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32nd European Modeling & Simulation Symposium
17th International Multidisciplinary Modeling & Simulation Multiconference
2724-0029 © 2020 The Authors.
doi: xx.xxxx/xxxx
CREATING THE FUTURE AIRPORT PASSENGER EXPERIENCE:
IMHOTEP
Miguel Mujica Mota1, Paolo Scala2,* , Ricardo Herranz3 , Michael Schultz4 and Edgar Jimenez5
1 Amsterdam University of Applied Sciences – Aviation Academy, Weesperzijde 190, Amsterdam, 1097 DZ, The Netherlands
2 Amsterdam University of Applied Sciences – Amsterdam School of International Business, Fraijlemaborg 133, Amsterdam, 1102 CV, The
Netherlands
3 Nommon Solutions and Technologies SL, Calle C. Coello 124, Madrid, 28006, Spain
4 Technical University of Dresden – Institute of Logistics and Aviation, Helmholtzstrasse 10, Dresden, 01069, Germany
5 Cranfield University – Centre for Air Transport Management, College road, Cranfield, MK43 0AL, United Kingdom
*Corresponding author. Email address: p.m.scala@hva.nl
Abstract
As the airport of the future is expected to become a multimodal connection platform, one of the main challenges is to create the conditions for travelers to
reach their destination by the most efficient and sustainable combination of modes. This will, furthermore, allow the airport and its surrounding region to
make a better use of their resources. In this context, the H2020-SESAR-2019-2 funded project IMHOTEP, aims at developing a concept of operations and a
set of data analysis methods, predictive models and decision support tools that allow information sharing, common situational awareness and real-time
collaborative decision-making between airports and ground transport stakeholders. In this paper, the IMHOTEP concepts are presented. The focus is on
the project proposed objectives and methodologies applied. Finally, the project expected results and limitations will be discussed.
Keywords: Airport, Ground Transport, Multimodal, Passenger Flows, Collaborative Decision Making
1.
Introduction
The European high-level vision on transport depicts a passenger-
centric system that takes travelers from their origin to their
destination in a seamless, efficient, predictable, environmentally-
friendly and resilient manner (European Commission, 2011).
Achieving this vision calls for enhanced modal integration not only
in terms of physical infrastructure, but also of business models,
operational processes and information systems. In particular,
significant potential remains untapped for improving the quality
and resilience of the door-to-door passenger journey and the
efficiency of airport operations through information sharing and
collaborative decision-making across transport modes. Airport
Collaborative Decision-Making (A-CDM) (EUROCONTROL, 2016) is
enhancing the efficiency of airport operations thanks to
information sharing and common situational awareness between
airports, airspace users (AUs), ground handlers and air navigation
service providers (ANSPs), but the concept has so far focused on
aircraft turnaround and pre-departure sequencing. Total Airport
Management (TAM) (SESAR, 2019) is a more holistic concept that
foresees closer integration of landside and airside processes, but
the passenger access and egress legs are still absent from the
picture. The inclusion of ground transport into TAM collaborative
decision-making process has been suggested as a future extension
that could enable a better integration between the airport and the
ground transport system, especially in situations of disruptions. A
key enabler for this enhanced integration is the development of
information platforms and services that provide airports and
ground transport stakeholders with a common and comprehensive
picture of the door-to-door passenger flows. In recent years, the
pervasive use of personal mobile devices has opened new
opportunities for the collection of high-resolution passenger
trajectory data with an unprecedented level of accuracy. These
data can in turn be blended with the information available from
different stakeholders (e.g., flight departure and arrival
information, schedules and travel times of ground access modes,
terminal processing times, etc.) not only to monitor passenger
flows in real-time, but also to build short-term predictive models
and develop look-ahead strategies. This shared, permanently
updated picture of the actual and future status of passenger flows
would facilitate coordination between transport modes, as well as
2 | 32nd European Modeling & Simulation Symposium, EMSS 2020
the development of passenger information services for the
planning and reconfiguration of the door-to-door journey,
enhancing the passenger experience and rendering the transport
system more resilient against disruptive events.
