Adapting Automated People Mover Capacity to
Real-Time Demand via Model-Based Predictive Control
Capacity to Real-Time Demand via
Model-Based Predictive Control
by
M.P. van Doorne BSc.
to obtain the degree of Master Master of Science
in Transport, Infrastructure & Logistics at the Delft University of Technology.
to be defended publicly on Wednesday December 2nd, 2015 at 14:00 PM.
Graduation Committee TU Delft Prof. dr. ir. G. Lodewijks TU Delft (3ME) dr.ir. W.W.A. Beelaerts van Blokland TU Delft (3ME) dr.ir. G. Homem de Almeida Correia TU Delft (CiTG)
Supervision NACO Royal HaskoningDHV
ir. T. van Vrijaldenhoven Airport Planning & Building Design ir. P.H. Ringersma Airport Planning & Building Design drs. ing. R. Lunstroo Airport Planning & Building Design
This report contains the findings of a research on the control of an Automated People Mover (APM) system. APMs are combined passenger transit systems that are an important asset for large airports to support intra-terminal passenger movements and/or provide inter-terminal tran-sit. While APMs were first introduced at airports in the early ’70s, the physical and operational system characteristics have not changed much since.
The problem is that the control system of any APM attempts to replicate a predefined schedule by constantly measuring parts of the system (i.e. trains) and take actions to diminish any errors. These schedules are already defined in the design phase of a system and should cope with a peak demand throughout the operational life cycle. Such a design approach inevitably results in that there will be a gap between the actual demand and the available capacity. Especially in airports with large demand fluctuations, this will e.g. incur large over-capacities during down periods. The objective of this research is to analyse the current fixed schedule control of an APM system and design a more intelligent control logic that adapts the capacity available to the real-time demand. This research will not only address the technical aspect to obtain such an Adaptive Control System (ACS), but will also consider the change in the system state compared to a conventional system. Which system state is better or worse depends on the requirements set by the system owner (i.e. airport). These requirements will be a trade-off between aspects that concern the economic and sustainable impact of the system and the passenger experience. This is summarised in a research objective:
Determine the feasibility of adapting Automated People Mover capacity to real-time demand, by taking economic, sustainable, passenger comfort and implementation aspects into consideration. To transform the objective into an actual design, a stepwise approach is taken in which first a conceptual outline is made of the ACS, after which a detailed design is tested and evaluated. To determine the generality of the ACS, it is applied to two test case (proposed) APM systems at Amsterdam Schiphol International Airport (AMS) and Shenzhen Ba’oan International Airport (SZX). Thereby, a set of (Key) Performance Indicators (KPIs) is formulated to quantitatively support any conclusion on the performance of an ACS, by measuring:
Passenger experience: platform dwell time, platform & train Level of Service (LoS); System cost: capital cost & operational Cost;
External effect: CO2pollution.
Conceptual Design of an Adaptive Control System
The technological development of Communication Based Train Control (CBTC) for APMs allows for a system that changes states according to a schedule. The goal of the ACS is to replace the currently used fixed schedule with a flexible schedule that adapts to real-time demand. It is therefore proposed that the available CBTC technology that currently controls train movements is combined with an hierarchically higher controller that can change the system capacity in terms
problem with the controller type used for this system, is that it reacts to demand initiated by a passenger with a push of a button. This is fine for a system in which a high number of vehicles is available to keep the reaction time low, but will be problematic in a typical APM system with few trains that need more time to anticipate. The result is that passengers have to wait uncomfortably long and instead some form of proactive control is required to activate a train in time.
It is therefore chosen to design an Adaptive Control System (ACS) by means of Model-based Predictive Control (MPC) method. An MPC calculates a set of future actions that as a com-bination satisfies an overall objective based on passenger demand forecast models. The prime objective of the MPC is to minimise the difference between the system capacity for the next n time steps and the demand forecast for that same period n. This demand is determined by aircraft movements that induce a high and narrow arrival distribution and a lower but broader departure distribution.
While demand characteristics can roughly be calculated based on historical data and airport forecasts, it is preferred to place a sensor at an appropriate distance before the platform such that the minimum forecast period is met. The capacity can either be changed by running more/less trains or increase/decrease the amount of cars per train. The complete control structure is summarised in Figure 2.
