Analysis of Passenger Group Behaviour and Its Impact on Passenger
Flow using an Agent-based Model
Lin Cheng, Clinton Fookes, Vikas Reddy and Prasad K.D.V. Yarlagadda
Queensland University of Technology, 2 George St, Brisbane, QLD, Australia
Keywords: Group Dynamics, Agent-based Model, Airport, Passenger Flow, Simulation.
Abstract: Group interaction within crowds is a common phenomenon and has great influence on pedestrian behaviour.
This paper investigates the impact of passenger group dynamics using an agent-based simulation method for
the outbound passenger process at airports. Unlike most passenger-flow models that treat passengers as
individual agents, the proposed model additionally incorporates their group dynamics as well. The
simulation compares passenger behaviour at airport processes and discretionary services under different
group formations. Results from experiments (both qualitative and quantitative) show that incorporating
group attributes, in particular, the interactions with fellow travellers and wavers can have significant
influence on passenger’s activity preference as well as the performance and utilisation of services in airport
terminals. The model also provides a convenient way to investigate the effectiveness of airport space design
and service allocations, which can contribute to positive passenger experiences. The model was created
using AnyLogic software and its parameters were initialised using recent research data published in the
Revenue of airports nowadays is gradually
transferring from aviation related sectors to non-
aviation sectors (retail revenues) and also from
traditional airline sources (lease arrangements) to
passengers (fees collected from ticket sales)
(Harrison et al., 2012, Schultz et al., 2010a). A
positive passenger experience is likely to result in
repeat visits, which not only helps further generate
airport’s financial profit, but also satisfies the needs
of other stakeholders such as operating airlines,
retailers, passengers and visitors (Popovic et al.,
2010). Hence, the passenger experience has become
a major factor that influences the success of an
In this context, passenger flow simulation has
become a significant approach in designing and
managing airports (Schultz et al., 2007,
Kleinschmidt et al., 2011). Although it has been
proven that social interactions greatly influence
crowd behaviours and decision making, far too little
attention has been paid to group behaviour when
developing passenger flow models (Qiu and Hu,
2010, Singh et al., 2009, Ma et al., 2012). This paper
aims to evaluate the impact of group dynamics on
passenger flow in an international departure terminal
using an agent-based model for the check-in process.
The remainder of the paper is organised as follows.
Section 2 reviews previous work related to airport
design and simulation techniques. Section 3
demonstrates the construction and configuration of
the agent-based model in the context of an
international airport. Section 4 provides the
simulation results and analysis and Section 5
summarises the major findings.
In recent years, there has been an increasing amount
of literature on airport passenger terminal design,
analysis and modelling. Tosic (1992) offered a
comprehensive review about global airport terminal
models. The review introduced the features and
proposed applications of the models, along with
their strengths and weaknesses. Generally, model
inputs are the physical layouts of the building, flight
schedules, arrival time of passengers and processing
rates. The evaluations of the model usually consist
of the queue length, utilities and waiting times at all
Cheng L., Fookes C., Reddy V. and Yarlagadda P..
Analysis of Passenger Group Behaviour and Its Impact on Passenger Flow using an Agent-based Model.
DOI: 10.5220/0005086807330738
In Proceedings of the 4th International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH-2014),
pages 733-738
ISBN: 978-989-758-038-3
2014 SCITEPRESS (Science and Technology Publications, Lda.)
facilities. Although some literature had considered
passengers as groups of people, how the group
dynamic influences group behaviour at each activity
and the overall system performance were not
illustrated nor analysed. In spite of this, those
models provided valuable references for future
model designs.
Schultz et al. (2010b) investigated passenger
dynamics in the airport terminal by analysing field
data from Dresden International Airport. The
research pointed out that approximately 50% of
passengers were travelling in groups and the group
size has significant influence on passenger speed.
Other factors that influence passenger speed are
gender, travel purpose (business/ leisure) and the
amount of carry-on baggage.
Popovic et al. (2009) presented an observation
technique that investigated how passenger activities
mediate people’s experience in the airport. In the
study, detailed passenger behaviour in the airport
was recorded. It was found that passengers travelling
in groups had a considerable waiting time at the
security process. The video showed that after the
security screening point, people wait for their group
members in the middle of the walkway to passport
control. The findings of the study provide valuable
information for modelling passenger behaviour and
group dynamics in this paper. Using the same
observation technique, Livingstone et al. (2012)
reported results of passenger landside retail
experience in airports. Through the data collection
from 40 passengers, researchers found that the
existence of passenger’s travel companions can
influence passenger’s landside dwell time and
shopping behaviour in discretionary activities. The
limitation of the observation technique is that
passengers who participated in the research were
aware that they were being recorded. Furthermore,
the low efficiency of video recording and data
processing restricted the technique to only a small
number of people.