IMHOTEP aims to seize these opportunities by developing a set
of enabling technologies able to provide a holistic view of the
airport processes, the ground transport system and the passenger
flows, with the ultimate purpose of improving the quality,
efficiency and resilience of the door-to-door passenger journey (see
Fig. 1).
Besides IMHOTEP, there are other similar projects that address
multimodality such as: TRANSIT
(
https://cordis.europa.eu/project/id/893209
), MODUS
(
https://cordis.europa.eu/project/id/891166
) and X-TEAM
(
https://cordis.europa.eu/project/id/891061
). In table 1, it can be
found a comparison between the project in terms of scope and
area of application. The table highlights the difference between
IMHOTEP and the mentioned projects. The scope of IMHOTEP is to
use airports and passenger information to enhance the passenger
travel experience from door-to-airport gate and vice versa, while
the scope of the other project is about the improvement of the air
transport within an intermodal context. The area of application of
IMHOTEP is bounded among the airport terminal and the ground
transport connecting to the airport. The area of application of the
other projects entails the whole door-to-door intermodal travel at a
higher level, as they have a more strategic focus (e.g. assessment of
intermodal concepts on transport system as a whole), while
IMHOTEP’s objective is to come up with decision support tools for
real-time decision making.
In this paper, the IMHOTEP project is discussed with emphasis
on the methodology applied, which comprises the use of novel
algorithms for passenger flow management along their door-to-
door journey and simulation models that will test, calibrate and
validate these algorithms. In particular, this paper pays attention
to the simulation part, by describing how the simulation models
are conceptualized. The simulation part, as it will be described in
more details in the next sections, comprises two models, one for
the airport access/egress, and another for the airport terminal.
The two models will be integrated into one for simulating the
entire passenger door-to-door travel experience.
Table 1. Comparison between IMHOTEP and similar projects
Project
Scope
Area of application
IMHOTEP
Integration of information
between airport and ground
transport stakeholders for a
better coordination of
operations.
Airport terminal operations and
airport external ground
transportation operations.
TRANSIT
Design a managin g system for
seamlessly integrated
intermodal transport.
Intermodal transportation
operations.
MODUS
Assessing the role of air
transport within an integrated,
intermodal approach.
Intermodal transportation
operations.
X-TEAM
Integrating air transport with
intermodal network to enable
the door-to-door connectivity,
up to 4 hours, between any
location in Europ e.
Intermodal transportation
operations.
Below the main contributions of the paper are listed:
•
First, the integration of the two models represents a key
contribution into the field, as most research has focused only
on the passenger flow within terminals, and not the entire
passenger’s door-to-door journey.
•
Second, this paper proposes a framework for developing the
two integrated models, pointing out the requirements both
from a technical and operational point of view.
•
Third, the challenges (technical and operational) brought by
the modeling part will be highlighted, as they represent the
starting point for future developments.
The remaining of the paper is as follows: in the next section,
the project is described with its operations and objectives. Then,
the different methodologies applied are presented, with a focus
on the development of the operation at terminal level which will
be part of the team that works in the respective work package.
Figure 1. IMHOTEP concept
First et al. | 3
Finally, a discussion on the expected results and challenges of the
project is given.
2.
IMHOTEP: Project Description
2.1.
Operations Description
The backbone of the IMHOTEP concept is the common view of the
real-time passenger flows and their short-term evolution. The way
the project proposes to materialize this concept is briefly outlined
as follows: Passenger flows will be represented in a disaggregated
manner, by means of the concept of Passenger Activity-Travel
Diary. A Passenger Activity-Travel Diary will be composed by the
sequence of activities and trip stages performed by the passenger.
