2018

Optimize Landside Airport Operations Using A Discrete Event Simulation

Thesis Final Report Marco Groot, BSc 1312898 Optimize Landside Airport Operations Using A Discrete Event Simulation

Preface This is the thesis report for the Aerospace Engineering Masters at Delft University of Technology. The thesis is the final part of the Master curriculum. Completion of the thesis will result in the graduation of the student.

The research described in this document gives a fundamental insight in landside airport processes for departing passengers. The layout of four different airports is analysed after which an optimization process will takes places based on the found results. This optimization aims at providing solutions to be able to cope with double passenger number compared to today’s level without the need to expand the terminal buildings.

A special word of gratitude goes to Paul Roling for his counselling during the thesis work. I would also like to thank my colleagues for all the advice, expertise and given opportunities at Scarabee Aviation Group.

Lastly, a big love to my parents who, during my studies in Delft, have always supported me and believed in me, even during the rough moments.

Zaandam, 30 March 2018

Marco Groot, BSc

Marco Groot, BSc 1

Optimize Landside Airport Operations Using A Discrete Event Simulation

Abstract The thesis subject described in this document optimizes landside airport operations by using a discrete event simulation. The landside operations included in this research are the check-in, baggage drop-off, security access, security check and immigration.

For each of the four areas in the landside process multiple methods exists. For check-in the distinction is made between serviced check-in, self-service check-in and internet check-in. The baggage drop-off knows four types, namely full serviced, limited serviced, retrofit self-service and integrated self-service. The security process itself is similar. However, the location at which the security check is performed might differ. This can be centralized (at a limited number of locations in the terminal) or decentralized (at each boarding gate). For the immigration the different methods are serviced or self-service.

A literature study has been conducted in the available solutions by different competitors regarding the check-in, baggage drop-off, security check and immigration. With the found data overall process times have been determined, which are used in the simulation program.

Four airports were chosen based on their characteristics. Together the four airports cover a ranges of small, medium and large sized airports, mostly leisure passengers, mostly business or a mix of leisure and business, dedicated domestic terminals, dedicated international terminals or domestic and international within the same terminal and airports based in different areas around the world. The airports chosen are Amsterdam Airport Schiphol (AMS), Tokyo International Airport Haneda (HND), London City Airport (LCY) and McCarran International Las Vegas (LAS).

In order to simulate the passenger flow through the airport, the number of passengers and the arrival schedule are needed. For the number of passengers, the most resent busiest day of the year was taken. By including a large amount of passengers in the simulation, the bottlenecks will arise. The arrival schedule is determined by means of the flight departure schedule, the aircraft used and the behaviour of passenger regarding the time of arriving at the airport before departure.

Passenger profiles have been determined in order to give a representable mix of passengers in the simulation program. These passenger profiles include the number of travel companions, travel purpose and type of baggage.

Site surveys have been conducted at the airports to determine the number of check-in kiosks/desks, baggage drop-off locations, security checkpoints and immigration booths. With this data and the process times of the solutions the airports were reconstructed in a simulation program. In the first phase the current terminal layout and passenger numbers are entered in the simulation models. After these models are verified, in the second phase the passengers numbers are doubled and the passenger profiles are included.

In the first phase different scenarios were run in order to verify the constructed simulation models. The aim of this verification process was to determine if the correct amount of passenger pass through the correct locations in the airport. Also, it was verified if running the same simulation scenario multiple times, these independent runs would give the same result.

Marco Groot, BSc 2

Optimize Landside Airport Operations Using A Discrete Event Simulation

The analysis of the results showed that, in terms of the process times, the simulations were accepted. Only 13 out of the 275 results were outside of the 95% acceptance criteria. As for the passenger movement through the airport, these were as expected for each of the performed scenarios.

Observing the passenger flow through the airport, bottlenecks in the process were found for AMS and LCY. For AMS the bottleneck occurs in Terminal 1, for the check-in kiosks as well as for the business class baggage drop-off. LCY has a mayor bottleneck at the baggage drop- off. This occurs for business class and economy class passengers. The main cause for this bottleneck lies with the used passenger profiles. For the simulations run for the verification process the passenger profiles have not been included. This means almost twice as much passengers are dropping of their baggage, which will not have done so when the passenger profiles are included. For HND and LAS, no bottleneck were found during the simulations.

After the simulation models are verified, the second phase was started. The passenger numbers are doubled and the passenger profiles included. The correct implementation of the passenger profiles have been verified. 92 out of the 96 results are accepted, which means 95.8%. This is an acceptable percentage, so it can be concluded that the passenger profiles are constructed correctly.

Based on the arrival pattern of passengers, the passenger numbers and the allowable maximum queueing times the number of required facilities are calculated. The queueing theory based on Little’s rule and Markov is used to calculate the required number of facilities. The required number of facilities depends on the number of passengers. As the arrival distribution show peak moments, the system might become over-designed. Therefore different peak time periods are used. This results in the required number of facilities for each of these peak time periods.

The simulation models for the four airports also give the number of required facilities. These results are compared with the results from the queueing theory. For AMS, LCY and LAS the simulation models show acceptable results for the peak time periods between 20 and 90 minutes. For HND the results of the simulation model are acceptable for the peak time periods of 90 to 180 minutes.

When comparing the number of required facilities according to the simulation models with the current day situation, the processes can be made more efficient. For AMS, HND and LAS twice as many passengers can pass through all stations, while less terminal space is required. At LCY a 40% increase in floorspace is required to facilitate double passenger numbers.

This research looks at the throughput of passengers at the different stations of the departure process. The time passengers spend moving between these stations is not taken into account. Also, disruptions in the system are not included. These items could be the subject of further studies.

Marco Groot, BSc 3

Optimize Landside Airport Operations Using A Discrete Event Simulation

Table of Contents Preface ...... 1 Abstract ...... 2 List of abbreviations ...... 9 List of figures ...... 10 List of tables ...... 11 Introduction ...... 13 1 Landside processes ...... 15 1.1 Check-in process ...... 15 1.1.1 Serviced check-in process ...... 15 1.1.2 Self-service check-in kiosk ...... 16 1.1.3 Internet check-in ...... 18 1.2 Baggage drop-off process ...... 19 1.2.1 Full serviced baggage drop-off ...... 19 1.2.2 Limited serviced baggage drop-off ...... 19 1.2.3 Retrofit baggage drop-off ...... 20 1.2.4 Integrated baggage drop-off ...... 21 1.3 Security ...... 22 1.3.1 Security measures ...... 22 1.3.2 Centralized versus decentralized security ...... 23 1.4 Immigration ...... 25 1.4.1 Serviced immigration ...... 25 1.4.2 Self-service immigration ...... 25 2 Airport selection ...... 26 2.1 Requirements ...... 26 2.2 Amsterdam Airport Schiphol ...... 28 2.2.1 Runways and terminals ...... 28 2.2.2 Passenger details ...... 29 2.2.3 Requirements covered ...... 29 2.3 Tokyo International Airport Haneda ...... 30 2.3.1 Runways and terminals ...... 30 2.3.2 Passenger details ...... 31 2.3.3 Requirements covered ...... 32 2.4 London City Airport ...... 33 2.4.1 Runways and terminals ...... 33 2.4.2 Passenger details ...... 33

Marco Groot, BSc 4

Optimize Landside Airport Operations Using A Discrete Event Simulation

2.4.3 Requirements covered ...... 34 2.5 McCarran International Las Vegas ...... 35 2.5.1 Runways and terminals ...... 35 2.5.2 Passenger details ...... 36 2.5.3 Requirements covered ...... 36 3 Passenger arrival schedule...... 37 3.1 Aircraft departures ...... 42 3.2 Available seats ...... 45 3.3 Passenger arrival distribution ...... 48 3.3.1 Amsterdam Airport Schiphol ...... 48 3.3.2 Tokyo International Airport Haneda ...... 48 3.3.3 London City Airport ...... 49 3.3.4 McCarran International Las Vegas...... 50 3.4 Passenger arrival distribution graph ...... 50 3.5 Remarks ...... 51 4 Solutions details ...... 52 4.1 Check-in solutions ...... 53 4.1.1 Serviced check-in ...... 53 4.1.2 Self-service check-in ...... 53 4.1.3 Internet check-in ...... 54 4.2 Baggage drop-off solutions ...... 54 4.2.1 Limited serviced baggage drop-off ...... 54 4.2.2 Retrofit self-service baggage drop-off ...... 55 4.2.3 Integrated self-service baggage drop-off ...... 56 4.3 Security check solutions ...... 57 4.4 Immigration ...... 59 4.4.1 Serviced immigration ...... 59 4.4.2 Self-service immigration ...... 59 5 Passenger profiles ...... 60 5.1 Passenger profiles determination ...... 60 5.2 Determine walking speeds per passenger profile ...... 62 5.3 Percentage of occurrence ...... 64 5.4 Reflection on chapter ...... 66 6 Build airports in simulation program ...... 67 6.1 General decisions ...... 67 6.1.1 Simulation program ...... 67

Marco Groot, BSc 5

Optimize Landside Airport Operations Using A Discrete Event Simulation

6.1.2 Process times ...... 68 6.1.3 Passenger profiles ...... 68 6.1.4 Passenger arrival distribution ...... 69 6.1.5 Simulation runtime ...... 69 6.2 Amsterdam Airport Schiphol ...... 70 6.3 Tokyo International Airport Haneda ...... 73 6.4 London City Airport ...... 75 6.5 McCarran International Las Vegas ...... 76 7 Model Verification ...... 78 7.1 Amsterdam Airport Schiphol ...... 78 7.1.1 Verification initial simulation ...... 78 7.1.2 Change of percentage variables ...... 81 7.1.3 Process times ...... 82 7.1.4 Passenger arrival distribution ...... 84 7.2 Tokyo International Airport Haneda ...... 86 7.2.1 Initial simulation ...... 86 7.2.2 Change of percentage variables ...... 87 7.3 London City Airport ...... 89 7.3.1 Initial simulation ...... 89 7.3.2 Change of percentage variables ...... 90 7.4 McCarran International Las Vegas ...... 91 7.4.1 Initial simulation ...... 91 7.4.2 Change of percentage variables ...... 92 7.5 Results overview and comparison ...... 93 8 Implementing passenger profiles...... 95 8.1 Percentage of occurrence ...... 95 8.2 Walking speeds ...... 99 8.3 Other changes to simulation programs ...... 99 9 Optimize airport equipment ...... 101 9.1 Check-in ...... 101 9.2 Baggage drop-off ...... 101 9.3 Security access ...... 102 9.4 Security ...... 102 9.5 Immigration ...... 103 9.6 Conclusions ...... 103 10 Airport optimization ...... 104

Marco Groot, BSc 6

Optimize Landside Airport Operations Using A Discrete Event Simulation

10.1 Allowed queueing times ...... 104 10.2 Queueing theory ...... 105 10.3 Simulation results ...... 107 10.4 Comparison queueing theory and simulation results ...... 107 11 Conclusions ...... 109 12 Recommendations ...... 111 Bibliography ...... 112 Appendix A Station solution companies ...... 124 Appendix A.1 Baggage drop-off retrofit solutions ...... 124 Appendix A.2 Baggage drop-off integrated solutions ...... 124 Appendix B Solutions details ...... 125 Appendix B.1 Serviced check-in ...... 125 Appendix B.2 Self-service check-in ...... 125 Appendix B.3 Internet check-in ...... 126 Appendix B.4 Limited serviced baggage drop-off ...... 126 Appendix B.5 Retrofit self-service baggage drop-off ...... 127 Appendix B.6 Integrated self-service baggage drop-off ...... 128 Appendix B.7 Security access ...... 129 Appendix B.8 Security check ...... 129 Appendix B.9 Serviced immigration ...... 130 Appendix B.10 Self-service immigration ...... 130 Appendix C Passenger profiles determination ...... 131 Appendix C.1 Walking speeds (Young, 1999) ...... 131 Appendix C.2 Walking speeds (Schultz, et al., 2008) ...... 131 Appendix C.3 Walking speeds baggage (Schultz, et al., 2008) ...... 131 Appendix C.4 Walking speeds travel purpose (Schultz & Fricke, 2011) ...... 131 Appendix C.5 Influence of baggage (Schultz, et al., 2008) ...... 132 Appendix D AMS airline group overview ...... 133 Appendix E Percentage of occurrence passenger profiles ...... 134 Appendix E.1 Percentage of occurrence AMS ...... 134 Appendix E.2 Percentage of occurrence HND ...... 135 Appendix E.3 Percentage of occurrence LCY ...... 136 Appendix E.4 Percentage of occurrence LAS ...... 137 Appendix F Simulation input parameters ...... 138 Appendix F.1 AMS ...... 138 Appendix F.2 HND ...... 139

Marco Groot, BSc 7

Optimize Landside Airport Operations Using A Discrete Event Simulation

Appendix F.3 LCY ...... 140 Appendix F.4 LAS ...... 141 Appendix G Process times used in phase 2 ...... 142 Appendix H Required facilities queueing theory ...... 143 Appendix H.1 Required facilities queueing theory AMS ...... 143 Appendix H.2 Required facilities queueing theory HND ...... 144 Appendix H.3 Required facilities queueing theory LCY ...... 144 Appendix H.4 Required facilities queueing theory LAS ...... 145 Appendix I Required facilities simulation ...... 146 Appendix I.1 Required facilities simulation AMS ...... 146 Appendix I.2 Required facilities simulation HND ...... 147 Appendix I.3 Required facilities simulation LCY ...... 147 Appendix I.4 Required facilities simulation LAS ...... 148 Appendix J Difference required facilities ...... 149 Appendix J.1 Difference required facilities AMS ...... 149 Appendix J.2 Difference required facilities HND ...... 150 Appendix J.3 Difference required facilities LCY ...... 150 Appendix J.4 Difference required facilities LAS ...... 151

Marco Groot, BSc 8

Optimize Landside Airport Operations Using A Discrete Event Simulation

List of abbreviations AMS Amsterdam Airport Schiphol ANA All Nippon Airways APC Automatic Passport Control ASK Available Seat Kilometre BHS Baggage Handling System DCS Departure Control System HND Tokyo International Airport Haneda IATA International Air Transport Association ICAO International Civil Aviation Organization JAL Japan Airlines KLM Royal Dutch Airlines (Koninklijke Luchtvaart Maatschappij) LAS McCarran International Las Vegas LCY London City Airport PLF Passenger Load Factor RFID Radio Frequency Identification RPK Revenue Passenger Kilometre SSCP Security Screening Checkpoint TSA Transportation Security Administration

Marco Groot, BSc 9

Optimize Landside Airport Operations Using A Discrete Event Simulation

List of figures Figure 1 Serviced check-in process flow (Miller, 2003) ...... 16 Figure 2 Self-service process flow diagram (Miller, 2003) ...... 17 Figure 3 Limited serviced baggage drop-off flowchart...... 20 Figure 4 Typical SSCP layout and elements (TSA, 2006a) ...... 23 Figure 5 Reason for travelling (Schiphol Group, 2016a) ...... 29 Figure 6 Purpose of travel for passengers by air in Japan (Nepal, et al., 2006) ...... 31 Figure 7 Total number of passengers by air travel in Japan (Trading Economics, 2016)...... 32 Figure 8 Purpose of visiting Las Vegas (GLS Research, 2014) ...... 36 Figure 9 Passenger arrival pattern for first class passengers (Chun & Mak, 1999) ...... 39 Figure 10 Passenger arrival pattern for economy class passengers (Chun & Mak, 1999) ...... 39 Figure 11 Arrival pattern intercontinental and European flights (Ashford, et al., 2013) ...... 40 Figure 12 Aircraft departures ...... 43 Figure 13 Aircraft departures intercontinental ...... 44 Figure 14 Aircraft departures continental ...... 44 Figure 15 Available seats ...... 46 Figure 16 Average number of seats per flight ...... 46 Figure 17 Passenger arrival distributions ...... 50 Figure 18 AMS simulation model ...... 71 Figure 19 HND simulation model ...... 73 Figure 20 LCY simulation model ...... 75 Figure 21 LAS simulation model ...... 77 Figure 22 Histogram run 4 initial simulation ...... 79 Figure 23 Histogram run 10 initial simulation ...... 80 Figure 24 Histogram double process times run 5 ...... 82 Figure 25 Histogram half process times run 3 ...... 83 Figure 26 Histogram 568 passengers per 15 minutes run 1 ...... 84 Figure 27 Histogram 1415 passengers per 15 minutes run 2 ...... 85 Figure 28 Histogram run 3 initial simulation ...... 87 Figure 29 Histogram run 4 initial simulation ...... 91 Figure 30 Passenger profiles single travelling passengers ...... 96 Figure 31 Passenger profiles groups of 2 or 3 persons ...... 97 Figure 32 AMS simulation using multi-server atoms ...... 100

Marco Groot, BSc 10

Optimize Landside Airport Operations Using A Discrete Event Simulation

List of tables Table 1 Centralized screening (Ashford, et al., 2013)...... 24 Table 2 Decentralized screening (Ashford, et al., 2013) ...... 24 Table 3 Category rules for business/leisure passengers ...... 26 Table 4 Chosen airports and covered requirements ...... 27 Table 5 Runways AMS (Schiphol Group, 2016a) ...... 28 Table 6 Haneda runways (JSC/JAA, 2016) ...... 30 Table 7 Operational hours London City airport runway (London City Airport, 2016c) ...... 33 Table 8 LAS runways (Federal Aviation Administration, 2016) ...... 35 Table 9 Selected days ...... 37 Table 10 Percentage passenger arrivals at check-in prior to flight (IATA, 2004) ...... 40 Table 11 Passenger profiles ...... 41 Table 12 Passenger profile arrival distribution minutes prior to departure (in percentage)..... 42 Table 13 Overview of aircraft departures ...... 45 Table 14 Solutions of departure process solutions ...... 52 Table 15 Profiles for groups of 1 person ...... 61 Table 16 Profiles for groups of 2 persons ...... 61 Table 17 Profiles for groups of 3 persons ...... 61 Table 18 Passenger profiles speed and standard deviation ...... 63 Table 19 Results field observation (James, 1953) ...... 64 Table 20 Percentage travel purpose per airport ...... 65 Table 21 Percentage of occurrence baggage types ...... 65 Table 22 Overview equipment departure terminals AMS ...... 70 Table 23 Passenger type per terminal ...... 72 Table 24 Overview equipment departure terminals HND ...... 73 Table 25 Overview equipment departure terminal LCY ...... 75 Table 26 Overview equipment departure terminals LAS ...... 76 Table 27 Time values initial simulation runs 1 through 5 ...... 79 Table 28 Time values initial simulation runs 6 through 10 ...... 79 Table 29 Setup variables simulation scenarios...... 81 Table 30 Passengers through T2 “KLM Non-Schengen 0%” ...... 81 Table 31 Passengers through T3 “Rest Non-Schengen 0%” ...... 81 Table 32 Time values double process times ...... 82 Table 33 Time values half process times ...... 83 Table 34 Time values 568 passengers per 15 minutes ...... 84 Table 35 Time values 1415 passengers per 15 minutes ...... 85 Table 36 Time values initial simulation runs 1 through 5 ...... 86 Table 37 Time values initial simulation runs 6 through 10 ...... 86 Table 38 Setup variables simulation scenarios...... 87 Table 39 Passengers through international terminal “International 0%” scenario ...... 88 Table 40 Passengers through domestic T1 terminal “T1 0%” scenario ...... 88 Table 41 Passengers through business class desks and security “Travel Class 0%” scenario . 88 Table 42 Passengers through terminals and kiosks “Internet check-in 100%” scenario ...... 88 Table 43 Setup variables simulation scenarios LCY...... 89 Table 44 Time values initial simulation runs 1 through 5 ...... 89 Table 45 Time values initial simulation runs 6 through 10 ...... 89 Table 46 Passengers through business class desks “Travel Class 0%” scenario ...... 90 Table 47 Passengers through internet check-in “Internet check-in 0%” scenario ...... 90 Table 48 Time values initial simulation runs 1 through 5 ...... 91

Marco Groot, BSc 11

Optimize Landside Airport Operations Using A Discrete Event Simulation

Table 49 Time values initial simulation runs 6 through 10 ...... 91 Table 50 Setup variables simulation scenarios LAS ...... 92 Table 51 Passengers through T3 “T1 100%” scenario ...... 92 Table 52 Business class passengers “Travel Class 0%” scenario ...... 92 Table 53 Passengers through internet check-in “Internet check-in 0%” scenario ...... 92 Table 54 Rejected values verification runs ...... 93 Table 55 Results verification passenger profiles implementation ...... 98 Table 56 Check-in optimal throughput overview ...... 101 Table 57 Baggage drop-off optimal throughput overview ...... 101 Table 58 Security access optimal throughput overview ...... 102 Table 59 Security optimal throughput overview ...... 102 Table 60 Immigration optimal throughput overview ...... 103 Table 61 Optimal waiting time standards for processing facilities (IATA, 2014b) ...... 104 Table 62 Average difference between required number of facilities simulation models and queueing theory ...... 107 Table 63 Peak time periods for which the results of the simulation models are within the allowed error range ...... 108 Table 64 Square meter comparison ...... 108

Marco Groot, BSc 12

Optimize Landside Airport Operations Using A Discrete Event Simulation

Introduction In 2014 the International Air Transport Association (IATA) released a 20-year passenger growth forecast (IATA, 2014a). This report forecasts an annual worldwide growth of 4.1%, which holds that in 2034 the passenger number will reach 7.3 billion (compared to 3.3 billion in 2014). When the passenger number is expected to double in the next 20 years, it is evident that airports need to take measures to cope with the increasing number of passengers. Additional capacity can be created by adding volume to the systems or by running the operations more efficient. This project will confine itself to increasing the efficiency.

Several studies have been performed on airport terminals, but limited research has been conducted on the departure process (check-in, baggage drop-off, security check and immigration) aimed at optimizing the processes to maximize the passenger throughput. This thesis will focus on optimizing the landside airport operation for departing passengers by looking at the number of passengers which can be processed per hour. This depends on a number of factors, such as passenger profiles, passenger arrival distributions, process time at check-in, baggage drop-off, security check and immigration, airport characteristics and level of service the airport aims to provide.

The foundation of the thesis lies with the research questions. The main question to be answered holds:

How can the landside airport operations be optimized in order to facilitate more passengers per hour?

In order to answer this question, also a number of sub questions needs to be answered. These questions are: - What kind of landside airport operations exists for the departure process? - Which methods are available to perform the landside airport operations? - How do passengers respond to the different methods and new technologies for performing the departure processes? - What are the best methods to use when considering process times? - How do the different methods interact with each other and does this influence the overall performance of the entire departure process? - In what way is the created model able to cope with changes in passenger profiles, flight schedules, new methods and disruptions?

During the thesis different solutions of check-in, baggage drop-off, security checks and immigration will be investigated. For the chosen airports discrete event simulation models are created. At first the current layout of the airports are simulated to verify the models. Second, these models are adjusted in order to perform an optimization of the landside operations.

Marco Groot, BSc 13

Optimize Landside Airport Operations Using A Discrete Event Simulation

This thesis will start off with a description of the different solutions for check-in, baggage drop- off, security checks and immigration. In Chapter 2 the airport selection is substantiated. Once the airports are selected, the passenger arrival schedule for each airport is created. This can be found in Chapter 3. In Chapter 4 a detailed research to solutions for check-in, baggage drop- off, security access, security check and immigration is stated, followed by passenger profiles in Chapter 5. Chapter 6 describes the simulation models with the current layout of the selected airport. The results of the simulation runs can be found in Chapter 7. In Chapter 8 the passenger profiles are included in the simulation models, while in Chapter 9 the optimal station solutions are determined and applied in the simulation models. Chapter 10 describes the optimization process. The discussion regarding the results of the optimization process are stated in Chapter 11. Finally, recommendations for improving the performed research and further study can be found in Chapter 12.

Marco Groot, BSc 14

Optimize Landside Airport Operations Using A Discrete Event Simulation

1 Landside processes Every airport can be divided into two areas where all the airport processes take place. These are the airside and the landside areas. The airside of an airport contains the processes in which there is a direct interaction with the aircraft. The landside processes involve all processes where the aircraft is not involved. The landside operations of an airport can be categorized in three sections (Ashford, et al., 2013). These are: 1. Transportation from and to the airport with the exception of air transportation. 2. Passenger arrival and departure processes. For arriving passengers this means all processes when the passenger enters the terminal at the gate until leaving the terminal at one of the ground transportation exits. For departing passengers this means all processes between when the passenger enters the airport terminal up until the gate where the aircraft is parked. 3. Non-passenger processes such as catering, mail and cargo processing.

This thesis will only focus at the passenger departing processes. These include the check-in, baggage drop-off, security access, security check and immigration. A detailed description of these processes can be found in this chapter. 1.1 Check-in process In the last 20 years the check-in in process has changed significant. These changes involve shifting the check-in process from the conventional method to self-service check-in kiosks and now even check-in via the internet or mobile phone. This chapter will elaborate on these changes. 1.1.1 Serviced check-in process The serviced check-in process is the process in which the passenger receives a paper boarding pass from an agent at a serviced check-in counter. Serviced check-in counters are dedicated to an airline or airline group. For example, Bangkok Airways check-in counters can be found at the passenger terminal building row F (Bangkok Airways, 2015) and the check-in counters of Tiger Airways at Singapore Changi Airport terminal 2 are at rows 11 and 12 (Tiger Airways, 2015).

At AMS the check-in counters are used by airline groups. Passengers can check-in for flights operated by Lufthansa, Austrian Airways and Swiss Airways at row 1. The check-in counters which are operated by KLM can also be used to check-in for flights of Air France and Delta Airlines.

Serviced check-in counters combine two processes of the departure passenger flow, namely check-in and baggage drop-off. The service agent prints the boarding pass for the passenger, but also prints the baggage labels. These labels are attached to the baggage and transported to the Baggage Handling System (BHS). The flowchart of the serviced check-in process can be found in Figure 1.

Marco Groot, BSc 15

Optimize Landside Airport Operations Using A Discrete Event Simulation

Agent calls Passenger Passenger waits Passenger passenger to enters check-in in queue until arrival at airport ticketing queue called by agent position

Agent looks up Passenger Agent assigns Agent prints reservation in provides paper seats to boarding passes computer ticket & ID to passenger system agent

Bags are tagged Agent gives Customer and sent to boarding pases proceeds to baggage and tickets to security and handling passenger gate

Figure 1 Serviced check-in process flow (Miller, 2003) 1.1.2 Self-service check-in kiosk Compared to the serviced check-in process the major difference with the self-service check-in is that the passenger does not require the assistant of an agent to check-in. All the steps which are performed by an agent at a serviced check-in counter are now performed by the passenger himself.

Self-service check-in is used since 1995, when Continental Airlines installed the first machines that enabled passengers to check-in themselves (Miller, 2003). Since then a large number of airlines and airport have also installed self-service check-in kiosks. In 2006, a survey was held under 2,869 leisure airline passengers, of which 86% had used a self-service check-in kiosk (Airport Technology, 2008).

Initiated by IATA, in 2003 the Common Use Self Service (CUSS) platform was created (ACRP, 2008). This platform allows the check-in of multiple airlines on a single kiosk. In Figure 1 the flow diagram of the serviced check-in process is shown. The flow changes when a passenger uses the self-service check-in kiosk. The flow diagram belonging to the self-service check-in method can be seen in Figure 2.

Marco Groot, BSc 16

Optimize Landside Airport Operations Using A Discrete Event Simulation

Figure 2 Self-service process flow diagram (Miller, 2003)

Marco Groot, BSc 17

Optimize Landside Airport Operations Using A Discrete Event Simulation

1.1.3 Internet check-in In sections 1.1.1 and 1.1.2 two check-in methods were discussed. Although there are situations in which the self-service kiosks are located outside of the terminal building (Finnair, 2015), both of these methods take place inside the airport terminal. However, the check-in process can also be performed outside of the airport terminal, as passengers can check-in via the internet (mostly 24 hours prior to departure). To simplify the check-in process, more and more airlines have solutions in which a passenger is automatically checked in. In this case, the passenger receives the boarding pass via a push message or email (Ghee, 2011). For passengers with a smartphone, airlines have developed apps. With these apps, passengers can book flights, check their flight status and check-in.

Internet check-in has some major advantages compared to the conventional check-in or check- in using the a kiosk. Passengers can check-in at a time which is convenient for them (or are even checked in automatically), do not have to wait in line for a counter or kiosk and spent less time at the airport terminal.

Especially this last advantage is important for the airport authorities. With the expected growth in passenger numbers, the level of service will decline if the check-in process does not change. When more passengers check-in outside of the airport terminal, more passengers can be processed with equal availability of check-in counters and kiosks.

A downside of internet check-in is that a passenger still needs to obtain a baggage label if the passenger has baggage to check-in. Baggage labels are not provided when a passenger checks in via the internet or mobile app. The baggage drop-off process will be discussed in the next chapter.

Marco Groot, BSc 18

Optimize Landside Airport Operations Using A Discrete Event Simulation

1.2 Baggage drop-off process Every passenger travels with baggage. This can be either carry-on baggage or hold baggage. Carry-on baggage is baggage which the passenger brings on the aircraft himself and is stored in the overhead compartments or underneath the seat of the passenger.

Hold baggage is checked in by the passenger and is loaded on the aircraft via the Baggage Handling System (BHS) of the airport. The hold baggage is stored in the baggage compartment of the aircraft, normally located underneath the passenger compartment. Passengers who want to store their baggage in the baggage compartment of the aircraft need to check in their baggage before going through security and customs. This chapter will discuss the different methods in which a passenger can check in hold baggage. 1.2.1 Full serviced baggage drop-off The full serviced method of check-in is discussed in section 1.1.1. Full serviced check-in also includes baggage drop-off. This means the full serviced baggage drop-off is actually the same as the full serviced check-in. The passenger hands the passport to the service agent, the agent will print a baggage label, attach the label to the bag and send the bag to the BHS. The flowchart of the full serviced check-in and baggage drop-off can be found in Figure 1. 1.2.2 Limited serviced baggage drop-off The limited serviced baggage drop-off counter is similar to the full serviced baggage drop-off. The only difference is that at the limited serviced baggage drop-off counter only baggage can be dropped off. It is not possible to check-in at these desks. Therefore a passenger needs to be checked in before being able to use the limited serviced baggage drop-off. Check in can be done by the methods described in sections 1.1.2 and 1.1.3.

The limited serviced baggage drop-off counters are serviced check-in counters in which the check-in is disabled. This means every baggage drop-off counter has the same dimensions and functionality as a serviced desk. Also, every serviced baggage drop-off counter is staffed.

For the limited serviced baggage drop-off method the flowchart from a passenger entering the airport until the passenger proceeds to customs and security is similar to the flowchart depicted in Figure 1. The flowchart for the limited serviced baggage drop-off counter is depicted in Figure 3. This flowchart is created by adjusting the flowchart of Figure 1.

