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The Rise of the Smart Passenger I: Analysis of
impact on Departing Passenger Flow in Airports
Miguel Mujica Mota∗, Paolo Scala†, Michael Schultz‡, Daniel Lubig‡, Mingchuan Luo‡, Edgar Jimenez Perez§
∗Aviation Academy, Amsterdam University of Applied Sciences, Amsterdam, The Netherlands
†Amsterdam School of International Business, Amsterdam University of Applied Sciences, Amsterdam, The Netherlands
‡Institute of Logistics and Aviation, University of Technology Dresden, Dresden, Germany
§Centre for Air Transport Management, Cranfield University, Cranfield, Bedfordshire, UK
Abstract—Airport infrastructure evolves alongside legacy sys-
tems and processes that limits the ability to fully realise the
efficiency potential of costly renovations. Airports will continue to
take advantage of current and future technologies. Nevertheless,
for such systems to work as efficiently as possible, the passenger
should play an active role. This paper analyzes the effect of a
new type of emerging ’smart passenger’, one that cooperates
to be enabled to use the most efficient processes for a seamless
experience. The technological and behavioural enhancements are
assessed with the simulation of two case studies: London City and
Palma de Mallorca airports. Results indicate that the introduction
of this type of passenger brings benefit in terms of level of
service indicators not only to this type of passenger but also
to the traditional ones (business, visitor and leisure). However,
the impact differs depending on the type of airport and the
proportion of ’smart passengers’.
Keywords—optimization, capacity, industry 4.0., simulation,
aviation, transport
I. INTRODUCTION
Technological evolution constantly challenges the design,
management and operation of airport facilities. Electronic
processing of passengers and bags has reduced processing
time, queues and space requirements and even enabled some
activities, such as check-in, to move off-terminal to a consid-
erable extent [1]. However, these gains in efficiency contrast
with the addition of processes and restrictions to ensure safety
and security throughout the air journey [2], further increased
by the focus on biosafety measures with the response to the
COVID-19 pandemic [3,4]. Moreover, passenger experience
is a result of the combined delivery of a variety of services
by multiple stakeholders using different systems and usually
following different objectives that consider quality of service
from various perspectives. As a result, airport operators strive
to put in place more advanced systems to make passenger
processing as fast as possible without sacrificing on security
and safety.
With the introduction of new technology within the realm of
industry 4.0, like artificial intelligence (AI), machine learning
(ML) and advanced sensor technology, aided by simulation
and optimization techniques, new opportunities to make airport
systems more efficient appear. This article analyses the impact
that some technologies could have if the transparency is
increased so that a more proactive ’smart passenger’ could
prepare to own more elements of the journey and take as
much advantage as the system allows in terms of time savings
and comfort. This type of passenger could have a smoother
journey and enjoy a faster trajectory through the system that
ultimately benefits all airport stakeholders (passengers, airport
operator, airlines, retailers and ground handlers). This concept
falls within the ambition of the IMHOTEP project where infor-
mation from the system is propagated in a transparent manner
across stakeholders to increase the awareness of the passengers
[5]. The impact of enabling a number of well informed,
smart passengers is evaluated by developing two case studies
considering substantially different types of airports: London
City (LCY), which is dedicated to serving a large proportion
of business travelers on short-haul trips where efficiency is
key to delivering its value proposition; and Palma de Mallorca
(PMI), which serves mainly leisure passengers and where very
high demand peaks strain infrastructure and processes. The
extreme and opposite characteristics of these airports make
the case studies relevant to study the potential implications at
different categories of airports, at least in Europe.
II. STATE OF THE ART ON EVALU ATIO N OF NE W SY ST EM S
AT AIRP ORT S
In this section, an overview of the studies regarding passen-
gers’ profiling and the modeling of airport terminal operations
using simulation techniques is presented. Regarding the latter
aspect, the literature will focus on articles where policies and
new technologies have been implemented.