Figure 2: Adaptive Control System
The MPC can adjust train scheduling and train composition based on changes in demand. The primary action of the controller is to add a 1-car train to the schedule in response to the initial creation of demand, such that the first passenger has to wait a maximum acceptable waiting time (based on system owner’s requirement). If the demand for a scheduled train exceeds the available capacity, there are two approaches to increase capacity further; an additional train can be scheduled before or after the first train and effectively increase frequency, or the train can be extended by an extra car. If in either approach a maximum is reached (no more trains able to be scheduled or no more cars to be added), the other approach should be used to further
expand capacity. This results in two concept ACS alternatives which can be tested and evaluated together with a reference alternative:
Reference Case: a representation of a conventional fixed schedule operation;
ACS 1 - Frequency: an ACS that favours changing train frequency over train capacity; ACS 2 - Capacity: an ACS that favours changing train capacity over train frequency.
Table 1: Summary of Simulation Results AMS
KPI Passenger Experience Cost (daily) External PI Wait(s) LoS Plat LoS Train Capital Operation kg CO2
Reference Case 90.611 A D $1,314 $560 9,424 ACS 1: Frequency 95.33 A D $2,012 $350 5,874 ACS 2: Capacity 94.18 A D $1,136 $346 5,806
1during daytime
Table 2: Summary of Simulation Results SZX
KPI Passenger Experience Cost (daily) External PI Wait(s) LoS Plat LoS Train Capital Operation kg CO2
Reference Case 92.31 C D $3,504 $4,454 110,751 ACS 1: Frequency 80 D D $3,107 $1,678 41,714 ACS 2: Capacity 80.42 D D $2,888 $1,757 43,674
Detailed Design of an Adaptive Control System
The conceptual design of the ACS is converted into detailed designs for proposed APM systems at Amsterdam Schiphol International Airport (AMS) and Shenzhen Bao’an International Airport (SZX). The simulation results, summarised in the tables 1 and 2, show a comparable result on the (Key) Performance Indicators (KPIs) for the two ACS alternatives in terms of dwell time, Level-of-Service (LoS), operational costs and CO2 pollution. Capital costs are however consistently
lower in ACS alternative 2 (capacity), as fewer cars have to be acquired.
This difference is caused by the distinctive decision logic of ACS alternative 1 (frequency), which can add an extra earlier train to decrease the waiting time of passengers. A mismatch can occur between the demand forecast and capacity availability when this train is added but passengers miss it and an extra car in the following train is required to compensate. The expected lower waiting time that should result from the logic is thereby insignificant with comparable average dwell times for the ACS alternatives.
The dwell time results of the ACS alternatives also show a distinctive difference between the two test cases, in which the results are better for SZX than AMS. This is the result of the ACS decision logic that makes a primary decision to schedule a 1-car train when demand is created and then it waits as long as possible to optimally fill that train (180 seconds). Any further adjustments to the schedule or train composition are possible when the demand surpasses the capacity of the first scheduled 1-car train. This does however only sporadically occur in the low demand system at AMS and is more frequent in the control of the SZX system, hence resulting in better results of the ACS in the latter test case.
higher platform utilization, which results in a drop in level of service for a singular platform in the SZX test case (from LoS C to Los D, on a ranking from A=best to F=worst). The LoS D measured on this platform at SZX is acceptable according to IATA standards for short periods, but should generally be redesigned to a higher LoS to meet client requirements. The LoS D measured for the trains in all alternatives is also low, but due to the short transit period transit it is deemed as an appropriate minimum LoS by industry experts.
The ACS alternatives differ significantly from the reference case on the KPIs cost and sustain-ability. As was concluded before, the ACS alternative that favours frequency over capacity (ACS alternative 1) consistently requires more cars, which results in a larger capital and total cost in the AMS test case. In the SZX case, the costs in the ACS alternatives are consistently lower, though. As the ACS alternatives in both test cases significantly reduce the total run distance, directly proportional operational costs and CO2 pollution are reduced as well.
The effect of an ACS is not only depended on the system scale but also on the design charac-teristics of an APM system. The relative reduction in run distance (and thus operational cost and CO2pollution) of the ACS alternatives compared to the reference case is for instance larger
for the APM system at SZX. This is due to the availability of parking spaces in the system and the single security status of APM passengers. AMS does not have parking spaces and has to make additional empty runs to vacate stations for scheduled train arrivals. There are also two passenger security states (Schengen and Non Schengen), which can result in an inefficiently large train composition. The AMS system on the other hand has much larger planned platforms, with as a result that the LoS remains the same for all alternatives.
