2019). In 2019, 16 people died and 101 were injured in a
© Keith Still, Marina Papalexi, Yiyi Fan and David Bamford. Published by Emerald Publishing
Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence.
Anyone may reproduce, distribute, translate and create derivative works of this article (for both
commercial & non-commercial purposes), subject to full attribution to the original publication and
authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/
legalcode
Case studies
and
application
Received 18 October2019
Revised 6 January2020
6 February 2020
14 February 2020
Accepted 24 February2020
Journal of Place Management and
Development
Emerald Publishing Limited
1753-8335
DOI 10.1108/JPMD-10-2019-0090
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/1753-8335.htm
human crush in Antananarivo, Madagascar before a concert at the Mahamasina Municipal
Stadium. The show was about to start and people, believing they could enter the stadium,
began to push, however, the doors were still closed (Still, 2019). Recently we have global
climate strikes on 6 December 2019, with disruptive demonstrations in 2,300 cities across
153 countries (Al Jazeera, 2019). Failures in both place design and place management are not
unique and crowd “mis”behaviour is not always the primary cause of accidents and
incidents. One common factor is the inappropriate utilisation of space. After major incidents
when facts are analysed the crowd is rarely the cause. More commonly the design and
management of the place is the problem.
The safety of humans in crowded environments has been recognised as a rapidly
growing research area and has been of significant concern to many government agencies
(Helbing et al., 2007). Increases in urban populations and mass events have raised interest
among researchers and authorities in regard to the problems of pedestrian and crowd
dynamics (Haghani and Sarvi, 2018). To date, there has been limited empirical research on
pedestrian and crowd behaviours, dynamics and motion (Shahhoseini et al., 2018).
Identifying and understanding the mechanisms that may lead to crowd disasters and
incidents are critical to ensuring safety in crowded environments (Helbing et al.,2007). In
addition to this, place management aims to identify and understand elements such as the
political, legal, economic, social and technological aspects of our environment, which ideally
lead to ensuring it is “fit for purpose”(Kalandides et al., 2016;Parker, 2008).
This paper reports on the implementation of a crowd safety management tool for places
of public assembly and major events and is a practitioner, not a conceptual paper. It
provides insight into strategic planning for places regard major events, specifically how to
potentially reduce crowd safety issues and as such makes a contribution to the place
management and development literature (Badiora and Odufuwa, 2019;Ibem et al., 2013;
Kalandides et al.,2016;Parker, 2008;Pasquinelli et al., 2018). The paper addresses a research
gap related to the implementation of analytic tools in characterising place crowd dynamics
identified by Helbing et al. (2007), the paper provides further clarity in discerning what
effective place management practice is, which is important because, as suggested by
Kalandides et al. (2016), improved knowledge of this can lead to the development of places
that are successful, liveable and equitable. Finally, it can make a defined and measurable
impact to place development, not just to specific crowd events but indeed to society as a
whole.
This paper will review a selection of the available literature on crowd safety and crowd
science in Section 2, followed by Section 3 outlining a crowd assessment safety risk model
(the design, information, management-ingress, circulation, egress (DIM-ICE) risk model)
used within this paper. Section 4 presents findings then reports on three case studies
applying said model and a discussion engages the cases with appropriate literature. Finally,
Section 5 describes conclusions and recommendations are made.
2. Literature review
2.1 Crowd dynamics
A crowd can be defined as follows: “a large group of individuals (N100 P) within the same
space and at the same time whose movements continue for a prolonged period of time
(t1960s) dependent on predominantly local interactions (k1P/m
2
)”(Duives et al., 2013,
p. 194). It can be seen from this definition that the number N(number of individuals), k
(density) and t(time) are the key elements of behaviour (movement/dynamics) of a crowd.
However, a crowd is not simply a collection of a number of individuals; rather, it may exhibit
highly complex dynamics.
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The dynamics of crowd behaviour and movement depend on various logical crowd-related
factors such as average speed, volume and density of the crowd, etc., as well as other
psychological and social factors. These dynamics can exhibit a number of unexpected collective
phenomena. The most commonly recognised of them discussed in the literature are as follows:
lane formation;
oscillations at bottlenecks;
the faster-is-slower effect; and
clogging at exit (Helbing et al., 2000,2005;Kretz et al., 2006;Georgoudas et al., 2010;
Shahhoseini et al., 2018).
High crowd density may result in serious safety issues and crowd disasters. A frequent cause of
crowd disasters is overcrowding (Haase et al., 2019). If congestion reaches critical levels, crowd
motion transitions into a stop-and-go pattern and eventually causes a phenomenon called crowd
turbulence (Helbing et al.,2007). Turbulent crowd motion is characterised by random, unintended
displacements of groups in all possible directions (mass motion), which trigger disasters. Large
gatherings of pedestrians are found in closed facilities such as shopping malls, stadiums, train
stations and in open facilities such as walkways and parks. The number and severity of tragedy
crowd disasters in high-density public events have risen significantly in the past decade (Haase
et al.,2019). In a recent stampede during Hajj on 24 September 2015, more than 700 people died
and more than 850 were injured. Therefore, understanding crowd behaviours and associated
risks in high-density environments have the potential to save lives.
2.2 Crowd risk
Static crowd density and moving/dynamic crowd density have risks and different limits.
Crowd risk level can be determined by integrating crowd density and flow rate as per
Figure 1 (Still, 2011). As shown in Figure 1, density is measured by the number of people per
m
2
and the flow rate is measured per metre per minute. Crowd risk increases with density
and flow rate and moves into high risk when the density exceeds a certain point, e.g. five-
Figure 1.