A. The emergence of the ’smart passenger’
The analysis of the passenger journey through an airport
system usually considers the existence of different pain points,
such as check-in, luggage drop-off, security screening and
migration control [6]. Pain points are normally associated to
waiting times, queuing and anxiety, therefore impacting the
overall passenger experience [7]. Technological advances de-
signed to improve passenger experience in relation to security
control are known as ”biometric identification and registered
passenger schemes” [2]. The combination of both enables
passenger profiling to better allocate resources in relation to
risk and represents a shift from the current ’one-size-fits-
all’ approach to airport security. Arguably, privacy concerns
and the limited practical implementations of registered or
trusted passenger schemes, diminishes the potential of new
technologies to substantially enhance passenger experience.
These pitfalls could be overcome with the use of a passenger-
centric solution where individuals agree to release personal
information when needed to facilitate their journey but remain
in control and have ownership of their own data.
The World Economic Forum has identified four critical
emerging technologies that would enable the implementation
of their concept for a Known Traveller Digital Identity: a
distributed ledger, cryptography, biometrics and mobile in-
terfaces and devices [6]. These could ensure the connection
between the physical and digital worlds to grant authorization
to access personal information securely without relying on a
single central authority. In fact, a passenger survey from SITA
suggests wider support for technologies that enable digital
identity management and shows a significant increase in the
use of mobile devices for checking-in (13% of respondents),
as well as of automated technologies for checking in (18%),
self-bag drop (24%), identity control (38%), boarding (18%)
and border control (24%) [8].
Increased implementation of these technologies should lib-
erate passengers from country- or airport-specific programs
of known passengers. In this sense, we propose a ’smart
passenger’ that has the ability to use specific processes or
facilities to speed up the journey, regardless of travel purpose
or ticket purchased. A ’smart passenger’ is enabled by mobile
technologies and biometric identification to travel through the
airport terminal unencumbered by luggage, as both hand and
hold luggage will be processed through the baggage handling
system through self-bag-drop kiosks. Then, the ’smart passen-
ger’ will enjoy preferential or exclusive access for departure
processes with the expectation (and sometimes certainty) that
processing times will be much lower than for the rest of
travellers. In this study, we assumed that the smart passengers
would be a novel category of passengers; however, it could be
case that in the future the categories will not be mutually
exclusive (i.e. there could be a smart-business and smart-
leisure ones).
B. Modeling airport terminal operations with simulation
Simulation techniques have been widely used for evaluating
airport terminal performance. Some researchers tackle airport
terminal individual operations or integrated approaches where
the flow of passengers is modeled throughout all the differ-
ent processes. Agent based models are commonly used for
modeling the passenger flow within the terminal. These allow
for the investigation of innovative concepts for passenger flow
guidance within terminals [9] or the evaluation of handling
processes, which would be useful for improving the efficient
use of concessionary areas and passenger level of service
[10]. Some studies [11] focused on the check-in operations,
which used an agent-based model to obtain insights about the
passenger flow behaviour and how to efficiently use ancillary
facilities such as cafes or information kiosks. Crucially, they
analyzed the effect of including additional attributes to the
passengers in the model, but none of those characteristics are
related to the use of specific technologies or processes.
A discrete event simulation model of the check-in area
was developed to optimize the check-in counter utilization in
order to provide a better level of service to the passengers
[12]. The model was coupled with an evolutionary algorithm
and it represents one of the first simulation-optimization
approaches developed in the field. An analysis on the impact
of different variables related to passenger or trip characteristics
to determine their impact on processing times concludes that
technological advancements could change the effect of the
variables under analysis, and therefore further research is
needed [13]. To simulate passenger boarding activities in
the aircraft cabin an environment is implemented using a
stochastic transition model via the cellular automata approach
[14]. This approach not only allowed to investigate essential
optimization approaches, but also to derive solutions taking
into account COVID-19 requirements [4]. Security screening
operations were the focus of other authors as well, who used a
discrete-event oriented approach with the objective of improv-
ing level of service indicators such as queue length, queuing
times and throughput [15,16]. In the latter, different policies
regarding the security line utilization based on passengers’
status and use of new technology were implemented.