Evaluating the feasibility of an ACS for APM systems
As there was no information available on the preferences of the system owners (i.e. the airport authorities) during the research, it was not possible to perform a Multi Criteria Decision Analysis (MCDA) and single out a most desirable solution for any of the two test cases. However, based on the research it can still be concluded that the ACS is a feasible concept that can effectively be implemented in APM systems and improve the performance thereof.
Both ACS alternatives show an overall better result compared to the reference case in terms of reducing costs (capital and operational) and increasing the sustainability of the system. While on the other hand the LoS decreases slightly for the SZX test case, the overall comfort that pas-sengers experience is still adjudged to be acceptable. It should be noted that system demand and design characteristics have a considerable effect on the relative result of the ACS implementation. Favouring a change of train capacity before changing train frequency (ACS alternative 2) is the best approach for an ACS in both test cases. The alternative shows a consistently better result in respect to capital costs, while the expected increase in dwell time compared to the first ACS alternative is only limited.
Recommendations
This research is an initial step towards an intelligent control of Automated People Movers. Logically, additional research is required to further determine the opportunities of an ACS.
It is important that NACO approaches companies within the APM industry and airport clients, to bring the idea of adapting APM capacity to demand in the attention. It should furthermore consider implementing any (future) intelligent control system in the physical design of new APM projects.
Scientific research is required on other APM systems and possibly on other combined passenger transit systems such as metros and (light) rail, to further test the generality of the ACS. Thereby, research is needed to test the robustness of an ACS to system failures and further test the effect of changing the relatively sensitive headway variable. Furtermore, it is recommended to identify implementation obstacles in respect to CBTC and system design.
1 Introduction 1
1.1 Research Context: Netherlands Airport Consultants . . . 1
1.2 Research Context: Automated People Mover . . . 2
1.3 Problem Description . . . 3
2 Research Approach 5 2.1 Research Relevance & Objective . . . 5
2.2 Research Questions . . . 6
2.3 Research Methodology: Engineering Design . . . 7
2.3.1 Conceptual Design: Controller Types . . . 7
2.3.2 Conceptual Design: Controller Structures . . . 9
2.3.3 Detailed Design: APM System Test Case(s) . . . 10
2.3.3.1 Process Analysis . . . 10
2.3.3.2 Modelling & Simulation . . . 11
2.3.3.3 Alternative Testing Framework . . . 12
2.4 Research Scope & Boundaries . . . 13
2.4.1 Automated People Movers . . . 13
2.4.2 APM Airport Function . . . 15
2.4.3 Test Case Airport Systems . . . 17
2.4.4 0% System Failures . . . 17
2.4.5 System Design Capacity . . . 18
2.4.6 Basic System Schedule Design . . . 18
2.5 Key Performance Indicators . . . 18
2.5.1 Measuring Passenger Experience . . . 18
2.5.2 Measuring Costs . . . 19
2.5.3 Measuring External Influences . . . 19
3 Conceptual Design of an Adaptive Control System for Automated People Movers in an Airport Environment 21 3.1 Available Control Systems . . . 21
3.2 ACS Controller Structure . . . 22
3.3 Central Model-Based Predictive Control Logic . . . 25
3.3.1 Internal Workings of the Decision Logic . . . 27
3.3.2 Demand Forecast Model . . . 29
4 Detailed Test Case Design For Amsterdam Airport Schiphol 31 4.1 Airport Region . . . 31
4.2 Airport Characteristics . . . 31
4.3 APM System Characteristics . . . 32
4.4 Alternative Description . . . 34
4.4.1 Reference Case . . . 34
4.4.2 ACS Alternative 1: Frequency . . . 36
4.4.3 ACS Alternative 2: Capacity . . . 37
4.5 Results . . . 38
4.5.1 Passenger Experience . . . 38
4.5.2 Cost . . . 39
5 Detailed Test Case Design For Shenzhen Bao’an International Airport 43
5.1 Airport Region . . . 43
5.2 Airport Characteristics . . . 44
5.3 APM System Characteristics . . . 45