Crowd density vs
crowd flow rate
Case studies
and
application
person/m
2
suggested by (Still, 2011). Daamen and Hoogendoorn (2003) outline the variance
and created a higher flow rate standard in Holland, which was entered into Dutch law.
Unfortunately, a “trained crowd”is not like a “tired crowd”leaving a venue or an “uncertain
crowd”during an incident. The usefulness of Figure 1 is in defining the language of flow/
density/risk, as the vocabulary is rather vague and the curve is difficult to visualise (without
graphics). Using the descriptors free-flowing, capacity and congested provide a clearer
definition of the “states”of the crowd flow.
Crowd risk analysis is considered an important aspect of crowd monitoring and
management (Smith, 2003). Still (2014a,2014b) suggests that there are particular issues
associated with conventional crowd safety risk assessment documents. He highlighted that
as follows:
these documents are biased towards overestimating risk;
there is often a “cut and paste”approach to the development of this type of
documentation; and most importantly
there is often a lack of the information required to address relevant crowd safety
issues.
Still (2014a,2014b, p. 48) states that as follows:
the standard crowd risk analysis process of multiplying the likelihood of the risk occurring and
the consequences of that risk fails the basic principles of Information Theory (Shannon, 1948)in
that it is impossible to reconstruct the conditions that give rise to many crowd related risks,
especially those which are dynamic in nature.
Still’s (2013) research on crowd disasters found that the design element was the fundamental
causality in over half of the incidents and concluded that an appropriate risk analysis of
crowds needs to be undertaken to significantly reduce fatalities and serious injuries. This
was also identified by Lak et al. (2019) and Ibem et al. (2013), who suggested that appropriate
design strategies, which consider participants’preferences, improve the quality of places in
different contexts and reduce safety risk. Similarly, Badiora and Odufuwa (2019)
highlighted the importance of developing environmental designs for enhancing crowd
safety. Fruin (2002) stated that there is a need for adequate formalised training in crowd
management principles and techniques, to raise awareness and provide information
regarding the appropriate tools that can be applied for an event to be organised successfully
avoiding crowd safety risks.
2.3 Crowd science
Crowd science is an emerging field of research (Chai et al., 2017), which is primarily
motivated by the crowd safety issues in congested environments. Crowd science refers to the
study of the effect of density, dynamics and behaviour on a crowd and crowd safety. The
concept stems from the initial work of Still (2000) on crowd dynamics, which can be defined
as “the study of how and where crowds form and move above the critical density of more
than one person per m
2
”(Still,2014a, 2014b, p. 93). Several techniques have been discussed
in relation to crowd science in the extant literature as follows:
crowd modelling;
crowd counting; and
crowd monitoring and management (including crowd risk analysis) (Still,2014a,
2014b).
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Each of these is discussed, in turn, below.
2.3.1 Crowd modelling. Crowd modelling, based on simulating the crowd scenarios under
various circumstances, is concerned with building robust representations of a crowd for
scene understanding and is primarily a process used in the development of a robust crowd
management system. The classical approaches for crowd modelling can be considered
under macroscopic and microscopic scales (Xu et al., 2014;Bellomo et al., 2016). Macroscopic
models treat the crowd system as a whole and are usually designed to achieve real-time
simulation for very large crowds, where each individual’s behaviour is not the primary
research interest. In contrast, microscopic models are only for smaller crowds to achieve
real-time simulation and focus on individual behaviours and their interactions based on
complex cognitive models. Various simulation techniques have been developed such as
situated cellular agents approach (Bandini et al., 2007). Virtual environment representations
have also been constructed for crowd simulations (Yersin et al., 2005;Yersin et al.,2009). A
number of behaviour models have been proposed to investigate crowd behaviour (Haghani
and Sarvi, 2018)suchasflow-based models and agent-based models. Crowd models may
also incorporate different facets of a crowd. Some of the work targets the extraneous
attributes of a crowd such as poses, movement patterns, appearance and coordinated
positions of individuals; and some other work targets how crowds social behaviour emerges
over time consequent to external events. However, the descriptive power of such
methodologies for practical applications has remained unclear. This has been largely
attributed to the lack of evidence-base against, which models can be calibrated or validated.
In general, pure mathematical approaches or analytic models are not adequate in
characterising crowd dynamics (Helbing et al.,2007).
2.3.2 Crowd counting. Crowd counting is an important task for operational, safety and
security purposes. Systems with these functions can be highly effective tools for crowd
management (Al-Zaydi et al., 2016). Pedestrian crowd events are common but assessing the
safety of such events has proven difficult. According to Duives et al. (2013), not only are the
layouts of the infrastructure different but also movements of pedestrians differ significantly
between events. Different non-visual and visual methods are used for crowd counting and
include various methods such as tally counters (Lev et al., 2008), differential weight counters
(dos Reis, 2014), infrared beams, wireless fidelity network and wireless sensor network
based counters (Yuan et al., 2011;Di Domenico et al.,2016). Visual-based crowd counting
systems can be deployed using different types of cameras. According to Al-Zaydi et al.
(2016), methods based on computer vision are one of the best choices because cameras have
become ubiquitous and their use increasing. There are an estimated six million CCTV
cameras installed in the UK (Birch et al.,2017). In comparison with computer vision-based
methodology, other non-visual methods need to be carefully planned and deployed for
specific purposes and the accuracy is often less than a computer vision-based technology.