The current study builds upon the latter work to propose
policies based on passenger profiles and the use of new
technologies for improving the efficiency of operations. The
main difference is methodological, as this paper incorporates a
dynamic agent-based simulation model in order to include the
interactions between passengers and between passengers and
physical facilities. Moreover, this study extends the evaluation
of the security area by including also the check-in process in
the departure trajectory of passengers (similar to the approach
followed by [17]). By identifying the dependencies of these
processes from the perspective of the passenger flow, this
research aims at unlocking the potential capacity of an airport
terminal depending on the types of passengers that use it.
III. METHODOLOGY
Aviation systems have different processes that can be stud-
ied by using different levels of abstraction, such as high-
level strategic analysis, to highly detailed passenger-level
operations. A multi-layered methodology has been explored
before with good results [18]–[20]. The current case focuses
on passenger trajectories within the airport terminals and their
interaction with the environment. In the models for the case
studies, the network of locations overlays a layout of the
terminal, generating high-detailed models where the different
performance indicators related to the passenger journey can
be evaluated and the dependencies of the different processes
identified.
As Figure 1 illustrates, after determining the objective, and
verifying and validating (V&V) the models; different scenarios
can be devised considering the novel characteristics of the
flowing entity (passenger) or the system (airport terminal
buildings). In our case it will be a combination of both. The
Figure 1. Multi-layered simulation methodology
passenger has novel characteristics and the system should
be modified considering the latest technology that allow a
smoother flow of passengers within the terminal. The airport
models developed for the IMHOTEP Project [5] are used for
the V&V step. These models have been verified and validated
under the project scope and provide exemplary cases of very
diverse types of airports. The evaluation on those two cases
provides insight about the benefit and/or impact for the airports
that fall within the categories of mostly-business and mostly-
leisure airports and can provide direction on which levers to
use when improving airport performance depending on its user
profile.
A. Simulating the ’smart passenger’
This section defines the ’smart passenger’ profile which
will be implemented in the simulation environment, including
their main characteristics in terms of travelling behavior. The
smart passengers are conceived as passengers who, regardless
of their travel purpose, want to make the most out of their
dwelling time within the terminal by avoiding unnecessary idle
time queuing at the airport facilities (i.e., check-in, security);
instead, spending more valuable time at recreational facilities,
such as restaurants, shops, or airline/airport lounges (cf. [21]).
In addition, these passengers are very well informed and
therefore control the course of their journeys independently
and dynamically (cf. self-connecting passengers) [22]. To
achieve this, the smart passengers obtain their boarding pass
online and use self drop baggage facilities for checking their
hold and cabin baggage. In this way, they will be bag-free
for the rest of the dwelling time within the airport terminal.
Moving within the terminal bag-free will allow them to move
quickly (higher walking speed than average passenger with
bag), and to have a quicker security screening process, as
they do not need to scan any bags. Table I compares the
characteristics of the smart passengers with those of the other
three passengers profiles that have been identified in this work,
namely ’business’, ’visitors’, and ’leisure’. These categories
were identified in the analysis of passenger surveys performed
in the IMHOTEP project.
TABLE I. PAS SEN GE RS PRO FIL E CHA RAC TE RIS TIC S
Passenger
profile
Groups Walking
speed
(m/s)
Bags to
check in
Items for
security
screening
Propensity
to
shop/eat
Business no 1.5 0 1 low
Visitor yes (1-2) 1 1-2 1 low
Leisure yes (1-4) 1 1-4 1 low
Smart no 1.5 1-2 0 high
Table I shows that smart passengers are similar to business
passengers; but business passengers do not carry any checked
baggage, and therefore, they skip the check-in process and
the baggage claiming process once they reach destination.