Marco Groot, BSc 19

Optimize Landside Airport Operations Using A Discrete Event Simulation

Passenger Passenger Passenger waits Passenger enters baggage checks-in using in queue until arrival at airport dop-off counter kiosk called by agent queue

Agent looks up Passenger Agent calls Bags are tagged reservation in provides passenger to and sent to BHS computer boarding pass baggage drop- system and ID to agent off counter

Passenger Agent gives proceeds to claimtag to security and passenger gate

Figure 3 Limited serviced baggage drop-off flowchart 1.2.3 Retrofit baggage drop-off Retrofit baggage drop-off counters are solutions in which the serviced baggage drop-off counters are modified such that passengers can drop off their baggage without the assistance of airline or airport staff. These retrofit solutions are relatively easy to implement as they are added to the serviced baggage drop-off counters. A list of companies which provides retrofit solution is stated in Appendix A.1. This table also includes at which airports these solutions have been implemented (as of October 2015).

The common difference between the retrofit solutions compared to the serviced baggage drop- off solutions is a reduction in staff. At the serviced baggage drop-off counters there is an agent stationed at each counter. For the retrofit solution, this is not needed. The passenger performs all actions for a successful drop-off, which eliminates the need of an agent at each station. The only agents around the baggage drop-off counters are agents who check if the passenger has the required documents and is checked in or who assist passengers with the baggage drop-off.

Most of the solutions offered are suitable for both one-step and two-step operations. With one- step operation, the baggage label is printed at the baggage drop-off location. This means the passenger performs all baggage operations at the same location. For two-step operation, the baggage label is printed at the check-in kiosk. After the passenger has obtained the baggage label, the passenger needs to go to the baggage drop-off location where the baggage is dropped off. The passenger needs to attach the baggage label either at the check-in kiosk or at the baggage drop-off location.

Marco Groot, BSc 20

Optimize Landside Airport Operations Using A Discrete Event Simulation

In the two-step operation, the baggage drop-off process is split in two locations. The benefit is that each location is occupied for a shorter time. This means the queuing time at a single location is shorter. However, the passenger needs to queue for both locations, so the total queuing time might not be shorter.

Also, passengers always need to go to a kiosk to obtain a baggage label. When a passenger needs to go to a kiosk for the check-in process, this is an effective location to obtain the baggage label. For passengers who have checked in before arriving at the airport there is no need to go to a check-in kiosk. In the two-step operation, the passenger is still forced to proceed to a kiosk. In the one-step operation, this passenger can walk straight to the baggage drop-off location. Therefore, the one-step operation is more efficient for passengers who are already checked in when arriving at the airport. 1.2.4 Integrated baggage drop-off With the integrated baggage drop-off solutions, the passenger can perform the entire process without assistance from an agent. In that perspective, the integrated solutions are comparable to the retrofit solutions. The difference between the two is the retrofit solution is an adjustment of the check-in counters, while the integrated solutions are fully integrated with the BHS.

When integrated solutions are installed in existing airport terminals, the check-in counters are completely removed. Different companies have developed integrated baggage drop-off solutions. A list of companies offering integrated solutions and the locations where these solutions are installed (as of October 2015) can be found in Appendix A.2.

The implemented baggage drop-off solution is more expensive and complex to integrate, as the old check-in counters need to be removed. Also, the integrated solutions need to be communicating with the BHS, while the check-in counters already have this connection.

The advantage of the integrated solution is that the entire airport terminal can be designed for a more efficient passenger flow. The integrated baggage drop-off solutions can be used in a one- step operation as well as in a two-step operation (BagDrop Systems BV, 2015).

With integrated baggage drop-off solutions, a reduction in agents can be achieved compared to the baggage drop-off solution from sections 1.2.1 and 1.2.2. The integrated solutions are self service solutions, which holds passengers can drop off their baggage without assistance from an agent. Just as with the retrofit solutions, agents are only present to verify if the passenger has the correct documents and to assist passengers when needed.

The newest development in the airline industry is to use more RFID instead of the 2D barcodes (Bite, 2010). Qantas Airways has developed personal tags which passengers can attach to their baggage at home (Qantas Airways, 2015). By using these tags, a passenger spends less time in the airport terminal for baggage drop-off as it is not necessary to print a baggage label. The integrated baggage drop-off solutions are suitable to work with RFID baggage tags (Swedberg, 2011).

Marco Groot, BSc 21

Optimize Landside Airport Operations Using A Discrete Event Simulation

1.3 Security In the previous two sections the check-in and baggage drop-off were covered. In the departure process flow, there are two more stations the passenger has to pass before reaching the aircraft. These are security and immigration. The security area will be discussed in this section, while the immigration can be found in the next section. 1.3.1 Security measures Airports are one of the most heavily secured structures in the world. Terrorists tend to attack on locations where large amount of people gather and which have a high disruptive or economic impact. Therefore, airports and aircraft face the threat of a terrorist attack every day. The two most well-known terrorist acts involving aircraft are the Lockerbie disaster (BBC, 1988) and the attacks of September 11, 2001 (BBC, 2001).

The security screening at airports at aimed at preventing passengers from taking any material on board which can be used for “unlawful interference with civil aviation” (ICAO, 2006). Examples of this are weapons, explosives of toxics.

After the attacks of September 11, 2001, the Aviation and Transportation Security Act was signed by the president of the United States of America (United State Congress, 2001). As a consequence, the Transportation Security Administration (TSA) was established. This changed the security measures at airports drastically. A number of security measures which are enforced by the TSA are security screening of all checked baggage, ban on use of metal cutlery and checking of laptops, jackets and shoes. Liquids can only be brought on board of aircraft as carry-on baggage when packed in containers not exceeding 100 ml. Also, these containers have to be carried in a transparent and re-sealable 1 litre plastic bag (Tan, 2007).

The TSA has written recommendations regarding airport security in which guidelines are stated for the design of Security Screening Checkpoints (SSCP) (TSA, 2006a). The typical SSCP layout and elements as depicted in this document can be found in Figure 4. The most important elements of the SSCP are the metal detector and the carry-on baggage X-ray machine. At some airports, the metal detectors are replaced by full body security scans (Schiphol, 2015a).

Marco Groot, BSc 22

Optimize Landside Airport Operations Using A Discrete Event Simulation

Figure 4 Typical SSCP layout and elements (TSA, 2006a) 1.3.2 Centralized versus decentralized security There are two forms of security screenings which can be carried out. These are called centralized and decentralized. The difference between the two is the location of the screening. An airport has centralized screening when the sterile area behind the screening serves multiple boarding gates. When the screening is carried out just before boarding at the boarding gate, this is called decentralized boarding (Ashford, et al., 2013).

Each of the two forms of screening has a set of advantages and disadvantages. For the centralized screening these are listed in Table 1. Table 2 states the advantages and disadvantages of the decentralized screening.

Marco Groot, BSc 23

Optimize Landside Airport Operations Using A Discrete Event Simulation

Table 1 Centralized screening (Ashford, et al., 2013)

Advantages Disadvantages Favoured by passengers Requires search of staff entering the sterile area Minimum security staff and equipment Food, merchandize and other materials must needed to process a given number of be scrutinized passengers Encourages passenger spending in Separation of arriving and departing restaurants and other commercial areas passengers is difficult to achieve Easier to concentrate security personnel in Only one standard of search is possible, one location whereas High-risk flights may require a more thorough search Surveillance of passengers is difficult at busy airports

Table 2 Decentralized screening (Ashford, et al., 2013)

Advantages Disadvantages The separation and surveillance problems Requires earlier call forward of passengers are eliminated Risk of collusion of staff and potential Results in loss of spending time and revenue attackers is minimized from restaurants, bars, shops, etc. Allows special measures to be taken on Involves long waiting times in crowded gate high-risk flights lounges with no amenities Requires more personnel and more equipment to process a given number of passengers Creates problems of search team availability if flight schedules go awry Makes an armed police presence difficult depending on the number of gates in use at one time Allows potential terrorist to get close to aircraft before search and risk of access to the apron is much raised due to emergency exits Enables terrorists to identify specific passenger groups and assembles them in attackable queues and groups Gate lounges must be enlarged to accommodate

Marco Groot, BSc 24

Optimize Landside Airport Operations Using A Discrete Event Simulation

1.4 Immigration The last area of the departure process which have to be discussed is the immigration. Although not every passenger has to go through this area, it is a vital part of the international departure process.

Every international travelling passenger needs to pass immigration. Passengers on domestic flights do not need to show their passport as they stay without their own country. Also passengers travelling within the Schengen countries do not have to go through the immigration process (Schiphol, 2015b). These passengers can proceed to the boarding gate immediately after passing through security.

The government of the country in which the airport is located is responsible for customs. These agencies check if the passenger has a valid document for entering other countries, is wanted by the police or still has taxes to pay (Schiphol, 2015d). 1.4.1 Serviced immigration With the serviced immigration every counter is staffed by an officer of the customs agency. The customs officer receives the passport from the passenger. Via a scan of the passport (or by manually entering the passenger’s information) the officer receives the passengers’ information from the computer. The officer then decides, based on the information from the computer, to let the passenger pass or escort the passenger for extra questioning.

With this method, the decision to let a passenger pass or not is made by personal interpretation of the data. This might cause passengers to be held unnecessary. Or passengers can pass while they were not allowed according to the rules. Also, since every counter is staffed, this method includes high personnel costs. 1.4.2 Self-service immigration In order to prevent mishandling passengers, reduce costs and shorten the queuing times the serviced immigration is being replaced by self-service immigration. There are 36 airports in which the Automated Passport Control (APC) by the U.S. Customs and Border Protection is installed for passengers who will enter the United States of America (U.S. Customs and Border Control, 2015). The self-service facilities are also installed in Japan (Ministry of Justice Japan, 2012), Germany (German Federal Police, 2015), The Netherlands (Schiphol, 2015c) and Australia (Passenger Self Service, 2015).

Passengers using the self-service immigration need to place their passport on the scanner. When a valid passport is presented, the first doors slide open and the passengers can proceed inside the self-service unit. Once the passenger is within the unit, the first doors close, after which a scan is made of the passengers face. This scan is compared to the photo in the passport. Where there is a match and there is no reason to refuse the passenger leaving the country, the second doors open and the passenger can proceed to the boarding gate. In case the scan of the passengers face does not match the photo on the passport, or when the passenger needs to be interview by the customs agency, the passenger needs to proceed to a serviced counter where the passenger will be checked by a customs officer.

According to (Global Gateway Alliance, 2015) the wait times has dropped with 22% after the installation of the self-service immigration. This holds that the average wait time at Terminal 4 of JFK airport in New York has decreased by 8.65 minutes.

Marco Groot, BSc 25

Optimize Landside Airport Operations Using A Discrete Event Simulation

2 Airport selection In this chapter the set of airports for the thesis will be chosen. The selection is based on a number of requirements. These requirements are listed in section 2.1. Sections 2.2 through 2.5 will cover the chosen airports and explain how these airports cover the requirements. 2.1 Requirements In order to perform an optimization of the terminal space, a selection of airports is made. The airports chosen should be based on differences in types of passengers. These differences are in terms of the origin and destination of the passenger, the purpose for travel and the total number of passengers in a terminal.

According to (Transportation Research Board, 2011) international passengers arrive at the airport earlier before the departure time compared to domestic passengers. Also, international passengers tend to have more baggage compared to domestic passengers. When business passengers are compared to leisure passengers, business passengers arrive closer to the departure time than leisure passengers. Leisure passengers travel with larger amounts of hold baggage. Business passengers tend to travel with only carry-on baggage (Transportation Research Board, 2011). In order to have a good comparison between business and leisure passengers, the selected airports should include an airport with mostly business passengers, an airport with mostly leisure passengers and an airport with a mix of business and leisure passengers. In order to determine which of these three passenger profiles is applicable to the airport, the ratio between business passengers over leisure passengers is considered. The three categories and their limits can be found in Table 3. In words, the categories can be seen as:

- At least twice as much business passengers as leisure passengers means mostly business passengers - At least twice as much leisure passengers as business passengers means mostly leisure passengers - All other ratios between business and leisure passengers means a mix of business and leisure passengers

Table 3 Category rules for business/leisure passengers

Category Ratio Mostly business passengers Larger and equal to 2.00 Mix of business and leisure passengers Larger to 0.50 and smaller to 2.00 Mostly leisure passengers Smaller and equal to 0.50

Marco Groot, BSc 26

Optimize Landside Airport Operations Using A Discrete Event Simulation

In a research performed by (Solak, et al., sd) a relationship was found between the walking speed within a terminal and the number of passengers per square meter. According to this research the walking speed reduces when the number of passengers increases. Therefore, the number of passengers in a terminal has an influence on the behaviour of passengers. Every year, Skytrax ranks the best airports worldwide. Some of the categories are based upon the annual total number of passengers (arriving, departing and transfer passengers). There are seven categories regarding number of passengers per year. The top category is “best airport for passenger numbers above 50 million”, while at the other end the category is “best airport for passenger numbers under 5 million” (Skytrax, 2016a). Using the same limits as Skytrax, for this research the distinction is made between large (above 50 million passengers annually) and small airports (under 5 million passengers annually). Airports with passenger numbers between 5 and 50 million passengers per year are considered medium sized airports.

Considering the above statements, a list of requirements for the set of airports to be chosen is formed. These requirements can be found below.

Requirements for set of airports 1. Large airport ( > 50 million passengers) 2. Medium airport ( > 5 million passengers and < 50 million passengers) 3. Small airport ( < 5 million passengers) 4. Dedicated international terminal 5. Dedicated domestic terminal 6. Mix of international and domestic in same terminal 7. Mostly business passengers 8. Mix of business and leisure passengers 9. Mostly leisure passengers 10. Airports from at least three different continents

In Table 4 the chosen airports can be found. As can be seen from Table 4 the airports chosen are from Europe, Asia and North America. This covers the requirement which states the chosen airports should be from at least three different continents. The airports are discussed in more detail in the next sections of this chapter.

Table 4 Chosen airports and covered requirements

Airport Airport Requirements covered code Amsterdam Airport AMS Large airport Schiphol Mix of international and domestic in same terminal Mix of business and leisure passengers Haneda International HND Large airport Tokyo Dedicated international terminal Dedicated domestic terminal Mix of business and leisure passengers London City LCY Small airport Mix of international and domestic in same terminal Mostly business passengers McCarran International LAS Medium airport Las Vegas Mix of international and domestic in same terminal Mostly leisure passengers

Marco Groot, BSc 27

Optimize Landside Airport Operations Using A Discrete Event Simulation

2.2 Amsterdam Airport Schiphol Amsterdam Airport Schiphol is the main airport of The Netherlands and is located 10 kilometres southwest of Amsterdam city centre (Google Maps, 2016a). AMS was voted “Best Airport in Western Europe” in 2014 by Skytrax (Skytrax, 2016a) and has won almost 200 awards in the last 30 years in the categories shopping, baggage, cargo and best airport (Schiphol Group, 2016d). The IATA airport code for Schiphol is AMS (IATA, 2016). 2.2.1 Runways and terminals AMS has 6 runways (Schiphol Group, 2016a). An overview of these runways can be found in Table 5.

Table 5 Runways AMS (Schiphol Group, 2016a)

Runway name Runway location Runway length Aalsmeer 18L – 36R 3,400 meter Buitenveldert 09 – 27 3,453 meter Kaag 06 – 24 3,500 meter Polder 18R – 36L 3,800 meter Schiphol Oost 04 – 22 2,014 meter Zwanenburg 18C – 36C 3,300 meter

There are three departure terminals at AMS, all located in one building. The departure terminals can be divided is four areas. These are reserved for check-in, security check, immigration checks and the lounge areas.

KLM Royal Dutch Airlines, is the largest airline at AMS. From the total of 450,000 aircraft movements on the airport in 2015, 220,000 were operated by KLM (Schiphol Group, 2016a). This means KLM needs a large check-in area to let all KLM passengers check-in. Therefore, the check-in areas of terminal 2 and half of terminal 1 is dedicated for KLM (together with SkyTeam partners). KLM operates terminal 2 for all non-Schengen flights and terminal 1 for all Schengen flights. The check-in area of the other half of terminal 1 is reserved for other airlines on Schengen flights. All passengers for non-Schengen flights and low-cost airlines can check-in at terminal 3.

On June 3rd, 2015, AMS switched to central security for departing passengers. There are five central security checkpoint located at the airport, which eliminates the need for security at the gates (Schiphol Group, 2016b). Three of the five security areas are for departure passengers, the other two for transfer passengers. The three security areas for departing passengers are located in terminal 1 (on the first floor), terminal 2 (on the second floor) and terminal 3 (on the second floor).

In terminal 1 only passengers for destinations in the Schengen area check-in and pass through security, there is no need for an immigration check. Passengers departing from terminals 2 and 3 are required to pass immigrations. Therefore, an immigration area can be found in these two terminals and are located after the security check.

Passengers who have cleared the check-in, security and (if needed) immigrations end up in the lounge area of the departure terminals. In this area shops, bars and restaurants can be found. This area functions mainly as the route towards the departure gates.

Marco Groot, BSc 28

Optimize Landside Airport Operations Using A Discrete Event Simulation

As the departure terminals are located in one building, passengers can walk easily between all three terminals. This can be done before security check, but also behind the security check. Passengers walking from departure lounge 1 to departure lounge 2 are required to pass an immigration area. 2.2.2 Passenger details According to (Schiphol Group, 2016a), 58.2 million passengers travelled through AMS in 2015. Of these 58.2 million passengers, 39.5% were transfer passengers and 60.5% origin and destination passengers. From a reason for travelling survey, it can be stated that 31% were business passengers and 46% were leisure passengers (Schiphol Group, 2016a) (see Figure 5).

Figure 5 Reason for travelling (Schiphol Group, 2016a) 2.2.3 Requirements covered From the requirements stated at the beginning of this chapter, AMS covers the following requirements. As 58.2 million passengers travelled through AMS in 2015, AMS falls under large airports ( > 50 million passengers).

All flights at AMS are to a destination outside The Netherlands. However, due to the Schengen convention, all flights to Schengen destinations can be considered as domestic flights (as there is no need for an immigration check). Although there is one dedicated terminal for Schengen flights and two terminals for non-Schengen flights, AMS fulfils the requirement of a mix of international and domestic in same terminal. This is since all terminals are located in one building and passengers can walk from one terminal to the other. As an example, passengers for a low cost airline to a Schengen destination need to check-in in terminal 3 and then proceed to terminal 1 for the security check.

Of all passengers, 31% are business passengers and 46% are leisure passengers. This means the ration business over leisure is 31/46 or 0.67. This means the airport has a mix of business and leisure passengers.

Marco Groot, BSc 29

Optimize Landside Airport Operations Using A Discrete Event Simulation

2.3 Tokyo International Airport Haneda The official name of the airport is Tokyo International Airport, but is mostly named Haneda International Airport to prevent confusion with Tokyo’s other airport, Narita. The IATA airport code for Haneda is HND (IATA, 2016).

HND is located 15 kilometres south of the Japanese capital Tokyo (Google Maps, 2016b). The airport is one of the best airports in the world, being in the top 3 of several Skytrax award categories in 2014 (Skytrax, 2016a). HND has been awarded with a 5-star rating for 2 consecutive years by Skytrax (Skytrax, 2016c) and is listed as the 5th best airport worldwide in 2015 (Skytrax, 2016b). 2.3.1 Runways and terminals HND has four runways. The two main runways are the A- and C-runways. The B- and D- runways are mainly used in south wind operations (JSC/JAA, 2016). An overview of the runways can be found in Table 6.

Table 6 Haneda runways (JSC/JAA, 2016)

Runway name Runway location Runway length A-runway 16R – 34L 3,000 meter B-runway 04 – 22 2,500 meter C-runway 16L – 34R 3,000 meter D-runway 05 – 23 2,500 meter

HND has three terminal buildings. These are Domestic Terminal 1, Domestic Terminal 2 and International Terminal (Japan Airport Terminal, 2016b), (Tokyo International Air Terminal, 2016b).

The domestic terminals are located between the A- and C-runways. Domestic terminal 1 is next to the A-runway, while domestic terminal 2 is facing the C-runway. The domestic terminals are connected via a underground subway station (belonging to the Keikyu line). One end of the station leads to terminal 1 while the other end of the station leads to terminal 2. Next to the subway station is a walkway, which allows passengers to walk between terminals 1 and 2. Passengers can also travel between the two domestic terminals using the monorail or a free shuttle bus (Japan Airport Terminal, 2016a).

Domestic terminal 1 hosts three airlines. These are Japan Airlines (JAL), Skymark and StarFlyer. The airlines All Nippon Airways (ANA), Air Do and Solaseed Air have their operations in domestic terminal 2 (Skytrax, 2016c).

The international terminal is located on the opposite side of the A-runway with regards to domestic terminal 1 (JSC/JAA, 2016). Passengers can travel between the domestic terminals and the international terminal using the subway (Keikyu line), the monorail or a free shuttle bus (Tokyo Internation Air Terminal, 2016a).

Marco Groot, BSc 30

Optimize Landside Airport Operations Using A Discrete Event Simulation

2.3.2 Passenger details HND is one of the busiest airports in the world regarding passenger numbers. In 2012 there were 58,8 million passengers in the two domestic terminals and 7,9 million passengers in the international terminal (Civil Aviation Bureau Japan, 2013). This brings the total number of passengers at HND at 66,7 million passengers in 2012.

As for the purpose of travel, between 1983 and 2003 the number of leisure passengers has grown significantly (see Figure 6) (Nepal, et al., 2006). When looking at the total number of passengers by air travel between the years 1983 and 2016 in Figure 7, it can be seen that the number of passengers in 2015 is approximately the same as in 2001 (Trading Economics, 2016). If the graphs of Figure 6 and Figure 7 are combined, it can be assumed that the purpose of travel, relative to the total number of passengers, in 2015 is the same as in 2001. Therefore it can be stated that in Japan 45% of all passengers are business passengers and 30% of all passengers are leisure passengers. These numbers are based on the total number of passengers in the country of Japan, so might not be applicable to HND. However, 60% of all domestic air travel passengers in Japan use HND either as origin or destination (Civil Aviation Bureau Japan, 2013). Since HND airport contributes to a significant amount (60%) to the data from Figure 6 and Figure 7, it can be assumed that the percentages found for business and leisure passengers can be used for Haneda airport.

Figure 6 Purpose of travel for passengers by air in Japan (Nepal, et al., 2006)

Marco Groot, BSc 31

Optimize Landside Airport Operations Using A Discrete Event Simulation

Figure 7 Total number of passengers by air travel in Japan (Trading Economics, 2016) 2.3.3 Requirements covered HND covers four of the requirements stated in section 2.1. First of all, the airport welcomes 66,7 million passengers per year. Since an airport is considered a large airport in this research when the number of passengers is larger than 50 million, HND is a large airport.

There are three terminal buildings, of which one is dedicated international and two are dedicated domestic. This means two requirements are covered, namely a dedicated international terminal and a dedicated domestic terminal.

Lastly, considering the purpose of travel, 45% are business passengers and 30% are leisure passengers. This means the ration business over leisure is 45/30 or 1.5. This means there is a mix of business and leisure passengers in all three terminals.

Marco Groot, BSc 32

Optimize Landside Airport Operations Using A Discrete Event Simulation

2.4 London City Airport London City Airport is one out of five airports in London, England (London & Partners, 2016). According to figures from (Civil Aviation Authority, 2016a), London City Airport is the smallest airport of these five. The small size of the airport makes it possible to be located close to the city centre, located 9 kilometres east of the Tower of London (Google Maps, 2016c). In 2014, the airport has won the award of “Best airport under 5 million passengers per year” (Skytrax, 2016a). The IATA airport code for London City is LCY (IATA, 2016). 2.4.1 Runways and terminals London City airport has only one runway. The runway has orientation 09 – 27 and is 1,199 meter. The runway has limited operational times, which are stated in Table 7 (London City Airport, 2016c).

Table 7 Operational hours London City airport runway (London City Airport, 2016c)

Day Operational hours Monday to Friday 6:30 – 22:00 Saturday 06:30 – 12:30 Sunday 12:30 – 22:00

LCY has one airport terminal. Departing passengers enter the terminal on the ground floor, where the check-in area and arrivals lounge are located. After check-in the passengers proceed to the first floor for the security check and further on to the gates for boarding (London City Airport Guide, 2016). 2.4.2 Passenger details As mentioned before, LCY is a small airport. In 2015, a total of 4,3 million passengers travelled to or from LCY (Civil Aviation Authority, 2016b). Since the airport is close to the city centre and due to the short check-in times (passengers with only hand luggage can check-in up to 15 minutes prior to departure (London City Airport, 2016a)) the airport is popular under business passengers. According to (London City Airport, 2016d) 63% of all passengers are travelling for business purposes and 37% are leisure passengers.

LCY does not have a transfer area. Therefore, transfer passengers need to clear arrivals and then proceed through the airport security via the departure lounge (London City Airport, 2016e). This research only focuses on passengers passing through the departure process. Since the transfer passengers at LCY are also passing through a departure process as all origin and destination departing passengers, the passenger numbers are considered to have no transfer passengers.

Marco Groot, BSc 33

Optimize Landside Airport Operations Using A Discrete Event Simulation

2.4.3 Requirements covered In 2015 around 4,3 million passengers travelled via LCY airport. This classifies the airport as a small airport since the total number of passengers is less than 5 million.

There is only one terminal and since the destinations from LCY are international and domestic destinations (London City Airport, 2016b) there is a mix of international and domestic passengers in the terminal.

Considering the purpose of travel, in the found literature (London City Airport, 2016d) only two purposes were classified. These were business and leisure. For all other airports in this thesis, there is a distinction between more than just business and leisure. Therefore, in order to compensate for only having two purposes at LCY, the found percentages for both business and leisure purposes are reduced for the classification of the type of passengers in the airport. The total of business and leisure combined for AMS, HND and LAS accounts for 80% of the total passengers. This leaves 20% for other purposes. Assuming 1/3 of this 20% was combined under “business” and the remaining 2/3 under “leisure” for LCY. This assumption is made since most of the other purposes for AMS, HND and LAS can be considered leisure instead of business. So, the percentage for business is reduced by 7% and leisure is reduced by 13%. This means the ratio business over leisure is 56/24 or 2.33. With this ration, this airport classifies as having mostly business passengers.

Marco Groot, BSc 34

Optimize Landside Airport Operations Using A Discrete Event Simulation

2.5 McCarran International Las Vegas The airport at the city of Las Vegas in the state of Nevada (United States of America) is named McCarran International and is located next to the world famous “The Strip”, the Las Vegas Boulevard (Google Maps, 2016d). In 2014, McCarran was ranked 6th as the world best airport with 40 to 50 million passengers per year (Skytrax, 2016a). The IATA airport code for McCarran International airport is LAS (IATA, 2016). 2.5.1 Runways and terminals LAS has four runways, which are placed as two sets of two parallel running runways. The details of the four runways can be found in Table 8 (Federal Aviation Administration, 2016). Table 8 LAS runways (Federal Aviation Administration, 2016)

Runway Length 01L – 19R 2,740 meter 01R – 19L 2,978 meter 07L – 25R 4,423 meter 07R – 25L 3,208 meter

There are two terminals at the airport, which are Terminal 1 and Terminal 3. Terminal 3 was opened in 2012 and replaced Terminal 2 (airport-technology.com, 2016).

Although there are two terminals, there are three buildings at the airport. Besides the buildings of terminals 1 and 3, there is also a satellite terminal. This is concourse D and can be reached using a people mover from terminals 1 and 3. Concourses A, B and C are located within the terminal 1 building (FlightStats, 2016b).

Terminal 1 only hosts domestic airlines. The destinations flown by these airlines are both domestic and international. The airlines which are located in terminal 1 are (McCarran International Airport, 2016d): - Allegiant Air - American Airlines - Delta Airlines - Omni Air - Southwest Airlines - Spirit Airlines

All foreign airlines and some domestic airlines are located in terminal 3. Also here, the flights can be both domestic as international. The domestic airlines in terminal 3 are (McCarran International Airport, 2016d): - Alaska Air - Frontier Airlines - Hawaiian Airlines - JetBlue Airways - Sun Country Airlines - United Airlines - Virgin America

Marco Groot, BSc 35

Optimize Landside Airport Operations Using A Discrete Event Simulation

2.5.2 Passenger details In 2015, a total of 45.3 million passengers travelled via LAS (McCarran International Airport, 2016b). These are the passengers combined in terminals 1 and 3. As can be seen in Figure 8, according to a research by (GLS Research, 2014), 61% of all passengers at LAS are leisure passengers. These are the passengers who have stated “Vacation/pleasure”, “Gambling” and “Wedding” as travel purpose. Combining the purposes “Convention/corporate meeting” and “Other business” gives 21% business passengers. According to (McCarran International Airport, 2016a) there were 5,2 million transfer passengers at LAS in 2015. This means 11.5% were transfer passengers and 88.5% were origin and destination passengers.

Figure 8 Purpose of visiting Las Vegas (GLS Research, 2014) 2.5.3 Requirements covered LAS can be classified as a medium sized airport. With 45.3 million passengers, the airport falls within the range of more than 5 million and less than 50 million passengers per year.

In both terminals passengers can check-in for domestic as well as international flights. Therefore, LAS has a mix of international and domestic passengers in the same terminal. The ratio business over leisure purpose is 21/61 or 0.34. Therefore LAS classifies as an airport with mostly leisure passengers.

Marco Groot, BSc 36

Optimize Landside Airport Operations Using A Discrete Event Simulation

3 Passenger arrival schedule In the previous chapter a set of airports was chosen. In order to find the best design of the airport terminal, the amount of passengers flowing through the terminal needs to be found. One can imagine the limitations of the terminal design will be reached with the highest amount of passengers. Therefore the design of the terminal will be based on the highest number of passengers within the terminal during a 24 hours time span.

The highest number of passengers is found by looking at the day with the highest number of passengers of a specific year. For AMS the busiest day in 2015 was on July 31 (Schiphol Group, 2016a). At HND the busiest day recorded in 2015 was December 29 (Shirakawa, 2016). LCY has high passenger peaks in early spring and in autumn. In 2014, the busiest day was on November 13 (Buying Business Travel, 2014). Lastly, the busiest day of 2015 at LAS was on May 21 (Quartz, 2015). To summarize, the selected days for the four airports can be found in Table 9.

Table 9 Selected days

Airport Selected day Amsterdam Airport Schiphol July 31, 2015 Tokyo International Airport Haneda December 29, 2015 London City Airport November 13, 2014 McCarran International Airport Las Vegas May 21, 2015

The next step in finding the passenger arrival schedule is to retrieve the aircraft departures of the selected days. Using (FlightStats, 2016a) the departures for AMS, HND, LCY and LAS are found. These lists contain the departure time, airline, flight number and destination. The aircraft type and maximum number of passengers of each flight are found by using (Pinkfroot/PlaneFinder, 2016). With the flight departure lists a graphical overview is created which states the distribution of the number of flights and number of available seats throughout the day. These graphs are stated and discussed in this chapter.

The number of available seats does not specify the number of passengers proceeding through the departure terminal. To calculate the number of passengers which pass through the departure terminal and when they arrive at the airport, there are three factors which needs to be taken into account.