Crowd counting based on computer vision can be classified into a line of interest and region
of interest (Li et al., 2011). Research into people counting in sparse environments is well
established, but there are still many challenges and limitations to overcome in crowded
environments (Hou and Pang, 2010). They report on a lack of knowledge of how to
handle occlusion (obstructions/blockages), which may only slightly affect crowd counting in
sparse environments, but its effect increases significantly in crowded environments.
Therefore, there is a need to develop a method to measure the level of occlusion, thereby
improving the accuracy of counting. Experience demonstrates that event organisers
typically inflate (grossly) their proposed numbers and the authorities then mobilise what
they consider to be a proportionate response. The authorities turn out in force (expecting a
larger crowd) and are seen as having provided a disproportionate response.
Case studies
and
application
2.3.3 Crowd monitoring and management (including crowd risk analysis). Crowd
monitoring deals with constructing systems for real-time decision support through the
statistical analysis of visual data. Furthermore, crowd management deals with the strategic,
tactical and operational handling of crowds ensuring safety in an uncompromising yet
efficient manner. Crowd risk analysis is considered to be an important aspect of crowd
monitoring and management (Smith, 2003). A number of studies highlight the shortcomings
of the traditional approach for assessing risks at mass gatherings. First, a traditional risk
assessment approach is insufficient to predict human behaviour (Upton, 2004). Second,
conventional risk assessment methods are biased towards overestimating risk (Still, 2019).
Despite the importance of the dynamic nature of crowd-related risks, the extant literature
has not progressed the notion of monitoring, assessing or describing crowds to underpin
interventions or controls. Also, there is limited evidence of practical applications for
dynamic crowd risk analysis and monitoring.
2.4 The limits of current knowledge in the field
Fruin (1984) and Sime (1993) highlighted that there is a need to understand the interaction of
efficient crowd management and place systems design for events, as these are the major factors
that affect crowd disasters. Berlonghi (1995) argued that mismanagement of crowd risk may
result in serious losses of life, health, property and money. Some operational guidelines and
legislation are available (although crowd safety legislation and guidance are different across
the UK, Europe, USA and Australia), which highlight measures that should be adopted in the
context of crowd risk management regard the success of the delivery of events. For example,
the “Green Guide”, in Guide to Safety at Sports Grounds (Health and Safety Executive [HSE],
1997;GSSG, 2008), the “Primrose Guide”in Guide to Fire Precautions in Existing Places of
Entertainment and Like Premises (Health and Safety Executive [HSE], 1998) and the “Purple
Guide”in Event Safety Guide (Health and Safety Executive [HSE], 1999). The Safety at Sports
Grounds 2018 Edition (Version 6) and the Primrose Guides (fire safety was replaced with a
series of guides specific to premises) were replaced by the UK Building. The Purple Guide has
also been extensively rewritten but is now subscription only.
However, there is still evidence that reveals clearly insufficient and inadequate planning at
high-density public events (Haase et al.,2019;Shahhoseini et al., 2018). Perhaps, these
guidelines do not go far enough; the Fire codes set the times for evacuation, but this is based on
the assumption of, namely, instant reaction to alarms; and the fire is no longer the only threat.
We now have chemical, biological, radiological and nuclear threats, active shooter, etc. This
means that the assumption of egress routes and viability, types of alarm, nature and direction
of a threat, etc are all new variables and not the same as the “fire”assumptions.
3. Research methodology
Our study aims to provide empirical evidence of using relevant techniques for dynamic risk
analysis to understand how to improve place crowd safety and throughput. To achieve this
objective, the paper adopts a multiple case study approach (Yin, 2018). However, gaining
access to organisations and having permission to share the outcomes for this type of
research can be difficult and is granted through a combination of good luck, effective
planning and hard work (Bell et al.,2018). The paper has, therefore, presented a summary of
projects that the lead author engaged with over time. The modelling tool presented in
Table 1, the “DIM-ICE risk model”was developed by Still (2009) from the application of
evidence-based mathematical formulae and direct interactions with clients over an extended
period. Primary data were collected based on the lead author’s experiences in these
interactions. The case studies reported in the findings section, chosen to be representative of
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Ingress Circulation Egress
Normal
Design Elements of the design that
influence the crowd during
ingress –this specifically
relates to the elements of
the design (such as barriers,
local geometry, width of
routes, paths and stairs,
entrances, turnstiles, etc.
Elements of the design that
influence the crowd during
circulation (this relates to
“mid-event”–moving around)
such as route widths, stairs,
layout and facilities
management, concessions, etc.
Elements of the design that
influence the crowd during
egress (getting out) –
specifically the egress
capacity, route complexity
and geometry (stairs,
corridors, doors, gates, etc.)
Information Prior to the event, many
things can influence crowd
behaviour such as advanced
notifications, media coverage,
tickets and posters, local
knowledge, previous event
history, nature of the band,
weather forecasts. Assess how
the information prior to the
event, near the event, on the
way to the event and at the
venue could influence the
crowd –specifically signage
and information systems
Mid-event there could be a
lot of conflicting
information, the
performance, the
concessions, signage, PA
announcements, stewards
and information points.
Assess how this influences
the crowds and how best to
inform the crowd of the
facilities
Signage and PA
announcements for departure
(non-emergencies) influence
not only the direction but the
distribution of the crowd.