Each activity or stage of the journey will have a start time, a start
location, an end time, an end location, and a set of additional
attributes selected from a list of pre-defined options for each type
of activity or stage (e.g., the activity prior to the trip of a departing
passenger or the subsequent activity to the trip of an arriving
passenger will have a an attribute called ‘activity type’ that may
take different possible values: ‘home’, ‘work’, ‘leisure’, etc.; the
access and egress legs will have an attribute called ‘transport
mode’ that may take the values ‘rail’, ‘metro’, ‘bus’, ‘private car’,
‘taxi’, etc.). For a more detailed representation of the Passenger
Activity-Travel Diary, please refer to Fig. 2.
The main operations related to the Passenger Activity-Related
Diary are Departures, Connections and Arrivals, and they are
performed in the following environments:
1.
Ground access: where passengers start or end their journey
(home, work, another place). Transport mode choice (bus,
train, private car, taxi).
2.
Airport landside: airport entries/egresses; check-in desks;
passengers transit areas.
3.
Airport airside: security check area; passport control area;
shopping area; gate area; luggage belt area.
4.
Air: passengers travelling, inside an aircraft, from origin to
destination.
2.2.
Objectives
The specific objectives of the project are the following:
•
Propose a concept of operations for the extension of airport
collaborative decision-making to ground transport
stakeholders, including local transport authorities, traffic
agencies, transport operators and mobility service providers.
•
Develop new data collection, analysis and fusion methods
able to provide a comprehensive view of the door-to-door
passenger trajectory through the coherent integration of
different types of high-resolution passenger movement data
collected from personal mobile devices and digital sensors.
•
Develop predictive models and decision support tools able to
anticipate the evolution of an airport’s passenger flows
within the day of operations and assess the operational
impact on both airport processes and the ground transport
system, with the aim of enabling real-time collaborative
decision-making between airports and ground transport
stakeholders and enhanced passenger information services.
•
Validate the proposed concept and the newly developed
methods and tools through a set of case studies conducted in
direct collaboration with airports, local transport authorities
Figure 2. IMHOTEP Operations
4 | 32nd European Modeling & Simulation Symposium, EMSS 2020
and transport operators.
3.
Methodology Applied
The proposed research methodology comprises five main stages.
(i) the development of an initial version of the IMHOTEP Concept
of Operations (ConOps) and defining the set of case studies that
will be used to develop and evaluate the proposed concept; (ii)
the collection and preparation of the datasets required for the
case studies; (iii) the development of the new data analysis
algorithms and the predictive models for the real-time
characterization and short-term forecasting of passenger
itineraries; (iv) the new data analysis algorithms and predictive
models will be then integrated into a prototype decision support
tool for collaborative decision-making between airports and
ground transport modes; (v) demonstration and evaluation of the
proposed concept through a set of case studies. In this paper, we
will focus on the third stage, in particular, in the development of
the simulation model of the passenger terminal.
3.1.
Development of the IMHOTEP ConOps and Definition of
the Case Studies
The IMHOTEP ConOps aims at extending the A-CDM process to
include airport access modes. The ConOps will describe the
characteristics of the proposed system from the point of view of
airports and ground transport stakeholders, through a set of use
cases. The development of the ConOps will encompass the
following steps:
•
analysis of the current trends in airport multimodal
connectivity and identification of new intermodal concepts
based on information sharing and collaborative decision-
making between the airport and ground transport modes.
•
identification of user needs and constraints.
•
translation of user needs into high-level operational and
technical requirements, including the definition of the
system architecture, the main interfaces and information
flows, the KPIs on which the collaborative decisions will be
based, and the required technical capabilities.
•
requirement validation. Once the ConOps is defined, the case
studies that will be used for the evaluation of the proposed
system will take place.
•
the input data required for the implementation of the case
studies.
3.2.
Data Collection and Preparation
The data sources used in the project include:
•
Anonymized mobile phone records. The position of each user
is recorded every 10-20 minutes and is given by the network
cell where the user is located. The data also include
sociodemographic information of the users, such as age and
gender.