Passenger load factor (PLF) The passenger load factor is the percentage of available seats being occupied by passengers. To be more precise, the passenger load factor is calculated by dividing the Revenue Passenger Kilometres (RPK) by the Available Seat Kilometres (ASK). The RPK is defined as the number of carried passengers times the distance flown. The ASK is the number of available seats times the distance flown (Jenatabadi & Ismail, 2007). The calculation of the PLF can be represented in a formula:

RPK PLF = ASK Equation 1

Marco Groot, BSc 37

Optimize Landside Airport Operations Using A Discrete Event Simulation

In this research the flights used as input can be considered as point-to-point. Therefore, the kilometres for the RPK and ASK are the same. As such, Equation 1 can be simplified to:

Number of passengers PLF = Available seats Equation 2 Origin passengers For this research only passengers which travel through the departure terminal are of interest. In general these are the passengers which have the airports selected for this research as origin. This means the number of transfer passengers needs to be subtracted from the total number of departed passengers.

HND posts an exception to this rule. As there are three separate terminals at HND, some transfer passengers have to change terminal. When passengers have to change terminal, the passenger has to collect their hold baggage and proceed to the arrival terminal. International arriving passengers even need to pass through customs. After this the passenger has to proceed to the correct terminal for their connecting flight. At this terminal the passenger drops off the hold baggage, pass through security and (for international passengers) proceed through the immigration process. As such, transfer passengers which switch between terminals can be treated as HND origin passengers. This means only the transfer passengers which stay within the same terminal should be subtracted from the total number of departing passengers.

Passenger arrival distribution Not every passenger arrives at the airport at the same time prior to departure. Some passengers arrive at the airport early to be sure not to miss the flight, while other passengers arrive as close to departure time as possible.

The arrival pattern is influenced by a number of factors. According to (Stefanik, et al., 2012) these are travel class (Business or Economy), departure time, travel purpose and flight duration (long haul versus short haul). Also, experienced passengers tend to arrive at the airport closer to the departure time compared to inexperienced passengers.

In a research conducted by (Chun & Mak, 1999) the arrival pattern of passengers is shown. In these graphs it can be seen there is a difference between arrival patterns of morning, afternoon and evening flights. Figure 9 shows the arrival pattern for first class passengers, while Figure 10 shows the arrival pattern for economy class passengers. It must be noted that in these two Figures AM stands for morning (flights departing before 10:00 hours), PM stands for afternoon (flights departing between 10:00 and 18:00 hours) and EV stands for evening (flights departing after 18:00 hours).

Marco Groot, BSc 38

Optimize Landside Airport Operations Using A Discrete Event Simulation

Figure 9 Passenger arrival pattern for first class passengers (Chun & Mak, 1999)

Figure 10 Passenger arrival pattern for economy class passengers (Chun & Mak, 1999) As regards to the different arrival times for flights departing in the morning, afternoon and evening, the results of the research of (Chun & Mak, 1999) is supported by (Stefanik, et al., 2012) and (IATA, 2004). The average arrival time for flight departing before 11:00 hours is 105 minutes, while for flights departing between 11:00 and 16:00 hours this average is 126 minutes. The average arrival time for flights departing after 16:00 hours is 127 minutes prior to departure (Stefanik, et al., 2012). Although (IATA, 2004) has slightly different time frames, the same trend can be seen. When Table 10 is considered, the arrival time lies closer to the departure time for flight departing in the morning compared to the arrival pattern for flights departing later in the day.

Marco Groot, BSc 39

Optimize Landside Airport Operations Using A Discrete Event Simulation

Table 10 Percentage passenger arrivals at check-in prior to flight (IATA, 2004)

Not only departure time and travel class have influence on the arrival pattern, also the purpose of travel is of influence. According to (Stefanik, et al., 2012) the average arrival time at the airport for business passengers is 116 minutes, while for leisure passengers this average is 121 minutes.

As for the flight duration, according to (Ashford, et al., 2013) passengers on intercontinental flights arrive earlier at the airport compared to passengers which fly continental. In Figure 11 the difference between arriving for intercontinental and European flights can be found.

Figure 11 Arrival pattern intercontinental and European flights (Ashford, et al., 2013) In this research, as regards to the passenger arrival pattern, only three of the four factors are taken into account. It is assumed that passengers travelling for business purposes are travelling in business class and passengers for leisure purposes are travelling in economy class. Therefore, the purpose of travel and the travel class are the same when considering these as factors for determining the arrival time of the passengers. As such, twelve passenger profiles can be determined. An overview of these passenger profiles can be found in Table 11.

Marco Groot, BSc 40

Optimize Landside Airport Operations Using A Discrete Event Simulation

Table 11 Passenger profiles

Passenger profile Travel class Departure time Flight duration BMI Business class Morning Intercontinental BMC Business class Morning Continental BAI Business class Afternoon Intercontinental BAC Business class Afternoon Continental BEI Business class Evening Intercontinental BEC Business class Evening Continental EMI Economy class Morning Intercontinental EMC Economy class Morning Continental EAI Economy class Afternoon Intercontinental EAC Economy class Afternoon Continental EEI Economy class Evening Intercontinental EEC Economy class Evening Continental

For passenger profiles BMC, BAC, BEC, EMC, EAC and EEC the data given by (Chun & Mak, 1999) is used. As the check-in for intercontinental flights closes one hour prior to departure (Ashford, et al., 2013), and since in Figure 9 as well as Figure 10 there is a percentage of passengers arriving less than one hour prior to departure, Figure 9 and Figure 10 represents the arrival pattern of passengers on continental flights. This means the arrival distributions for the passenger profiles BMC, BAC and BEC are stated in Figure 9, while the arrival patterns for passenger profiles EMC, EAC and EEC are stated in Figure 10. It must be noted that in order to properly use the arrival patterns as stated by (Chun & Mak, 1999), the assumption is made that the arrival pattern for first class passengers is the same as for business class passengers.

When the difference in arrival pattern for intercontinental and continental passengers is compared (see Figure 11), it can be seen that passengers for intercontinental flights arrive approximately 20 minutes earlier compared to passengers for continental flights. As the intervals used by (Chun & Mak, 1999) are 15 minutes, also the difference between the arrival pattern of passengers for intercontinental and continental flights are taken to be 15 minutes. When this is applied to the arrival patterns of Figure 9 and Figure 10, the arrival patterns for passenger profiles BMI, BAI, BEI, EMI, EAI and EEI are found. An overview of the percentage of passengers arriving per 15 minute interval for each passenger profile can be found in Table 12.

Marco Groot, BSc 41

Optimize Landside Airport Operations Using A Discrete Event Simulation

Table 12 Passenger profile arrival distribution minutes prior to departure (in percentage)

105 120 135 150 165 180 195

90

30 45 60 75

>210

<30

-

------

Pax. 105

45 60 75 90 120 135 150 165 180 195 210

profile

BMI 0 0 2 9 13 15 17 22 8 10 3 1 0 0 BMC 0 2 9 13 15 17 22 8 10 3 1 0 0 0 BAI 0 0 1 10 21 12 18 15 12 6 2 3 0 0 BAC 0 1 10 21 12 18 15 12 6 2 3 0 0 0 BEI 0 0 2 4 10 11 11 10 17 15 7 4 6 3 BEC 0 2 4 10 11 11 10 17 15 7 4 6 3 0 EMI 0 0 1 6 10 16 17 18 15 6 5 6 0 0 EMC 0 1 6 10 16 17 18 15 6 5 6 0 0 0 EAI 0 1 1 2 8 10 15 21 13 14 10 2 3 0 EAC 1 1 2 8 10 15 21 13 14 10 2 3 0 0 EEI 0 0 1 3 5 6 11 12 16 20 14 4 5 3 EEC 0 1 3 5 6 11 12 16 20 14 4 5 3 0

In the following sections the departure distribution of the four airports are described. In order to find the passenger arrival distribution first the aircraft departures needs to be found. Once this is known, based on the aircraft type, the distribution of available seats can be made. The number of available seats is multiplied by the PLF and the number of transfer passengers is deducted. Lastly, the arrival patterns are applied according to the passenger profiles. This results in the passenger arrival distribution. 3.1 Aircraft departures As mentioned before, the first step in finding the passenger arrival distribution is to list the aircraft departures. In this section the aircraft departures for the days specified in Table 9 are stated. The aircraft departures at the four airports can be found in Figure 12. The data for the aircraft departures is collected from (FlightStats, 2016a).

Marco Groot, BSc 42

Optimize Landside Airport Operations Using A Discrete Event Simulation

Aircraft departures 70

60

50

40 AMS 30 HND

20 LCY

Number of flights Numberofflights perhour LAS 10

0

Hour

Figure 12 Aircraft departures From Figure 12 a number of differences between the airports can be seen. First of all, LCY is the smallest airport, with the least amount of aircraft departures. This can be explained since LCY only has one runway, whereas the other airports have at least 4 runways. So in terms of capacity it is expected LCY has the least amount of aircraft departures.

Another aspect in the number of the departures is the spreading throughout the day. For every airport periods with a large amount of departures can be distinguished, alternated with lower numbers of departures. Especially for AMS this can be seen, where there is one large morning peak, one large evening peak and three smaller peaks in the afternoon. For HND, even though the number of departures is almost constant between 06:00 hours and 20:00 hours, there are periods in the morning and evening where the number of departures peak. For LCY the morning and evening peaks are more distinct. The peaks at LAS are less obvious. However, it can be seen that the number of departures are below average from 19:00 hours onwards.

Since there is a distinction between intercontinental and continental passenger regarding the arrival distribution, separate graphs are made for these types of flights. The aircraft departures for intercontinental flights can be found in Figure 13, while the aircraft departures for continental flights are stated in Figure 14.

Marco Groot, BSc 43

Optimize Landside Airport Operations Using A Discrete Event Simulation

Aircraft departures intercontinental 20 18 16 14 12 10 AMS 8 HND 6 LCY

Number of flights Numberofflights perhour 4 LAS 2 0

Hour

Figure 13 Aircraft departures intercontinental

Aircraft departures continental 60

50

40

30 AMS HND 20 LCY

Number of flights Numberofflights perhour LAS 10

0

Hour

Figure 14 Aircraft departures continental At all four airports the number of continental flights is higher compared to the number of intercontinental flights. An overview of the total number of intercontinental and continental flights for the four airports can be found in Table 13.

Marco Groot, BSc 44

Optimize Landside Airport Operations Using A Discrete Event Simulation

Table 13 Overview of aircraft departures

AMS HND LCY LAS Intercontinental 128 98 0 29 Continental 502 500 150 482 Total 630 598 150 511 Percentage 20.3% 16.4% 0.0% 5.7% Intercontinental

AMS and HND have a high peak of intercontinental departing aircraft in the morning. For LAS the intercontinental flights departed spread out through the day. LCY has no intercontinental flights, as all flights are to Schengen destinations. The continental departures looks almost the same as the total aircraft departures schedule from Figure 12. 3.2 Available seats The number of departing flights does not specify the number of passengers in the terminal. When larger aircraft are used, more passengers will be in the terminal compared to when smaller aircraft are used. Therefore, in order to find the distribution of passengers entering the airport terminal, the distribution of available seats needs to be considered.

In order to find the number of available seats, the aircraft types for all departing flights are considered. For example, at AMS flights KL1957 to Zurich and KL691 to Toronto both depart at 09:35 hours. On the flight to Zurich a Boeing 737 is used, while the leg to Toronto is flown with a Boeing 777. On the flight to Zurich there are 122 available seats, whereas on the flight to Toronto there are 425 seats available (Pinkfroot/PlaneFinder, 2016). That means that on these two flight a total number of 547 seats are available. When this principle is applied to every flight departing, a distribution is created which shows the total number of available seats departing throughout the day. This can be seen in Figure 15. The number of available seats on every flight for all four airports is found by using (Pinkfroot/PlaneFinder, 2016).

Marco Groot, BSc 45

Optimize Landside Airport Operations Using A Discrete Event Simulation

Available seats 12000

10000

8000

6000 AMS HND 4000 LCY

Available Available seatsper hour LAS 2000

0

Hour

Figure 15 Available seats When Figure 12 and Figure 15 are compared, it can be seen that the two distributions are different. This is due to the different number of seats on the aircraft used for all flights. When looking at Figure 16 the average number of seats throughout the day can be seen.

Average number of seats 300

250

200

150 AMS HND 100 LCY LAS

50 Average Average numberofsetas perhour 0

Hour

Figure 16 Average number of seats per flight

Marco Groot, BSc 46

Optimize Landside Airport Operations Using A Discrete Event Simulation

When looking at the average number of seats per flight for AMS, it can be seen there are two peaks at which the average number of seats is low. This occurs around 09:00 and 16:00 hours. These are the times at which the smaller aircraft for continental destinations depart (see Figure 14) while at the same time the number of intercontinental departures is low (see Figure 13). A high peak in average number of seats can be seen between 23:00 and 01:00 hours. This occurs since at these hours only intercontinental flights depart.

When looking at the distribution for HND it can be seen this looks similar to the distribution of departing flights. A peak for the morning and afternoon can be seen. Also, between 14:00 and 16:00 hours a small peak of higher number of average seats can be seen. Remarkable on the HND chart is the high peak of average number of seats around midnight. This can be explained since no domestic flights depart anymore (see Figure 14). So around midnight only intercontinental flights depart (see Figure 13). These flights are performed by larger aircraft compared to the aircraft for domestic flights.

For LCY and LAS it can be seen that the average number of seats is constant. For LCY this is caused since the airport has a short runway. As such, only smaller aircraft can land on LCY. Therefore the average number of seats does not deviate between the aircraft types used.

For LAS the most used aircraft type is the Boeing 737. Out of the 511 flights departing from LAS, 295 were performed with aircraft of this type. Also, 106 flights were used with Airbus A319 or Airbus A320. So in total 78.5% of all flights are performed by B737, A319 or A320. These three aircraft types have roughly the same number of seats. This explains the constant number in average seats. The peak at 01:00 hours for LAS is explained since at this time only four flights depart, of which one is an Airbus A330 with 294 seats and the other three aircraft are B737 or A319. With three average sized aircraft and one which has almost double the capacity of the average aircraft at LAS, this causes a peak in average seats.

Marco Groot, BSc 47

Optimize Landside Airport Operations Using A Discrete Event Simulation

3.3 Passenger arrival distribution As mentioned before in this chapter, in order to find the passengers arrival distribution the number of available seats will be multiplied with the PLF. Also, the number of transfer passengers will be deducted in order to find the total number of passengers travelled through the departure terminal. Lastly, the arrival patterns of the passenger profiles are taken into account. 3.3.1 Amsterdam Airport Schiphol For AMS, the PLF in July, 2015 was 87% (Schiphol Group, 2016a). According to (Schiphol Group, 2016a) in July 2015 the percentage of O&D passengers was 62.4%. Therefore, the number of passengers passing through the departure terminal on July 31 was:

Number of passengers with AMS as origin = = available seats ∗ PLF ∗ origin passenger percentage = = 100.418 ∗ 0.87 ∗ 0.624 = 54.515 passengers Equation 3 As mentioned before, for the arrival pattern it is assumed that all passengers travelling for business purposes are travelling in business class. This means that for AMS 31% of all passengers are business class passengers (Schiphol Group, 2016a). The remaining 69% are passengers travelling in economy class.

After applying the PLF, origin passenger percentage and percentage of business class passengers the arrival pattern of the passenger profiles from Table 11 can be applied in order to obtain the passenger arrival distribution.

3.3.2 Tokyo International Airport Haneda In order to calculated the PLF for HND in December 2015, the information provided by (ANA, 2016) and (JAL, 2016) are considered. For ANA, only the PLF and number of passengers for the entire month of December was found (ANA, 2016). This data is not useful, as the month of December is, with the exception of the Christmas/New Year holiday period, a low peak period. The data presented by (JAL, 2016) only reflects the holiday period. As the day considered for HND in this research falls right in the middle of the Christmas/New Year holiday period, only the data from (JAL, 2016) is used to calculate the PLF for HND.

The literature found specifies the number of passengers and the load factor for international and domestic separate. In order to calculate the average load factor, the following formula is used:

∑(PLF ∗ number of passengers)

∑ number of passengers Equation 4 If the numbers from (JAL, 2016) are substituted in this formula the average PLF for HND is found. Performing the calculation results in a PFL of 78.0%.

Marco Groot, BSc 48

Optimize Landside Airport Operations Using A Discrete Event Simulation

As for the percentage of origin passengers, no actual data could be retrieved. However, data on the percentage of transfer passengers for Tokyo Narita airport could be found (CAPA, 2016). The number of transfer passengers for Narita is 17% in 2015. Narita is located near Tokyo (Google Maps, 2016e) and is around half the size of HND in terms of passenger traffic (CAPA, 2016). For this research it is assumed the number of transfer passengers for HND is the same as for Narita. Earlier in this chapter it was discussed only transfer passengers which stay within the same terminal (e.g. international-international and domestic-domestic transfer passengers) should be treated as transfer passengers and passengers which need to switch between terminals (e.g. international-domestic and domestic-international transfer passengers) are considered origin passengers. As detailed data could not be retrieved, it is assumed all transfer passengers stay within the same terminal.

Applying the numbers for PLF and origin passenger percentage to the number of available seats, gives:

Number of passengers with HND as origin = = available seats ∗ PLF ∗ origin passenger percentage = = 130.722 ∗ 0.78 ∗ 0.83 = 84.629 passengers Equation 5 In order to apply the arrival pattern to the total number of passengers, the number of business class passengers needs to be known. As mentioned in section 2.3, this is 45%. Using the passenger profiles from Table 12 the passenger arrival distribution can be created. 3.3.3 London City Airport As for the PLF, according to (Wright & Ruiz-Celada, 2016) the average number of seats per flight in 2014 was 80, while the average number of passengers per flight was 53. This means the average PLF in 2014 was 53/80 is 66.3%. However, as November 13 was the busiest day of the year at the airport (Buying Business Travel, 2014), the PLF used should be one which reflects a peak period. According to (York Aviation, 2015), the PLF used to calculate the loading on the infrastructure at peak periods is 85%. As such, for this research a load factor of 85% is used for LCY.

In section 2.4 it was already stated there is no transfer terminal at LCY. All passengers arriving have to proceed through the arrivals terminal. Passengers which have a connecting flight need to pass the departure process as being a passenger which has LCY as origin airport. Therefore, the percentage of origin passengers at LCY is considered to be 100%. When this, together with the PLF, is substituted in the formula previously used, the number of departing passengers can be calculated:

Number of passengers with LCY as origin = = available seats ∗ PLF ∗ origin passenger percentage = = 12.090 ∗ 0.85 ∗ 1.00 = 10.277 passengers Equation 6 At LCY 56% of all passengers are business passengers. Using this percentage and applying the passenger profiles from Table 12, the arrival distribution can be found.

Marco Groot, BSc 49

Optimize Landside Airport Operations Using A Discrete Event Simulation

3.3.4 McCarran International Las Vegas Just as for the other three airports, the number of available seats will be multiplied by the PLF and the percentage of origin passengers. According to (United States Department of Transportation, 2016) the PLF in May 2015 was 86.1%. As for the rate of transfer passengers, a total number of 2.001.201 passengers boarded an aircraft at LAS in May 2015 (McCarran International Airport, 2016e). According to (McCarran International Airport, 2016c) there were 195.295 connecting passengers which have boarded an aircraft in May 2015 at LAS. Therefore the percentage of transfer passengers is 195.295/2.001.201 is 9.8%. And as such, 90.2% are origin passengers. Using these numbers the actual amount of passengers departing from LAS can be calculated.

Number of passengers with LAS as origin = = available seats ∗ PLF ∗ origin passenger percentage = = 77.238 ∗ 0.861 ∗ 0.902 = 59.985 passengers Equation 7 As mentioned in section 2.5 the amount of business passengers accounts for 21% of the total number of passengers. Therefore 21% of all passengers are assumed to be business class passengers. With this, combined with the arrival pattern stated in Table 12, the arrival distribution can be created. 3.4 Passenger arrival distribution graph Since the method for finding the passenger arrival distribution has been completed, the distribution can be created. The passenger arrival distribution for the four airports can be found in Figure 17.

Passenger arrival distribution 2000 1800 1600 1400 1200 1000 AMS 800 HND 600 LCY 400 LAS

Numberofpassengers permins 15 200 0

Hour

Figure 17 Passenger arrival distributions As can be seen from Figure 17 the arrival of passengers at the airport terminal is not constant throughout the day. But considering the aircraft departures from Figure 12 this was expected. Just as the aircraft departures show peaks, so does the arrival pattern of the passengers.

Marco Groot, BSc 50

Optimize Landside Airport Operations Using A Discrete Event Simulation

At AMS the morning and evening peaks can be seen clearly. Also the three smaller peaks during the day can be distinguished. This pattern is similar to the aircraft departures.

For HND the peaks are not as steep as at AMS. Still there are peaks in the morning and the evening. The peak between 22:00 and 23:00 hours is not as high in absolute terms, but does have a steeper approach and decline. This peak is caused by the departure of international flights, as no domestic flights depart between 21:00 and 06:00 hours.

Two peaks can be seen at LCY. Again, these are morning and evening peaks. As the average aircraft size used at LCY show almost no variation, the passenger arrival distribution was expected to have the same shape as the aircraft departures graph.

The last airport to be considered is LAS. Four small peaks can be found. One early in the morning, one in the late morning, a peak during noon and the last peak is in the late afternoon. In the evening also a peak can be seen, although the absolute value of this peak is lower than the average arrival distribution. Therefore this is not considered to be a peak in the passenger arrivals. 3.5 Remarks To conclude this chapter, some final remarks needs to be given regarding the graphs in the chapter. Figure 12, Figure 15 and Figure 16 show the respective data per hour. This means that all data from flights departing within a one hour time frame are added to one data point in the graph. For example, the number of departures between 12:00 and 12:59 hours are added to one data point. This is different compared to Figure 17, where the data is separated by 15 minute intervals.

The 15 minute interval used in Figure 17 is needed to create a more realistic arrival pattern. Also, the data presented by (Chun & Mak, 1999), (Stefanik, et al., 2012) and (IATA, 2004) shows 10 or 15 minute intervals. Since data from their research was used, it was useful to use 15 minute intervals for this thesis as well.

In order to have consistency throughout the research, the graphs for aircraft departures and available seat distribution should also depict data per 15 minute interval. However, for Figure 12, Figure 15 and Figure 16 this was not needed, as the purpose of these three graphs was to show an overall image of the distribution throughout the day. Showing data per 15 minute interval would be too detailed.

Marco Groot, BSc 51

Optimize Landside Airport Operations Using A Discrete Event Simulation

4 Solutions details An extensive discussion regarding the arrival patterns of passengers for the four airports can be found in the previous chapter. From this discussion the amount of passengers arriving during the day is known. Once these passengers have arrived at the airport terminal, the passengers need to pass through a number of stations in order to complete the departure process. Each of these stations have a specific process time. The combination of these process times influences the total amount of time needed for a passenger to complete the departure process.

Earlier in this research the different solutions for each station were determined. An overview of the four stations within the departure process, with their corresponding solutions, can be found in Table 14.

Table 14 Solutions of departure process solutions

Station Solutions Check-in Serviced check-in Self-service check-in Internet check-in Baggage drop-off Fully serviced baggage drop-off Limited serviced baggage drop-off Retrofit self-service baggage drop-off Integrated self-service baggage drop-off Security check Centralized security Decentralized security Immigration Serviced immigration Self-service immigration

In order to find the solution in which the most amount of passengers can be processed in the least amount of time, some data regarding each solution is needed. The data needed includes: - The amount of floor space needed for a single solution - The average process time for a single passenger to successfully complete the process - The quickest time in which a single passenger can successfully complete the process - The amount of passengers which can use a single solution entity simultaneous - The percentage of failed processes - The average time a passengers spends at a solution during a failed process

For the items in the above list, it must be noted that a failed process for the security check means that the passengers needs an additional security check, since possible prohibited and restricted items are found during the initial check.

As mentioned earlier in this research, the “Serviced check-in” solution for the check-in station and the “Fully serviced baggage drop-off” solution for the baggage drop-off station are the same solution. Therefore, this solution will only be treated once in this chapter.

Marco Groot, BSc 52

Optimize Landside Airport Operations Using A Discrete Event Simulation

4.1 Check-in solutions For the check-in station there are three solutions. These are: - Serviced check-in - Self-service check-in - Internet check-in

Regarding the six items listed earlier in this chapter, these are only needed for the serviced and self-service check-in. These are not needed for internet check-in, as this takes place outside of the airport terminal. 4.1.1 Serviced check-in The serviced check-in is dependent on the airport. Each airport has slightly different check-in desks and might have different process times. An overview of the required data for the serviced check-in desks for the four airports can be found in Appendix B.1.

It must be noted that only specific data for the size of the check-in desk was found for AMS and HND. For LCY and LAS the data from (IATA, 2004) was used. As for the average process times, only data for AMS was retrieved. For the other three airports data from (Transportation Research Board, 2010) was used.

At a serviced check-in desk groups of passengers can be checked in simultaneous. However, the baggage can only be checked in one at a time. As such, for the simulation it is assumed only 1 passenger at a time can be processed.

A failed check in at a serviced desk only occurs when the equipment of the desk itself breaks down. As no data could be found on the malfunctions of a serviced desk, for the simulation it is assumed a check-in at a serviced desk never fails. 4.1.2 Self-service check-in Self-service check-in is performed using check-in kiosks at the airport. There are a number of different manufacturers for check-in kiosks. An overview of the data regarding the equipment from these manufacturers can be found in Appendix B.2.

No information could be retrieved for the SITA solution. For the other competitors, only the size of the kiosk could be found. An exception to this is the kiosk from OKI, for which also process duration data was collected. Also, since the size of the OKI solution is average compared to the other solutions, for the simulation only the OKI kiosk data will be used.

Normal practice for kiosk check-in is that all passengers in the same booking can check in at the same time. So for example, if a family of four arrives at the kiosk, all four family members can be checked in during a single process (Groot, 2015).

Regarding the number of processes the check-in via the kiosk fails, no information could be found. In real practice, passenger which fail to check in using the kiosk will proceed to a service desk. During observations at AMS and HND no single failed check-in was seen. As such, it is assumed the fail rate of the kiosk check-in is 0 percent.

Marco Groot, BSc 53

Optimize Landside Airport Operations Using A Discrete Event Simulation

4.1.3 Internet check-in With internet check-in the methods of check-in which are performed outside of the airport terminal are meant. These are check-in using the website of the airline, the mobile app or via automatic check-in. As this method of check-in is performed out of the airport terminal, no data regarding the size of the solution or process duration is needed. The only data needed is the percentage of passengers which use internet check-in. This data is needed since the passengers checking in online do not have to proceed to a check-in kiosk or check-in counter when arriving at the airport. The percentage of passengers checking in via the internet per airport are stated in Appendix B.3.

It must be noted that no information could be found regarding the percentage of passengers using internet check-in in Japan. However, data could be retrieved for South Korea and China. As both of these countries are Asian and have similar internet usage (InternetLiveStats.com, 2016), (Incitez Pte Ltd., 2016) the average value of the percentage of passengers using internet check-in from South Korea and China was used. 4.2 Baggage drop-off solutions As mentioned in Table 14 there are four solutions for the baggage drop-off at an airport. However, the fully serviced baggage drop-off is the same as the service check-in solution. Therefore, in this section only the following three solutions will be treated: - Limited serviced baggage drop-off - Retrofit self-service baggage drop-off - Integrated self-service baggage drop-off 4.2.1 Limited serviced baggage drop-off The limited serviced baggage drop-off solution is a conventional serviced desk. However, with this solution passengers needs to be checked in before proceeding to this solution. Only baggage can be dropped here. As such, the size of the desk is the same as for the serviced check-in solution. However, since check-in is not performed here, but only baggage drop-off, the process times should be lower compared to the serviced check-in. The data for the limited serviced baggage drop-off solution at the four airports can be found in Appendix B.4.

The size of the limited serviced baggage drop-off counters is the same as for serviced check-in counters. However, as check-in is not performed the average process time is slightly lower. As the average process time of serviced check-in at AMS is lower compared to the other three airports, the information stated by (Abdelaziz, et al., 2010) needs to be adjusted for AMS. Since the average process time of limited serviced baggage drop-off is 4.7% lower compared to serviced check-in for the airports HND, LCY and LAS, assumed is that the process time at AMS is also 4.7% lower. A such, the process time used in the simulation for AMS is 85.8 seconds.

As for the number of passengers which can be serviced simultaneously and the fail rate, the same assumptions as for the serviced check-in apply. This means 1 passenger can drop-off baggage and the fail rate is 0%.

Marco Groot, BSc 54

Optimize Landside Airport Operations Using A Discrete Event Simulation

4.2.2 Retrofit self-service baggage drop-off Retrofit self-service baggage drop-off solutions are solutions which are installed on conventional service desks in order to change the desk from a serviced to a self-service facility. There are a number of manufactures which offer this kind of solution. The data regarding the solutions of these manufacturers can be found in Appendix B.5.

Regarding the process duration, only information for the solutions of SITA, ICM and Materna were found. The SITA solution can be used in both a 1-step as a 2-step process. The data for the ICM solution stated in Appendix B.5 is for a 2-step process. The Materna solution facilitates a 1-step process.

Only one passenger at a time can use the solution. During a 1-step process the boarding pass of the passenger is scanned, the baggage label is printed, the baggage label is attached to the bag and the baggage label is scanned. During a 2-step process, as the baggage label was already printed and attached to the baggage at a kiosk, only a scan of the boarding pass, followed by a scan of the baggage label is needed. With both a 1-step and 2-step process, scanning of the boarding pass and baggage label is needed to verify the baggage belongs to the correct passenger. As such, only 1 passenger at a time can use the retrofit solution.

When looking at the percentage of processes which could fail, no actual data could be retrieved. However, as the retrofit solution itself is just a service desk with a label scanner, not much could go wrong there. The difference between a retrofit solution compared to a service desk is that the passenger can place baggage which will exceed the baggage allowance. If this is the case, the baggage will not be accepted and the passenger is asked to go to a service desk. At the service desk, the passenger can pay additional fees.

The number of passengers with overweight baggage is independent of the solution. From (Scarabee Aviation Group, 2016a) data is available regarding the percentage of processes in which the baggage allowance was exceeded. This percentage is also taken for the retrofit solution. This means the failed rate which will be used in the simulation is 6.4%. Regarding the average time of a failed process, overweight is determined once the passenger details are retrieved from the DCS and the bag is weighted. This is roughly halfway a process. Therefore the average time of a failed process is taken to be half of the average time of a successful process.

Marco Groot, BSc 55

Optimize Landside Airport Operations Using A Discrete Event Simulation

4.2.3 Integrated self-service baggage drop-off The integrated self-service baggage drop-off solutions are solutions in which the conventional service desk is removed and the self-service solution is installed. In some cases the self-service solution has a smaller footprint, which allows an increase in baggage drop-off positions while using the same amount of floor space. The data for the solutions offered by different manufacturers can be found in Appendix B.6.

Little information could be found regarding the integrated self-service baggage drop-off solutions, only (partial) information was found for five different companies. No information regarding Materna, Amadeus, CCM, Vision-box and Cofely could be found.