Ensure that all routes are
clearly signed –checking for
lines of sight to ensure all exit
routes are visible
Management Stewards, security and
police management not only
divert the crowd to the most
appropriate areas but also
influence the crowd’s
behaviour (such as reducing
the element of hooliganism
by increasing the visibility
of police –this is also
information). Queues can be
actively managed and
evenly distributed if
approach routes allow good
sightlines
During the event, the
stewards can actively
manage queues and crowd
movements
During egress departing
crowds can be actively
managed = specifically car
parks can be made more
efficient if actively managed
(rather than allowing a free-
for-all dash for the exit)
Emergency
Design How does the ingress
system cope during an
emergency –you may need
to consider a “stay out”
strategy and assess how the
design copes with turning
the crowd back from an
internal threat
Mid-event how quickly can
this site evacuate –
typically the type of
calculation a fire/safety
officer would perform to
ensure the site had
sufficient egress routes and
capacity for clearance
How does the egress system
cope during an emergency –
you may need to consider a
“stay put”strategy and
assess how the design copes
withholding the crowd back
from an external threat
Information During ingress how would
the crowds be informed of
an emergency? What type
of information, in what
Mid-event how would the
crowds be informed of an
emergency? What type of
information, in what form
and what content is
During egress how would
the crowds be informed of
an emergency? What type
of information, in what
form and content is
(continued)
Table 1.
The DIM-ICE risk
model
Case studies
and
application
the scope and scale of place management within the word limits of a journal paper,
contribute to the evidence base on the adoption and adaptation of place crowd safety and
crowd science “best practice”within organisations.
This “DIM-ICE risk model”can be used to identify the multiple and complex issues
associated with crowd safety by taking into account the density, behaviour and dynamics of
crowds (Still, 2009). Still’s DIM-ICE risk model considers three fundamental categories of
systemic failure that are applicable to all crowd-related incidents, these are:
(1) design;
(2) information; and
(3) management-related failures.
The DIM-ICE risk model assesses these categories against three identified phases of crowd
movement in high-density environments, namely:
(1) ingress (arrival);
(2) circulation (movement within the venue); and
(3) egress (departure).
The model combines these elements into a matrix framework (Table 1). It is important to
present this here to provide sufficient detail regards the different elements involved and to
provide some contextualisation for the application of theory in the cases.
Table 2 presents the Crowd Safety Projects from 1999 to 2019 and provides a useful
overview of the scope and scale of application of the techniques discussed in the paper.
Importantly, it provides the evidence of the application of the DIM-ICE risk model to 68
projects and, when the singular public interactions per site (post-intervention) are counted,
Ingress Circulation Egress
form and content is
required?
required? Ensuring the
crowd moves away from
the threat requires more
than just a please leave an
announcement
required? For this, you need
to consider the crowd in the
process of normal egress
Management During ingress, there may
be more people trying to
gain entry than is
physically possible (for
example, a “free”event).
The crowds may need
active management to
prevent overcrowding in
the event space. This would
be considered an emergency
situation as there is a risk of
crushing if the event does
not have an active
management system
During the event, the crowd
may need to be managed
(directed) away from a
threat. Consider the
information (above) and the
management of the egress
for a direction that ensures
the crowd moves as quickly
as possible away from the
source of danger
The crowd may need to be
managed after vacuation
(say on a holding area) to be
kept safe until the threat/
danger has passed
You may need to keep
managing the crowd for
several hours during a
holding operation. You will
need to keep the crowd
informed until it is safe to
let the crowd disperse
Source: Still, 2009
Table 1.
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Type of event Sports events Religious festivals Major events Citycentre and retail
Festivals and street
events Rail Leisure Evacuation
Sydney Olympics
Beijing Olympic Stadium
London Olympics
Twickenham (UK)
Swedbank Arena Project
(Sweden)
Commonwealth Games
(UK)
Millennium Stadium (UK)
Wembley Stadium (UK)
Football Licensing
Authority Concourses
(UK)
Hong Kong Jockey Club
Premier League persistent
standing (modelling
project)
Penn State (USA)
Everton Fan Zone (UK)
Etihad –Manchester City
Stadium (UK)
Besiktas–Vodafone
Arena (Turkey)
Liverpool Stadium
Murray field Stadium
Jamarat Bridge, Saudi
Arabia
Al-Haram, Saudi Arabia
Al Mashaaer
AlMugaddassah metro
project bid Saudi Arabia
Diwali (Leicester)
Royal wedding
MCFC and MUFC victory
parades, Manchester
Leicester Caribbean
Carnival
Glastonbury music
festival, UK
Hampton Court flower
show
Great Manchester run
London New Year Event
Aberdeen Hogmanay
Mathew Street Festival
(Liverpool)
Penn Stadium (Nebraska v
Penn State)
Lincoln Christmas Market
Birmingham Christmas
Market
Kendal Torchlight Parade
T in the Park (Scotland)
Canada Day (Ottawa)
Dubai New Year
Paradise Street
Development Area,
Liverpool
Jabal Omar development
(Makkah –Saudi Arabia)
West Kowloon Cultural
District bid
Covent Garden Ticket Hall
Retail Analysis
Austin, Texas (South by
Southwest Music Festival)
Manchester Arena
EMAAR –Dubai Mall –
UAE
Toronto (Metrolinx)
Chelsea flower show
Lewes fireworks
Aberdeen fireworks
Royal Parks (London)
Protest March
(Manchester –police
planning)
Cubic Transportation Ltd
(London Underground)
Easingwold training
courses (London
Underground Limited and
British Transport Police),
Dwell modelling –Alstom,
Porterbrook, Bombardier,
Interfleet
Wembley Complex Station
(Marshalling Study)
Chesterfield Railway
Station
Covent Garden Ticket Hall
Retail Analysis
Kuala Lumpur
Conference Centre
Aquarium
Cleethorpes Outdoor
Arena Development
Manchester Museum
National Arenas
Association
Liverpool Arena and
Conference Centre
Premier League (UK
football) –safety in
stands (modelling
project)
Canary Wharf (London
Financial District)
Labour Party Conferences
Amsterdam Police
Barclays Bank
AWE Aldermaston (public
enquiry),
Bluewater shopping mall
and
Westfield (Olympics
gateway)
Sum of projects 17 4 16 84667
Annual number of people who then
attend the places
39,599,850 10,000,120 9,907,000 174,169,000 1,270,000 1,304,360,500 3,050,100 76,765,020
Table 2.