5.4 Alternative Description . . . 47
5.4.1 Reference Case . . . 47
5.4.2 ACS Alternative 1: Frequency . . . 49
5.4.3 ACS Alternative 2: Capacity . . . 50
5.5 Results . . . 50
5.5.1 Passenger Experience . . . 50
5.5.2 Cost . . . 51
5.5.3 External Effect . . . 53
6 Conclusion & Recommendation 55 6.1 Recommendations for NACO . . . 57
6.2 Recommendations for Further Scientific Research . . . 58
Appendices 58 A Airport Pax Transit Systems 61 A.1 Personal/Group Rapid Transport . . . 61
A.2 Metro . . . 62
A.3 Other APTS Solutions . . . 62
B APM System Benchmark 63 B.1 Hartsfield-Jackson Atlanta International Airport . . . 64
B.2 Beijing Capital International Airport . . . 66
B.3 Birmingham International Airport . . . 68
B.4 O’Hare International Airport . . . 70
B.5 Dallas/Forth Worth International Airport . . . 72
B.6 Detroit International Airport . . . 74
C Arena Simulation Model 77 C.1 Arena Software Methodology . . . 77
C.2 Model/Simulation Set Up, Validation & Verification . . . 77
C.3 Model Description . . . 81
C.4 Amsterdam Airport Schiphol Model Set Up . . . 88
C.5 Shenzhen Bao’an Airport Model Set Up . . . 88
D Results 93
Bibliography 95
2 Adaptive Control System . . . iv
1.1 Bombardier. . . 2
1.2 Mitsubishi . . . 2
1.3 Siemens . . . 2
1.4 Network layouts . . . 3
2.1 Basic Control System principle (Negenborn and Hellendoorn, 2010) . . . 8
2.2 Multi criteria decision support methodology (Vreeker et al., 2002) . . . 13
2.3 Test Case APM vehicle . . . 15
2.4 Available actions between APM and Aircraft movement . . . 16
3.1 FBTC (Top) vs CBTC (Bottom) . . . 22
3.2 Example Fixed Schedule for Singapore Changi (Lea+Elliot, 2014) . . . 23
3.3 Hierarchy in control logic . . . 24
3.4 ACS capacity change approaches . . . 27
3.5 Increased Train Frequency Favoured Over Increased Train Capacity . . . 28
3.6 Increased Train Capacity Favoured Over Increased Train Frequency . . . 29
4.1 Arrival and Departure Pattern AMS . . . 33
4.2 Example Block Schedule AMS . . . 33
4.3 Proposed APM trajectory at AMS . . . 33
4.4 Proposed APM Network Layout at AMS . . . 34
4.5 Passenger Type distribution (Lea+Elliot, 2014) . . . 35
4.6 APM Demand AMS (1 run) . . . 37
5.1 Pearl River Delta . . . 44
5.2 Arrival and Departure Pattern SZX . . . 45
5.3 Proposed APM trajectory at SZX . . . 46
5.4 Proposed APM Network Layout at SZX . . . 46
5.5 APM demand SZX (1 run) . . . 48
5.6 The location of the sensor at Terminal 3 . . . 50
A.1 WVU PRT (Wikipedia) . . . 62
A.2 Heathrow PRT (Wikipedia) . . . 62
C.1 Simple representation of a typical system analysed with SIMAN (Pegden, 1983) . 77 C.2 Model Sections AMS . . . 81
C.3 Module Schedule Generator . . . 84
C.4 Module Passenger Generation . . . 84
C.5 Module Platform Distributor . . . 85
C.6 Module ACS Favour Frequency . . . 85
C.7 Module ACS Favour Capacity . . . 85
C.8 Module Platform Waiting Process . . . 85
C.9 Module Outbound Station Process . . . 86
C.10 Module Inbound Station Process . . . 86
C.11 Module Parking Process . . . 86
C.12 Module Train Continuation Process . . . 86
C.13 Train Schedule Module . . . 87
C.15 Tornado diagrams for AMS Sensitivity Analysis . . . 90 C.16 Tornado diagrams for SZX Sensitivity Analysis . . . 90
1 Summary of Simulation Results AMS . . . v
2 Summary of Simulation Results SZX . . . v
1.1 APM characteristics . . . 2
2.1 Types of Systems (Ackoff, 1999) . . . 11
2.2 Airport Passenger Transit Systems . . . 13
2.3 Differences Between APM and PRT systems (Furman et al., 2014) . . . 14
2.4 Test Case Airports . . . 17
4.1 Round trip Single Service AMS (v=50kph, acc=1m/s2, dec=1m/s2) . . . 34
4.2 Headway and Train Composition Combinations AMS . . . 36
4.3 Results Dwell Time (Day Time Results Between Brackets) . . . 38
4.4 Results Level-of-Service . . . 39
4.5 Results Cost . . . 41
4.6 Results CO2 pollution . . . 42
5.1 Round trip Single Service SZX (v=50kph, acc=1m/s2, dec=1m/s2) . . . . 46
5.2 Headway and Train Composition Combinations SZX . . . 48
5.3 Results Dwell Time . . . 51
5.4 Results Level-of-Service . . . 52
5.5 Results Cost . . . 54
5.6 Results CO2 pollution . . . 54
6.1 Summary of Simulation Results AMS . . . 56
6.2 Summary of Simulation Results SZX . . . 56