When looking at the information which is available, it can be seen that the size of the solutions differs significantly. IER and BagDrop have solutions with the same size. The SITA solution is wider, but less deep. For ICM, it can be seen that the width is more than double compared to IER and BagDrop. For the depth of the ICM solution, as well as for the depth and width of the Alstef solution, no information could be found. Also, BagDrop has two different units installed with different widths. The machines at HND are smaller compared to the machines in AMS. At HND, the BagDrop solution is only installed in one of the domestic terminals. Passengers flying domestic have smaller pieces of baggage compared to passengers on intercontinental flights. A such, the unit could be made smaller.

When looking at the process durations, it can be seen that the average duration of the ICM and Alstef solutions are faster compared to the BagDrop solution. This is since the ICM and Alstef solutions are 2-step solutions, while the BagDrop solutions is a 1-step process. So for the simulation the average of the times of ICM and Alstef will be used to simulate an integrated self-service baggage drop-off solution in a 2-step process, while the data from BagDrop will be used for a 1-step process.

A last remark needs to be made regarding the average process times given by BagDrop. During high peak moments the average process duration decreases. The cause of this is due to passenger observing the process performed by other passengers while standing in the queue. As such, passengers are familiar with the process and perform all tasks quicker compared to passengers who are not familiar with the process. For the ease of the simulation, only the average duration during non-peak moments will be used.

Just as with the retrofit solution, the passenger needs to scan the boarding pass for identification. As such, the baggage which is checked in will be coupled to the passenger. This means only one passenger at a time can use the self-service baggage drop-off solution.

When looking at the percentage of failed processes, BagDrop makes detailed reports in which this data is included. A failed process can be caused by overweight baggage, passenger aborting the process or technical failure. As for the average process time during a failed process, no actual data is recorded. However, the failure of a process can appear at virtually any moment in the process. Right at the beginning or just before a process would have been successfully completed. As such, the average process time of a failed process is assumed to be half of the average time of a successful process.

Marco Groot, BSc 56

Optimize Landside Airport Operations Using A Discrete Event Simulation

4.3 Security check solutions For the security check there are two types of solutions determined. These are centralized and decentralized security. With centralized security, all passengers proceed through the security checkpoint at one (or a limited) number of locations. Decentralized security is located at the boarding gate itself. Therefore a decentralized security needs as much security points as there are boarding gates.

For the centralized security, the security check area is only accessible by passengers with a valid boarding pass. Therefore there is a check at the entrance of the security check area. During this check, it is verified the person which want to enter the area has a valid boarding pass. This check can be performed by a person or by an automated gate. In Appendix B.7 an overview of the data regarding a personal check and automated checks can be found.

The size of the Kaba and IER gates are almost the same. As such, the average size of these two are used as input for the simulation. The quickest duration of the Gunnebo and IER gates are also the same, while the quickest process duration for the Kaba gate is only 0.5 seconds slower. As the solutions are mechanically and in operational procedure almost the same, it can be assumed the average process time is the almost the same for all solutions. Therefore the known data of Gunnebo will be used in the simulation.

As for the personal solution, a staff member can read the boarding pass and determine if access is allowed in 4 seconds. Handing over the boarding pass from the passenger and the staff member takes 1 second. This data is retrieved from an instruction video (Scarabee Aviation Group, 2016b). Assuming a group hands over all boarding passes at once, the process duration for a personal solution can be stated as:

푃푟표푐푒푠푠 푡𝑖푚푒 = 2 푥 ℎ푎푛푑표푣푒푟 + 푛푢푚푏푒푟 표푓 푝푎푠푠푒푛푔푒푟푠 푥 푠푐푎푛푛𝑖푛푔 푡𝑖푚푒 = 2 푥 1 + 푔푟표푢푝 푠𝑖푧푒 푥 4 [푠푒푐] Equation 8 As for the size of the personal solution, an educated guess has to be made. When looking at the width, this should be taken as the length between two passenger access points. This definition also applies for the access gates. So for the width of the personal solution the same value can be taken as for the access gates. The access gates are designed with multiple sensors which determine if the gate is free to open the doors, senses when the doors can be closed after the passenger has passed the gate and when someone is trying to walk in the wrong direction. In order to be able to observe this, extra sensors have been placed behind the doors of the access gates. When a personal solution is chosen, the staff member has the overview. So the area required for the sensors is not needed in case of a personal solution. As such, the length of the personal solution can be half of the length of a self-service access gate.

The purpose of the gates is to allow passengers access to the security area, while denying access to non-passengers. As such, the boarding pass is scanned. If a correct boarding pass is scanned, the person in front of the gate is allowed access. Only one boarding pass at a time can be presented, resulting that only one passenger at a time can use a access gate. For a personal check this can be different, as all boarding passes of the travel group can be handed over to the staff member at the same time. So the number of passengers passing the access control performed by a person is determined by the group size.

Marco Groot, BSc 57

Optimize Landside Airport Operations Using A Discrete Event Simulation

Beside a mechanical malfunction of the access gate, a failure of the access process means a person without a boarding pass approached the access area. For the simulation only persons with a valid boarding pass are in the system, so the failure rate of the security access is 0 percent.

For the security check itself, Scarabee has installed security lanes at different locations. At all these locations, the security lanes were installed at a centralized security system. Depending on the location, the size of the lane varies. The lanes installed have a divestment area, X-ray scanner, body scanner or metal detector and a reclaim or recheck area. The total area of this equipment is, in the largest variation, 5,000 by 20,500 mm.

When a decentralized security situation occurs, the security check is performed at the departure gate. Due to space restrictions, the layout of a security lane at the gate contains two divest tables, X-ray, metal detector and two composure tables. Following the TSA design guide (TSA, 2006b) this holds the security lane is 1,969 by 7,557 mm.

As for the average process duration, the same steps has to be taken at centralized and decentralized security. Those are placing the baggage on the belt, X-ray screening of the baggage, screening of the passenger and reclaiming the baggage. As such, both process durations are the same.

The main difference between the centralized solution provided by Scarabee compared to the decentralized option involves the design of the lanes. For the centralized security there are 3 divestment positions, 8 passengers can reclaim their belongings simultaneously (including remote reclaim station) and 2 passenger can be positioned at recheck. This means a total of 14 passengers can use the lane at the same time. For the decentralized security, 1 passenger can divest and 1 passenger is reclaiming his/her belongings or is located at recheck. As such, a total of 3 passengers can use the lane at the same time. An overview of the found details can be found in Appendix B.8.

Marco Groot, BSc 58

Optimize Landside Airport Operations Using A Discrete Event Simulation

4.4 Immigration The immigration check is used to monitor the passengers leaving the country (or Schengen area). This immigration check can be performed by serviced or self-service solutions. 4.4.1 Serviced immigration The serviced immigration process might be different at the four airport locations. Therefore data was gathered to obtain the differences or similarities. The results can be found in Appendix B.9.

Hardly any information could be retrieved regarding the serviced immigration. As such, the design values stated by IATA will be used in the simulation. According to (IATA, 2004), the average process time for departing passengers is 18 seconds.

No data could be retrieved regarding the number of failed processes. For the immigration check a failed process means additional checks are needed, the passenger is not allowed to leave the country immediately. When looking at the immigration statistics, 229,000 out of 362 million passengers were refused entry to the United States of America in 2013 (Seghetti, 2015). This is 0.063%. The legislation for arriving passengers is stricter compared to those of arriving passengers as visa’s for entry a mostly needed when travelling internationally. For departing a country a visa is not needed. So assuming the percentage of additional checks is lower for departing passengers compared to arriving passengers, the rate of failed processes is negligible. As such, for the simulation it is set to 0%. 4.4.2 Self-service immigration With self-service immigration, the immigration process is automated such that almost no staff is needed. Only when a passenger needs additional checks, the passenger is guided towards immigration staff. When no additional checks are needed, passengers can proceed through immigration without the assistance of staff. There are a number of manufacturers providing automated immigration solutions. The data regarding these solutions can be found in Appendix B.10.

The size of the self-service immigration solutions are similar. For the simulation program the average size will be used. When looking at the average process duration, only for the solutions provided by SITA and Morpho data was available. The process durations of these two manufacturers differ significantly as the SITA solutions takes 4.5 times as long as the Morpho solution The Morpho solution is close to the IATA standard of serviced immigration (IATA, 2004), so the values of Morpho regarding process durations will be used in the simulation.

For the percentage of failed processes, just as for the serviced immigration, no data could be retrieved. However, the fail rate for the self-service immigration is the same as for the serviced immigration. So also for self-service immigration the fail rate is set to 0% in the simulation.

Marco Groot, BSc 59

Optimize Landside Airport Operations Using A Discrete Event Simulation

5 Passenger profiles In the previous chapter the data for the different solutions were found. However, the throughput inside the airport terminal is not only determined by the equipment, but also depends on the passengers. More specific, the types of passengers and their characteristics. In order to take this into account, different passenger profiles are defined, which holds the group size, gender, travel purpose and type of baggage. In this chapter the explanation can be found on the determination on the different passenger profiles and the characteristics of these profiles. 5.1 Passenger profiles determination As a basis for the data used for determining the passenger profiles and their characteristics, three researches which have focussed on passenger dynamics, passenger handling at airport terminals and passenger walking speeds are used.

First of all, (Young, 1999) has conducted measurements at San Francisco International airport and Cleveland Hopkins International airport. Randomly selected passengers were followed through the terminal. As the distance between the start and end point of the measurements was known, and the time the passengers took to walk between these two points was measured, the average walking speed could be determined. A distinction was made between different passenger groups, such as gender, travel type and if the passenger had baggage or not. The results of these measurements are stated in Appendix C.1.

The second research to consider is performed by (Schultz, et al., 2008) at Dresden International airport. In this research a similar measurement has been performed compared to the research of (Young, 1999). Just as in the research by (Young, 1999) the average walking speeds based on gender and travel purpose was found. Additionally, also the walking speed of groups consisting of 1, 2 or 3 persons was analysed. The results of (Schultz, et al., 2008) can be found in Appendix C.2.

Besides the walking speeds (Schultz, et al., 2008) also looked at the walking speeds when passengers have baggage. A distinction between business and leisure passengers was made regarding walking speeds with baggage. The results found by (Schultz, et al., 2008) regarding this matter are stated in Appendix C.3.

The last research which will be discussed here are the results from the research of (Schultz & Fricke, 2011) which was presented at the ninth USA/Europe Air Traffic Management Research and Development Seminar. Again the movement of passengers at Dresden International airport was analysed. Using this analysis, average walking speeds were found for passenger groups of 1, 2 and 3 persons based on the travel purpose. The results found by (Schultz & Fricke, 2011) can be found in Appendix C.4.

When above three researches are compared, it can be seen there is a pattern in the distinction between passengers. These distinctions are between male and female, business and leisure travel purpose, group size and travelling with or without baggage. If these distinctions are combined, in total 24 passenger profiles are created. The passenger profiles belonging to groups of 1 person, groups of 2 persons and groups of 3 persons can be found in Table 15, Table 16 and Table 17 respectively.

Marco Groot, BSc 60

Optimize Landside Airport Operations Using A Discrete Event Simulation

Table 15 Profiles for groups of 1 person

Profile Gender Travel purpose Bag MBN Male Business No bag MBT Male Business Trolley bag MBC Male Business Baggage cart MLN Male Leisure No bag MLT Male Leisure Trolley bag MLC Male Leisure Baggage cart FBN Female Business No bag FBT Female Business Trolley bag FBC Female Business Baggage cart FLN Female Leisure No bag FLT Female Leisure Trolley bag FLC Female Leisure Baggage cart

Table 16 Profiles for groups of 2 persons

Profile Travel purpose Bag 2BN Business No bag 2BT Business Trolley bag 2BC Business Baggage cart 2LN Leisure No bag 2LT Leisure Trolley bag 2LC Leisure Baggage cart

Table 17 Profiles for groups of 3 persons

Profile Travel purpose Bag 3BN Business No bag 3BT Business Trolley bag 3BC Business Baggage cart 3LN Leisure No bag 3LT Leisure Trolley bag 3LC Leisure Baggage cart

A distinction between the gender of the passengers is made for the first 12 passenger profiles only. For single passenger groups it is easy to determine the gender, while for groups there are many variations. For a group of 2 persons, the group can consist of 2 males, 2 females or 1 male and 1 female. Applying this would mean the total number of profiles in Table 16 would grow to 18. Even more, the number of profiles in Table 17 would grow to 24. So applying the gender of the group members for groups of 2 and 3 persons will result in 54 passenger profiles.

When looking at the purpose of the passenger profiles in this research, having 54 passenger profiles is too extensive. The passenger profiles are not the area of interest, but the behaviour of the interaction between the station solutions. For this it was decided to only incorporate the gender for the groups of 1 person. Also, in the studies discussed in this chapter there has been no distinction between the composition in genders within groups of 2 and 3 persons. Therefore, if the gender would be incorporated in groups of more than 1 person, no data is available to average walking speeds.

Marco Groot, BSc 61

Optimize Landside Airport Operations Using A Discrete Event Simulation

The last point to discuss for the passenger profiles is the baggage carried. (Schultz, et al., 2008) have found the influence in walking speed when carrying different types of baggage. These results are stated in Appendix C.5. It must be noted that the walking speed of passengers with a rucksack were taken as the baseline, from which the walking speeds of passengers with other types of baggage were compared to.

Additional to the data from Appendix C.5 (Young, 1999) has found that passengers without baggage travel 2.24% slower compared to the average walking speed. One might expect travelling without baggage would increase the walking speed. (Young, 1999) does not provide an explanation to why the results are opposite to what might be expected.

It might seem unintuitive that passengers with a trolley bag walk faster compared to passengers with a rucksack. (Schultz, et al., 2008) give as explanation that passengers with a trolley bag often are frequent travellers, which are familiar with moving through the airport. So the walking speed is not correlated to the type of baggage.

The walking speed of passengers with a rucksack is 1.32 m/s (Schultz, et al., 2008) which is only 1.49% slower compared to the average walking speed found by (Young, 1999). For passengers with handbags this difference is only 0.75%. As the difference in walking speeds between passengers with handbags and rucksacks is almost the same as the average walking speed, it was decided to remove these types of baggage from the passenger profiles. This leaves only three types of baggage to be taken into account when creating the passenger profiles. These three types are no bags, trolley bags and baggage carts. 5.2 Determine walking speeds per passenger profile After finding the passenger profiles, the next step is to find the corresponding walking speed for each profile. To do so, a base speed is defined. This base speed are the speeds defined in Appendix C.4. To find the correct walking speed for each profile, adjustments to this base speeds needs to be made.

First of all, the base speed is changed according to the difference in walking speeds based on the gender of the passenger. When the data from (Young, 1999) is considered, the walking speeds of male and female are compared to the average walking speed. This holds males walk 5.22% faster than average, while females walk 4.48% slower. It must be noted that the adjustment for the gender of the passenger on the walking speed is only applied to groups of 1 person.

Secondly, the base walking speeds needs to be adjusted based on the type of baggage carried by the passenger. For the profiles where the passenger does not have baggage, the difference between the walking speed of passengers without baggage and the average walking speed as found by (Young, 1999) is used. This means passengers without baggage walk 2.24% slower. As for passengers with trolley bags or carts, the difference with the average speed as stated in Appendix C.5 are used.

To complete the walking speed determination, the procedure used to adjust base values according to the characteristics of the profiles is applied to the standard deviation. As an exception to this, the difference in the standard deviation for the profiles where passengers have trolley bags or baggage carts are not known. An overview of the adjustment and final values for all passenger profiles can be found in Table 18.

Marco Groot, BSc 62

Optimize Landside Airport Operations Using A Discrete Event Simulation

Table 18 Passenger profiles speed and standard deviation

Passenger Base Base Diff. Diff. st. Diff. Diff. st. Profile Profile Profile speed stand. speed dev. speed dev. speed st. dev. (m/s) dev. gender gender bag bag (m/s) (m/s) (m/s) (%) (%) (%) (%) MBN 1.38 0.21 5.22 7.41 -2.24 -11.11 1.41 0.17 MBT 1.38 0.21 5.22 7.41 7.60 n/a 1.51 0.28 MBC 1.38 0.21 5.22 7.41 -3.80 n/a 1.39 0.28 MLN 1.19 0.25 5.22 7.41 -2.24 -11.11 1.22 0.21 MLT 1.19 0.25 5.22 7.41 11.20 n/a 1.35 0.32 MLC 1.19 0.25 5.22 7.41 -2.80 n/a 1.21 0.32 FBN 1.38 0.21 -4.48 7.41 -2.24 -11.11 1.31 0.17 FBT 1.38 0.21 -4.48 7.41 7.60 n/a 1.41 0.28 FBC 1.38 0.21 -4.48 7.41 -3.80 n/a 1.30 0.28 FLN 1.19 0.25 -4.48 7.41 -2.24 -11.11 1.12 0.21 FLT 1.19 0.25 -4.48 7.41 11.20 n/a 1.26 0.32 FLC 1.19 0.25 -4.48 7.41 -2.80 n/a 1.12 0.32 2BN 1.17 0.17 n/a n/a -2.24 -11.11 1.15 0.06 2BT 1.17 0.17 n/a n/a 7.60 n/a 1.25 0.17 2BC 1.17 0.17 n/a n/a -3.80 n/a 1.13 0.17 2LN 0.97 0.20 n/a n/a -2.24 -11.11 0.95 0.09 2LT 0.97 0.20 n/a n/a 11.20 n/a 1.08 0.20 2LC 0.97 0.20 n/a n/a -2.80 n/a 0.94 0.20 3BN 1.04 0.23 n/a n/a -2.24 -11.11 1.02 0.12 3BT 1.04 0.23 n/a n/a 7.60 n/a 1.12 0.23 3BC 1.04 0.23 n/a n/a -3.80 n/a 1.00 0.23 3LN 0.93 0.17 n/a n/a -2.24 -11.11 0.91 0.06 3LT 0.93 0.17 n/a n/a 11.20 n/a 1.04 0.17 3LC 0.93 0.17 n/a n/a -2.80 n/a 0.90 0.17

Marco Groot, BSc 63

Optimize Landside Airport Operations Using A Discrete Event Simulation

5.3 Percentage of occurrence In the last part of this chapter the occurrence of each passenger profile will be calculated. For this the percentage of occurrence of group sizes, gender, travel purpose and baggage type are considered. Just as for the determination of the walking speed of the passenger profiles, the gender is only applicable to groups of 1 person.

During a study performed by (James, 1953), field observations were made regarding the occurrence of different groups. These observations were made in Eugene and Portland (Oregon, United States of America). The results regarding the group size are stated in Table 19.

Table 19 Results field observation (James, 1953)

Number of persons in Frequency Percent of total group 1 10,149 65.54 2 3,945 25.47 3 1,075 6.94 4 238 1.54 5 65 0.42 6 14 0.09 Total 15,486 100.00

In this research only groups of 1, 2 and 3 persons are considered. Therefore, the percentage of occurrence of the groups with 3, 4, 5 and 6 persons from the research of (James, 1953) are added together and taken as the percentage of occurrence for the group of 3 persons.

As for the occurrence of gender, this was mentioned in the research by (Schultz, Schulz and Fricke, 2010). There it was stated that 52% of the passengers followed were male, 44% was female and 4% were children. For this research, only males and females are taken into account. Therefore, it is assumed that the children were 50% male and 50% female and added to the male and female groups. This holds that for this thesis the percentage of occurrence for males is 54% and 46% for females.

The percentage of occurrence for the travel purpose has been discussed previously in Chapter 2. In Chapter 2 the ratio between business and leisure was used to classify the airport. This ratio will be used again to calculate the percentage of occurrence of business and leisure passengers.

In order to use this ratio, two formulas need to be used. The first one is the formula for the ratio between business and leisure (see Equation 9). The second formula is used to convert the ration to percentages, by having the sum of business and leisure to be 100 (see Equation 10). This holds there are 2 formulas, with 2 unknowns. Thus the unknowns can be solved and the percentages of occurrence for business and leisure as travel purpose are found.

퐵푢푠𝑖푛푒푠푠 = 푟푎푡𝑖표 퐿푒𝑖푠푢푟푒 Equation 9

퐵푢푠𝑖푛푒푠푠 + 퐿푒𝑖푠푢푟푒 = 100 Equation 10

Marco Groot, BSc 64

Optimize Landside Airport Operations Using A Discrete Event Simulation

When the ratios of the airports as found in Chapter 2 are used, the corresponding percentages for business and leisure travel purpose are found (see Table 20).

Table 20 Percentage travel purpose per airport

Airport Ratio Business (%) Leisure (%) AMS 0.67 40.10 59.90 HND 1.50 60.00 40.00 LCY 2.33 70.00 30.00 LAS 0.34 25.40 74.60

The last item to be discussed in this section is the percentage of occurrence of the different types of baggage. In the research conducted by (Berdowski, et al., 2009) it was found that 16.30% of all passenger does not have any checked baggage. This percentage is taken as the ‘No bag” category in this research.

As for the other two baggage types, (Davis & Braaksma, 1988) discovered that during flow measurements at Toronto Pearson International airport 43.11% of passengers carrying baggage were pushing a baggage cart. As mentioned before, 16.30% of all passengers do not have baggage. This holds that 83.40% of all passenger do have baggage, of which 43.11% are pushing a baggage cart. This means that 83.40% * 43.11% = 36.08% of all passengers are pushing a baggage cart.

Now the percentage of passengers without baggage and the percentage of passengers with a baggage cart are known, the remaining passenger must be carrying a trolley bag. This is 100.00% - 16.30% - 36.08% = 47.62% of all passengers.

In Table 21 an overview is given with the percentage of passengers carrying the baggage types specified for this research.

Table 21 Percentage of occurrence baggage types

Baggage type Percentage (%) No bag 16.30 Trolley bag 47.62 Baggage cart 36.08

The percentage of occurrence of business and leisure passengers depend on the airport and are therefore different for AMS, HND, LCY and LAS. In order to find the percentage of occurrence for each passenger profile, the percentage occurrence of group size, gender, travel purpose and bag type are all multiplied. As an example, for profile 1 of AMS:

65.54% * 54.00% * 40.10% * 16.30% = 2.31%. Equation 11 The percentage of occurrences of the passenger profiles for the airports AMS, HND, LCY and LAS are stated in Appendices E.1 through E.4 respectively.

Marco Groot, BSc 65

Optimize Landside Airport Operations Using A Discrete Event Simulation

5.4 Reflection on chapter In this chapter data from several researches has been used. Before the chapter is concluded, some remarks are made regarding this data.

The researches used for the walking speed determination ( (Young, 1999), (Schultz, et al., 2008) and (Schultz & Fricke, 2011)) show similar results. Also, these researches have been performed in the United States of America and Germany, meaning the data does not only represent the behaviour of passengers in one location. Furthermore, these data were collected by observing passengers at airport terminals, which means the same environment as for which the data is used in this research.

When looking at section 5.3, (Berdowski, et al., 2009) and (Davis & Braaksma, 1988) researched the occurrence of types of baggage carried by passengers. As these measurements were taken at airports as well, it gives a good representation for using this data in the simulation.

Besides above statements, there are also some critical notes. First of all, in this research only three types of baggage are used (no bags, trolley bags and baggage carts). In reality there are more types of baggage (e.g. handbags and rucksacks). However, as handbags and rucksacks do not have a (large) influence on the walking speed of passengers (Schultz, et al., 2008), these can be ignored in this research.

Second, the occurrence of group sizes has been limited to groups of 1, 2 and 3 persons. At airports, also larger groups occur. However, due to lack of data regarding the walking speed of these larger groups and since the percentage of occurrence for these groups is low (James, 1953), these are taken out of the simulation input. Additionally, the data was gathered in 1953 and in public spaces. This does not represent the occurrence of group sizes at airports in 2016. But since more recent data could not be found, the data from (James, 1953) is used.

Another remark regarding the group size, (James, 1953) has gathered data in two cities in the United States of America. This is not representative for group sizes around the world. Also, groups sizes might be influenced by the travel purpose. One can image that on an airport with more leisure travel, larger groups will occur compared to airports with mostly business travel. To create a more accurate model for occurrences of different group sizes at the airports chosen in this research, more measurements should be taken at the four airports. However, due to time and financial limitations, it was not possible to perform these measurements for this research.

Marco Groot, BSc 66

Optimize Landside Airport Operations Using A Discrete Event Simulation

6 Build airports in simulation program In Chapter 4 all the available data regarding the solutions for check-in, baggage drop-off, security access, security check and immigration were stated. With this input, the airports can be built in the simulation model. The design of the simulation models of the four airports are discussed in this chapter. Additionally, the choices made during the construction of the simulation models are discussed as well. 6.1 General decisions Although four simulation models are build (one for each airport), the basics of the models are the same. This means that the fundamental principles are applied to all four models. 6.1.1 Simulation program The simulation platform used is Enterprise Dynamics. The benefits of Enterprise Dynamics include easy to use graphical user interface, no model limitations and extended reporting capabilities of the simulation results.

Within Enterprise Dynamics all elements entered in the model are called atoms. In the models of this research three types of atoms are used. The blue atoms are queues, the orange atoms are servers (used for kiosks, check-in desks, self-service baggage drop-off, security access and immigration) and the black atoms are multi-servers (used for security lanes). The elements which are processed through the system are called products. In the simulations for this research each product represents a passenger.

Queue atoms are used to direct products to free server atoms. When there are no free server atoms, the product waits inside the queue atom until a server atom becomes available. The queue atoms work via the First In First Out (FIFO) principle.

The server atoms are used for modelling different processes. This includes kiosks, check-in desks, (self-service) baggage drop-off, security access and immigration. Server atoms are only capable to process a single product. This means a second product can only enter the server atom after the first product has left.

Multi-server atoms work almost the same as server atoms. The only difference is that multiple products can be processed simultaneously. In other words, a second product can enter the multi- server atom while the first product is still being processed by that multi-server atom. Because of the process time distribution it can happen that product two enters after product one, but leaves before product one does.

Marco Groot, BSc 67

Optimize Landside Airport Operations Using A Discrete Event Simulation

6.1.2 Process times In Chapter 4 data was found regarding the process times of the solutions. These process times were average process times and/or quickest process times. This data is used as a starting point for the process times used in the simulation models.

For a more realistic simulation, a distribution is used for the process times. However, no accurate data regarding the distribution of the process times of the solutions used in the simulation model are available. As such, the distribution of the process times are assumed to be triangular. In order to set up the triangular distribution, the minimum, average and maximum process times are needed. Also, Enterprise Dynamics has the limitation that the mean needs to be in a certain value range, based on a set formula in which the minimum and maximum values are variables. According to Enterprise Dynamics, the mean needs to oblige to:

(2 ∗ 푚𝑖푛𝑖푚푢푚) + 푚푎푥𝑖푚푢푚 푚푒푎푛 ≥ 3 Equation 12

푚𝑖푛𝑖푚푢푚 + (2 ∗ 푚푎푥𝑖푚푢푚) 푚푒푎푛 ≤ 3 Equation 13 In the previous Chapter 4 the values for the mean process times are found for all solutions. For some of the solutions the quickest process times were found as well. In the cases where the mean and the minimum process times are known, the range of maximum process times are calculated by the above two formulas. When only the average process time is known, the minimum and maximum values are taken for solving the two equations where the “≤” and “≥” are replaced by an “=” in Equation 12 and Equation 13. 6.1.3 Passenger profiles In Chapter 5, 24 passenger profiles were defined. These profiles include the group size (single person, two persons travelling together or groups of 3 or more persons) and the type of baggage (no bag, trolley bag or baggage cart). Also the walking speeds of the different passenger profiles are determined.

For the first phase of building the simulation models, these defined passenger profiles are not yet implemented. The purpose of this first phase is to verify and validate the models with regards to the stations and passenger throughput. In order to have the correct passenger numbers through the correct terminals and stations (international versus domestic, business versus leisure), a simplified model for passenger profiles is used. As the passenger profiles from Chapter 5 are not implemented, also the types of baggage carried by the passengers are not taken into account. Furthermore, the walking speeds of passengers is only needed when the travel between the stations is taken into account. This research does not look at the total time spend inside the terminal building, but the throughput at the stations. Therefore, the walking routes and speeds are not included.

Marco Groot, BSc 68

Optimize Landside Airport Operations Using A Discrete Event Simulation

The simplified model for passenger profiles determines is a passenger is flying business class or economy class, domestic or international (Schengen or non-Schengen for AMS) and if a passenger has checked in via internet or still needs to check in at the airport. Not included in this simplified version is the type of baggage, sex of single passengers, group size and walking speeds. As such, all passengers are single passengers with 1 piece of check-in baggage.

By not including the passenger profiles from Chapter 5 some considerations need to be made when analysing the results of the simulation. The time a passenger spends inside the terminal, according to the simulation model, only includes queueing and processing times. The time a passenger walks through the terminal from one process to another is not included. Also, all passengers only have 1 piece of check-in baggage, while there were passenger profiles defined with no or multiple pieces of check-in baggage. This means the baggage drop-off locations different usage as compared to when the passenger profiles of Chapter 5 would have been implemented.

Considering the simplification of the passenger profiles will create a worse scenario in terms of process times and equipment usage, the decision to not implement the passengers profiles defined in Chapter 5 does not negatively influence the simulation models. Therefore the choice not to implement the passenger profiles is justified. 6.1.4 Passenger arrival distribution In Chapter 3 the passenger arrival distribution throughout the day was found. This distribution was based on a 15 minute time interval. This same distribution is taken as input for the passengers. At the beginning of the 15 minute interval, all passengers which should enter the airport in the next 15 minutes are created. These passengers are released within the next 15 minutes by means of a random pattern. At the end of the 15 minute interval, all passenger which were created have been released into the airport. When the next 15 minute period starts, this sequence is repeated. 6.1.5 Simulation runtime Each run simulates 1 day at the airport. However, in real life there is a continues operation. This means passengers could arrive at the airport at day 1, but will depart on day 2 (e.g. a departure time at 00:30 hours). As such, the simulation will run for 25 hours to allow passengers which have arrived just before midnight to complete the entire departure process (and are not in the middle of the terminal when the simulation run stops).

Marco Groot, BSc 69

Optimize Landside Airport Operations Using A Discrete Event Simulation

6.2 Amsterdam Airport Schiphol In order to model AMS a site survey was conducted on January 12, 2017. During this survey all equipment in the departure terminals were counted. In section 2.2.1 it was stated AMS only has 3 departure terminals. During the site survey it was found that Terminal 3 consists of 2 parts. The smallest section of the terminal is for Schengen flights. As such, 4 terminals are built in the simulation model. Terminal 3 is the Non-Schengen part, while Terminal 4 is the Schengen part. An overview of the kiosks, baggage drop-off locations, check-in desks, security access, security lanes and immigration booths can be found in Table 22.

Table 22 Overview equipment departure terminals AMS

T1 T2 T3 T4 Kiosks 27 37 30 14 Check-in desks 66 46 125 44 Self-service baggage drop-off 7 12 8 0 Security gates 11 11 11 2 Security lanes 14 12 14 5 Serviced immigration 0 9 10 0 Self-service immigration 0 6 12 0

The layout of the four departure terminals are entered in the simulation program. An overview of the model can be found in Figure 18.