Crowd safety
projects from
1999 to 2019
Case studies
and
application
this equates to a total overall annual impact on 1,619,121,590 individuals who subsequently
attended the venues.
Interestingly, presenting the data from Table 2 in a slightly different manner
demonstrates that the projects split into three main groups (Figure 2), according to the level
of crowd density in the crowd safety projects from 1999 to 2019. Figure 2 shows the small,
medium and large scale application of the DIM-ICE risk model across a number of
representative places (e.g. religious festivals in the far east, multiple Olympic games, mass
transit, etc.) characterised by crowd density. The following section reports on the actual
application of the model at three large and challenging crowd safety projects as follows:
(1) Sydney Olympics project;
(2) Canary Wharf project;
(3) Murrayfield Stadium project.
The case studies report on the use of the DIM-ICE risk model. The rationale of choosing
these three projects was guided by our main research objective, to report on the
implementation of an analytic tool in characterising place crowd dynamics. As discussed in
the literature, there is a lack of clarity on the descriptive power of such methodologies for
practical applications. Choosing three cases also provides us with opportunities to both
expand this evidence base and to validate the DIM-ICE model.
The DIM-ICE risk model is a technique, which acts as a predictor of crowd activity and
behaviour; it identifies the principles that we should be looking for to prevent future
incidents. It assists in understanding the routes of crowds and highlights areas and times of
high risk, which enable to organisers to deal with those risks and control them through
effective design, information systems and management strategies. Initially, the DIM-ICE
model is a blank template. By thinking through the details that need to go in each box, we
encourage a structure to both the risk analysis and understanding of the causes of accidents
and incidents. The next phase of the process is to categorise the elements of the DIM-ICE
model into three identified group, namely:
(1) things that go well;
(2) things that need to be monitored; and
(3) things that require improvement.
Figure 2.
Crowd density and
crowd safety projects
taken place from 1999
to 2019
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The DIM-ICE risk model also assisted in creating a one-way system to optimise crowd
movement by using Network analysis theory (Still, 2019). This one-way system included all
possible options available to the attendees and introduced additional nodes. As part of it, a
command and control tool was designed, which was an excel spreadsheet where information
such as events start times, transit times, transit capacity, queuing times for security and
screening, walk time to get to the stadium and the seating time, were all included. This
approach assisted in identifying the number of people that would be in circulation (people
moving between events) rather than in the stadiums. It provided an estimation of the
expected number of pedestrians moving around the common domain. The method provided
information to patrons that enabled them to manage their schedule to arrive at the stadium
on time without experiencing unpleasant incidents such as long waiting times or an
accident.
4. Findings
This section presents the findings from the application of the DIM-ICE risk model as
Case 1, Case 2 and Case 3, respectively, in chronological order (from 2000 to 2018) to
showcase the use of this model. Each of these cases outlines the context and impact of
application; however, please note that some of the exact detail (especially regarding
metrics and operational elements) have understandably had to be withheld for reasons
of security.
4.1 Case 1 –Sydney Olympics project (2000)
The 24th Olympic games were held in September 2000 in Sydney, New South Wales,
Australia. Approximately 11,000 athletes participated in the games and a total of 6.7 million
games tickets were sold (92.4 per cent of the availability). Before the Sydney 2000 Games
started, the organisers realised that they had to deal with a significant challenge as follows:
how to design optimal throughput, minimise the exposure to crowd safety risk and provide
a successful and pleasant event. Crucially, they had to deal with non-event ticketing
spectators (people that just wanted to be part of the Olympics atmosphere). Although they
knew the capacity of the domain, they could not estimate the number of non-event ticketing
spectators attending the games. Therefore, they requested external advice on how to face
this issue because they did not have the knowledge and skills in-house. Table 3 presents a
summary of the Sydney Olympic Games project.
The DIM-ICE risk model was applied to the circulation and movement of the people
attending the event(s). The implementation of the model aimed to identify areas that
required improvement (e.g. expected high-density) and reduce the risk of crowd safety-
related issues. Figure 3 presents the DIM-ICE risk model created for the Sydney Olympics
project. Specifically, as Figure 3 summarises, during normal conditions, the main challenges
that the model identified were as follows:
Design category: difficulties in estimating the number of non-event ticketing
spectators, which caused a risk of overcrowding during the ingress, circulation and
egress phases.
Management category: difficulties in managing peoples’movements, especially
from North-South in the event area during the ingress and circulation phases.
Under an emergency condition, the DIM-ICE risk model assisted in realising that there
were a number of potential crowd safety-related issues that the organisers had to address
to offer a safe environment.