B.1 APM test cases . . . 63
C.1 Arena Modules . . . 78
C.2 Results 18 run replications AMS . . . 88
C.3 Results 10 Run replications SZX . . . 89
C.4 Sensitivity Analaysis . . . 91
D.1 Raw Output Data AMS Simulation . . . 93
The document in front of you is the final step towards the degree of Master of Science in Trans-port, Infrastructures & Logistics. For my graduation thesis, I was commissioned by the Airport Planning & Building Design department of NACO to improve the operations of Automated Peo-ple Movers at airports. After 8 months of thorough research, it is this document that contains the final report on my findings that entail two effective alternatives.
Thanks go out to my university committee; Gabriel Lodewijks, Wouter Beelaerts van Blokland and Goncalo Homem de Correia for their positive, but critical feedback during the project. I also want take them for recommending me to do my thesis on a subject that is somewhat out of my ’comfort zone’ (i.e. Automated People Movers), to challenge myself and widen my overall knowledge on airport systems.
I also want to thank my supervisors at NACO; Tim van Vrijaldenhoven, Piet Ringersma and Ronald Lunstroo. It has been a great experience to be part of this professional and exciting company. While agendas were full and meetings were short, there has always been someone to talk to, brainstorm with or to critically review my progress. The last 8 months gave me the opportunity to experience the normal work environment of an airport consultant, with as highlight the chance to participate in the site visit at Shenzhen Bao’an International Airport.
M.P. van Doorne BSc. November 17th 2015
The air transportation sector has expanded rapidly in the last decades. After the deregulation of the industry in the U.S. in 1978 and the easing of many bilateral agreements in the following years, the sector has grown at an annual rate of about 5% globally (Reynolds-Feighan, 1998, De Neufville and Odoni, 2003, IATA, 2015b). This changing demand does not only mean that airlines have to expand their fleets and operations, but airports need to adapt as well.
Airports are therefore expanding their terminal buildings and/or construct new terminals to accommodate future passenger demand. For such projects, they generally call in the help of an expert third party to consult, improve and develop. The Netherlands Airport Consultants (NACO) is such an expert party for airport (re)development and is therefore always in search of new technologies to design good solutions for their customers.
This thesis is on the improvement of airport passenger transit systems (APTS) and in particular Automated People Movers (APM), which are an integral part of many airport terminal designs. Knowledge within the company on ’simple’ systems such as travelators and buses is adequate but for projects that contain APMs, the help of partner companies such as Lea+Elliot1 is required.
NACO is therefore interested to improve its in-house knowledge on APMs, as this will aid in creating better and more comprehensive designs for its clients.
This introduction contains a brief explanation of the company and APMs, so to familiarise the reader with the research context. The chapter is concluded with the problem description.
1.1
Research Context: Netherlands Airport Consultants
Netherlands Airport Consultants (NACO) was founded by Royal Dutch Airlines (KLM) director-president dr. Albert Plesman back in 1949 and the company became renowned for its design of e.g. Amsterdam Airport Schiphol. NACO was taken over by engineering firm DHV in 2003, which in 2012 merged with Royal Haskoning to Royal HaskoningDHV (de Voogt, 2014). The company has done some major airport projects that include the airports of Kuala Lumpur, Bei-jing and Hong. It has recently won a tender for another major airport together with architectural firms Foster & Partners and Fernando Romero for Mexico-City (Royal HaskoningDHV, 2015, Foster+Partners, 2015). With these and other projects, NACO has been involved in circa 550 airport developments in over 100 countries, making it one of the major parties in the airport design industry (NACO, nd).