Marco Groot, BSc 70

Optimize Landside Airport Operations Using A Discrete Event Simulation

Figure 18 AMS simulation model When looking at Figure 18, ten columns can be seen. From left to right, these columns are: 1. Queues for the check-in kiosks (Blue) 2. Check-in kiosks (Orange) 3. Queues for the check-in desks and self-service baggage drop-off (Blue) 4. Check-in desks and self-service baggage drop-off (Orange) 5. Queues for the security gates (Blue) 6. Security gates (Orange) 7. Queues for the security lanes (Blue) 8. Security lanes (Black) 9. Queues for the immigration (Blue) 10. Immigration (Orange)

In the simulation model, terminal 1 is located at the bottom and terminal 4 is located at the top. Only terminals 2 and 3 have an immigration area, as these are the Non-Schengen terminals. The top two blocks of check-in kiosks and check-in desks are located in terminal 4.

Marco Groot, BSc 71

Optimize Landside Airport Operations Using A Discrete Event Simulation

For the check-in desks and the security check, a number of desks, gates and lanes are dedicated for business class passengers. This means there is a separation of equipment used by business class and economy class passengers. Furthermore, business class passengers which have not checked-in online are checked in at a check-in desk. As such, none of the business class passengers use the check-in kiosks.

An assumption is made for the check-in kiosks, check-in desks and baggage drop-off points. The models is constructed such that this equipment is common use. At AMS, terminal 2 and half of terminal 1 have common use facilities. These parts of the airport are for KLM and SkyTeam partners and passengers for these airlines use the same kiosks, check-in desks and baggage drop-off facilities. At the other parts of the airport, the kiosks are common use for the airlines other than KLM and SkyTeam partners. The check-in desks and baggage drop-off points are not common use.

In order to facilitate the common use features in the model two groups are defined. The first group are the passengers for KLM and SkyTeam partners. This group is called “KLM”. The second group are the passengers for all other airlines. This group is called “Rest”. An overview of the airlines belonging to each group can be found in Appendix D.

Separating the passengers into two groups based on their airline gives the following usage of the terminals (see Table 23).

Table 23 Passenger type per terminal

Terminal Passenger type Terminal 1 (lower half) Rest Schengen Terminal 1 (top half) KLM Schengen Terminal 2 KLM Non-Schengen Terminal 3 Rest Non-Schengen Terminal 4 Rest Schengen

Marco Groot, BSc 72

Optimize Landside Airport Operations Using A Discrete Event Simulation

6.3 Tokyo International Airport Haneda Just as for AMS, a site survey was conducted at HND in order to determine the number and location of the kiosks, check-in desks, security gates, security lanes and immigration booths. The site survey was conducted on February 21, 2017. An overview of the site survey can be found in Table 24.

Table 24 Overview equipment departure terminals HND

International Domestic 1 Domestic 2 Kiosks 56 65 49 Check-in desks 148 95 73 Self-service baggage drop-off 0 0 39 Security gates 2 0 0 Security lanes 14 28 30 Serviced immigration 6 0 0 Self-service immigration 23 0 0

With the information from the site survey, the simulation model was build. The model of HND can be seen in Figure 19.

Figure 19 HND simulation model

Marco Groot, BSc 73

Optimize Landside Airport Operations Using A Discrete Event Simulation

There are three terminals at HND. In Figure 19 the international, domestic T1 and domestic T2 terminals can be seen from top to bottom respectively. The setup for the model is comparable to the model for AMS. This means the most left orange column represents the check-in kiosks. The column with orange atoms to the right of these are the check-in desks and self-service baggage drop-off locations. The black atoms are the security lanes and the column with orange atoms at the most righthand side is the immigration. The immigration is only present at the international terminal. Also, at the international terminal there are two security gates, used for the access to the priority security lanes. The layout of the domestic terminals is such that there is no need for security gates.

In the model the blue atoms represent the queues. Compared to the AMS model some extra blue atoms can be seen behind the kiosks and check-in desks. These do not have a purpose for the simulation itself, but are used to simplify the construction of the model.

As mentioned before, there are three terminal buildings at HND. All passengers for international flights travel through the international terminal. Passenger on domestic flights for JAL, StarFlyer and Skymark Airlines depart from domestic T1, while passengers on domestic flights for ANA, Solaseed Air and Air Do depart from domestic T2.

There is a separation between economy class and business class passengers at the domestic terminals. The check-in desks for business class passengers are located in separate areas. From these areas passengers are guided towards priority security lanes. As the priority security lanes can only be accessed through the business class check-in areas, there is no need for security gates. At the international terminal the separation between economy class and business class passengers is the same as in the AMS model.

Marco Groot, BSc 74

Optimize Landside Airport Operations Using A Discrete Event Simulation

6.4 London City Airport Where a site survey was conducted for AMS and HND, Google Maps was used to get the required information to construct the simulation model for LCY. By means of this virtual site survey (which was conducted on June 5, 2017) the equipment installed in the terminal was found. An overview is stated in Table 25.

Table 25 Overview equipment departure terminal LCY

International Kiosks 35 Check-in desks 17 Self-service baggage drop-off 0 Security gates 3 Security lanes 4 Serviced immigration 0 Self-service immigration 0

LCY consists of one terminal building. All passengers travel through this terminal. The layout for the simulation model can be found in Figure 20.

Figure 20 LCY simulation model As can be seen in Figure 20, LCY is a small airport with a small terminal. Just as for the models mentioned before, the most left column with orange atoms are the check-in kiosks. The middle and right column with orange atoms are for the check-in desks and security gates, respectively. The black atoms are again for the security lanes. There is no immigration.

Just as in the model for HND, the blue atoms in front of the kiosks, check-in desks, security gates and security lanes are for the queues. The blue atoms immediately behind the kiosks and check-in desks are created to simplify the construction of the model.

The small row of two check-in desks are dedicated for business class passengers. Beside these there is no separation between business class and economy class passengers. This means all passengers go through the same security gates and security lanes.

Marco Groot, BSc 75

Optimize Landside Airport Operations Using A Discrete Event Simulation

6.5 McCarran International Las Vegas The last airport to be built in the simulation program is LAS. No site survey could be conducted on this airport. As Google Maps does not allow a virtual walk through the airport, the numbers of equipment needs to be estimated. The method used is taking the number of equipment per passenger for AMS and HND airport and multiply this factor to the number of passengers at LAS. LCY was not taken into account when determining the factor coefficient, as LCY is five times as small (in terms of passenger numbers) and has a completely different passenger profile. Additionally, no information was found which indicates self-service equipment is installed at LAS. As such, it is assumed there is no self-service equipment at LAS. An overview of the equipment in the two terminals of LAS can be found in Table 26.

Table 26 Overview equipment departure terminals LAS

T1 T3 Kiosks 119 48 Check-in desks 241 98 Self-service baggage drop-off 0 0 Security gates 24 10 Security lanes 51 20 Serviced immigration 57 23 Self-service immigration 0 0

The two terminals at LAS are reconstructed in the simulation program. These terminals contain the equipment as stated in Table 26. The simulation model of LAS can be found in Figure 21.

The setup of the simulation model is the same as for HND and LCY. This means the four orange columns represent the kiosks, check-in desks, security gates and immigration (from left to right). The security lanes are black. The blue columns right in front of the orange and black columns are the queues for the respective equipment. The extra blue columns are for the construction of the model itself. Lastly, Terminal 1 is located at the top, Terminal 3 at the bottom.

Marco Groot, BSc 76

Optimize Landside Airport Operations Using A Discrete Event Simulation

Figure 21 LAS simulation model

Marco Groot, BSc 77

Optimize Landside Airport Operations Using A Discrete Event Simulation

7 Model Verification In order to have confidence the model behaves as designed, the model is verified against the design criteria. As four models are build (one for each airport) also four verification processes needs to be completed. However, the principles behind the models are the same. As such, the first model build goes through an extensive verification process, while the other three models only are verified where there is a difference compared to the first model.

For all four models an initial simulation is performed. The variables in these simulations are set as found in the previous chapters. Ten independent simulation runs are performed on each initial simulation in order to verify a consistent functionality. In order to verify the model further, additional simulations are performed in which variables are changed. Due to the extensive runtimes of the simulations, only five independent runs are performed for each of these simulations. 7.1 Amsterdam Airport Schiphol The first model build is for AMS. In Chapter 6 the layout of the build terminal was stated (see Figure 18). As discussed in previous chapters, the minimum, average and maximum process times are needed in order to create a process time distribution. In Chapter 6 it was explained which values were chosen.

Besides the process time distribution other parameters are needed as input for the model. For AMS the passengers are divided based on the airline with which the passengers fly. By making use of the departure schedule it is known which percentage of the passengers fly with KLM and their SkyTeam partners. Based on the destinations of the flights, the percentage of passengers flying to Schengen destinations is known. This percentage was retrieved for KLM passengers as well as passengers classified as flying with the “Rest” airlines. Thirdly, from Chapter 2 it was concluded that 31% of the passengers are business class passengers. Lastly, the percentage of passenger which have checked in online is taken into account. A complete overview of the variables used in the model for AMS is stated in Appendix F.1. 7.1.1 Verification initial simulation Using the variable settings an initial simulation was performed. In total 10 independent runs are performed in order to verify the consistency in the behaviour. The primary result which should give approximately the same value, is the time a passenger is in the system. So this is the time from entering the model until leaving the model. Since this is known for each passenger, a histogram can be created. As an example, the histogram of run 4 can be found in Figure 22.

Marco Groot, BSc 78

Optimize Landside Airport Operations Using A Discrete Event Simulation

Figure 22 Histogram run 4 initial simulation Looking at the histogram, five values are of importance: - Min: time of the quickest passenger through the system - Mean: average time of all passengers through the system - Max: time of the slowest passenger through the system - Median: time for which 50% of the passengers are quicker and the other 50% is slower - Mode: time which occurs the most (range of whole minutes)

An overview of these five values for the simulations runs of the initial simulation can be found in Table 27 (runs 1 through 5) and Table 28 (runs 6 through 10).

Table 27 Time values initial simulation runs 1 through 5

1 2 3 4 5 Min 00:02:50 00:03:00 00:02:57 00:03:03 00:03:01 Mean 00:27:26 00:29:30 00:27:35 00:28:51 00:28:39 Max 03:28:11 04:04:17 03:21:58 03:42:18 03:36:20 Median 00:18:59 00:19:16 00:19:26 00:19:14 00:19:08 Mode 00:07:00 00:07:00 00:08:00 00:07:00 00:08:00

Table 28 Time values initial simulation runs 6 through 10

6 7 8 9 10 Min 00:02:50 00:02:56 00:03:01 00:02:48 00:00:47 Mean 00:28:16 00:28:31 00:26:33 00:28:23 00:24:35 Max 03:39:27 03:44:14 03:32:59 03:22:05 03:06:04 Median 00:19:09 00:19:17 00:18:34 00:19:12 00:18:28 Mode 00:07:00 00:08:00 00:08:00 00:07:00 00:08:00

Marco Groot, BSc 79

Optimize Landside Airport Operations Using A Discrete Event Simulation

The data in Table 27 and Table 28 shows consistency throughout the independent runs. The only exception is run 10, where the min, mean and max are lower compared to the other 9 runs. The min and max are based on a single passenger (the fastest and the slowest), which means this can differ compared to the other runs. However, the mean should be approximately the same. When looking at the histogram of run 10 (see Figure 23) it can be seen that the number of passengers exceeding 1 hours in the terminal is lower compared to run 4 (see Figure 22). This explains the mean is lower for run 10. The median and mode are comparable to the other 9 runs.

Figure 23 Histogram run 10 initial simulation

Marco Groot, BSc 80

Optimize Landside Airport Operations Using A Discrete Event Simulation

7.1.2 Change of percentage variables Six variables used are percentages. These represent which portion of passengers belongs to a certain group. In order to verify these variables additional simulations are performed. An overview of the performed simulations, settings and expected outcome can be found in Table 29. All variables in the simulations of are kept the same as stated in Appendix F.1, expect for the variable in column 2 of Table 29.

Table 29 Setup variables simulation scenarios

Simulation Changed setting Expected outcome KLM 100% KLM = 100 No passengers through T1 (lower half), T3 and T4 KLM 0% KLM = 0 No passengers through T1 (top half) and T2 KLM Non-Schengen 100% KLM Non-Schengen = 100 No passengers through T1 top half KLM Non-Schengen 0% KLM Non-Schengen = 0 No passengers through T2 Rest Non-Schengen 100% Rest Non-Schengen = 100 No passengers through T1 (lower half) and T4 Rest Non-Schengen 0% Rest Non-Schengen = 0 No passengers through T3 Travel Class 100% Travel Class = 100 No passengers at kiosk, economy desks, gates and lanes Travel Class 0% Travel Class = 0 No passengers at business desks, gates and lanes Internet Check-in 100% Internet Check-in = 100 No passengers at kiosks Internet Check-in 0% Internet Check-in = 0 All economy passengers go to kiosks

All ten simulation scenarios stated in Table 29 resulted in the expected outcome. As an example, the number of passengers through T2 of the “KLM Non-Schengen 0%” simulation runs are stated in Table 30, while the passengers through T3 in the “Rest Non-Schengen 0%” simulation runs can be found in Table 31.

Table 30 Passengers through T2 “KLM Non-Schengen 0%”

1 2 3 4 5 Input T2 0 0 0 0 0

Table 31 Passengers through T3 “Rest Non-Schengen 0%”

1 2 3 4 5 Input T3 0 0 0 0 0

Marco Groot, BSc 81

Optimize Landside Airport Operations Using A Discrete Event Simulation

7.1.3 Process times Additional scenarios are performed, aimed to verify the process times of the different stations. The process times have been respectively doubled and divided by 2 compared to the initial simulation. Histograms of these two simulation scenarios can be found in Figure 24 and Figure 25. The time values can be found in Table 32 and Table 33.

Figure 24 Histogram double process times run 5

Table 32 Time values double process times

1 2 3 4 5 Min 00:06:11 00:05:55 00:05:47 00:05:34 00:06:02 Mean 01:52:55 01:54:24 01:53:07 01:55:37 01:53:46 Max 11:39:16 12:05:46 11:22:01 12:14:29 12:02:39 Median 01:00:47 00:59:38 01:00:16 01:02:51 01:01:10 Mode 00:14:00 00:14:00 00:15:00 00:14:00 00:15:00

Marco Groot, BSc 82

Optimize Landside Airport Operations Using A Discrete Event Simulation

Figure 25 Histogram half process times run 3

Table 33 Time values half process times

1 2 3 4 5 Min 00:01:30 00:01:26 00:01:34 00:01:30 00:01:30 Mean 00:17:32 00:17:23 00:17:25 00:17:26 00:17:24 Max 03:04:39 03:08:13 03:32:22 02:21:54 02:59:43 Median 00:12:55 00:12:41 00:12:47 00:12:54 00:12:49 Mode 00:04:00 00:04:00 00:04:00 00:04:00 00:04:00

As can be seen from Figure 24 and Table 32, doubling the process times will exponentially increase the total time a passengers spend in the terminal. This is due to the fact that the queues are more than doubled. For the quickest passenger, it can be seen that the time value is doubled. This is for a passenger where there are no queues. The maximum time is increased by a factor 3.

When looking at Figure 25 and Table 33 it can be seen that the minimum process time is half compared to the minimum process time of the initial simulation. The maximum time is lower, but only decreased by around 30 minutes. The passenger for which the maximum time applies proceeds through the terminal during a peak moment. This means this passenger is most likely to experience queues. Although the process times are quicker, these have a marginal influence of the total duration the passenger is in the terminal compared to the influence of the queues.

Marco Groot, BSc 83

Optimize Landside Airport Operations Using A Discrete Event Simulation

7.1.4 Passenger arrival distribution Lastly, the passenger arrival schedule was altered. In one of these scenarios the passenger arrival schedule is taken as the average number of passengers per 15 minutes from the initial simulation model and applied throughout the entire day. For the second simulation scenario, the maximum number of passengers within a 15 minute time interval of the initial simulation is taken and applied throughout the entire day. This means in both of these scenarios a constant passenger input is used, with 568 passengers per 15 minutes and 1415 passengers per 15 minutes respectively. The results of these two scenarios can be found below.

Figure 26 Histogram 568 passengers per 15 minutes run 1

Table 34 Time values 568 passengers per 15 minutes

1 2 3 4 5 Min 00:02:59 00:02:16 00:02:45 00:02:46 00:01:37 Mean 00:19:48 00:19:54 00:19:47 00:19:57 00:19:53 Max 02:48:29 03:02:20 02:30:17 03:10:14 02:41:38 Median 00:15:15 00:15:19 00:15:12 00:15:18 00:15:14 Mode 00:07:00 00:08:00 00:08:00 00:08:00 00:08:00

Marco Groot, BSc 84

Optimize Landside Airport Operations Using A Discrete Event Simulation

Figure 27 Histogram 1415 passengers per 15 minutes run 2

Table 35 Time values 1415 passengers per 15 minutes

1 2 3 4 5 Min 00:02:46 00:02:45 00:02:49 00:02:51 00:02:49 Mean 01:10:52 01:13:31 01:13:05 01:13:06 01:11:53 Max 11:10:00 11:21:20 11:21:20 11:16:23 11:14:37 Median 00:19:42 00:19:48 00:20:35 00:19:52 00:20:09 Mode 00:08:00 00:09:00 00:08:00 00:09:00 00:09:00

For both scenarios the minimum time is the same as with the initial simulation. This is as expected, as the process times are the same and the minimum time represents a passenger with no queues.

With 568 passengers per 15 minutes, there is no peak moment. Therefore, the queues are less, as queues are mostly created during high peak moments. As such, the max and median are lower.

In the scenario with 1415 passengers per 15 minutes, there is an increase in the number of passengers. There are almost three times as much passengers compared to the initial simulation. As the airport is not designed for this high number of passengers queues will form. This result in an increase of the mean and maximum time value.

Marco Groot, BSc 85

Optimize Landside Airport Operations Using A Discrete Event Simulation

7.2 Tokyo International Airport Haneda The simulation model for HND was the second model to be created. As the basic principles of the model are equal to the model of AMS, the verification process does not have to be as extensive as for the AMS model. In more detail, the process times of the equipment and the arrival schedule of the passengers are identical implemented as to the AMS model. As such, only the change of percentage variables needs to be verified. However, before the change of percentage variables can be verified, first the initial measurement needs to be performed. 7.2.1 Initial simulation In order to run the initial simulation, the variables needs to be set. The variables for the initial simulation are the percentage of passengers departing form the different terminals (as found in Chapter 3), travel class and internet check-in (as found in Chapter 2) and process times of the different types of equipment in the terminals (as found in Chapter 4). The overview of all variable settings for the initial simulation of HND can be found in Appendix F.2.

For the initial simulation ten independent runs are performed. The time values of these runs can be found in Table 36 and Table 37. Also, one of the histograms of the independent runs is shown in Figure 28.

Table 36 Time values initial simulation runs 1 through 5

1 2 3 4 5 Min 00:02:56 00:02:53 00:02:51 00:02:42 00:02:50 Mean 00:22:56 00:22:51 00:23:16 00:22:53 00:23:02 Max 02:59:41 03:08:38 02:42:44 02:52:20 03:02:34 Median 00:18:01 00:18:05 00:18:24 00:18:05 00:18:06 Mode 00:09:00 00:09:00 00:10:00 00:09:00 00:09:00

Table 37 Time values initial simulation runs 6 through 10

6 7 8 9 10 Min 00:02:54 00:02:57 00:02:39 00:02:58 00:02:46 Mean 00:22:26 00:23:11 00:23:14 00:22:37 00:22:55 Max 03:27:51 03:38:57 02:53:01 03:23:16 03:09:43 Median 00:17:44 00:18:18 00:18:15 00:17:58 00:18:08 Mode 00:09:00 00:09:00 00:09:00 00:09:00 00:09:00

Marco Groot, BSc 86

Optimize Landside Airport Operations Using A Discrete Event Simulation

Figure 28 Histogram run 3 initial simulation When comparing the values of the ten runs consistency can be seen. The mean time lies between 22:26 and 23:16 minutes, so the range is 50 seconds. Compared to the initial simulation of AMS, the results for HND are more close to each other. Also the histograms of the independent runs have similar shapes. 7.2.2 Change of percentage variables There are four percentage variables within the model for HND. It needs to be verified if these are implemented correctly. This is done by simulating the following four scenarios stated in Table 38. Also, the expected result of each of these scenarios are stated.

Table 38 Setup variables simulation scenarios

Simulation Changed setting Expected outcome International 0% International = 0 No passengers through international terminal T1 0% T1 = 0 No passengers through T1 Travel Class 0% Travel Class = 0 No passengers through business class desks and security lanes Internet Check-in 100% Internet Check-in = 100 No passengers go to kiosk

For the scenarios stated in Table 38, five independent runs were performed. The results which indicates the simulates were completed successfully can be found in Table 39 through Table 42.

Marco Groot, BSc 87

Optimize Landside Airport Operations Using A Discrete Event Simulation

Table 39 Passengers through international terminal “International 0%” scenario

1 2 3 4 5 Input International 0 0 0 0 0

Table 40 Passengers through domestic T1 terminal “T1 0%” scenario

1 2 3 4 5 Input T1 0 0 0 0 0

Table 41 Passengers through business class desks and security “Travel Class 0%” scenario

1 2 3 4 5 Input BD Desk BC I 0 0 0 0 0 Input BD Desk BC T1 0 0 0 0 0 Input BD Desk BC T2 0 0 0 0 0 Input CS I BC 0 0 0 0 0 Input CS T1 BC 0 0 0 0 0 Input CS T2 BC 0 0 0 0 0

Table 42 Passengers through terminals and kiosks “Internet check-in 100%” scenario

1 2 3 4 5 Input Kiosk I 0 0 0 0 0 Input Kiosk T1 0 0 0 0 0 Input Kiosk T2 0 0 0 0 0

Marco Groot, BSc 88

Optimize Landside Airport Operations Using A Discrete Event Simulation

7.3 London City Airport LCY has only one terminal. Also, the implementation of the process times of the equipment and the passenger arrival schedule are identical to the AMS and HND models. As such, only three different scenarios are needed to verify the model. Besides the initial simulation these are the two scenarios stated in Table 43.

Table 43 Setup variables simulation scenarios LCY

Simulation Changed setting Expected outcome Travel Class 0% Travel Class = 0 No passengers through business class desks and security lanes Internet Check-in 0% Internet Check-in = 0 All economy class passengers go to kiosk 7.3.1 Initial simulation For the initial simulation the same procedure is applied as for AMS and HND. This means ten independent runs are performed. The variable settings used for the initial simulation are stated in Appendix F.3. The results of the runs can be found in Table 44 and Table 45.

Table 44 Time values initial simulation runs 1 through 5

1 2 3 4 5 Min 00:04:38 00:03:41 00:03:51 00:04:07 00:04:02 Mean 03:06:25 03:01:36 03:09:55 03:03:43 03:04:04 Max 11:08:10 11:31:12 11:28:00 11:18:24 11:17:05 Median 02:33:57 02:29:22 02:38:39 02:29:30 02:30:04 Mode 02:00:00 01:55:00 02:00:00 02:00:00 02:00:00

Table 45 Time values initial simulation runs 6 through 10

6 7 8 9 10 Min 00:03:57 00:03:22 00:04:32 00:03:46 00:03:51 Mean 03:02:56 02:58:26 03:00:27 03:03:04 03:05:56 Max 11:23:32 11:11:56 11:16:22 11:22:56 11:39:57 Median 02:29:25 02:25:54 02:25:38 02:29:58 02:33:10 Mode 02:00:00 01:53:00 01:58:00 01:56:00 01:59:00

When looking at the results of the simulation runs, consistency throughout the numbers is found. The results itself might seem unexpected. As LCY is a small airport, a mean process time of over 3 hours is long. Especially when compared to AMS and HND, where, respectively, 5 and 8 times as many passengers pass through the airport.

The reason for these numbers is found in the passenger profiles. When taking groups as a single entity and assuming business class passengers with a trolley bag can take the bag as cabin baggage, 49,7% of the passengers do not have to drop-off their baggage. As mention in Chapter 6, it is assumed in the model every passenger has one piece of baggage to check-in. In the simulations for LCY the bottleneck lies at the check-in desks. This causes queues up to 3000 passengers. When taking the passenger profiles into account, around 5000 passengers will not go through the check-in desks, removing the queues to a large extent.

Marco Groot, BSc 89

Optimize Landside Airport Operations Using A Discrete Event Simulation

7.3.2 Change of percentage variables For the verification of the percentage variables, two scenarios needs to be completed. The two scenarios are the changes of the “Travel class” percentage and the change of “Internet check- in” percentage. The results of the expected outcomes of the scenarios can be found in Table 46 and Table 47.

Table 46 Passengers through business class desks “Travel Class 0%” scenario

1 2 3 4 5 Input BD queue BC 0 0 0 0 0

Table 47 Passengers through internet check-in “Internet check-in 0%” scenario

1 2 3 4 5 Input Internet Check-in 0 0 0 0 0

Marco Groot, BSc 90

Optimize Landside Airport Operations Using A Discrete Event Simulation

7.4 McCarran International Las Vegas In order to verify the simulation model for LAS, three scenarios are run (besides the initial simulation run). 7.4.1 Initial simulation During the initial simulation, ten independent runs are performed. The variable settings used during the initial simulation are stated in Appendix F.4. The results of the ten runs can be found in Table 48 and Table 49. Also, one of the histogram of the independent runs is shown in Figure 29.

Table 48 Time values initial simulation runs 1 through 5

1 2 3 4 5 Min 00:03:46 00:03:50 00:03:48 00:03:50 00:03:48 Mean 00:20:52 00:20:43 00:20:47 00:20:41 00:20:51 Max 02:37:42 02:56:25 03:12:54 02:41:33 02:57:12 Median 00:16:14 00:16:12 00:16:13 00:16:04 00:16:17 Mode 00:08:00 00:09:00 00:09:00 00:09:00 00:08:00

Table 49 Time values initial simulation runs 6 through 10

6 7 8 9 10 Min 00:03:53 00:02:44 00:03:48 00:03:26 00:03:52 Mean 00:20:59 00:20:46 00:20:49 00:20:52 00:20:48 Max 02:56:09 03:12:06 03:19:59 03:03:31 03:01:04 Median 00:16:21 00:16:14 00:16:15 00:16:18 00:16:18 Mode 00:09:00 00:09:00 00:08:00 00:09:00 00:09:00

Figure 29 Histogram run 4 initial simulation

Marco Groot, BSc 91

Optimize Landside Airport Operations Using A Discrete Event Simulation

The results of the initial simulation runs are consistent. The mean time is ranged between 20:41 and 20:59 minutes. While the quickest process time of run 7 is low, the minimum process times of the other 9 runs are within a 27 seconds span. Overall, the process times are as might be expected at an airport. 7.4.2 Change of percentage variables As mentioned before, three additional scenarios are performed to verify the model. An overview of these scenarios can be found in Table 50.

Table 50 Setup variables simulation scenarios LAS

Simulation Changed setting Expected outcome T1 100% T1 = 100 All passengers travel through Terminal 1 Travel Class 0% Travel Class = 0 No passengers through business class desks and security lanes Internet Check-in 0% Internet Check-in = 0 All economy class passengers go to kiosk

In the tables below the results of the outputs from which the expected outcome can be verified are stated.

Table 51 Passengers through T3 “T1 100%” scenario

1 2 3 4 5 Input T3 count 0 0 0 0 0

Table 52 Business class passengers “Travel Class 0%” scenario

1 2 3 4 5 Input T1 business passengers 0 0 0 0 0 Input T3 business passengers 0 0 0 0 0

Table 53 Passengers through internet check-in “Internet check-in 0%” scenario

1 2 3 4 5 Input T1 Internet check-in 0 0 0 0 0 Input T3 Internet check-in 0 0 0 0 0

Marco Groot, BSc 92

Optimize Landside Airport Operations Using A Discrete Event Simulation

7.5 Results overview and comparison The main objective of the simulations performed in this chapter is to verify the models. As can be seen from the results stated in this chapter, all test scenarios have been completed successfully.

When looking at the tables in this chapter which state the time values of the min, max, mean, median and mode of the process times, it can be concluded that these values are within an acceptable range. The criteria for acceptable values was taken to be such that values needs to be within the 95% range of the normal distribution. This means:

푥̅ − 1.96𝜎푥 ≤ 푥 ≤ 푥̅ + 1.96𝜎푥 Equation 14 As an example, from the ten “min” values from the initial run of HND (Table 36 and Table 37) the average and standard deviation were calculated. These values were substituted in Equation 14 to find the accepted “min” values for the initial simulation runs of HND. This is done for the 7 scenarios for which the process time tables are stated in this chapter.

The only values rejected based on the criteria in Equation 14 are stated in Table 54. This means from the 275 measurement points, only 13 points are rejected. Also, the rejected data points are found in 10 different runs. Only run 10 from AMS and run 3 from LCY have multiple points rejected. These results are satisfactory to conclude the verification of the simulation runs was successful.

Table 54 Rejected values verification runs

Airport Values rejected AMS Table 27, run 2, max Table 28, run 10, min, mean and median HND Table 36, run 3, mode Table 37, run 6, median LCY Table 44, run 3, mean and median Table 45, run 7, mode Table 45, run 10, max LAS Table 48, run 4, median Table 49, run 6, mean Table 49, run 7, min

When looking at the scenarios for which the percentage variables were changed, all simulations showed the expected outcome. As an example, no passengers travelled through T3 at LAS when the percentage for T1 was set to 100%.

Marco Groot, BSc 93

Optimize Landside Airport Operations Using A Discrete Event Simulation

When comparing the process times of the initial simulation runs of the four airports, it can be seen that the passengers at LAS travel through the airport the quickest. Although the minimum time required to pass through the airport is quicker at AMS and HND, the average process time is the quickest at LAS. Due to the passenger profiles and layout of the airport, it was expected that LCY would show the quickest process times. However, since the passenger profiles have not been included in the simulations, LCY shows large queues. It can be concluded that if every passenger at LCY would need to check in hold baggage, the airport could not cope with the passenger flow. This would them result in an average mean process time of 3:03 hours and an average maximum process time of 11:21 hours.

There are two bottlenecks in the process at AMS. These are the kiosks at Terminal 1 and the business class baggage drop-off in Terminal 1. The average queueing times are 16:22 minutes and 53:37 minutes, respectively. For LCY the queueing times for the baggage drop-off also create a bottleneck. At LCY, the average queueing time for economy class is 2 hours and 21 minutes. For business class this is 5 hours and 47 minutes. As mentioned before, the queueing times for the baggage drop-off at LCY is not representative, as the passenger profiles have not been taken into account. At HND and LAS there are no queues which could be considered to be a bottleneck for the entire process.

Marco Groot, BSc 94

Optimize Landside Airport Operations Using A Discrete Event Simulation

8 Implementing passenger profiles In Chapter 5 the passenger profiles were defined for all four airlines. Two features were established, namely the percentage of occurrence and waking speeds. For the validation and verification of the simulation programs these passenger profiles were not included.

As stated in the previous chapter the validation and verification process was successful. Therefore the second phase of this research can start. This means implementation of the passenger profiles, optimizing the equipment used at all stations and double the number of passengers. In this chapter the implementation of the passenger profiles is described. 8.1 Percentage of occurrence The simulation tool has an ‘Empirical Distribution’ atom in which the percentage of occurrence for the passenger profiles can be set. Arriving passengers are classified according to this distribution and channelled to the corresponding profile atom. Passengers which are classified as travelling is groups of 2 or 3 persons are grouped together before proceeding to the airport terminal atoms.