Case studies
and
application
Design category: during the circulation phase, there was a limited flow capacity.
Considering that the number of non-event ticketing spectators could not be
estimated, this may have caused difficulties in attendees’movements and safety
issues.
Information category: there was unclear signage during the circulation and egress phases.
Management category: the model identified that a detailed evacuation plan was
missing, another potential crowd safety issue.
The data analysis revealed that, approximately, it would take at least 2 h to get from places like
Sydney into the seating area. However, the attendees were informed, by the organisers of the
Sydney 2000 Games that it would take 4 h to arrive at the venue. The reason, to avoid crowd
safety-related issues and provide a greater distribution of people’s arrival times. Patrons were
satisfied because there was a variety of events/entertainment in the concessions around the
stadiums in the common domain spaces; plus, they arrived in the venue on time/in plenty of
time (under promise and over deliver).
Based on the data analysis, an estimation regarding the demand at the maximum point of
the system was achieved. In total, 80,000 people per hour was the full capacity of the system –
this was calculated by optimising routes, making them wide enough (e.g. 20 m wide to facilitate
aflow rate of 82 people per metre per minute), etc. As a result, the DIM-ICE risk model helped to
designed resilience into the system and ensured that the peak demand represented 80 per cent
of the system’s capacity consistently allowing greater efficiency.
4.2 Case 2 –Canary Wharf project (2003)
In the early 2000s Canary Wharf in London, UK (a major finance hub) experienced at least
two “bomb threats”per week (Mullin et al., 1996). Reports of vehicles laden with explosives
or the existence of so-called “parcels bombs”in particular places were a regular occurrence.
Table 3.
The summary of the
Sydney Olympics
project
Project Sydney Olympics project, Sydney, Australia
When 2000
Aim Optimising the crowd throughput
Minimising the exposure to crowd safety risk
Providing a successful and pleasant event
Problem A new building was developing –would the crowd adapt to the new
environment?
There were two minor injuries on site –were the existing safety procedures
adequate?
Solution The DIM-ICE risk model applied emphasising more on the circulation
movement of the people attending the event
The development of a one-way system optimising crowd movement by using
the Network analysis theory
Benefits The identification of the expected number of pedestrians moving in the common
domain
Information provided to patrons that enabled them to manage their schedule to
arrive to the stadium on time without experiencing any unpleasant incident
The design of the queuing system was improved to achieve more efficient inflow
A successful event was achieved
Customer satisfaction was enhanced
People impacted by the
project recommendations
6,800,000 during the games
JPMD
The UK’s London Metropolitan Police Service was facing complications in dealing with
these issues because it did not have an official or legal authority to call for an evacuation of
areas or premises unless there was an imminent risk to life (there are both legal and financial
restrictions to evacuating a major banking centre due to the high number of false alarms).
The Metropolitan Police Service could not force an evacuation because of the potential for
false alarms and there was no system in place to quantify the level of threat. Therefore, it
was decided that a top-down command and control evidenced-based procedure was required
for any building to be evacuated; this was serving the dual purpose of protecting lives and
minimising the potential risk of litigation.
The unique positioning of Canary Wharf London is complicated because it is an island
surrounded by water, it, therefore, has limited exit points and emergency access routes. The
Figure 3.
The DIM-ICE risk
model –the Sydney
Olympics project
Case studies
and
application
challenge of providing directional information (i.e. which was the safe way to go) to people
to move away from the potential threats become complex. Table 4 provides an overview of
the Canary Wharf project.
Applying the DIM-ICE risk model, a transparent and robust network analysis was
conducted. This served to analyse the capacity of available roads and optimised the crowd
moved away from the location of the threat. Figure 4 presents the DIM-ICE risk model
applied for the Canary Wharf project. The areas that required further improvements for any
potential crowd safety issues to be avoided were identified. As Figure 4 summarises, under
normal conditions, the main challenges were as follows:
Design category: the limited capacity of available roads, which could cause serious
safety issues as there were difficulties in directing people to move away from
potential threats during the ingress, circulation and egress phases.
Management category: the Metropolitan Police Service was facing difficulties in
managing information regarding the capacity and availability of routes during the
ingress phase.
Under emergency conditions, the DIM-ICE risk model identified similar potential
crowd safety-related issues.
Design category: the limited capacity of available roads negatively impacted the
design of an optimum route to keep people away from potential threats.
Information category: a significant issue was the lack of information to identify the
severity of the threat and on routes that had the maximum capacity to direct people
to move away from potential threats.
Management category: the model identified the need for a detailed evacuation plan,
to help the Metropolitan Police Service follow standardised procedures, avoiding
potential issues related to crowd safety.
Based on the DIM-ICE risk model, bespoke software was developed by the London
Metropolitan Police Service for Canary Wharf, representing the entire site using coordinated
grids. The location of any potential threat was indicated by a specific grid reference and the
severity of the threat identified by a physical radius. The algorithms within the software
Table 4.
The summary of the
Canary Wharf
project
Project Canary Wharf project, London, UK
When 2003
Aim Optimising the crowd throughput
Problem Canary Wharf was experienced an incredible crowd safety restated tread
and the police did not have the official or legal authority to call an
evacuation
Solution The use of DIM-ICE risk model assisted in analysing the capacity of all
the existing available roads and optimising the crowd movement away
from the location of the thread
Benefits Information provided regarding the route that had the maximum
capacity to direct people and move them away from the thread
People impacted by the project
recommendations
90,000 per day
Notes: White squares = areas that are sufficient, no improvements are required; light grey squares =
require improvement; and grey squares = require much improvement to avoid crowd safety issues.