However, business passengers carry a cabin bag during their
time spent within the terminal, making the security screening
process more time- consuming. Smart passengers, as already
mentioned, focus on maximizing the dwelling time within the
terminal in the departing journey by being bag-free. Visitor
passengers are a category of passengers who travel with the
purpose of visiting family and friends. They are similar to the
leisure passengers as they share similar characteristics such as
traveling in groups and checking in their bags at the check-
in counter. However, leisure passengers generally travel in
bigger groups (families) and therefore carry more bags. Due
to the limitation of time, the propensity to consume within the
terminal is lower than the one for the smart passengers.
On the airport side, a change in the facilities technology
and processes policies can incentivize the ’smart’ behavior
of passengers, for instance by implementing self kiosk bag-
gage drop and millimetre-wave body scanners at the security
screening process [23]. The latter could drastically decrease
the security control processing time as the smart passengers,
who do not carry any cabin bag, will not need to use trays for
scanning their belongings, and could just go directly through
the body scanner. This solution cannot be implemented for the
other passengers profiles as they will need to scan their cabin
baggage. Figure 2 depicts the departure passengers’ itinerary
within the terminal according to their profile.
The flowchart of Figure 2 shows how smart passengers
Figure 2. Departure passengers’ itinerary within the terminal based on the
passengers’ profile
have dedicated self-service kiosk for the baggage drop off and
dedicated lines for the security process; this can incentivize
the ’smart’ behavior and improve both airport performance
and passengers’ travel experience. The models in this study
consider different processing times for each of the processes
modeled (check-in and security screening) based on the types
of passengers. Table II summarizes the processing times
applied to this study for each of the passenger profiles. These
processing times have been defined based on observations of
actual processing times at the case study airports.
TABLE II. PROCES SI NG TI MES A CCO RD ING T O PASS ENG ER P ROFI LES
Distribution of processing times (s)
Passenger profile (Self) Check-in Security screening
Business 0 uniform(30,32)
Visitor 1 bag: uniform(70,90) uniform(50,51)
2 bags: uniform(90,110)
Leisure 1 bag: uniform(70,90) uniform(50,52)
2 bags: uniform(90,110)
3 bags: uniform(110,130)
4 bags: uniform(130,150)
Smart 1 bag: uniform(35,45) uniform(14,16)
2 bags: uniform(45,55)
IV. EXP ER IM EN TAL DESIGN AND RESULTS
Different scenarios are used to evaluate how the implemen-
tation of the smart passengers and related technologies impact
on airport performance. These scenarios were based on the
proportion of smart passengers out of the total travelers, and
the new technologies implemented in the check-in and security
screening areas. The focus is on the departure processes, with
emphasis in the check-in and security screening areas, which
are known to be the most problematic areas to manage [16,24].
Airport performance is monitored in terms of queue length
and queuing time while increasing the percentage of smart
passengers for each scenario, as summarized in Table III.
In addition, two case studies were considered to make
a thorough analysis of the impact of the smart passengers
to airport performance. They refer to two airport terminals
having different characteristics (LCY and PMI), both in terms
of passenger profiles and terminal layouts. In the following
sections the two case studies will be described together with
TABLE III. SCENAR IO S EXP ER IME NTE D
Scenario Share of smart passengers
Base case 0%
Scenario 1 (S1) 10%
Scenario 2 (S2) 20%
Scenario 3 (S3) 30%
the analysis of the results which present the impact for the
regular passengers (business, visitor and leisure categories)
and the emerging smart passenger. As the smart passengers
require the use of dedicated facilities (check-in and security),
the results compare the performance of the facilities used by
them with the the rest of the (regular) passengers.
Following the simulation methodology, several replications
ran for each scenario in order to get representative results.
Therefore, the results include the median, 95% percentile and
maximum values. Due to computational power and software
limitations, the simulations only covered the peak hours during
part of a typical busy day (considering pre-Covid-19 pandemic
levels) as to evaluate the system in the most stressed config-
uration.