In Figure 30 the passenger profiles for single travelling passengers are shown. The atoms in the left column are the atoms where all passengers are send which come from the atom which ‘creates’ the passengers. The atoms in the right column are used to attach label values to the passengers. These labels include number of bags, if the passenger has already been checked in via the internet and the destination of the passenger.

When looking at Figure 31, the same structure for the passenger profiles can be seen in terms of the left and right columns. However, there is an additional column in the middle compared to Figure 30. The atoms in this column are used to group the individual passenger together to form a group of 2 or 3 persons.

Between the atoms in the left column and the atoms in the right column of Figure 30 no action takes place. The only reason for the atoms in the right column are to attach the label values to the passengers. This could have been done in the atoms of the left column as well. For the passengers travelling in groups of 2 or 3 persons it is required to attach the label values to the entire group instead of the individual passengers. It was chosen to have a consistent method for all passenger profiles. As such, the label values are added to the single passenger profiles with the use of the atoms in the column on the right-hand side.

Marco Groot, BSc 95

Optimize Landside Airport Operations Using A Discrete Event Simulation

Figure 30 Passenger profiles single travelling passengers

Marco Groot, BSc 96

Optimize Landside Airport Operations Using A Discrete Event Simulation

Figure 31 Passenger profiles groups of 2 or 3 persons The same structure of including the passenger profiles is used for all four airports. After the implementation a verification is performed on the passenger profiles. In Table 55 an overview of this verification can be found. As can be seen, the resulting percentages are comparable to the designed values.

Marco Groot, BSc 97

Optimize Landside Airport Operations Using A Discrete Event Simulation

Table 55 Results verification passenger profiles implementation

Passenger AMS AMS HND HND LCY LCY LAS LAS profile design result design result design result design result (%) (%) (%) (%) (%) (%) (%) (%) MBN 2.31 2.28 3.46 3.45 4.04 3.94 1.47 1.53 MBT 6.76 6.65 10.11 10.14 11.8 11.80 4.28 4.32 MBC 5.12 5.04 7.66 7.69 8.94 8.77 3.24 3.27 MLN 3.46 3.47 2.31 2.31 1.73 1.63 4.30 4.20 MLT 10.10 9.94 6.74 6.70 5.06 5.12 12.57 12.39 MLC 7.65 7.57 5.11 5.15 3.83 3.86 9.53 9.57 FBN 1.97 1.96 2.95 2.92 3.44 3.55 1.25 1.23 FBT 5.76 5.69 8.61 8.57 10.05 9.87 3.65 3.80 FBC 4.36 4.36 6.53 6.49 7.61 7.70 2.76 2.72 FLN 2.94 2.92 1.97 1.95 1.47 1.49 3.67 3.54 FLT 8.60 8.72 5.74 5.74 4.31 4.46 10.71 10.73 FLC 6.52 6.63 4.35 4.37 3.26 3.15 8.11 7.98 2BN 1.66 1.75 2.49 2.46 2.91 2.90 1.05 1.10 2BT 4.86 5.02 7.28 7.32 8.49 8.39 3.08 3.15 2BC 3.69 3.61 5.51 5.54 6.43 6.50 2.33 2.35 2LN 2.49 2.44 1.66 1.67 1.25 1.26 3.10 3.10 2LT 7.27 7.31 4.85 4.87 3.64 3.62 9.05 9.09 2LC 5.50 5.74 3.68 3.70 2.76 2.83 6.86 6.85 3BN 0.59 0.58 0.88 0.85 1.03 0.97 0.37 0.35 3BT 1.72 1.76 2.57 2.61 3.00 3.06 1.09 1.08 3BC 1.30 1.30 1.95 1.94 2.27 2.40 0.82 0.83 3LN 0.88 0.83 0.59 0.59 0.44 0.47 1.09 1.12 3LT 2.56 2.59 1.71 1.70 1.28 1.31 3.19 3.33 3LC 1.94 1.86 1.30 1.27 0.97 0.96 2.42 2.38 Total 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00

In section 7.5 the requirements for a successful verification process were determined. For the verification of the correct implementation of the passenger profiles the same criteria is used. This means the values from the simulation should be within a 5% margin. This means the result values should be within the range of:

0.95푥푑푒푠𝑖𝑔푛 ≤ 푥푟푒푠푢푙푡 ≤ 1.05푥푑푒푠𝑖𝑔푛 Equation 15 When using this criteria a total of 89 out of the 96 results are accepted. When also accepting results which are within an absolute range of 0.05%, 92 out of the 96 results are accepted. The three additional results which are accepted by this have small design values, which means the results are only allowed to deviate with a small absolute number to be accepted. An example is the 3BN profile for LAS.

Since 92 out of 96 results are accepted, this means 95.8% of all results are accepted. This holds the verification is passed.

Marco Groot, BSc 98

Optimize Landside Airport Operations Using A Discrete Event Simulation

8.2 Walking speeds The walking speeds of passenger have influence on the total time passengers are within the airport terminal. For the same distance, passengers with slower walking speeds occupy terminal space for a longer time compared to passengers with a faster walking speed. This means the lower the walking speeds, the fewer passengers can travel through the same terminal space.

In this research five stations of the departure process are included. Passengers travelling between these stations is not in the scope of the research. As such, it was decided to not make alterations to the simulation programs for the walking speeds of the passengers. 8.3 Other changes to simulation programs In the simulation programs for which the validation and verification was performed (Chapter 7) used atoms to separate business class and economy class passengers. Due to the implementation of the passenger profiles, this separation is included within these different profiles. As such, the business class and economy class atoms are removed in the new simulation programs.

Furthermore, the entire airport was reconstructed in the models discussed in Chapter 6. For each piece of equipment in the terminal a single atom was used in the simulation program. For example, 108 kiosk atoms were used for the AMS simulation. In order to make the simulation program more maintainable and to be able to easily make changes to the program in order to find the optimal number of equipment needed, the atoms are replaced with multi-server atoms. Using these multi-server atoms, all 108 kiosk can be simulated by one single atom. In Figure 32 the new AMS simulation can be seen. Five columns with multi-server atoms can be seen (black atoms). From left to right these are the kiosks, check-in desks/self-service baggage drop- off, security access, security check and immigration. This new simulation model has the same functionalities as the model from Figure 18.

Also in Figure 32, between the second and third columns with multi-server atoms, purple atoms can be seen. These atoms are used to split the passenger groups into individual passengers. For all stations behind this point are single-passenger operated. This means a process can only cope with a single passenger and not with a group of passengers. For example, the access gates to the security area are designed for a single person. The passenger presents the boarding after which the gate opens. Only one passenger can pass through the gate, the doors close behind the passenger. The second passenger needs to present his/her own boarding pass. Also for the security check and immigration, in general, a group of passengers cannot combine into one group process. Exceptions can be for a parent carrying a small child on his/her arm, but these passengers are not taken into account.

Lastly, some alterations regarding the process times are made. With the values used originally, only the actual process interaction was taken into account. However, just before starting and after completing the process, passengers occupy the equipment without actually using the equipment. Examples are approaching the equipment, searching for the required documents, gathering all belongings and walking away from the equipment. For the new simulations this is taken into account. The new process times are stated in Appendix G.

Marco Groot, BSc 99

Optimize Landside Airport Operations Using A Discrete Event Simulation

Figure 32 AMS simulation using multi-server atoms

Marco Groot, BSc 100

Optimize Landside Airport Operations Using A Discrete Event Simulation

9 Optimize airport equipment The goal of optimizing the airport layout is to process as many passengers as possible per square meter. In Chapter 4 detailed information regarding all solution options were stated. The first step in the optimization process is to find the best option for each station in terms of passenger throughput per square meter. This is calculated by dividing the footprint of the equipment by the number of passengers which can simultaneously use the equipment and multiply this with the average process time (Equation 16). The best option is the one which gives the lowest result from this equation.

푒푞푢𝑖푝푚푒푛푡 푓표표푡푝푟𝑖푛푡 표푝푡𝑖푚푎푙 푡ℎ푟표푢푔ℎ푝푢푡 = ∗ 푎푣푒푟푎푔푒 푝푟표푐푒푠푠 푡𝑖푚푒 푛푢푚푏푒푟 표푓 푝푎푥 푝푒푟 푝푟표푐푒푠푠 Equation 16 9.1 Check-in For the check-in process three options were discussed. These are the serviced check-in, self- service check-in and internet check-in. In Table 56 the results of the calculation of Equation 16 for each of these options can be found.

Table 56 Check-in optimal throughput overview

Serviced Self-service Internet check-in check-in check-in Equipment footprint (m2) 3.6 0.265 0 Pax/process All in booking All in booking All in booking Average process time (sec) 115.5 80 80 Result Equation 16 (m2*s/pax) 415.8 21.179 0

As can be seen from Table 56 the internet check in would be the most favourable option. The footprint of this option is 0 m2 as the internet check-in does not take place in the airport terminal. However, not all passengers might have the option to check-in online. As such, there still need to be equipment in the airport terminal. The self-service check-in option is more efficient compared to the serviced check-in. 9.2 Baggage drop-off For the baggage drop-off four options were discussed. Just as for the check-in, Equation 16 is used to determine the most efficient solution.

Table 57 Baggage drop-off optimal throughput overview

Serviced Baggage Retrofit desk Integrated desk drop desk baggage drop Equipment footprint (m2) 3.6 3.6 3.6 + 0.265 2.8 Pax/process All in All in 1 1 booking booking Average process time 115.5 143 28 + 80 87 (sec) Result Equation 16 415.8 514.8 417.392 243.6 (m2*s/pax)

Marco Groot, BSc 101

Optimize Landside Airport Operations Using A Discrete Event Simulation

Although the process time at the retrofit desk itself is only 28 seconds, passengers using the retrofit solution will always have to go through the check-in kiosk first to print a baggage label. Even if the passenger is already checked in via the internet. A such, the process times (and footprint) of the kiosk and retrofit needs to be added.

From Table 57 it can be seen that the integrated baggage drop solutions gives the best throughput. Although with the serviced desk and the baggage drop desk all passengers in a single booking can be assisted in the same process, where a new process is required per individual passenger for the retrofit and integrated solutions, the most time consuming is printing a label, attaching the label to the bag and transporting the bag to the BHS. For multiple passengers this needs to be done in series. So in Table 57 the process times of the serviced and baggage drop desks can be considered as being for 1 individual passenger.

The values stated in Table 57 are for the AMS simulation. For the other three airports the footprint of the desks is smaller and the process time of the integrated baggage drop is higher. But even with the values for these airports the integrated baggage drop solutions gives the best result. 9.3 Security access Two options were described for the security access, personal and automated. As the average process time is the same for both options and the footprint for the automated solution is smaller, the automated solution is more favourable (see Table 58).

Table 58 Security access optimal throughput overview

Personal security access Automated security access Equipment footprint (m2) 2 1.521 Pax/process 1 1 Average process time (sec) 8 8 Result Equation 16 (m2*s/pax) 16 12.168 9.4 Security The security can be centralized or decentralized. Although the footprint of the centralized lane is much larger compared to the decentralized security lane, more passengers can be served simultaneously with the centralized security lane. Using Equation 16 it is found that the centralized security gives the best throughput (Table 59).

Table 59 Security optimal throughput overview

Centralized security Decentralized security Equipment footprint (m2) 92.5 14.880 Pax/process 14 2 Average process time (sec) 165 165 Result Equation 16 (m2*s/pax) 1090.179 1227.578

Marco Groot, BSc 102

Optimize Landside Airport Operations Using A Discrete Event Simulation

9.5 Immigration The last station to consider in the departure process is the immigration. The two options are serviced and self-service. In Table 60 the results of the calculation for the best throughput can be found. The self-service solution gives a better result.

Table 60 Immigration optimal throughput overview

Serviced immigration Self-service immigration Equipment footprint (m2) 7.525 2.111 Pax/process 1 1 Average process time (sec) 30 30 Result Equation 16 (m2*s/pax) 225.75 63.318 9.6 Conclusions When looking at the results of this chapter, the following options give the largest number in terms of passenger throughput per square meter: - Internet check-in - Integrated baggage drop-off - Automated security access - Centralized security - Self-service immigration

Two remarks need to be made. As not all passengers will check in via the internet, check-in facilities need to be present in the airport terminal. Check-in kiosks are more favourable compared to check-in desks in terms of passenger throughput per square meter.

For business class passengers, airlines offer additional benefits and service. This includes, for example, personal contact at check-in desks. Business class passengers value this service and choose to fly with certain airlines based on their level of service.

When creating the simulation models, the above two items will be taken into account. This means check-in kiosks will be placed and serviced check-in desks will be used for business class passengers.

Marco Groot, BSc 103

Optimize Landside Airport Operations Using A Discrete Event Simulation

10 Airport optimization In the previous chapters the equipment to be used, passenger profiles, variable values and passenger arrival schedules are determined. Using these inputs the airport layout can be optimized to find the best passenger throughput per square meter.

Three sections can be found in this chapter. At first the allowed queueing times per processing facility are determined. Second, the number of required facilities (based on the allowed queueing times) are determined by using queueing theory. Lastly, the number of required facilities are found using the simulation programs. 10.1 Allowed queueing times When designing an airport, the number of equipment is determined by the desired queueing times. When the queueing times are too short, the airport terminal is over-designed. To the contrary, the airport terminal is considered under-designed when the queueing times are too long. In the Airport Development Reference Manual (IATA, 2014b) standards are defined for the waiting times of process facilities in airport terminals. These standards are used to find the optimal number of equipment for all four airports in this research. In Table 61 an overview is given for the allowed queueing times in an optimal designed airport.

Table 61 Optimal waiting time standards for processing facilities (IATA, 2014b)

Allowed queueing times (minutes) Check-in kiosk 0 – 2 Baggage drop economy class 0 – 5 Check-in desk business/first class 0 – 3 Security access 0 – 0.5 Security check economy class 5 – 10 Security check business/first class 0 – 3 Immigration 10

Marco Groot, BSc 104

Optimize Landside Airport Operations Using A Discrete Event Simulation

10.2 Queueing theory According to Little’s rule, the mean number of customers in the queue can be found by multiplying the mean rate of arrival with the mean wait time in the queue. This leads to:

퐿푞 = 휆푊푞 Equation 17 In the simulation models the inter-arrival time and the service time of the facilities are not constant. Even though a triangular distribution is assumed in the simulation, an adaption of Little’s rule for exponential distribution is used. This results in:

𝜌2 퐿 = 푞 1 − 𝜌 Equation 18 In Equation 18, ρ is the utilization of the facility. As in the simulation models a single queue is used for multiple facilities, the equation for the utilization becomes the mean rate of arrival divided by the number of facilities times the mean service rate. Or:

휆 𝜌 = 푐휇 Equation 19 Combining Equation 18 and Equation 19, plus taking into account the probability there are zero passengers in the queue (P0) leads to:

휆 푐 푃0 (휇) 𝜌 퐿 = 푞 푐! (1 − 𝜌)2 Equation 20 where

푐−1 (푐𝜌)푚 (푐𝜌)푐 푃 = 1/ [ ∑ + ] 0 푚! 푐! (1 − 𝜌) 푚=0 Equation 21

Using the above five equations, the required number of facilities c can be calculated for each of the processes at all four airports. The mean wait time of the queue Wq is taken the maximum allowed time from Table 61, such that the minimum number of facilities is found without having too long queues.

One of the variables in the above equations is the mean rate of arrival. In Chapter 3 the arrival distributions were found. These arrival distributions are not constant throughout the day. Each airport has multiple peaks, causing an irregular arrival pattern. Calculating the required number of facilities based on the arrival pattern at a peak moment is undesirable, since this causes an over-designed terminal. Using the average arrival rate causes an under-designed terminal during peak hours.

Marco Groot, BSc 105

Optimize Landside Airport Operations Using A Discrete Event Simulation

For calculating the required number of facilities nine time periods are taken. These are 10, 20, 30, 40, 50, 60, 90, 120 and 180 minutes. The maximum number of arriving passengers throughout the day for each time period is found. This number is divided by the time period and multiplied by 10, to get the average number of arriving passengers per 10 minutes based on the taken time period. An example is stated below.

The arrival pattern is based on 10 minute time intervals. When considering the 90 minute time interval, the number of arriving passengers of 9 consecutive intervals of 10 minutes are added. This gives the number of arriving passengers from x - 90 to x minutes. The mentioned calculation is performed for x = 0 to x = 1440 (as 1440 is total number of minutes per day), considering x can only be complete tens (so 10, 20, 30, 40, etc.). Also, for x smaller than 90, the arrivals before midnight the previous day are taken into account. For a single day this gives 144 values of arriving passengers within a 90 minute time interval. The maximum number of these values is taken. Finally, this number is divided by the time interval and multiplied by 10. This gives the average number of arriving passengers per 10 minutes based on the 90 minute time interval. This value is taken as input to determine the required number of facilities.

As nine time periods are taken, eventually nine values of arriving passengers are found. This method is used to take the shape of the arrival distribution into account. Below example is used to explain this:

Consider a 40 minute time period is which 1000 passengers arrive at an airport facility. As with the arrival of passengers in Chapter 3, periods of 10 minutes are taken. This means in the 40 minutes time period of this example, there are 4 periods of 10 minutes. Consider three possible arrival patterns: - Pattern 1: 4 periods of 250 passengers - Pattern 2: 2 periods of 500 passengers and 2 periods of 0 passengers - Pattern 3: 1 period of 1000 passengers and 3 periods of 0 passengers So within the same time period (40 minutes) the same number of passengers (1000 passengers) have arrived. But the arrival pattern is completely different. Pattern 1 is an evenly distributed pattern, while pattern 3 is one large peak. Pattern 2 can be 2 peaks, or 1 peak which lasts 20 instead of 10 minutes.

When designing an airport considering a maximal queueing time, one can imagine more facilities are needed when the arrival pattern is shaped as pattern 3 compared to pattern 1. As the four airports have different arrival patterns (AMS has peaks of 1 hour, while HND has one peak which is over 3 hours), looking at different time intervals could give a different answer to the required number of facilities needed.

Using the formulas mentioned in this section this results in the minimum required number of facilities for which the queueing times are allowed (according to Table 61) considering the nine different time periods of passenger arrivals. The results can be found in Appendix H for the airports AMS, HND, LCY and LAS.

One remark has to be made regarding the number of facilities needed for the security check as depicted in the tables below. This information states the number of facilities if only one passenger per facility can be serviced. As stated in Chapter 9, at the centralized security 14 passengers can use the facility simultaneous. As such, for the number of required lanes, the number depicted in the tables needs to be divided by 14 and rounded up.

Marco Groot, BSc 106

Optimize Landside Airport Operations Using A Discrete Event Simulation

10.3 Simulation results When calculating the required number of facilities via the queueing theory, the maximum allowed queueing time is taken as the starting point. From these allowed queueing times, using the equations stated in section 10.2, the required number of facilities is found.

A different approach is used with the simulation models. A more iterative process takes place, where the number of facilities is entered in the model and the result of the simulation is the corresponding maximum queueing time. Based on the resulting maximum queueing time, the number of facilities in the model is changed and the simulation is run again. This is repeated until the maximum queueing time is as close to, but not exceeding, the maximum allowed queueing time. The number of facilities for which this is achieved for the airports AMS, HND, LCY and LAS can be found in Appendix I. 10.4 Comparison queueing theory and simulation results Using the queueing theory and the simulation models are two different methods to calculate the required number of facilities. In this section the two methods are compared to see if the simulation models offer a reliable alternative to the queueing theory method.

In order to conclude the results from the simulation models are comparable to the results when using the queueing theory, the results of the two methods need to be within a certain error margin range. At the verification of the of the simulation models and the implementation of the passenger profiles an error margin of 5% was used. To be consistent throughout this research, again the same error margin is used. To be able to conclude the simulation model is a reliable alternative method for using the queueing theory, the results should hold:

0.95푥푞푢푒푢푒𝑖푛𝑔 푡ℎ푒표푟푦 ≤ 푥푠𝑖푚푢푙푎푡𝑖표푛 푚표푑푒푙 ≤ 1.05푥푞푢푒푢푒𝑖푛𝑔 푡ℎ푒표푟푦 Equation 22 In Appendix J the difference between the required number of facilities according to the simulation models and the queueing theory are stated. The shown percentages are found by:

푥 퐷𝑖푓푓푒푟푒푛푐푒 푠𝑖푚푢푙푎푡𝑖표푛 푚표푑푒푙 푎푛푑 푞푢푒푢푒𝑖푛푔 푡ℎ푒표푟푦 = 푠𝑖푚푢푙푎푡𝑖표푛 푚표푑푒푙 ∗ 100% 푥푞푢푒푢푒𝑖푛𝑔 푡ℎ푒표푟푦 Equation 23 In Table 62 the difference in required number of facilities according to the queueing theory and the simulation models is stated. This difference is calculated for each of the timer periods used when calculating the required number for facilities with the queueing theory.

Table 62 Average difference between required number of facilities simulation models and queueing theory

10 20 30 40 50 60 90 120 180 AMS 94% 96% 96% 97% 98% 99% 103% 112% 118% HND 88% 90% 92% 93% 93% 94% 95% 96% 97% LCY 92% 98% 101% 102% 102% 103% 105% 106% 111% LAS 93% 99% 102% 103% 103% 105% 105% 107% 109%

Marco Groot, BSc 107

Optimize Landside Airport Operations Using A Discrete Event Simulation

When looking at certain peak time periods, applying Equation 22, the simulation models give acceptable results compared to the queueing theory models. However, there are also some results from the simulation models which are outside the 5% error margin. So it can be concluded the simulation models are a reliable alternative, but this is only the case when certain peak time periods. An overview for the peak time periods for which the simulation models are sufficiently accurate is stated in Table 63.

Table 63 Peak time periods for which the results of the simulation models are within the allowed error range

Airport Peak periods AMS 20 to 90 minutes HND 90 to 180 minutes LCY 20 to 90 minutes LAS 20 to 90 minutes

As can be seen from Table 63, for three out of the four airports the simulation model give accurate results compared to the queueing theory when the peak time period is taken between 20 and 90 minutes. Only at HND the results are only accurate for peak time periods of 90 minutes or more.

Looking at the arrival distributions in Figure 17, the peak period of AMS and LAS is 45 to 60 minutes. For HND and LCY this is between 3 to 4 hours. So for AMS, HND and LAS the peak time periods for which the results of the simulation models are within the allowed range correspond to the peak time period of the arrival distribution.

Exception to this is LCY, where the peak period range for which the simulation results are acceptable is between 20 and 90 minutes, while the peak in arrival distribution is 3 hours. However, when looking at the 5 stations individually, only for the 120 and 180 minutes peak time periods the solutions of the simulation model are within the 5% error margin range.

With this it can be concluded the results of the simulation model are valid and can be used to answer the research questions. When looking at all facilities currently installed at the airports and the facilities required when the passenger numbers are doubled, the total square meters can be compared. For AMS, HND and LAS the operations can be made more efficient, meaning less square meters are required compared to current day situation (see Table 64). AMS and HND can handle all passengers with a reduction of around 40% of current used floorspace. LAS needs only one third compared to the current day situation. LCY is a different story. In order to facilitate all passengers, the terminal space needs to increase with 40%. The main difference for LCY lies with the number of check-in desks and baggage drop-off units required. These grow from 17 now to 47 when the number of passengers is doubled. At the other three airports these are the station where a large reduction in required floorspace is seen.

Table 64 Square meter comparison

Airport m2 currently used m2 required with double Difference passenger numbers AMS 5988.1 3599.4 60.1% HND 8142.4 4597.7 56.5% LCY 484.4 684.0 141.2% LAS 9183.6 2930.8 31.9%

Marco Groot, BSc 108

Optimize Landside Airport Operations Using A Discrete Event Simulation

11 Conclusions This research looks at the landside airport operations and how these can be optimized in order to facilitate more passengers per hour. The underlying question is if it is possible to cope with twice as many passengers, compared to the current situation, without expanding the terminal buildings. In order to answer this question, six sub questions were stated to assist in finding the answer.

The landside airport process consists of check-in, baggage drop-off, security access, security check and immigration. For check-in, baggage drop-off, security access and immigration serviced and self-service options are available. More and more self-service facilities are installed in airports. Passenger surveys show that passengers are keen to use these new self- service solutions as it is faster, allow for more flexibility and is user-friendly. For the security check the two possible options are centralized and decentralized security.

The stations hardly interact with each other. With full serviced check-in desks the check-in and baggage drop-off are combined. But other interaction between the processes and facilities does not exists. Mostly, passengers proceed from check-in at a kiosk to a baggage drop-off point, followed by the security check and finally to the immigration. There is no information exchange between these points.

As the stations do not have any influence on the process times of other stations, the maximum number of passengers per hour is achieved when the solution with the highest number of passengers per hour per square meter is used. This means the integrated self-service baggage drop-off, automated security access gates, centralized security and self-service immigration. It is preferred to have all passengers arrive at the airport terminal already checked-in and in the possession of a boarding pass. As not all passengers are able to check-in at an off-airport location, check-in facilities are needed inside the terminal. Therefore self-service kiosks are needed. Lastly, as business class passengers demand more personal assistance, serviced check- in desks are required for these passengers.

When comparing the number of required facilities according to the simulation models with the current day situation, the processes can be made more efficient. For AMS, HND and LAS twice as many passengers can pass through all stations, while less terminal space is required. At LCY a 40% increase in floorspace is required to facilitate double passenger numbers.

Looking at the created simulation models, there are benefits in calculating the required number of facilities compared to using the queueing theory. When changing a single parameter in the simulation model, the required number of facilities for all stations can be calculated. If the same parameter is changed and the queueing theory method is used, the calculations for all stations needs to be performed separately. Furthermore, the simulation models give a graphical representation regarding the passenger flow in the terminal. The user can see the build-up of queues and when peak moments at each station occur.

Marco Groot, BSc 109

Optimize Landside Airport Operations Using A Discrete Event Simulation

In order to facilitate more passengers per hour the utilization at all stations in the terminal needs to increase. Especially at the check-in desks and baggage drop-off points many facilities are not used during an extensive period of time. Airlines have their own dedicated facilities. When there are no flights for that airline, the facilities are closed. These could be used by other airlines, thus increasing the utilization. Creating common-use facilities increases the utilization even more. Furthermore, processes can be performed outside the terminal building. More and more passengers which arrive at the airport are already checked-in and in possession of a boarding pass. This means a reduction in time spend inside the terminal. Lastly, the facilities currently used can be replaced with facilities with a faster process time in order to increase the throughput of passengers.

Marco Groot, BSc 110

Optimize Landside Airport Operations Using A Discrete Event Simulation

12 Recommendations In this research a large number of assumptions had to be made. These assumptions relate to process times of different station options, passenger profiles, passenger numbers and passenger arrival distributions. Although the assumptions made have been derived from literature studies, the accuracy of the simulation models can be improved when actual data is used. In order to obtain these data, field measurements are required or data needs to be provided by airport authorities, airlines and suppliers of station equipment.

For the defined passenger profiles characteristics for the percentage of occurrence, type of baggage and walking speeds were defined. As this research focusses on the process times at the stations, the time required between the stations was left out of the models. As such, the characteristics regarding the walking speeds of the passenger profiles was not used. If the entire period a passenger spends inside the airport terminal (station processes and travelling between the stations) needs to be investigated, this can be included in the created simulation models. Further research is required regarding the walking paths and distances between the stations in order to include this in the models.

Lastly, one of the research questions asked was how the model can cope with disruptions. As this can have a major impact on the operation, the system needs to be able to recover quickly in order to make sure passengers arrive at their gate in time before the aircraft departs. Disruptions are unpredictable in occurrence and duration. Therefore, a thorough investigation in disruptions can become extensive. Also, since this research is aimed at finding an answer to optimize the passenger throughput in normal process operations, disruptions were considered to be out of scope. A research regarding the system behaviour during different disruptions could be performed during another study.

Marco Groot, BSc 111

Optimize Landside Airport Operations Using A Discrete Event Simulation

Bibliography Abdelaziz, S. G., Hegazy, A. A. & Elabbassy, A., 2010. Study of Airport Self-service Technology within Experimental Research of Check-in Techniques. International Journal of Computer Science Issues, Volume 7(Issue 3), pp. p.17-26.

ACI, 2016. Revolutionising airport baggage processing. [Online] Available at: http://www.airport-business.com/2013/04/revolutionising-airport-baggage- processing/ [Accessed 25 November 2016].

ACRP, 2008. Common Use Facilities and, Washington D.C.: TRANSPORTATION RESEARCH BOARD.

Airport Technology, 2008. Self Check-in security. [Online] Available at: http://www.airport-technology.com/features/feature1510/ [Accessed 26 July 2015]. airport-technology.com, 2016. Terminal 3, McCarran International Airport (LAS/KLAS), Nevada, United States of America. [Online] Available at: http://www.airport-technology.com/projects/mccarran-international-airport- terminal-us/ [Accessed 25 February 2016].

Alstef, 2015. BAGXpress (automatic baggage drop-off). [Online] Available at: http://www.alstef.com/BAGXpress-automatic-baggage-drop-off [Accessed 16 August 2015].

Alstef, 2016. BagXpress(automatic baggage drop-off). [Online] Available at: http://www.alstef.com/BAGXpress-automatic-baggage-drop-off [Accessed 10th August 2016].

ANA, 2016. Press Release. [Online] Available at: https://www.ana.co.jp/eng/aboutana/press/2015/pdf/160202.pdf [Accessed 17 April 2016].

Ashford, N. J. et al., 2013. Airport Operations. Third ed. New York: McGraw Hill.

BagDrop Systems BV, 2015. BagDrop Modular Compact. [Online] Available at: http://bagdrop.com/?page_id=153 [Accessed 16 August 2015].

BagDrop Systems BV, 2016a. BagDrop Performance Statistics - Weekly Totals and Averages Amsterdam Airport, Amsterdam: s.n.

BagDrop Systems BV, 2016b. BagDrop Performance Statistics - Weekly Totals and Averages Haneda Airport, Amsterdam: s.n.

Bangkok Airways, 2015. Airport check-in. [Online] Available at: http://www.bangkokair.com/pages/view/check-in-airport [Accessed 26 July 2015].

Marco Groot, BSc 112

Optimize Landside Airport Operations Using A Discrete Event Simulation

BBC, 1988. BBC News. [Online] Available at: http://news.bbc.co.uk/onthisday/hi/dates/stories/december/21/newsid_2539000/2539447.stm [Accessed 29 August 2015].

BBC, 2001. BBC News. [Online] Available at: http://news.bbc.co.uk/onthisday/hi/dates/stories/september/11/newsid_2514000/2514627.stm [Accessed 29 August 2015].

BCS, 2015a. BCS Airports and Airlines Case Studies. [Online] Available at: http://www.bcsgroup.biz/case-studies/airports-airlines.aspx [Accessed 15 August 2015].

BCS, 2015b. BCS Hardware Solutions. [Online] Available at: http://www.bcsgroup.biz/solutions-for-airports-airlines/hardware.aspx [Accessed 15 August 2015].