•
Data on the ground transport system, including the road and
rail networks, the available transport services, public
transport ticketing and smart card data and road traffic
counts, among others.
•
Airport data. These data will include data on the airport
systems and processes (e.g., airport layout, flight schedules,
gate allocation, etc.), aggregated data on the use of airport
facilities (e.g., data on the use of the parking facilities,
passenger flows at the check-in counters, passport control
and security checkpoints, etc.), and disaggregated data on
the passenger itineraries in the terminal generated by airport
sensors, such as boarding pass scanners and Wi-Fi sensors.
3.3.
Modeling and Short-term Forecasting of Passenger Flows
The development of new data analysis algorithms and predictive
models for the real-time reconstruction and short-term
forecasting of passenger flows comprise two activities that will
run in parallel: the modelling of the passenger terminal flows and
the modelling of the access and egress legs.
3.3.1. Modeling of Passenger Terminal Process
The modelling of the passenger terminal processes will
encompass two main steps as depicted in Figure 3.
Figure 3. Steps for modeling the passenger terminal process
First, a set of algorithms will be developed for the detailed
reconstruction of the passengers’ kerb-to-gate and gate-to-kerb
itineraries. Second, a simulation model based on the airport
simulation software CAST (ARC, 2020), will be developed. CAST
software is a multi-agent simulation framework which allows the
modelling of pedestrian, vehicle and aircraft traffic and of
landside and airside processes.
The airport agent will be designed to provide, collect, and
react on the information included in the Passenger Activity-Travel
Diaries. Based on this new situational awareness, the airport
system will be dynamically controlled to optimize the utilization of
infrastructure and resources in real-time, with the aim of
increasing operational efficiency and passenger comfort. A more
detailed description of the simulation model of the airport
terminal processes will be given in section 4.2.
3.3.2. Modeling of Passenger Access and Egress
The modelling of the airport access and egress legs will also
comprise two steps (see Fig. 4).
First, we will develop algorithms for the detailed
reconstruction of the passengers’ door-to-kerb and kerb-to-door
itineraries. Particular attention will be paid to the characterization
of the different types of passengers (e.g., business travelers vs
tourists), as they have very different impacts on the handling
process. In a second step, the information about passengers’
airport access behavior will be combined with the information on
the supply of transport services to/from the airport to build and
First et al. | 5
calibrate a multimodal transport model based on the traffic
simulation software Aimsun Live (Aimsun, 2020).
Figure 4. Steps for modeling the access-egress process
The full door-to-kerb and kerb-to-door trajectories of the
passengers will be modelled, in order to generate the access or
egress segments of the Passenger Activity-Travel Diaries. The
model will be coupled with an emissions model to estimate the
environmental impact of the airport landside access. Ground
transport modes will be provided with the capability to
dynamically re-plan their services based on the information
provided by the Passenger Activity-Travel Diaries.
3.4.
Development of Decision Support Toolset
Once the models of the passenger terminal flows and of the
access and egress legs are developed, they will be integrated in
order to provide a comprehensive view of the full Passenger
Activity-Travel Diaries. The integration will involve three main
activities:
•
the development of the required interfaces so that each
model can take as inputs some of the outputs produced by
the other models.
•
the implementation of the KPIs on which the collaborative
decisions will be based. The set of KPIs, specified in the
IMHOTEP ConOps, will measure the quality, sustainability,
efficiency and resilience of the passenger journey.
•
a prototype dashboard will be developed to allow the users
to filter, visualize and analyze the KPIs of their interest
according to different criteria.
3.5.
Demonstration and Evaluation
The last stage of the project will be devoted to the demonstration,
validation and evaluation of the IMHOTEP concept and the newly
developed tools in collaboration with the partner airports and
transport operators, as well as with other stakeholders. The
evaluation process will include the following steps:
•
revise and specify in more detail the case studies defined in
the first stage of the project in the light of the results of the
data analysis and modelling tasks.