A. Case study 1: Leisure-oriented airport
The Case study 1 (PMI), is an airport which serves mostly
leisure passengers. Some of the main characteristics of these
passengers are that they often travel in groups, carry multiple
baggage and often arrive at the airport based on tour operators
schedules. The airport is a large-size airport that carries
around 25 million passengers per year, therefore, the terminal
features large areas for check-in, security screening and gates.
Moreover, in this airport the shopping/catering areas occupy
a large area of the terminal. Table IV shows the passenger
profile shares among the different scenarios, while Table V
gives an overview of the terminal facilities.
TABLE IV. CASE ST UDY 1PAS SEN GE R PRO FILE S
Passenger profile Base case S1 S2 S3
Business 10% 9% 8% 7%
Visitor 15% 13% 12% 10%
Leisure 75% 68% 60% 53%
Smart 0% 10% 20% 30%
TABLE V. CA SE S TUDY 1FACILITIES
Facility Amount
Check-in counters 204
Boarding pass readers 40
Security lines 19
Gates 75
Physically, the airport has 204 check-in desks available;
however, not all of them are in use simultaneously, as they are
assigned to ground handlers who in turn assign them to specific
flights. Table VI shows the amount of check-in counters used
for each ground handler, and the total amount of check-in
counters used. As it can be noticed, 80 counters out of 204
are used, leaving room for a better utilization. For this case
study, it was assumed that each ground handler would add two
more check-in counters and turn them into self baggage drop-
off to be used by smart passengers only. Regarding security
screening, it was assumed that four security lines (out of the
19 available) would be dedicated to smart passengers, and
would use different technology for body scanning to enable
walk-through security screening, leading to a drastic decrease
in the processing time as shown in Table II. It is assumed that
dedicating 20% of the security capacity to smart passengers
was a reasonable assumption, but this can be tested in future
work with a sensitivity analysis.
TABLE VI. CHECK -IN COUNTER ALLOCATION
Ground handler Check-in counters used
GH 1 16
GH 2 16
GH 3 8
GH 4 16
GH 5 18
GH 6 4
GH 7 2
Total 80
The case study was run simulating the time-window from
4:00 AM to 10:00 AM which represents the busiest hours of
the day, having in total 101 departures and 16,560 passengers
expected to transit in the terminal; the traffic was based on
real data.
Figure 3. Departure flights over the day (case study 1)
1) Case study 1: Experimental results: In this section
we illustrate the results provided by evaluating the different
scenarios, focusing on the check-in area and the security
area. These graphs present the distinction between passengers’
profiles, in this case smart and regular (business, visitor and
leisure). Figure 4 shows the queue length at the check in
area, here we notice that the queue length for the regular
passengers improves by introducing the smart passengers, on
the other hand, the smart passengers queue length has a sharp
increase in S2 and S3 when we increase their percentage
by 20% and 30%, respectively. In these two scenarios, we
observe that the 95% percentile of the smart passengers is
higher than the one of the regular ones. This situation is not
ideal, as it would negatively affect the benefit of being a smart
passengers. This phenomenon is confirmed in Figure 5, which
shows that the queuing time for smart passengers grows until
it exceeds the queuing time of regular passengers in S3. The
best scenario for smart passengers is found in S1, while for
regular passengers is S3. However, a trade-off can be found
in S2, where the queuing time has lowered for the regular
passengers, while the smart passengers can still benefit from
a lower queuing time compared to the regular ones. This trend
reveals that, as the percentage of smart passengers increases,
the capacity for them is limited, with the consequence of
longer queues in check-in. A similar phenomena has been
identified in a previous study [16].
Figure 4. Queue length at the check-in area (case study 1)
Figure 5. Queuing time at the check-in area (case study 1)
In Figures 6 and 7, the security area queue length and
queuing time values are depicted. Both graphs show low
values of queue length and queuing time, suggesting that, for
this specific airport, with this specific traffic and resources,
the security area would not be a bottleneck for the system.