BCS, 2015c. Brisbane Airport's Self-Service Baggage Solution. [Online] Available at: http://www.bcsgroup.biz/solutions-for-airports-airlines/hardware.aspx [Accessed 16 August 2015].

Berdowski, Z. et al., 2009. Survey on standard weights of passenger and baggage. Zoetemeer: EASA.

Bite, K. E., 2010. Improving on Passenger and Baggage Processes at Airports with RFID. In: C. Turcu, ed. Sustainable Radio Frequency Identification Solutions. s.l.:InTech.

Buying Business Travel, 2014. London City airport posts record traveller numbers. [Online] Available at: http://buyingbusinesstravel.com/news/2023434-london-city-airport-posts- record-traveller-numbers [Accessed 5 March 2016].

CAPA, 2016. Seoul Icheon airport: 49 million passengers in 15 years. Well on the way to 100 million in 2030. [Online] Available at: http://centreforaviation.com/analysis/seoul-incheon-airport-49-million- passengers-in-15-years-well-on-the-way-to-100-million-in-2030-274544 [Accessed 17 April 2016].

CCM, 2016. Self Baggage Drop - MyCheck. [Online] Available at: http://www.ccmairports.com/technology/bagcheck.html [Accessed 25 November 2016].

Chambost, G. & Renard, J.-D., 2005. In: La grande aventure de L'Airbus A380. Bordeaux: s.n., p. 12.

Chun, H. W. & Mak, R. W. T., 1999. Intelligent Resource Simulation for an Airport Check-In Counter Allocation System. IEEE Transactions on Systems, Man and Cybernetics-Part: Applications and Reviews, 29(3), pp. 325-335.

Marco Groot, BSc 113

Optimize Landside Airport Operations Using A Discrete Event Simulation

Civil Aviation Authority, 2016a. Airport Data 2015. [Online] Available at: https://www.caa.co.uk/Data-and-analysis/UK-aviation- market/Airports/Datasets/UK-Airport-data/Airport-data-2015-12/ [Accessed 22 February 2016].

Civil Aviation Authority, 2016b. UK Airport Data. [Online] Available at: https://www.caa.co.uk/Data-and-analysis/UK-aviation- market/Airports/Datasets/UK-airport-data/ [Accessed 22 February 2016].

Civil Aviation Bureau Japan, 2013. Tokyo International Airport. [Online] Available at: http://www.mlit.go.jp/koku/15_hf_000051.html [Accessed 21 February 2016].

Cofely, 2015. Cofely Besix Airports. [Online] Available at: http://www.cofely-besix-airports.com/wp-content/uploads/2014/04/UDrop- Brochure.pdf [Accessed 15 August 2015 ].

Cofely, 2016. UDrop Brochure. [Online] Available at: http://www.cofely-besix-airports.com/wp-content/uploads/2014/04/UDrop- Brochure.pdf [Accessed 25 November 2016].

Davis, D. G. & Braaksma, J. P., 1988. Adjusting for Luggage-Laden Pedestrians in Airport Terminals. Transpn. Res. A., 22(5), pp. 375-388.

Dinkelmann, R., 2016. Chief Technical Officer ICM [Interview] (31 August 2016).

DSG Materna, 2016. Retrofit - DSG BagDrop. [Online] Available at: http://www.dsg-bagdrop.com/retrofit [Accessed 25 November 2016].

DSG Systems, 2015. Self service bag drop. [Online] Available at: http://dsg-systems.no/productcategory/self-service-bag-drop/en [Accessed 15 August 2015].

Federal Aviation Administration, 2016. Las Vegas - McCarran International Airport. [Online] Available at: https://www.faa.gov/nextgen/snapshots/airport/?locationId=35 [Accessed 25 February 2016].

Finnair, 2015. LOCATIONS OF SELF-SERVICE CHECK-IN KIOSKS. [Online] Available at: http://www.finnair.com/hk/gb/information-services/check-in/self-service-check- in-kiosk [Accessed 26 July 2015].

FlightStats, 2016a. Historical Airport Departures and Arrivals. [Online] Available at: http://www.flightstats.com/go/HistoricalFlightStatus/flightStatusByAirport.do [Accessed 3 April 2016].

Marco Groot, BSc 114

Optimize Landside Airport Operations Using A Discrete Event Simulation

FlightStats, 2016b. McCarran International Airport. [Online] Available at: http://www.flightstats.com/go/AirportTerminalMaps/airportTerminalMaps.do?airportCode=L AS [Accessed 25 February 2016].

German Federal Police, 2015. EasyPass. [Online] Available at: http://www.easypass.de/EasyPass/EN/What_is_EasyPASS/home_node.html [Accessed 23 August 2015].

Ghee, R., 2011. Is the check-in process a thing of the past? - Future Travel Experience. [Online] Available at: http://www.futuretravelexperience.com/2011/03/is-the-check-in-process-a- thing-of-the-past/ [Accessed 26 July 2015].

Global Gateway Alliance, 2015. NEW DATA: CUSTOMS KIOSKS DRAMATICALLY REDUCE WAIT TIMES AT JFK, NEWARK AIRPORTS, s.l.: s.n.

GLS Research, 2014. Las Vegas Visitor Profile, Las Vegas: s.n.

Google Maps, 2016a. Google Maps. [Online] Available at: https://www.google.nl/maps/place/Schiphol/@52.3568826,4.7894368,12z/data=!4m19!1m16! 4m15!1m6!1m2!1s0x47c63fb5949a7755:0x6600fd4cb7c0af8d!2sAmsterdam!2m2!1d4.89516 79!2d52.3702157!1m6!1m2!1s0x47c5e134e0fd162d:0xc7d51583f1cef188!2sSchiphol,+Evert +van+de+Beek [Accessed 14 February 2016].

Google Maps, 2016b. Google Maps. [Online] Available at: https://www.google.nl/maps/place/Luchthaven+Haneda/@35.5493975,139.7776499,17z/data =!3m2!4b1!5s0x601863f4691ece0f:0x7411314143346d11!4m2!3m1!1s0x6018640ba43192e 3:0xd32c3a9d146f8df [Accessed 21 February 2016].

Google Maps, 2016c. Google Maps. [Online] Available at: https://www.google.nl/maps/place/Luchthaven+Londen+City/@51.5065923,- 0.0313175,12.28z/data=!4m2!3m1!1s0x47d8a8880ebb3687:0x774e8a0a64b1a4b [Accessed 22 February 2016].

Google Maps, 2016d. Google Maps. [Online] Available at: https://www.google.nl/maps/place/McCarran+International+Airport/@36.09223,- 115.1599857,14z/data=!4m2!3m1!1s0x80c8c59f1f049c5d:0x471359241ec41e1e [Accessed 25 February 2016].

Marco Groot, BSc 115

Optimize Landside Airport Operations Using A Discrete Event Simulation

Google Maps, 2016e. Google Maps. [Online] Available at: https://www.google.nl/maps/place/Narita+International+Airport/@35.6982495,140.2164361, 11z/data=!3m1!5s0x6022f30bbc0d9317:0x1406c81d944fe019!4m2!3m1!1s0x6022f379d1bd 3757:0xd56e29a162771aa1 [Accessed 17 April 2016].

Groot, M., 2015. Amsterdam Airport Schiphol, Haneda International Airport: s.n.

Gunnebo, 2016a. Security Gates for Airports - ImmSec. [Online] Available at: http://www.gunnebo.nl/producten/toegangscontrole/Security-Poorten-Voor- Luchthavens/ImmSec [Accessed 10th August 2016].

Gunnebo, 2016b. Security Gates for Airports - PreSec. [Online] Available at: http://www.gunnebo.nl/producten/toegangscontrole/Security-Poorten-Voor- Luchthavens/PreSec [Accessed 10th August 2016].

IATA, 2004. Airport Development Reference Manual. 9th ed. Montreal: IATA.

IATA, 2014a. New IATA Passenger Forecast Reveals Fast-Growing Markets of the Future. [Online] Available at: http://www.iata.org/pressroom/pr/pages/2014-10-16-01.aspx [Accessed 25 July 2015].

IATA, 2014b. The new ADRM (10th Edition). Barcelona, Passenger Terminal Expo - IATA Day.

IATA, 2016. Airline and Airport Code Search. [Online] Available at: http://www.iata.org/publications/Pages/code-search.aspx [Accessed 22 February 2016].

ICAO, 2006. International Standards and Recommended Practices Annex 17. 8th edition ed. s.l.:ICAO.

ICM Airport Technics, 2014. The world’s most proven self-service bag drop solution. s.l.:s.n.

ICM Airport Technics, 2015. Self Service Bag Drop. [Online] Available at: http://autobagdrop.com.au/solutions/self-service-bag-drop/ [Accessed 15 August 2015].

ICM, 2014. The world's most proven self-service bag drop solution. Sydney: s.n.

ICM, 2016. Self Service Bag Drop. [Online] Available at: http://autobagdrop.com.au/solutions/self-service-bag-drop/ [Accessed 25 November 2016].

IER, 2016a. Automated Gate for Access Control & Boarding. s.l.:s.n.

Marco Groot, BSc 116

Optimize Landside Airport Operations Using A Discrete Event Simulation

IER, 2016b. Self-Service Bag Drop: IER 780 Automated Bag Drop. [Online] Available at: http://www.ier.com/_/uk/market/air-transportation/_/uk/rubrique/self-service- bag-drop/_/uk/product/ier-780-automated-bag-drop/_index.html [Accessed 9th August 2016].

IER, 2016c. Self-service Tagging & Check-in Kiosk. s.l.:s.n.

Incitez Pte Ltd., 2016. Whitepaper: China internet Statistics & User Insights. [Online] Available at: https://www.chinainternetwatch.com/whitepaper/china-internet-statistics/ [Accessed 25 November 2016].

Ink Aviation, 2015. Ink Aviation - Innovation Simplified. [Online] Available at: http://inkaviation.com/ [Accessed 15 August 2015].

Innova, 2015. Products/Kiosks. [Online] Available at: http://www.innova.com.tr/kiosk/english/kiosks.asp [Accessed 9th August 2016].

InternetLiveStats.com, 2016. Internet Users by Country (2016). [Online] Available at: http://www.internetlivestats.com/internet-users-by-country/ [Accessed 25 November 2016].

JAL, 2016. Press Release. [Online] Available at: http://press.jal.co.jp/en/bw_uploads/20160104%20- %20NY%202016%20Results%20Final%201.pdf [Accessed 17 April 2016].

James, J., 1953. The Distribution of Free-Forming Small Group Size. American Sociological Review, 18(5), pp. 569-570.

Japan Airport Terminal, 2016a. Access Haneda Airport. [Online] Available at: http://www.tokyo-airport-bldg.co.jp/en/access/ [Accessed 21 February 2016].

Japan Airport Terminal, 2016b. Airport Map. [Online] Available at: https://www.tokyo-airport-bldg.co.jp/en/map/ [Accessed 21 February 2016].

Jenatabadi, H. S. & Ismail, N. A., 2007. The Determination of Load Factors in the Airline Industry. International Review of Business Research Papers, 3(4), pp. 125-133.

JSC/JAA, 2016. Japan Schedule Coordination/Japan Aeronautic Association. [Online] Available at: http://www.schedule- coordination.jp/archives/arc_hnd/2010/operational_procedure_at_hnd.pdf [Accessed 21 February 2016].

Kaba, 2016. Kaba Luchthavens - Self-boarding gates. [Online] Available at: http://www.kaba.nl/branches/luchthavens/518902/self-boarding-gates.html [Accessed 3 December 2016].

Marco Groot, BSc 117

Optimize Landside Airport Operations Using A Discrete Event Simulation

Koninklijke Marechaussee, 2015. Taken Marechaussee. [Online] Available at: https://www.defensie.nl/organisatie/marechaussee/inhoud/taken [Accessed 23 August 2015].

London & Partners, 2016. London Airports Map. [Online] Available at: http://www.visitlondon.com/traveller-information/travel-to- london/airport/london-airport-map?ref=mosaic [Accessed 22 February 2016].

London City Airport Guide, 2016. London City Terminal Maps. [Online] Available at: http://www.london-city-airport-guide.co.uk/terminal-map.html [Accessed 22 February 2016].

London City Airport, 2016a. London City Airport Check-in. [Online] Available at: http://www.londoncityairport.com/travelandbooking/page/check-in [Accessed 23 February 2016].

London City Airport, 2016b. London City Airport Destinations. [Online] Available at: http://www.londoncityairport.com/destinations [Accessed 23 February 2016].

London City Airport, 2016c. London City Airport Facilities. [Online] Available at: http://www.londoncityairport.com/aboutandcorporate/page/airlinepartnersfacilities [Accessed 22 February 2016].

London City Airport, 2016d. London City Airport Passenger Profile. [Online] Available at: http://www.londoncityairport.com/aboutandcorporate/page/airlinepartnerspassengerprofile [Accessed 23 Fabruary 2016].

London City Airport, 2016e. Get In Touch, Questions and Answers. [Online] Available at: http://www.londoncityairport.com/getintouch/qanda [Accessed 25 February 2016].

Materna IPS, 2016. IPS Phoenix Kiosk. [Online] Available at: http://www.materna-ips.com/ips/fileadmin/data/ips%20Phoenix.pdf [Accessed 9th August 2016].

Materna, 2015. Materna Self Bag Drop. [Online] Available at: http://www.materna-ips.com/ips/?page_id=6626 [Accessed 15 August 2015].

McCarran International Airport, 2016a. 2015 Detailed Passenger Type, Las Vegas: Clark County Department of Aviation.

McCarran International Airport, 2016b. 2015 Emplaned/Deplaned Combined, Las Vegas: Clark County Department of Aviation.

McCarran International Airport, 2016c. 2015 Connecting Emplaned Passengers, Las Vegas: McCarran Airport.

Marco Groot, BSc 118

Optimize Landside Airport Operations Using A Discrete Event Simulation

McCarran International Airport, 2016d. Which Terminal?. [Online] Available at: https://www.mccarran.com/Fly/WhichTerminal.aspx [Accessed 25 February 2016].

McCarran International Airport, 2016e. 2015 Emplaned Passengers, Las Vegas: McCarran Airport.

Miller, J. K., 2003. Reducing the Air Travel Hassle Factor Through Self-Service Check-in Process, Boston: Massachusetts Institute of Technology.

Ministry of Justice Japan, 2012. Automated gates. [Online] Available at: http://www.moj.go.jp/nyuukokukanri/kouhou/nyuukokukanri07_00015.html [Accessed 23 August 2015].

Negoro, K., 2016. Project Manager OKI [Interview] (26th May 2016).

Nepal, K. P., Hibino, N., Kanda, Y. & Morichi, S., 2006. Time Series Analysis of Domestic Aviation Passenger Travel Patterns in Japan. Nagoya, Proceedings of the 10th ATRS conference.

New Zealand Customs Service, 2015. New Zealand Customs Service. [Online] Available at: http://www.customs.govt.nz/about/Pages/default.aspx [Accessed 23 August 2015].

OKI, 2016. Specificaties - Productinformatie - Informatiekiosk terminal Sukit. [Online] Available at: https://www.oki.com/jp/kiosk/products/spec.html [Accessed 9th August 2016].

Passenger Self Service, 2015. Australia to install biometric e-gates for departure. [Online] Available at: http://www.passengerselfservice.com/2015/04/australia-to-install-biometric- egates-for-departures/ [Accessed 23 August 2015].

Pinkfroot/PlaneFinder, 2016. Plane Finder Data. [Online] Available at: https://planefinder.net/data/flight/ [Accessed 3 April 2016].

Qantas Airways, 2015. The Q Bag Tag. [Online] Available at: http://www.qantas.com.au/travel/airlines/q-bag-tag/global/en [Accessed 16 August 2015].

Quartz, 2015. The day before Thanksgiving actually isn’t the busiest time to fly. [Online] Available at: http://qz.com/545014/the-day-before-thanksgiving-is-not-the-busiest-day-to-fly- in-the-us/ [Accessed 5 March 2016].

Safran Morpho, 2016. MorphoWay. [Online] Available at: http://www.morpho.com/en/aviation-border-security/secure-and-manage- borders/automated-solution/morphoway [Accessed 10th August 2016].

Marco Groot, BSc 119

Optimize Landside Airport Operations Using A Discrete Event Simulation

Scarabee Aviation Group, 2016a. Logging AMS and HND units between 1 and 7 December 2016, s.l.: s.n.

Scarabee Aviation Group, 2016b. Process demonstration video, Amsterdam: s.n.

Scarabee Aviation Group, 2016c. Reporting SSL KPI Statistics, s.l.: s.n.

Scarabee Aviation Group, 2016d. SSBPC logging Gunnebo. s.l.:s.n.

Schiphol Group, 2016a. 2015 Traffic Review, Amsterdam: Schiphol Group.

Schiphol Group, 2016b. Greater comfort as a result of central security. [Online] Available at: https://www.schiphol.nl/Travellers/News/GreaterComfortAsAResultOfCentralSecurity1.htm [Accessed 14 February 2016].

Schiphol Group, 2016c. Plattegronden Schiphol. [Online] Available at: https://www.schiphol.nl/Reizigers/OpSchiphol/Plattegronden.htm [Accessed 14 February 2016].

Schiphol Group, 2016d. Schiphol Awards. [Online] Available at: http://www.schiphol.nl/Reizigers/OverSchiphol/Awards.htm [Accessed 14 February 2016].

Schiphol, 2015a. Airport Security Scan. Amsterdam: s.n.

Schiphol, 2015b. Security checks upon departure. [Online] Available at: http://www.schiphol.nl/Travellers/AtSchiphol/CheckinControl/SecurityChecksUponDepartur e.htm [Accessed 23 August 2015].

Schiphol, 2015c. Self-service passport control launched. [Online] Available at: http://www.schiphol.com/B2B/RouteDevelopment/NewsPublications1/RouteDevelopmentNe ws/SelfServicePassportControlLaunched.htm [Accessed 23 August 2015].

Schiphol, 2015d. Veiligheid. [Online] Available at: http://www.schipholjunior.nl/schiphol-hoe-werkt-dat/veiligheid.html [Accessed 23 August 2015].

Schultz, M. & Fricke, H., 2011. Managing Passenger Handling at Airport Terminals. s.l., s.n.

Schultz, M., Schulz, C. & Fricke, H., 2008. Passenger Dynamics at Airport Terminal Environment. Pedestrian and Evacuation Dynamics 2008, Volume 2010, pp. 381-396.

Seghetti, L., 2015. Border Security: Immigration Inspections at Ports of Entry. Washington, D.C.: Congressional Research Service.

Shirakawa, N., 2016. CEO Scarabee Aviation Group Japan [Interview] (24 February 2016).

Marco Groot, BSc 120

Optimize Landside Airport Operations Using A Discrete Event Simulation

SITA, 2016a. Passenger IT Trend Survey 2016. [Online] Available at: https://www.sita.aero/globalassets/docs/surveys--reports/passenger-it-trends- survey-2016-factsheet-south-korea.pdf [Accessed 22 October 2016].

SITA, 2016b. Automated Passport Control Kiosk. [Online] Available at: http://www.sita.aero/solutions-and-services/products/automated-passport- control-kiosk [Accessed 10th August 2016].

SITA, 2016c. Passenger IT Trends Surevy 2016. [Online] Available at: https://www.sita.aero/globalassets/docs/surveys--reports/passenger-it-trends- survey-2016-factsheet-china-english.pdf [Accessed 22 October 2016].

SITA, 2016d. Passenger IT Trends Survey 2016. [Online] Available at: https://www.sita.aero/globalassets/docs/surveys--reports/passenger-it-trends- survey-factsheet-europe.pdf [Accessed 22 October 2016].

SITA, 2016e. Passenger IT Trends Survey 2016. [Online] Available at: https://www.sita.aero/globalassets/docs/surveys--reports/passenger-it-trends- survey-2016-factsheet-usa.pdf [Accessed 22 October 2016].

SITA, 2016f. SITA BagDrop. [Online] Available at: http://www.sita.aero/solutions-and-services/solutions/sita-bagdrop [Accessed 25 November 2016].

SITA, 2016g. SITA BagDrop. [Online] Available at: https://www.sita.aero/solutions-and-services/solutions/sita-bagdrop [Accessed 9th August 2016].

Skytrax, 2016a. The World's Best Airports in 2014. [Online] Available at: http://www.worldairportawards.com/Awards/airport_award_winners_2014.html [Accessed 14 February 2016].

Skytrax, 2016b. The World's Top 10 Airports of 2015. [Online] Available at: http://www.worldairportawards.com/Awards/worlds_best_airport.html [Accessed 21 February 2016].

Skytrax, 2016c. Tokyo Haneda 5-star Airport Rating. [Online] Available at: http://www.airlinequality.com/ratings/tokyo-haneda-airport-quality-rating/ [Accessed 21 February 2016].

Solak, S., Clarke, J.-P. B. & Johnson, E. L., n.d. Airport Terminal Capacity Planning using Delay Time Approximations and Multistage Stochastic Programming, s.l.: s.n.

Stefanik, M., Kandera, B. & Badanik, B., 2012. Aspects influencing arrival behavioural pattern of air travellers. Transport System Telematics, 5(1), pp. 35-40.

Marco Groot, BSc 121

Optimize Landside Airport Operations Using A Discrete Event Simulation

Swedberg, C., 2011. Tagsys, ICM Airport Technics Market RFID Bag Tag to Airlines. [Online] Available at: http://www.rfidjournal.com/articles/view?8966 [Accessed 16 August 2015].

Tan, S. H., 2007. Airport Security. Santiago, Chile, s.n.

Tiger Airways, 2015. Useful check-in information. [Online] Available at: http://www.tigerair.com/sg/en/check_in.php [Accessed 26 July 2015].

Tokyo Internation Air Terminal, 2016a. Access Information. [Online] Available at: http://www.haneda-airport.jp/inter/en/access/ [Accessed 21 February 2016].

Tokyo International Air Terminal, 2016b. Floor map Haneda airport. [Online] Available at: http://www.haneda-airport.jp/inter/en/map/floormap/map.html [Accessed 21 February 2016].

Trading Economics, 2016. Air Transport - Passengers Carried In Japan. [Online] Available at: http://www.tradingeconomics.com/japan/air-transport-passengers-carried-wb- data.html [Accessed 3 March 2016].

Transportation Research Board, 2010. Airport Passenger Terminal Planning and Design - Volume 1: Guidebook. Washington D.C.: s.n.

Transportation Research Board, 2011. Resource Manual for Airport In-terminal Concessions. 1st edition ed. Burlingame, CA: LeighFisher.

Travel News, 2015. More than half of London City's passengers resort to online check-in. [Online] Available at: http://www.travel-news.co.uk/10177/2015/09/more-than-half-of-london-citys- passengers-resort-to-online-check-in/ [Accessed 23rd August 2016].

TSA, 2006a. Recommended security guidelines for airpot planning, design and construction. s.l.:s.n.

TSA, 2006b. Security Checkpoint Layout Design/Reconfiguration Guide. s.l.:s.n.

Type22/SITA, 2015. Eindhoven Airport about Scan&Fly. [Online] Available at: https://vimeo.com/channels/scanfly/120367454 [Accessed 25 November 2016].

Type22, 2015a. Scan&Fly. [Online] Available at: http://www.scanfly.aero/#/start [Accessed 15 August 2015].

Marco Groot, BSc 122

Optimize Landside Airport Operations Using A Discrete Event Simulation

Type22, 2015b. Our Products. [Online] Available at: http://www.type22.aero/our-products/ [Accessed 16 August 2015].

U.S. Customs and Border Control, 2015. Automated Passport Control. [Online] Available at: http://www.cbp.gov/travel/us-citizens/automated-passport-control-apc [Accessed 23 August 2015].

U.S. Department of Homeland Security, 2015. U.S. Customs and Border Protection. [Online] Available at: http://www.cbp.gov/ [Accessed 23 August 2015].

United State Congress, 2001. U.S. Government Publishing Office. [Online] Available at: http://www.gpo.gov/fdsys/pkg/PLAW-107publ71/content-detail.html [Accessed 2015 August 2015].

United States Department of Transportation, 2016. Load Factor (passenger-miles as a proportion of available seat-miles in percent (%)). All Carriers - Las Vegas, NV: McCarran International (Origin Airport). [Online] Available at: http://www.transtats.bts.gov/Data_Elements.aspx?Data=5 [Accessed 17 April 2016]. van Someren Greve, C., 2016. Service Manager BagDrop systems BV [Interview] (15th June 2016).

Wright, C. & Ruiz-Celada, D., 2016. Whole-system thinking in a capacity-constrained airport. Cologne, London City Airport.

York Aviation, 2015. City Airport Development Programme, London: York Aviation.

Young, S. B., 1999. Evaluation of Pedestrian Walking Speeds in Airport Terminals. Transportation research record, Issue 0824, pp. 20-26.

Marco Groot, BSc 123

Optimize Landside Airport Operations Using A Discrete Event Simulation

Appendix A Station solution companies Appendix A.1 Baggage drop-off retrofit solutions

Company Airports implemented Source BCS/SITA Auckland, Christchurch, (BCS, 2015a), (BCS, 2015b) Wellington Cofely UDrop Montreal (Cofely, 2015) DSG Systems Barcelona, Copenhagen, (DSG Systems, 2015) Helsinki, Oslo, Stockholm ICM Airport Technics London Heathrow, Paris (ICM Airport Technics, 2015) Charles de Gaulle, Sydney Ink Aviation London Gatwick (Ink Aviation, 2015) Materna Düsseldorf, Hamburg, (Materna, 2015) Nürnberg Type22 Brussels, Eindhoven, Hong (Type22, 2015a) Kong, Rotterdam Appendix A.2 Baggage drop-off integrated solutions

Company Airports implemented Source Alstef Paris Orly (Alstef, 2015) BagDrop Systems BV AAS, Tokyo Haneda (BagDrop Systems BV, 2015) BCS/SITA Brisbane, Lisbon, Melbourne (BCS, 2015c) ICM London Heathrow, Paris (ICM Airport Technics, 2015) Charles de Gaulle, Sydney Type22 Currently not installed (Type22, 2015b)

Marco Groot, BSc 124

Optimize Landside Airport Operations Using A Discrete Event Simulation

Appendix B Solutions details Appendix B.1 Serviced check-in AMS HND LCY LAS Floor space 1800x20001 1500x18001 1980x18002 1980x18002 required (mm) Average 901 1503 1503 1503 duration successful (sec) Quickest No info No info No info No info duration successful (sec) Number of 1 1 1 1 passengers simultaneous Percentage No info No info No info No info failed (%) Average No info No info No info No info duration failed (sec) 1 (van Someren Greve, 2016) 2 (IATA, 2004) 3 (Transportation Research Board, 2010) Appendix B.2 Self-service check-in SITA OKI Innova IER Materna Floor space No info 430x7121 620x4273 585x4924 570x6905 required (mm) 620x6003 Average No info 802 No info No info No info duration successful (sec) Quickest No info 402 No info No info No info duration successful (sec) Number of No info All All All All passengers passengers in passengers in passengers in passengers in simultaneous booking booking booking booking Percentage No info No info No info No info No info failed (%) Average No info No info No info No info No info duration failed (sec) 1 (OKI, 2016) 2 (Negoro, 2016) 3 (Innova, 2015) 4 (IER, 2016c) 5 (Materna IPS, 2016)

Marco Groot, BSc 125

Optimize Landside Airport Operations Using A Discrete Event Simulation

Appendix B.3 Internet check-in Airport Percentage of passengers using internet check-in (%) AMS 311 HND 142 LCY 533 LAS 654 1 (SITA, 2016d) 2 (SITA, 2016a), (SITA, 2016c) 3 (Travel News, 2015) 4 (SITA, 2016e) Appendix B.4 Limited serviced baggage drop-off AMS HND LCY LAS Floor space 1800x20001 1300x12001 1980x18002 1980x18002 required (mm) Average duration No info 1433 1433 1433 successful (sec) Quickest duration No info No info No info No info successful (sec) Number of 1 1 1 1 passengers simultaneous Percentage failed No info No info No info No info (%) Average duration No info No info No info No info failed (sec) 1 (van Someren Greve, 2016) 2 (IATA, 2004) 3 (Abdelaziz, et al., 2010)

Marco Groot, BSc 126

Optimize Landside Airport Operations Using A Discrete Event Simulation

Appendix B.5 Retrofit self-service baggage drop-off SITA ICM Materna CCM Cofely Floor space Same as Desk + Same as Same as Same as required desk1 440x2454 desk6 desk7 desk8 (mm) Average No info 405 No info No info No info duration successful (sec) Quickest 15 (2-step)2 255 Less than 50 No info No info duration 28 (1-step)3 seconds2 successful (sec) Number of 1 1 1 1 1 passengers simultaneous Percentage No info No info No info No info No info failed (%) Average No info No info No info No info No info duration failed (sec) 1 (SITA, 2016f) 2 (ACI, 2016) 3 (Type22/SITA, 2015) 4 (ICM, 2016) 5 (Dinkelmann, 2016) 6 (DSG Materna, 2016) 7 (CCM, 2016) 8 (Cofely, 2016)

Marco Groot, BSc 127

Optimize Landside Airport Operations Using A Discrete Event Simulation

Appendix B.6 Integrated self-service baggage drop-off IER SITA ICM BagDrop Alstef Floor space 1500x25001 1750x16002 3400x?3 1500x25004, No info required 7 (mm) 1300x25004, 8 Average No info No info 469 72 (57 3010 duration peak)5, 7 successful 74 (68 (sec) peak)6, 8 Quickest No info No info 303 295, 7 1210 duration 336, 8 successful (sec) Number of 1 1 1 14 1 passengers simultaneous Percentage No info No info No info 85, 6 No info failed (%) Average No info No info No info No info No info duration failed (sec) 1 (IER, 2016b) 2 (SITA, 2016g) 3 (ICM, 2014) 4 (van Someren Greve, 2016) 5 (BagDrop Systems BV, 2016a) 6 (BagDrop Systems BV, 2016b) 7 At AMS 8 At HND 9 (Dinkelmann, 2016) 10 (Alstef, 2016)

Marco Groot, BSc 128

Optimize Landside Airport Operations Using A Discrete Event Simulation

Appendix B.7 Security access Personal Gunnebo Kaba IER Floor space No info 1170x13003 1050x20504 1006x20985 required (mm) Average 6 (for 1 5.42 No info No info duration passenger successful group)1 (sec) Quickest No info 1.53 24 1.5 (opening and duration closing door)5 successful (sec) Number of Group 1 1 1 passengers simultaneous Percentage No info No info No info No info failed (%) Average No info No info No info No info duration failed (sec) 1 (Scarabee Aviation Group, 2016b) 2 (Scarabee Aviation Group, 2016d) 3 (Gunnebo, 2016b) 4 (Kaba, 2016) 5 (IER, 2016a) Appendix B.8 Security check Centralized security Decentralized security Floor space 5000x185001 1969x75572 required (mm) 5000x205001 Average 1511 No info duration successful (sec) Quickest No info No info duration successful (sec) Number of 141 2 passengers simultaneous Percentage 301 No info failed (%) Average 1861 No info duration failed (sec) 1 (Scarabee Aviation Group, 2016c) 2 (TSA, 2006b)