•
execute a set of simulation experiments to validate the
predictive capabilities of the models.
•
emulate the real-time operation of the decision support tools
in several working sessions with relevant stakeholders in
order to assess the potential of the new tools to support
collaborative decision-making.
•
assess the performance impact of the proposed concept
along different dimensions, with particular focus on the
benefits for the passenger, but also looking at the efficiency
in the use of airport resources and the sustainability of the
airport access modes.
Once the evaluation is completed, the results of the case
studies will be complemented with a feasibility analysis of the
proposed concept. The knowledge extracted from the case
studies and the feasibility study will be synthesized into a set of
guidelines and recommendations on the applicability of the
IMHOTEP C
4.
Passenger Terminal Process Simulation
The goal of the passenger terminal process simulation model is to
develop and calibrate a modeling tool that reconstructs and
simulates the flows of passengers inside the airport terminal. In
this section, a brief state of the art regarding the modeling of
airport terminal, and a description of the simulation model
developed are given.
4.1.
State of the Art regarding the Simulation of Airport
Terminals
The most commonly used paradigms for the simulation of
passenger terminal flows are discrete event simulation and agent-
based modelling. Discrete event simulation has been used to
analyze the times spent at each stage of the compulsory activities
at the airport (e.g., security check), identify bottlenecks, and
design possible strategies to minimize these times (Alodhaibi et
al., 2017; Rausch and Kljajić, 2006). Agent-based modelling
provides a more detailed representation of the individual
passenger trajectories, allowing the study of how the passengers
move inside the airport, including discretionary activities (e.g.,
shopping), and the analysis of how the visited locations and the
time spent at each of them varies with personal preferences, trip
purposes, etc. Relevant examples can be found in Schultz et al.
(2011), where agent-based modelling is used to study the
passenger handling processes and test the impact of adaptive
airport signage, and in Ma et al. (2011), where an agent-based
model is used to evaluate the level of use of different airport
facilities; the probability that a passenger chooses a certain
activity is modelled as a function of the passenger characteristics
(sociodemographic profile, number of bags, etc.), the available
time, and a walking distance threshold. Outside academia, there
exists commercial software for the microscopic simulation of the
passenger movements, such as CAST.
4.2.
Development of the Passenger Terminal Simulation
Model
The simulation model for the airport passenger terminal, will be
developed by following the steps: model specifications; model
data analysis; model design and development; model validation.
Figure 5 shows how these steps are linked together.
4.2.1. Model Specifications
The technical specification of the model of the passenger
terminal processes, will be developed starting from the high-level
system specification provided by the IMHOTEP ConOps.
6 | 32nd European Modeling & Simulation Symposium, EMSS 2020
The passenger terminal model will include the handling
processes of passenger arrival:
Figure 5. Steps for developing the airport passenger terminal simulation model
In the following subsection, each step is discussed.
•
Check-in.
•
Border control.
•
Security.
•
Boarding pass control.
and departure:
•
Deboarding.
•
Border control.
•
Baggage handling.
•
Custom.
The layout and amount of facilities of the airport terminal will
be provided by the airport operators that will cooperate in the
project.
The specification will also include the interface requirements
for the integration of the passenger terminal model with the
access-egress model (see section 3.3.2).
4.2.2. Model Data Analysis
A set of data fusion and analysis methodologies aimed at
reconstructing the passenger behavior inside the terminal will be
derived from a variety of data generated by personal mobile
devices and airport sensors (mobile phone records, Wi-Fi sensors,
boarding pass scanners, etc.). With the development of
algorithms, the type of passenger (business traveler, tourist, etc.)
will be identified and the time spent by different types of
passengers at the different terminal processes as well as in other
discretionary activities (e.g., shopping) will be measured. The data
from personal mobile devices will also be blended with other data
(e.g., airport surveys) to enrich the characterization of the
passengers’ sociodemographic profile and other relevant factors
that may explain the passenger behavior inside the terminal.