Scenarios S1, S2 and S3 improve the queue length perfor-
mance for the regular passengers when compared to the ’base
case’; however, for all scenarios these values are already low.
The smart passengers do not present any significant queue
length or queuing time for all scenarios, this suggests that
the implementation of the smart passengers category is not
significant for the security area.
Results show that by including specific policies for the
smart passengers impacts mostly performance of the check-
in area, while the security area does not seem to be the
affected by it. Furthermore, we identified interesting behaviour.
In check-in area smart passengers’s indicators degrade as the
percentage increases suggesting that the facilities are reaching
their capacity. For the regular ones, as more smart passengers
use the facilities, the performance of regular passengers is
improved. Since the values for smart passengers are high,
it would be necessary to increase the facilities for them or
investigate different policies.
Figure 6. Queue length at the security area (case study 1)
Figure 7. Queuing time at the security area (case study 1)
B. Case study 2: Business-oriented airport
Case study 2 considers LCY, an airport largely oriented to
business travelers, although used by leisure passengers as well.
The business proposition of the airport promotes short periods
between the terminal access and aircraft entry. Therefore,
fast processing times and low queuing times at the terminal
facilities are required. Business passengers are primarily ex-
perienced in traveling by air and know the necessary terminal
procedures, which leads to relatively lower processing times.
These passengers travel mainly with hand luggage, which
reduces the check-in time significantly. Table VII exhibits the
distribution of passenger profiles. Visitors (passengers visiting
friends and relatives) are not considered in case study 2
given the focus on business passengers in the airport’s market
proposition. The airport is designed compactly to allow short
distances between the individual facilities. The number of
existing airport facilities are shown in Table VIII.
TABLE VII. CASE ST UDY 2PAS SEN GE R PRO FILE S
Passenger profile base case S1 S2 S3
Business 57% 52% 47% 42%
Visitor 0% 0% 0% 0%
Leisure 43% 38% 33% 28%
Smart 0% 10% 20% 30%
TABLE VIII. CASE ST UDY 2FACILITIES
Facility Amount
Check-in counters 19
Boarding pass readers 8
Security lines 6
Gates 15
The business-oriented airport focus on the use of electronic,
digital, and self-service facilities. The majority of check-in and
boarding pass control procedures are performed using self-
service kiosks. The check-in desks are primarily available for
business travelers and families with infants or small children.
During peak periods, the use of check-in facilities at manned
and unmanned counters is coordinated by the airport to avoid
long waits and queues. The security control is divided in to
adjacent areas. The simulation is performed for the busy period
between 2:00 PM and 7:00 PM based on a real flight plan,
with a demand peak in the second half of this period. The
number of departure flights is shown in Fig. 8.
1) Case study 2: Experiment results: Due to the high
rate of passengers using a self-service check-in facility, the
resulting waiting times and queue length in the base case are
significantly lower compared to Case study 1. Less than half
of the passengers did not have to wait for check-in, and the
average 95% percentile equals approximately 2 minutes. The
check-in processes at the business airport are not congested,
which results in only small and insignificant improvements by
an increasing rate of smart passengers. Since mainly flight
experienced business passengers with fewer bags use the
airport, the processing times are lower than for the leisure
passengers that dominate in Case study 1.
Figure 8. Departure flights over the day (Case study 2)
Fig. 9 exhibits characteristic security area values for all sce-
narios divided into regular and smart passengers. The median
value for the base case shows a low queue length of 10 waiting
passengers. However, the 95% percentile and the maximum
are significantly higher than the median. This results from
a traffic peak during the observed period and the occurring
high demand, which exceeds the available capacity at the
security facilities. The fast check-in procedure results in a high
throughput leading to quick access to the security area. In the
scenario considering the introduction of the smart passengers
(S1), one security lane is dedicated for this passenger type.
The regular passengers are using the 5 remaining facilities.