Ma, et al. (2011, 2012) introduced an individual
agent decision model to simulate stochastic
passenger behaviour in airport departure terminals.
Using Bayesian networks, the conditional
probabilities of passengers’ advanced traits
(shopping preference, hunger level, technology
preference, etc) were calculated through the basic
traits (age, gender, nationality, flight class, etc.). By
considering the restriction factors (such as remaining
time and walking distance) passengers in the
simulation can behave autonomously based on the
results of Bayesian network inferences. However,
the simulation did not explain how the group
dynamics influence the passengers’ decision making
process and what will happen if passengers were in a
group where group members have very different
behaviour in their advanced traits.
Cheng et al. (2014) conducted a case study to
demonstrate how the agent-based passenger flow
model can be used to examine the efficiency of an
airport evacuation strategy. By comparing
evacuation time of individual passengers and
passengers in groups, the impact of group dynamics
during an airport evacuation process was analysed.
The simulation results shows that group dynamics
can significantly impact passenger behaviour during
airport evacuation processes and consequently
affects the total evacuation time.
3.1 Airport Environment
In agent-based modelling, three key elements need
to be identified and modelled: agents, their
environment, and their interactions with other agents
and the environment (Macal and North, 2010). The
model environment is an airport departure terminal,
which is divided into landside and airside.
Figure 1: The International airport departure processes.
-in Securit
Customs Boardin
Discretionary Activities
Landside Airside
The landside of the terminal is open to the public,
while the airside of the terminal is only accessible
for passengers.
Figure 1 illustrates a high-level description of the
passenger departure processes in the model.
Passenger activities are categorised into processing
and discretionary activities (Kraal et al., 2009).
Processing activities are mandatory for passengers
before boarding the plane. On the landside of the
terminal, passengers check-in for their flights, and
pass through security screening and border
processing before entering airside and boarding.
Discretionary activities are considered as any other
activities undertaken by passengers during non-
processing time (Kraal et al., 2009, Livingstone et
al., 2012). Discretionary activities can happen
between two sequential mandatory activities as
shown in Figure 1. Examples of discretionary
activities in the proposed model include random
walking, store browsing, having food and using
other airport services. Retail shops and airport
services are located at both landside and airside to
emulate the real-world scenario.
In order to guarantee there is ample time left for
security measures, passengers are often advised to
arrive at the airport three hours before the standard
flight departure time. In the model, the flight check-
in process starts at 2.5 hours prior to flight
departures and closes on 25 minutes before the
departure time. A row of check-in service counters
(eight counters per row) are assigned to the check-in
process of each flight. Among the eight counters,
there are two counters for business class passengers
and six counters for economy passengers.
3.2 Pedestrian Configuration
Pedestrians in the model are categorised into
passengers and wavers. Passengers are those who
will board on the plane, while wavers are fellow
companions who accompany the passengers to the
airport but do not board the flight. Age, gender,
residential status and travel purpose are four basic
characteristics of passengers in the model. These
four factors can influence advanced passenger
characteristics such as mobility and shopping
Table 1 summarises the distribution of airport
passengers’ age and gender provided by the global
passenger survey conducted by IATA (2013).
According to the country of residence, passengers in
the model are divided into Australian resident and
overseas visitors. The Australian Bureau of Statistics
(ABS) provided the information of departure
passengers’ country of residence and passengers
main reasons for their journey in 2012-2013
financial year (ABS, 2013). These four basic
characteristic factors: age, gender, country of
residence and travel purpose will be initialised to
each agent according to the percentage rate showed
in the Table 1. The age and gender are assigned to
each agent when the agent enters the system. Since
passenger groups usually share common features of
country of residence and travel purpose, these two
factors are initialised to each agent after the
pedestrian group has finished assembling and will
assume passengers in the same group have a
common country of residence and travel purpose.
Based on these four basic characteristic factors,
passenger groups in the model are initialised with
different speeds and activity preferences, which
enable agents to act autonomously in the simulation.
Table 1: Basic passenger characteristics in the airport.