4.2.3. Model Design and Development
To develop an airport terminal simulation model, incorporating
the extended A-CDM mechanisms defined in the IMHOTEP
ConOps, a simulation model of the terminal passenger flows will
be developed using CAST multi-agent airport simulation software.
The model will include the airport layout, the airport processes,
and the passengers’ behavior. CAST terminal software allows to
model airport terminal processes and to obtain a clear and
detailed representation of the them, as they appear in real world
airport terminals. A snapshot of the animation of CAST terminal
model can be seen in Figure 6. The model will include the
necessary mechanisms to simulate the airport decision-making
processes considered in the IMHOTEP ConOps (e.g., dynamic
assignment of airport resources based on the short-term forecast
of terminal passenger flows).
Figure 6. CAST terminal model animation. So urce: https: //arc.de/cast-terminal-
simulation/
4.2.4. Model Calibration and Validation
The simulation model will be calibrated and validated by
comparing the model results with the information measured by
means of the algorithms as mentioned in section 4.2.2. The model
will be verified and validated based on diverse statistical
techniques making an intensive use of the datasets from the
project. Then the results of the statistical analysis will be
translated into empirical probability distributions of the passenger
processes. Diverse techniques from Machine learning or data
mining will be used for predicting the behavior of the passengers
within the terminal.
5.
Contribution and Challenges of the IMHOTEP Project
The results of IMHOTEP are expected to set the path for the
development of a multimodal airport collaborative decision-
making environment. Beyond the A-CDM context, the new data
analysis algorithms and the simulation solutions developed by
IMHOTEP could be the basis for new solutions for the planning
and management of transport systems. Airport managers are one
of the stakeholders that will benefit from the outcome of the
IMHOTEP project, as they will be able to make more accurate and
reliable decisions. The integration of the data between ground
transport and airports will enhance the accuracy in the flow of
First et al. | 7
passengers in and out of the airport, and consequently both
airports and ground transport can make a better planning of their
resources. IMHOTEP itself will reveal the potential improvement
of performance once information is shared not only with the
stakeholders of the system itself but with the users of the
systems. Furthermore, IMHOTEP will enable the passengers to
perform a self-regulated operation as they will be able to
perceive, assess and make informed decisions based on the
information sources of the system. This approach has the
potential to give way to new types of systems where transparency
and decentralized decision making are the key characteristics; in
other words, IMHOTEP will reveal the potential of new paradigms
of systems management.
The use of simulation allows to evaluate the potential of
information sharing and also different policies and scenarios to
validate the overall IMHOTEP ConOps, however, it comes with
some challenges:
1.
Validation. The validity of the simulation model outcome
relies on the data collected about airport process
specifications, provided by the airport operator, and the
data on passenger behavior, collected by using passengers’
personal mobiles and airport sensors. Therefore, the data
should be reliable and available and in a form that is useful
for the validation purposes.
2.
Integration of multi-model approach. The different models
of IMHOTEP need to be integrated to interchange
information amongst them. This will be a challenge as it is
foreseen a large-scale model.
3.
Personal data protection, as the proposed concept relies on
the anonymization of different personal data, adequate
data security policies and measures ensuring compliance
with GDPR are required.
All these challenges require a particular effort from a modeling
point of view, especially regarding the integration of the different
models. Running the models on-line or off-line is a critical decision
that the modelers need to make, which impacts the way how the
two models will communicate (via a shared database or a
synchronous exchange of data).
Funding
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.
Acknowledgements
The authors would like to thank the Amsterdam University of
Applied Sciences for the support in this research, as well as the
Dutch Benelux Simulation Society (www.dutchbss.org) and
EUROSIM (www.eurosim.info, www.eurosim2022.eu) for
disseminating the results of this work.
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