JPMD
provided the information required to identify routes that had the maximum capacity to
direct people and move them away as safely as possible. A complicating factor was that the
accessibility of routes was constantly changing (building works, repairs, road layout
changes, etc.), which affected the capacity of specific routes. Therefore, the software
required constant updating and development for significant information regarding the
capacity and availability of routes to be as robust and accurate as possible. The use of the
DIM-ICE risk model provided the much-needed evidence-based solution to a highly complex
and changeable problem area.
Figure 4.
The DIM-ICE risk
model –the Canary
Wharf project
Case studies
and
application
The project had three core modelling element, a generic routing diagram (as a lookup
table) and a specific threat location exclusion network (only showing viable routes
depending on location and severity of the threat). This was coupled to information-based
maps and video clips for training the occupants and a “pied piper approach”to egress. That
is, instead of pushing people out of buildings, marshals were trained to lead people away
from the threat. The instructions for the occupants were to “follow the crowd and keep
going”. In essence, this meant there developed a directional approach to all possible threat
locations. This was coupled to a training programme and situation awareness for all levels
of command and control for the site.
4.3 Case 3 –Murrayfield stadium project (2018)
Murrayfield Stadium is a sports stadium located west of Edinburgh, the capital of Scotland.
It is the largest and most impressive stadium in Scotland and the fifth largest in the UK with
a seated capacity of 67,144. It is the home of Scottish rugby and Murrayfield Stadium has
also hosts musical events.
In November 2018, expert advice on crowd safety was required for two reasons as
follows: a new building was to be developed, close to the site, which would impact upon the
layout of the merchandising area both during and after its construction. The physical
change to the site were known, but the impact during construction and on the
merchandising locations post-build were under negotiation. An investigation into how the
crowd was going to adapt to the new environment was required; there were two minor
injuries on-site, which made the operations team of the Murrayfield stadium seek advice on
whether existing crowd safety procedures were sufficient from both a health and safety
perspective and potential exposure to litigation. The use of the DIM-ICE risk model assisted
in analysing the current situation and providing a set of recommendations. Table 5
summarises the Murrayfield Stadium project.
To develop the DIM-ICE risk model, data was collected through conducting a site survey
(a day on the site as a customer –walking to various areas, setting up cameras, extensive
videoing of ingress, circulation and egress); using the data from the stadium CCTV cameras
Table 5.
The summary of the
Murrayfield Stadium
project
Project Murrayfield Stadium, Edinburgh, UK
When 2018
Aim Optimising the crowd throughput
Problem A new building was developing –would the crowd adapt to the new
environment?
There were two minor injuries on site –were the existing safety
procedures adequate?
Solution The use of DIM-ICE risk model assisted in analysing the current situation
and providing a set of recommendation
Benefits >90% of Murrayfield Stadium’s safety operations were appropriate
Areas that required improvement regarding the existing risk assessment
policy were identified
The design of the queuing system was improved to achieve the more
efficient inflow
Murrayfield Stadium demonstrate continual assessment and
improvement to the risk management process
Customer satisfaction was enhanced
People impacted by the project
recommendations
67,000 per event
JPMD
and also reviewing the existing risk assessment policies. Based on the data analysis, the
DIM-ICE matrix was created, which highlighted areas that required improvements. Figure 5
presents the DIM-ICE risk model applied to the Murrayfield Stadium project. More than 90
per cent of Murrayfield Stadium’s safety operations were well above the standards
appropriate for stadia of this size, but there were critical elements that required review.
Areas that required improvement were identified and the design of the queuing system
improved to achieve more efficient inflow. The analysis of the data demonstrated that there
was a significant risk on the site –a narrow area at the west end of the stadium with the
rather high-density flow (as a result merchandising in this area was highly profitable).
There were also waste bins in that area, which further reduced the width. Specifically, as
Figure 5 summarises, the main areas that required involvements were related to the
existence of an emergency situation as follows:
Design category: the identified narrow area could cause overcrowding at the
ingress, circulation and egress phases.
Information category: visitors might have difficulties in exiting the stadium due to
unclear signage, which could cause safety issues.
Management category: the model identified that the risk assessment processes had
to be updated, which was a critical requirement for creating a safe environment.
In addition, the development of the new building, by changing dynamics of flow, changed
the routing and footfall moving in and out the stadium, specifically if an emergency
situation required a directional egress close to the construction site during the build. A
number of recommendations were made, aiming to provide solutions to the identified safety
issues. Initially, the operations team were recommended to: update the risk assessment
processes, to increase the width of the west end area with the tight footfall (density); and to
move the waste bins units out of the high footfall (volume) area, which would reduce the
flow constraint to make it more efficient and accessible for patrons. Finally, an engagement
between the operations team and the building construction engineers was recommended to
ensure that specific corridors would remain clear and available for the crowd, during the
construction phase. Note: one of the modelling tools used here was the risk/congestion
mapping, a visual approach to identifying areas of high footfall and potential congestion at
specific times during an event (ingress/circulation/egress).
5. Discussion and final remarks
To add value to the discussion of safe place management and make a defined contribution
the paper has reported on the use and intervention of a crowd assessment safety risk model,
the “DIM-ICE risk model”. The aim of the study was to report on the impact and use of this
intervention on place crowdsafety and the application of crowd science. In addition, we offer
the following learning points for practitioners, drawing upon the significant background
provided by the presented cases.