Since the share of smart passengers in the first scenario is
only 10%, no queue is observed, and the smart passenger
security lane remains unused most of the time. For the regular
passenger, the waiting times are increasing compared to the
base case due to the reduction in available capacity for this
passenger type. Scenarios S2 and S3 exhibit a shift in queue
length figures for the regular passengers as in the previous
case study. In particular, the 95% and the maximum parameters
decrease by over 60% (S2) resp. 80% (S3) when the proportion
of smart passengers is increased. The queue length for smart
passengers increases but is still negligible. The median values
remain stable in all three scenarios.
A similar result trend can be observed for the waiting time
of regular passengers in the security lanes shown in Fig. 10
since queue length and waiting time correlate to each other. It
is important to note that as the percentage of smart passengers
increases, the performance values for the regular ones improve,
and the dispersion of values is also reduced, meaning that a
better level of service is achieved to all the passengers when
more smart passengers use the terminal. These results are
similar to the ones of the previous case study 1.
V. CONCLUSIONS AND FUTURE WO RK
This research presented the analysis of introducing a new
category of passengers (the ’smart passenger’). These passen-
Figure 9. Queue length at the security area (case study 2)
Figure 10. Queuing time at the security area (case study 2)
gers takes more control over its journey with the objective
of overcoming in a short time the pain points all passen-
gers face in a terminal for enjoying the non-aeronautical
facilities available. By using simulation, the notion of this
new passenger type was tested in two airports with very
different characteristics (Palma de Mallorca and London City).
The simulation of the passenger journey in those airports
revealed the potential impact these new passengers might
have in the future together with knock-on effects in the
system. For a leisure-oriented airport (Case 1), characterized
by high peak traffic, the introduction of the smart passengers
brings benefit both for them and for the regular travelers in
the areas analyzed. Problems of capacity appear to smart
passengers when their proportion grows, an effect already
identified in a previous study [16], revealing that there are
tipping points when it is necessary to adapt the system for
the increasing demand of smart passengers. For a business-
oriented airport (Case 2) with a high percentage of self-service
check-in and digitally-assisted processes, the introduction of
smart passengers is also positive. However, when the amount
of smart passengers is low as in scenario S1, the regular
passengers suffer from the reduction in capacity for processing
them with the consequence of a bigger dispersion in the
performance values compared to the base case. Remarkably,
when the amount of smart passengers increases, the whole
system gets the benefit. It is revealed by the reduction of
mean and dispersion values in the performance for regular
passengers and smart ones bringing an overall better level
of service for all the passengers in the terminal. This results
suggest that technology together with the incentives for smart
passengers in terminals would unlock valuable capacity, which
in turn would affect positively all the passengers (smart and
traditional).
This study opens new research lines which will be explored
by the authors in the future. For instance, given that resource
management is important, a sensitivity analysis could help
identifying what would be the right amount of resources
(e.g.security lines or counters) dedicated to the smart passen-
gers. Furthermore, as it is documented that variability plays an
important role in dynamic systems, an analysis of variance to
identify which elements contribute the most to the variability
of the system could provide managerial insight. The option of
flexible (shared) lines when the amount of smart passengers is
low, as well as other interesting concepts like virtual queuing
or pre-ordered services can also be evaluated. Furthermore,
the results of the IMHOTEP Project and survey data available
on line could improve the accuracy for the parameters that
simulate behaviour. Last but not least other policies will be
evaluated like virtual queuing and new designs of airport
terminals considering the potential behaviour identified.
ACKNOWLEDGMENT
This project has received funding from the
SESAR/European Union’s Horizon 2020 research and
innovation program under grant agreement No 891287. The
opinions expressed herein reflect the authors’ view only.
Under no circumstances shall the SESAR Joint Undertaking is
responsible for any use that may be made of the information
contained herein. The authors also thank Dutch Benelux
Simulation Society (www.dutchbss.org) and EUROSIM
(www.eurosim.info) for disseminating the results of this
work.
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