Marco Groot, BSc 129

Optimize Landside Airport Operations Using A Discrete Event Simulation

Appendix B.9 Serviced immigration AMS HND LCY LAS Floor space 3500x21501 3500x21501 3500x21501 3500x21501 required (mm) Average No info No info No info No info duration successful (sec) Quickest No info No info No info No info duration successful (sec) Number of 1 1 1 1 passengers simultaneous Percentage No info No info No info No info failed (%) Average No info No info No info No info duration failed (sec) 1 (Transportation Research Board, 2010) Appendix B.10 Self-service immigration IER SITA Gunnebo Morpho Kaba Floor space 1006x20981 No info 1034x28903 1058x24104 1050x20505 required (mm) Average No info 902 No info 204 No info duration successful (sec) Quickest 1.5 seconds No info No info 74 No info duration (opening and successful closing (sec) door)1 Number of 1 1 1 1 1 passengers simultaneous Percentage No info No info No info No info No info failed (%) Average No info No info No info No info No info duration failed (sec) 1 (IER, 2016a) 2 (SITA, 2016b) 3 (Gunnebo, 2016a) 4 (Safran Morpho, 2016) 5 (Kaba, 2016)

Marco Groot, BSc 130

Optimize Landside Airport Operations Using A Discrete Event Simulation

Appendix C Passenger profiles determination Appendix C.1 Walking speeds (Young, 1999)

Speed (m/s) Standard Deviation (m/s) Average 1.34 0.27 Male 1.41 0.29 Female 1.28 0.29 Passenger without bags 1.31 0.24 Passenger with bags 1.37 0.22 Business passenger 1.39 0.22 Leisure passenger 1.33 0.24 Appendix C.2 Walking speeds (Schultz, et al., 2008)

Speed (m/s) Standard Deviation (m/s) Male 1.40 0.22 Female 1.27 0.22 Business passenger 1.36 0.22 Leisure passenger 1.00 0.23 Group of 1 person 1.36 0.23 Group of 2 persons 1.06 0.21 Group of 3 persons 0.96 0.19 Appendix C.3 Walking speeds baggage (Schultz, et al., 2008)

Speed Business Standard Speed Standard (m/s) Deviation Leisure Deviation Leisure Business (m/s) (m/s) (m/s) Trolley bag 1.42 0.20 1.19 0.19 Handbag 1.33 0.22 1.07 0.22 Rucksack 1.32 0.25 1.07 0.21 Baggage 1.27 0.24 1.04 0.20 cart Appendix C.4 Walking speeds travel purpose (Schultz & Fricke, 2011)

Speed Standard Speed Standard Speed Standard Business Deviation Leisure Deviation Average Deviation (m/s) Business (m/s) Leisure (m/s) Average (m/s) (m/s) (m/s) Group of 1 person 1.38 0.21 1.19 0.25 1.36 0.23 Group of 2 persons 1.17 0.17 0.97 0.20 1.06 0.21 Group of 3 persons 1.04 0.23 0.93 0.17 0.96 0.19

Marco Groot, BSc 131

Optimize Landside Airport Operations Using A Discrete Event Simulation

Appendix C.5 Influence of baggage (Schultz, et al., 2008)

Type of baggage Difference with average Difference with average speed business passengers speed leisure passengers (%) (%) Trolley bag 7.60 11.20 Handbag 0.80 0.00 Rucksack 0.00 0.00 Baggage cart -3.80 -2.80

Marco Groot, BSc 132

Optimize Landside Airport Operations Using A Discrete Event Simulation

Appendix D AMS airline group overview KLM Rest (1) Rest (2) KLM Germanwings Air Canada Air France Lufthansa Cathay Pacific Delta LOT TUI Alitalia Swiss China Airlines Transavia Austrian Aeroflot Kenya Airways Finnair Egyptair China Southern Adria Airways Turkish Airlines Belavia Air Malta Garuda Xiamen Estonian Air Korean Air Malaysia Airlines SAS United Airlines CityJet TAP American Airlines TAROM HOP Singapore Airlines Ukrain International Aer Lingus Easyjet Britisch Airways Emirates Aegean Atlasjet Croatia Airlines Tunisair Skywork Onur air FlyBe Royal Air Maroc Iberia Norwegian Air Vueling Jet2.com Air Baltic Bulgaria Air Icelandair Pegasus Air Serbia Air Europa Air Astana Aereos Czech airlines Avion Express Etihad El Al Qatar Surinam Airways Royal Jordanian Air Canada

Marco Groot, BSc 133

Optimize Landside Airport Operations Using A Discrete Event Simulation

Appendix E Percentage of occurrence passenger profiles Appendix E.1 Percentage of occurrence AMS

Passenger Occurrence Occurrence Occurrence Occurrence Total (%) profile group size (%) gender (%) travel bag type purpose (%) (%) MBN 65.54 54.00 40.10 16.30 2.31 MBT 65.54 54.00 40.10 47.62 6.76 MBC 65.54 54.00 40.10 36.08 5.12 MLN 65.54 54.00 59.90 16.30 3.46 MLT 65.54 54.00 59.90 47.62 10.10 MLC 65.54 54.00 59.90 36.08 7.65 FBN 65.54 46.00 40.10 16.30 1.97 FBT 65.54 46.00 40.10 47.62 5.76 FBC 65.54 46.00 40.10 36.08 4.36 FLN 65.54 46.00 59.90 16.30 2.94 FLT 65.54 46.00 59.90 47.62 8.60 FLC 65.54 46.00 59.90 36.08 6.52 2BN 25.47 n/a 40.10 16.30 1.66 2BT 25.47 n/a 40.10 47.62 4.86 2BC 25.47 n/a 40.10 36.08 3.69 2LN 25.47 n/a 59.90 16.30 2.49 2LT 25.47 n/a 59.90 47.62 7.27 2LC 25.47 n/a 59.90 36.08 5.50 3BN 8.99 n/a 40.10 16.30 0.59 3BT 8.99 n/a 40.10 47.62 1.72 3BC 8.99 n/a 40.10 36.08 1.30 3LN 8.99 n/a 59.90 16.30 0.88 3LT 8.99 n/a 59.90 47.62 2.56 3LC 8.99 n/a 59.90 36.08 1.94 Total 100.00

Marco Groot, BSc 134

Optimize Landside Airport Operations Using A Discrete Event Simulation

Appendix E.2 Percentage of occurrence HND

Passenger Occurrence Occurrence Occurrence Occurrence Total (%) profile group size (%) gender (%) travel bag type purpose (%) (%) MBN 65.54 54.00 60.00 16.30 3.46 MBT 65.54 54.00 60.00 47.62 10.11 MBC 65.54 54.00 60.00 36.08 7.66 MLN 65.54 54.00 40.00 16.30 2.31 MLT 65.54 54.00 40.00 47.62 6.74 MLC 65.54 54.00 40.00 36.08 5.11 FBN 65.54 46.00 60.00 16.30 2.95 FBT 65.54 46.00 60.00 47.62 8.61 FBC 65.54 46.00 60.00 36.08 6.53 FLN 65.54 46.00 40.00 16.30 1.97 FLT 65.54 46.00 40.00 47.62 5.74 FLC 65.54 46.00 40.00 36.08 4.35 2BN 25.47 n/a 60.00 16.30 2.49 2BT 25.47 n/a 60.00 47.62 7.28 2BC 25.47 n/a 60.00 36.08 5.51 2LN 25.47 n/a 40.00 16.30 1.66 2LT 25.47 n/a 40.00 47.62 4.85 2LC 25.47 n/a 40.00 36.08 3.68 3BN 8.99 n/a 60.00 16.30 0.88 3BT 8.99 n/a 60.00 47.62 2.57 3BC 8.99 n/a 60.00 36.08 1.95 3LN 8.99 n/a 40.00 16.30 0.59 3LT 8.99 n/a 40.00 47.62 1.71 3LC 8.99 n/a 40.00 36.08 1.30 Total 100.00

Marco Groot, BSc 135

Optimize Landside Airport Operations Using A Discrete Event Simulation

Appendix E.3 Percentage of occurrence LCY

Passenger Occurrence Occurrence Occurrence Occurrence Total (%) profile group size (%) gender (%) travel bag type purpose (%) (%) MBN 65.54 54.00 70.00 16.30 4.04 MBT 65.54 54.00 70.00 47.62 11.80 MBC 65.54 54.00 70.00 36.08 8.94 MLN 65.54 54.00 30.00 16.30 1.73 MLT 65.54 54.00 30.00 47.62 5.06 MLC 65.54 54.00 30.00 36.08 3.83 FBN 65.54 46.00 70.00 16.30 3.44 FBT 65.54 46.00 70.00 47.62 10.05 FBC 65.54 46.00 70.00 36.08 7.61 FLN 65.54 46.00 30.00 16.30 1.47 FLT 65.54 46.00 30.00 47.62 4.31 FLC 65.54 46.00 30.00 36.08 3.26 2BN 25.47 n/a 70.00 16.30 2.91 2BT 25.47 n/a 70.00 47.62 8.49 2BC 25.47 n/a 70.00 36.08 6.43 2LN 25.47 n/a 30.00 16.30 1.25 2LT 25.47 n/a 30.00 47.62 3.64 2LC 25.47 n/a 30.00 36.08 2.76 3BN 8.99 n/a 70.00 16.30 1.03 3BT 8.99 n/a 70.00 47.62 3.00 3BC 8.99 n/a 70.00 36.08 2.27 3LN 8.99 n/a 30.00 16.30 0.44 3LT 8.99 n/a 30.00 47.62 1.28 3LC 8.99 n/a 30.00 36.08 0.97 Total 100.00

Marco Groot, BSc 136

Optimize Landside Airport Operations Using A Discrete Event Simulation

Appendix E.4 Percentage of occurrence LAS

Passenger Occurrence Occurrence Occurrence Occurrence Total (%) profile group size (%) gender (%) travel bag type purpose (%) (%) MBN 65.54 54.00 25.40 16.30 1.47 MBT 65.54 54.00 25.40 47.62 4.28 MBC 65.54 54.00 25.40 36.08 3.24 MLN 65.54 54.00 74.60 16.30 4.30 MLT 65.54 54.00 74.60 47.62 12.57 MLC 65.54 54.00 74.60 36.08 9.53 FBN 65.54 46.00 25.40 16.30 1.25 FBT 65.54 46.00 25.40 47.62 3.65 FBC 65.54 46.00 25.40 36.08 2.76 FLN 65.54 46.00 74.60 16.30 3.67 FLT 65.54 46.00 74.60 47.62 10.71 FLC 65.54 46.00 74.60 36.08 8.11 2BN 25.47 n/a 25.40 16.30 1.05 2BT 25.47 n/a 25.40 47.62 3.08 2BC 25.47 n/a 25.40 36.08 2.33 2LN 25.47 n/a 74.60 16.30 3.10 2LT 25.47 n/a 74.60 47.62 9.05 2LC 25.47 n/a 74.60 36.08 6.86 3BN 8.99 n/a 25.40 16.30 0.37 3BT 8.99 n/a 25.40 47.62 1.09 3BC 8.99 n/a 25.40 36.08 0.82 3LN 8.99 n/a 74.60 16.30 1.09 3LT 8.99 n/a 74.60 47.62 3.19 3LC 8.99 n/a 74.60 36.08 2.42 Total 100.00

Marco Groot, BSc 137

Optimize Landside Airport Operations Using A Discrete Event Simulation

Appendix F Simulation input parameters Appendix F.1 AMS

Variable Value Remarks KLM 65 Percentage KLM and SkyTeam passengers KLM Non-Schengen 47 Percentage Rest Non-Schengen 57 Percentage Travel Class 31 Percentage business class passengers Internet Check-in 31 Percentage Kiosk Mean 80 Seconds Kiosk Min 40 Seconds Kiosk Max 120 Seconds Business Desk Mean 90 Seconds Business Desk Min 45 Seconds Business Desk Max 180 Seconds Economy Desk Mean 85.5 Seconds Economy Desk Min 40 Seconds Economy Desk Max 150 Seconds SBD Mean 57 Seconds SBD Min 27 Seconds SBD Max 115 Seconds Security Gate Mean 5.4 Seconds Security Gate Min 1.8 Seconds Security Gate Max 12 Seconds Security Mean 151 Seconds Security Min 120 Seconds Security Max 210 Seconds Staffed Immigration Mean 18 Seconds Staffed Immigration Min 10 Seconds Staffed Immigration Max 30 Seconds Self-Service Immigration Mean 20 Seconds Self-Service Immigration Min 7 Seconds Self-Service Immigration Max 45 Seconds

Marco Groot, BSc 138

Optimize Landside Airport Operations Using A Discrete Event Simulation

Appendix F.2 HND

Variable Value Remarks International 18.2 Percentage international passengers T1 48.4 Percentage domestic passengers T1 Travel Class 45 Percentage business class passengers Internet Check-in 14 Percentage Kiosk Mean 80 Seconds Kiosk Min 40 Seconds Kiosk Max 120 Seconds Business Desk Mean 150 Seconds Business Desk Min 75 Seconds Business Desk Max 300 Seconds Economy Desk Mean 143 Seconds Economy Desk Min 70 Seconds Economy Desk Max 280 Seconds SBD Mean 68 Seconds SBD Min 33 Seconds SBD Max 115 Seconds Security Access Mean 6 Seconds Security Access Min 3 Seconds Security Access Max 12 Seconds Security Mean 151 Seconds Security Min 120 Seconds Security Max 210 Seconds Staffed Immigration Mean 18 Seconds Staffed Immigration Min 10 Seconds Staffed Immigration Max 30 Seconds Self-Service Immigration Mean 20 Seconds Self-Service Immigration Min 7 Seconds Self-Service Immigration Max 45 Seconds

Marco Groot, BSc 139

Optimize Landside Airport Operations Using A Discrete Event Simulation

Appendix F.3 LCY

Variable Value Remarks Travel Class 45 Percentage business class passengers Internet Check-in 53 Percentage Kiosk Mean 80 Seconds Kiosk Min 40 Seconds Kiosk Max 120 Seconds Business Desk Mean 150 Seconds Business Desk Min 75 Seconds Business Desk Max 300 Seconds Economy Desk Mean 143 Seconds Economy Desk Min 70 Seconds Economy Desk Max 280 Seconds SBD Mean 57 Seconds SBD Min 27 Seconds SBD Max 115 Seconds Security Access Mean 5.4 Seconds Security Access Min 1.8 Seconds Security Access Max 12 Seconds Security Mean 151 Seconds Security Min 120 Seconds Security Max 210 Seconds Staffed Immigration Mean 45 Seconds Staffed Immigration Min 53 Seconds Staffed Immigration Max 80 Seconds Self-Service Immigration Mean 40 Seconds Self-Service Immigration Min 120 Seconds Self-Service Immigration Max 150 Seconds

Marco Groot, BSc 140

Optimize Landside Airport Operations Using A Discrete Event Simulation

Appendix F.4 LAS

Variable Value Remarks T1 71 Percentage passengers through T1 International 7.5 Percentage international passengers Travel Class 21 Percentage business class passengers Internet Check-in 65 Percentage Kiosk Mean 80 Seconds Kiosk Min 40 Seconds Kiosk Max 120 Seconds Business Desk Mean 150 Seconds Business Desk Min 75 Seconds Business Desk Max 300 Seconds Economy Desk Mean 143 Seconds Economy Desk Min 70 Seconds Economy Desk Max 280 Seconds SBD Mean 68 Seconds SBD Min 33 Seconds SBD Max 115 Seconds Security Access Mean 6 Seconds Security Access Min 3 Seconds Security Access Max 12 Seconds Security Mean 151 Seconds Security Min 120 Seconds Security Max 210 Seconds Staffed Immigration Mean 18 Seconds Staffed Immigration Min 10 Seconds Staffed Immigration Max 30 Seconds Self-Service Immigration Mean 20 Seconds Self-Service Immigration Min 7 Seconds Self-Service Immigration Max 45 Seconds

Marco Groot, BSc 141

Optimize Landside Airport Operations Using A Discrete Event Simulation

Appendix G Process times used in phase 2

Variable AMS (sec) HND (sec) LCY (sec) LAS (sec) Kiosk Mean 80 95 95 95 Kiosk Min 40 55 55 55 Kiosk Max 120 135 135 135 Business Desk Mean 120 180 180 180 Business Desk Min 75 105 105 105 Business Desk Max 210 330 330 330 Economy Desk Mean 115.5 173 173 173 Economy Desk Min 70 100 100 100 Economy Desk Max 180 310 310 310 SBD Mean 87 98 98 98 SBD Min 57 63 63 63 SBD Max 145 145 145 145 Security Access Mean 8 8 8 8 Security Access Min 4 4 4 4 Security Access Max 12 12 12 12 Security Mean 165 165 165 165 Security Min 135 135 135 135 Security Max 225 225 225 225 Staffed Immigration Mean 30 30 n/a 30 Staffed Immigration Min 20 20 n/a 20 Staffed Immigration Max 50 50 n/a 50 Self-Service Immigration Mean 30 30 n/a 30 Self-Service Immigration Min 15 15 n/a 15 Self-Service Immigration Max 60 60 n/a 60

Marco Groot, BSc 142

Optimize Landside Airport Operations Using A Discrete Event Simulation

Appendix H Required facilities queueing theory Appendix H.1 Required facilities queueing theory AMS

10 20 30 40 50 60 90 120 180 Kiosk T1 KLM 37 37 37 37 37 36 34 32 29 Kiosk T1 Rest 12 12 11 11 11 11 11 11 11 Kiosk T2 32 32 32 32 31 31 30 28 26 Kiosk T3 21 21 21 21 20 20 20 19 17 Kiosk T4 8 8 8 8 8 7 7 6 5 Check-in T1 Rest BC 7 7 7 7 7 7 7 6 6 Check-in T1 Rest EC 19 18 17 17 16 16 16 16 15 Check-in T1 KLM BC 21 21 21 21 20 20 19 18 17 Check-in T1 KLM EC 66 65 64 63 63 63 63 61 56 Check-in T2 KLM BC 19 19 18 18 18 18 17 16 15 Check-in T2 KLM EC 60 58 58 57 57 56 55 52 48 Check-in T3 Rest BC 14 14 13 13 13 13 12 11 10 Check-in T3 Rest EC 41 40 40 39 39 38 37 35 32 Check-in T4 Rest BC 5 4 4 4 4 4 4 3 3 Check-in T4 Rest EC 15 15 15 14 14 14 14 13 12 Security access T1 BC 6 6 6 6 6 6 5 5 5 Security access T1 EC 7 7 7 7 7 7 6 6 6 Security access T2 BC 4 4 4 4 4 4 3 3 3 Security access T2 EC 5 5 5 5 5 5 5 5 4 Security access T3 6 6 6 6 6 5 5 5 5 Security access T4 2 2 2 2 2 2 2 1 1 Security check T1 BC 80 80 80 80 79 79 77 73 68 Security check T1 EC 110 110 109 109 108 108 108 108 108 Security check T2 BC 51 50 50 50 49 49 47 44 40 Security check T2 EC 82 81 80 80 80 80 80 80 76 Security check T3 88 87 87 87 87 87 87 86 83 Security check T4 23 23 23 23 22 22 22 21 20 Immigration T2 24 24 24 24 24 24 24 23 23 Immigration T3 15 15 15 15 15 15 15 15 15

Marco Groot, BSc 143

Optimize Landside Airport Operations Using A Discrete Event Simulation

Appendix H.2 Required facilities queueing theory HND

10 20 30 40 50 60 90 120 180 Kiosk International 27 26 24 24 24 24 24 23 23 Kiosk T1 56 55 53 53 53 52 51 50 49 Kiosk T2 57 55 55 54 54 54 54 53 52 Check-in International 34 31 30 30 30 29 29 28 28 BC Check-in International 40 40 39 38 38 38 37 36 35 EC Check-in T1 BC 68 65 63 63 62 61 61 61 60 Check-in T1 EC 78 76 74 74 73 73 73 72 71 Check-in T2 BC 76 72 70 69 68 68 67 66 65 Check-in T2 EC 80 79 79 78 78 78 77 76 75 Security access 4 4 4 4 4 4 4 4 4 International BC Security check 66 64 63 62 62 62 61 61 59 International BC Security check 51 49 49 48 48 47 47 47 46 International EC Security check T1 BC 126 126 125 123 122 122 121 120 119 Security check T1 EC 104 102 102 102 101 101 101 100 99 Security check T2 BC 138 133 131 129 129 128 128 128 127 Security check T2 EC 113 109 109 108 107 107 107 106 106 Immigration 14 13 13 13 13 13 13 13 13 International BC Immigration 9 9 9 9 9 9 8 8 8 International EC Appendix H.3 Required facilities queueing theory LCY

10 20 30 40 50 60 90 120 180 Kiosk 21 20 19 19 19 19 18 18 17 Check-in BC 34 33 32 31 31 31 30 29 27 Check-in EC 24 24 23 22 22 22 22 21 20 Security access 6 5 5 5 5 5 5 5 5 Security check 92 92 91 90 89 88 88 87 82

Marco Groot, BSc 144

Optimize Landside Airport Operations Using A Discrete Event Simulation

Appendix H.4 Required facilities queueing theory LAS

10 20 30 40 50 60 90 120 180 Kiosk T1 47 45 41 41 41 39 38 38 37 Kiosk T2 21 19 18 18 18 17 17 17 16 BC Check-in T1 35 31 30 30 29 28 28 27 26 EC Check-in T1 162 147 140 140 140 138 136 134 131 BC Check-in T2 18 16 14 13 13 13 13 12 12 EC Check-in T2 66 63 60 60 60 58 57 56 55 BC Security access T1 4 4 4 4 4 4 4 4 4 EC Security access T1 10 10 10 10 10 10 10 9 9 BC Security access T2 2 2 2 2 2 2 2 2 2 EC Security access T2 5 4 4 4 4 4 4 4 4 BC Security check T1 63 57 55 55 54 53 53 51 50 EC Security check T1 194 191 190 189 188 186 186 185 185 BC Security check T2 29 26 25 25 24 24 24 23 23 EC Security check T2 86 81 80 79 79 78 78 77 75 Immigration T1 46 45 45 45 45 45 45 45 44 Immigration T2 20 19 19 19 19 19 19 19 18

Marco Groot, BSc 145

Optimize Landside Airport Operations Using A Discrete Event Simulation

Appendix I Required facilities simulation Appendix I.1 Required facilities simulation AMS

Number of facilities Kiosk T1 KLM 35 Kiosk T1 Rest 10 Kiosk T2 31 Kiosk T3 21 Kiosk T4 7 Check-in T1 Rest BC 6 Check-in T1 Rest EC 15 Check-in T1 KLM BC 19 Check-in T1 KLM EC 61 Check-in T2 KLM BC 18 Check-in T2 KLM EC 54 Check-in T3 Rest BC 12 Check-in T3 Rest EC 37 Check-in T4 Rest BC 5 Check-in T4 Rest EC 13 Security access T1 BC 5 Security access T1 EC 6 Security access T2 BC 4 Security access T2 EC 5 Security access T3 9 Security access T4 2 Security check T1 BC 77 Security check T1 EC 105 Security check T2 BC 49 Security check T2 EC 77 Security check T3 84 Security check T4 21 Immigration T2 22 Immigration T3 14

Marco Groot, BSc 146

Optimize Landside Airport Operations Using A Discrete Event Simulation

Appendix I.2 Required facilities simulation HND

Number of facilities Kiosk International 24 Kiosk T1 48 Kiosk T2 52 Check-in International BC 29 Check-in International EC 32 Check-in T1 BC 59 Check-in T1 EC 68 Check-in T2 BC 63 Check-in T2 EC 73 Security access International BC 4 Security check International BC 55 Security check International EC 43 Security check T1 BC 117 Security check T1 EC 91 Security check T2 BC 124 Security check T2 EC 98 Immigration International BC 12 Immigration International EC 8 Appendix I.3 Required facilities simulation LCY

Number of facilities Kiosk 24 Check-in BC 27 Check-in EC 20 Security access 6 Security check 78

Marco Groot, BSc 147

Optimize Landside Airport Operations Using A Discrete Event Simulation

Appendix I.4 Required facilities simulation LAS

Number of facilities Kiosk T1 42 Kiosk T2 18 BC Check-in T1 28 EC Check-in T1 127 BC Check-in T2 15 EC Check-in T2 53 BC Security access T1 5 EC Security access T1 10 BC Security access T2 3 EC Security access T2 5 BC Security check T1 51 EC Security check T1 172 BC Security check T2 23 EC Security check T2 71 Immigration T1 42 Immigration T2 18

Marco Groot, BSc 148

Optimize Landside Airport Operations Using A Discrete Event Simulation

Appendix J Difference required facilities Appendix J.1 Difference required facilities AMS

10 20 30 40 50 60 90 120 180 Kiosk T1 KLM 95% 95% 95% 95% 95% 97% 103% 109% 121% Kiosk T1 Rest 83% 83% 91% 91% 91% 91% 91% 91% 91% Kiosk T2 97% 97% 97% 97% 100% 100% 103% 111% 119% Kiosk T3 100% 100% 100% 100% 105% 105% 105% 111% 124% Kiosk T4 88% 88% 88% 88% 88% 100% 100% 117% 140% Check-in T1 Rest BC 86% 86% 86% 86% 86% 86% 86% 100% 100% Check-in T1 Rest EC 79% 83% 88% 88% 94% 94% 94% 94% 100% Check-in T1 KLM 90% 90% 90% 90% 95% 95% 100% 106% 112% BC Check-in T1 KLM 92% 94% 95% 97% 97% 97% 97% 100% 109% EC Check-in T2 KLM 95% 95% 100% 100% 100% 100% 106% 113% 120% BC Check-in T2 KLM 90% 93% 93% 95% 95% 96% 98% 104% 113% EC Check-in T3 Rest BC 86% 86% 92% 92% 92% 92% 100% 109% 120% Check-in T3 Rest EC 90% 93% 93% 95% 95% 97% 100% 106% 116% Check-in T4 Rest BC 100% 125% 125% 125% 125% 125% 125% 167% 167% Check-in T4 Rest EC 87% 87% 87% 93% 93% 93% 93% 100% 108% Security access T1 83% 83% 83% 83% 83% 83% 100% 100% 100% BC Security access T1 86% 86% 86% 86% 86% 86% 100% 100% 100% EC Security access T2 100% 100% 100% 100% 100% 100% 133% 133% 133% BC Security access T2 100% 100% 100% 100% 100% 100% 100% 100% 125% EC Security access T3 150% 150% 150% 150% 150% 180% 180% 180% 180% Security access T4 100% 100% 100% 100% 100% 100% 100% 200% 200% Security check T1 96% 96% 96% 96% 97% 97% 100% 105% 113% BC Security check T1 95% 95% 96% 96% 97% 97% 97% 97% 97% EC Security check T2 96% 98% 98% 98% 100% 100% 104% 111% 123% BC Security check T2 94% 95% 96% 96% 96% 96% 96% 96% 101% EC Security check T3 95% 97% 97% 97% 97% 97% 97% 98% 101% Security check T4 91% 91% 91% 91% 95% 95% 95% 100% 105% Immigration T2 92% 92% 92% 92% 92% 92% 92% 96% 96% Immigration T3 93% 93% 93% 93% 93% 93% 93% 93% 93% Average 94% 96% 96% 97% 98% 99% 103% 112% 118%

Marco Groot, BSc 149

Optimize Landside Airport Operations Using A Discrete Event Simulation

Appendix J.2 Difference required facilities HND

10 20 30 40 50 60 90 120 180 Kiosk International 89% 92% 100% 100% 100% 100% 100% 104% 104% Kiosk T1 86% 87% 91% 91% 91% 92% 94% 96% 98% Kiosk T2 91% 95% 95% 96% 96% 96% 96% 98% 100% Check-in 85% 94% 97% 97% 97% 100% 100% 104% 104% International BC Check-in 80% 80% 82% 84% 84% 84% 86% 89% 91% International EC Check-in T1 BC 87% 91% 94% 94% 95% 97% 97% 97% 98% Check-in T1 EC 87% 89% 92% 92% 93% 93% 93% 94% 96% Check-in T2 BC 83% 88% 90% 91% 93% 93% 94% 95% 97% Check-in T2 EC 91% 92% 92% 94% 94% 94% 95% 96% 97% Security access 100% 100% 100% 100% 100% 100% 100% 100% 100% International BC Security check 83% 86% 87% 89% 89% 89% 90% 90% 93% International BC Security check 84% 88% 88% 90% 90% 91% 91% 91% 93% International EC Security check T1 93% 93% 94% 95% 96% 96% 97% 98% 98% BC Security check T1 88% 89% 89% 89% 90% 90% 90% 91% 92% EC Security check T2 90% 93% 95% 96% 96% 97% 97% 97% 98% BC Security check T2 87% 90% 90% 91% 92% 92% 92% 92% 92% EC Immigration 86% 92% 92% 92% 92% 92% 92% 92% 92% International BC Immigration 89% 89% 89% 89% 89% 89% 100% 100% 100% International EC Average 88% 90% 92% 93% 93% 94% 95% 96% 97% Appendix J.3 Difference required facilities LCY

10 20 30 40 50 60 90 120 180 Kiosk 114% 120% 126% 126% 126% 126% 133% 133% 141% Check-in BC 79% 82% 84% 87% 87% 87% 90% 93% 100% Check-in EC 83% 83% 87% 91% 91% 91% 91% 95% 100% Security access 100% 120% 120% 120% 120% 120% 120% 120% 120% Security check 85% 85% 86% 87% 88% 89% 89% 90% 95% Average 92% 98% 101% 102% 102% 103% 105% 106% 111%

Marco Groot, BSc 150

Optimize Landside Airport Operations Using A Discrete Event Simulation

Appendix J.4 Difference required facilities LAS

10 20 30 40 50 60 90 120 180 Kiosk T1 89% 93% 102% 102% 102% 108% 111% 111% 114% Kiosk T2 86% 95% 100% 100% 100% 106% 106% 106% 113% BC Check-in T1 80% 90% 93% 93% 97% 100% 100% 104% 108% EC Check-in T1 78% 86% 91% 91% 91% 92% 93% 95% 97% BC Check-in T2 83% 94% 107% 115% 115% 115% 115% 125% 125% EC Check-in T2 80% 84% 88% 88% 88% 91% 93% 95% 96% BC Security access 125% 125% 125% 125% 125% 125% 125% 125% 125% T1 EC Security access 100% 100% 100% 100% 100% 100% 100% 111% 111% T1 BC Security access 150% 150% 150% 150% 150% 150% 150% 150% 150% T2 EC Security access 100% 125% 125% 125% 125% 125% 125% 125% 125% T2 BC Security check 81% 89% 93% 93% 94% 96% 96% 100% 102% T1 EC Security check 89% 90% 91% 91% 91% 92% 92% 93% 93% T1 BC Security check 79% 88% 92% 92% 96% 96% 96% 100% 100% T2 EC Security check 83% 88% 89% 90% 90% 91% 91% 92% 95% T2 Immigration T1 91% 93% 93% 93% 93% 93% 93% 93% 95% Immigration T2 90% 95% 95% 95% 95% 95% 95% 95% 100% Average 93% 99% 102% 103% 103% 105% 105% 107% 109%

Marco Groot, BSc 151