5.1 What do we learn from the application of the design, information, management-ingress,
circulation, egress model?
The model was developed from the analysis of past disasters and their fundamental causes. So the
model helps users consider the phases and influences of crowd behaviour through the lens/filter of
causality. In essence, it draws the attention of the user to think through the event in time (ICE) and
controls (DIM). In response to these issues, the DIM-ICE risk model was used to compare
operational situations during so-called “normal”and then “emergency”conditions for each phase
Case studies
and
application
and aspect of proceedings to fully scope out crowd dynamics at an event. Helbing et al. (2007)
clearly state that that pure mathematical approaches and analytic models are not adequate for this
purpose, that the conventional risk assessment process is mathematically biased.
5.2 What works, in what contexts?
It works because the user has to put some description and colour code each of the 18 boxes.
A gap/space on the matrix means a gap in their knowledge –which then needs to be filled.
For clarification and enhanced communication, we recommend the use of red/amber/green
colour coding in the DIM-ICE matrix to highlight areas that are as follows:
well organised (green);
those that require improvement or monitoring during an event (amber); and
those that must be improved (red).
Figure 5.
The DIM-ICE risk
model –the
Murrayfield Stadium
project
JPMD
Haase et al. (2019) reported on overcrowding being a frequent cause of crowd disasters, this
highly visual method serves to clearly pinpoint where and potentially when this might occur.
When applied and facilitated by an informed cross-discipline operational team the approach
allows the potential rapid identification of high-risk areas. Alternative place design,
communication and management requirements can then be identified, discussed, considered and
implemented to allow the development and adoption of appropriate operational strategies on site.
This goes beyond the rather rudimentary aspects outlined in the available literature by Helbing
et al. (2000),Helbing et al. (2005),Kretz et al. (2006),Georgoudas et al. (2010) and Shahhoseini et al.
(2018), e.g. lane formation, oscillations at bottlenecks, the faster-is-slower effect and so-called
“clogging”at exits. This contribution is important as Haase et al. (2019) described the number and
severity of fatal crowd disasters in high-density public events rising significantly.
As per Duives et al. (2013), adequate crowd movement information (facilities, locations, correct
exits in an emergency, etc.) must be clearly communicated to the attendees/public in a manner
that removes any scope for ambiguity. The DIM-ICE risk model, therefore, has the potential to
provide an evidence-based comprehensive pre-event analysis, which properly forecasts potential
crowd issues ensuring a successful event, where the attendees enjoy a positive experience (Al-
Zaydi et al., 2016). Upton (2008) suggested that successful planning of an event requires an in-
depth risk assessment of crowd safety, which the reported intervention of the DIM-ICE risk model
achieves. The DIM-ICE risk model can be used across the spectrum –to show green/amber/red in
context and as a methodology for systematic and rigorous assessment of the risks using phases of
behaviour and influences on behaviour through the design of the place, information and
management. In essence, it provides a “how to shape the crowds’behaviour”guide.
5.3 What does not work?
The DIM-ICE risk model is risk/causality-based. It does not define routing-area-movement-
people. However, when used with other tools and knowledge it is a comprehensive risk-based
analysis method. The implementation of the model assists event planners to identify locations
and potentially times of high risk and control them, through effective place design, information
systems and management strategies. Still (2015a,2015b) suggested that this model provides
solutions to complex safety issues by simplifying them into elements for consideration, a
multiscale approach to the overall process of event planning (zoom into a section, then zoom out
to see the overall impact on the event), providing the information required for the planning phase.
It sits between the traditional approaches of crowd modelling, considered under macroscopic
(treat the crowd system as a whole) and microscopic (smaller crowds, real-time simulation,
individual behaviour and interactions) scales (Xu et al.,2014;Bellomo et al., 2016)andthe
impressive but resource-intensive virtual environment representations (Yersin et al.,2005;Yersin
et al.,2009). In a similar manner to the above, the application of the DIM-ICE risk model is
required prior to an event to establish a crowd risk plan; for example, ingress and egress routes
have to be of sufficient size to safely accommodate predicted crowds, therefore, avoiding
congestion that may occur. However, it is the robustness and relevance of its application that
appears to make a difference (Still,2014a, 2014b) creating a much-needed evidence base (Helbing
et al.,2007) to inform operational decision making.
5.4 What limitations this model presents?
It is only one of a number of tools that can/should be used regards safety in place
management. Together they cover the risk dynamics. So it is part of a suiteof tools –specific
to each place. The case studies presented within this paper seek to inform the challenges and
limitations of crowded environments that event organisers face, as highlighted by Hou and
Pang (2010). The paper provides information regarding the application of an evidence-based
Case studies
and
application
approach (the DIM-ICE risk model), addressing the research gap related to the
implementation of analytic tools in characterising crowd dynamics identified by Helbing
et al. (2007). It contributes to what Chai et al. (2017) identified as the emerging field of
research motivated by crowd safety issues in our increasingly congested environments. It
also provides further understanding to place management terms as “improved knowledge
and more effective place management practice can ultimately lead to places that are more
successful, more liveable and more equitable”(Kalandides et al.,2016, p. 358).
To take this development further we suggest more research into aspects of crowd safety,
crowd science and its application. Ultimately this goes beyond simple reporting and has the
very great potential to make a defined and measurable impact, not just to specific crowd
events but also to society as a whole.
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Corresponding author
David Bamford can be contacted at: d.bamford@mmu.ac.uk
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