Source Detailed factors Percentage in
total passengers
Age range IATA global passenger survey
(IATA, 2013)
< 25 10%
25 - 34 31%
35 - 44 23%
45 - 54 18%
55 - 64 11%
>65 7%
Gender IATA global passenger survey
(IATA, 2013)
Male 59%
Female 41%
Country of residence Australian Bureau of Statistics
(ABS, 2013)
Australian resident 58%
Overseas visitor 42%
Travel purposes Australian Bureau of Statistics
(ABS, 2013)
Business 15%
Leisure 85%
3.3 Pedestrian Group Interaction
Pedestrians in the airport are predominantly driven
by specific goals: passengers want to finish airport
processes and board their flights; and wavers
accompany passengers in the airport and send them
off. In this paper we focus explicitly on the
interactions within pedestrian groups. There are
some basic rules that govern the relationship and
interactions of a group:
During movement, pedestrians in the same
group generally move toward the same
All group members will try to keep a uniform
speed, except during situations such as
avoiding obstacles and collision with other
If group members fall behind due to any
reason, other group members will slow down
until the stalled group member catches up.
At mandatory processes such as check-in,
passengers who finish the process faster need
to wait for all other group members to
complete the process before moving on.
If time is allowed for discretionary activities,
pedestrians in the same group will generally
undertake the same activity together once the
activity is chosen.
The model takes the departure flight schedule
and passenger numbers for each flight as input. The
attributes of the agents such as group size, speed,
flight schedule and class, and shopping preference
are initialised. The interactions between the agents
and the environment are defined. Based on the flight
schedule, the agents are appropriately introduced in
the environment. Detailed micro-activities in each
process, for instance, the passenger behaviour at
check-in process is modelled based on observational
data collected by the Human System team of the
Airports of the Future project (Kraal et al., 2009).
The 3D simulation environment of an international
airport departure terminal is shown in Figure 2. The
model is built within the AnyLogic 6.8 platform to
simulate the daily operation of the airport. Activities
of each agent in the system were updated
successively according to preset characteristics
within a discrete-event structure of the AnyLogic
simulation software.
To evaluate the effect of group dynamics on
facilitation and overall congestion at the check-in
area, we ran simulations under three different
scenarios. These are passengers travelling: (a) alone;
(b) in groups of varying size; (c) in groups of
varying size with wavers. Figure 3 illustrates the
screenshots taken for the same flight and timeline of
the simulation. From the model observation, it was
noted that passengers who travel in groups will wait
for group members in the pathway after finishing the
check-in process. This waiting behaviour of
passenger groups can cause congestion in the
pathway behind the check-in area and slow down the
passenger flow. More severe congestion can be seen
in the scenario where passenger groups are
accompanied by wavers [Figure 3 (c)].
Data collected from three different simulations at
the check-in process show that passenger group
dynamics influence the check-in queue time and
dwell time (Figure 4). The check-in dwell time is the
average time elapsed between passengers entering
the check-in area and leaving it with their
companions (if there are any), while check-in queue
time is the average time elapsed between passengers
entering the queuing area and getting served by the
check-in staff. From the table in Figure 4, we note
passengers travelling in groups or with wavers spend
approximately 3 minutes to regroup after the
process. This leads to a longer dwell time in the
check-in process.
(a) (b)
Figure 2: Airport departure terminal simulation environment (a) landside of the terminal; (b) airside of the terminal.
(a) (b) (c)
Figure 3: Facilitation and overall congestion at check-in for three different scenarios. Passenger travelling: (a) alone; (b) in
groups; (c) in groups with wavers.
Figure 4: Regroup, queue and dwell times at the check-in process for the three different scenarios.
The model results also suggest that the time
passengers spend in queuing can be influenced by
group structure. It can be seen that passengers
travelling alone spend approximately 2 minutes less
in the queue when compared to passengers travelling
in groups. A possible explanation for witnessing
such a trend could be the congestion caused by
people waiting to regroup with their fellow travellers
around the queuing area. In essence, ignoring group
dynamics in agent based modelling may yield results
that may not accurately represent the real-world
Through the simulation of the check-in process, it is
shown that agent-based modelling can be used to
analyse group dynamics of pedestrians in a complex
environment. The results in this study suggest that
the group dynamics can potentially lead to
congestion and longer check-in dwell times. Such
scenarios can lead to potential flight delays and thus
contribute to a lower level of service (LOS) and poor
passenger experience. Furthermore, they may also
leave the passengers with less time for discretionary
activities which may not be favourable for airport
retail operators. Therefore, from the airport
management perspective, it would be beneficial if
terminal operators could run the simulation
beforehand by inputting flight schedules and
passenger quantity details into the model. The
simulation results will provide valuable information
for airport operators to respond proactively to any
potential congestion. Furthermore, the advanced
modelling incorporating group dynamics provides a
more powerful long-term planning tool and airport
design analysis tool.
The Airports of the Future research project is
supported under the Australian Research Council’s
Linkage Projects funding scheme (LP0990135). The
authors would like to thank Philip Kirk who
provided some of the model parameters used in the
simulation. The authors also acknowledge the
contribution of the industry stakeholders involved in
this project. More details on the project can be found
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