The company has its head office in the Hague and the ± 150 people that work there account for the fast majority of the company’s employees. A couple of employees are stationed at smaller offices outside the Netherlands to better serve the local markets in e.g. Mexico, Saudi Arabia, U.A.E. and South Africa.
1.2
Research Context: Automated People Mover
Automated People Mover (APM), also referred to as Automated Airport People mover, Auto-mated Transit System, Airport People Mover, Automatic People mover or simply People Mover, is a collective noun for systems that transport passengers without the interference and/or control of human beings (i.e. automated). APMs are part of a large group of airport passenger transit systems (APTS) that also includes e.g. buses, metros, personal rapid transit (PRT or Pods) and travelators (moving walkways).
The latter is probably the most common APTS and it functions as a backbone for many intra-terminal connections, allowing passengers to move quickly through lengthy hallways and piers. While technically both the travelator and PRT are fully automated and can thus be denoted as APMs, one generally refers to high capacity vehicles that run on a fixed route and schedule. Currently there are 48 of these systems in operation at airports, transporting passengers on land-side, airside or both. The systems not only differs from other guided transport on automation, but also on the short track distance (average <2km), narrow vehicles, high frequency and high standees to seating ratio.
APM Types
Upon indexing the different systems, it was found that the type of vehicles generally are quite similar based on capacities and vehicle sizes, but are very different when it comes to the support, propulsion and guidance system. The used solutions are summarized in table 1.1.
Table 1.1: APM characteristics
Support Propulsion Guidance Rubber tires AC Electric motor Guide rail Steel wheel DC Electric motor Conventional rail Levitation Cable pulled Monorail
Side wheels
The majority of systems make use of a rubber tired system. In most cases, this is either a Bombardier Innovia APM (versions C-100, CX-100, 100, 200 or 300), the Mitsubishi Crystal Mover or the Siemens Airval, of which examples are shown in Figures 1.1, 1.2 and 1.3. The Bombardier system and new Airval system make use of a central guide rail (Bombardier, 2015, Siemens, 2014) and the Mitsubishi and earlier Siemens systems using side-mounted wheels to find their way (Mitsubishi HI, 2010, Siemens, nd). This type of system is a derivative of the VAL introduced back in 1983 by Matra, now Siemens (Lardennois, 1993).
Cable pulled APMs are also used at airports, mainly in combination with levitating vehicles. The cars are lifted 2 millimetres above the surface by means of a single 10 bhp electric motor and pulled along the track (Bares, nd). These systems are slightly more limited than the afore-mentioned rubber tired systems that can change tracks at will (assuming the track has change points).
Only a single application is found of a monorail system for inter-terminal transport at Tokyo Haneda, which in fact also functions as a transport system further towards the city. A marginally larger number is found for rail mounted APM system, with the 2 station (cable pulled) APM at Birmingham and the 3 station (cable pulled) APM at Toronto.
Network Layouts
Different network layouts are used based on e.g. the vehicle characteristics, amount of stations and flexibility required. Elliott and Norton (1999) have determined the different layouts possible, which are shown in figure 1.4.
Figure 1.4: Network layouts
Systems can differ from a single vehicle that runs back and forth on a single rail up to multiple vehicles that operate at the same time on a pinched loop layout. Generally it is the latter that is used due to high capacity and vehicle flexibility (e.g. London Heathrow and Atlanta) in combination with the rubber tired APM vehicles. This layout is however not compatible with cable pulled APMs, which generally use a single lane with bypass or dual lane configuration (e.g. Detroit and Zurich).
Full details and characteristics of the different APM systems currently in use at airports are given in Appendix B. An example case is included for all systems.
1.3
Problem Description
While the aforementioned APMs serve their goals in transporting passengers at an airport, their operations are still far from optimised. An important inefficiency is that all systems use a predefined capacity (ACRP, 2012b). In many cases, the capacity is fixed throughout the day based on the peak demand and in some cases this approach is marginally improved by running
Such an approach is relatively simple and generally fulfils the required task, but is de facto inefficient. Demand fluctuations throughout a day are a common phenomenon at airports (espe-cially at transfer/hub airports), with substantial differences between peak and off-peak periods. When one would use an APM at an airport nowadays, he or she will notice that outside peak periods, the trains are as good as empty. On the other hand, riding the same train during peak periods can be a cramped experience and passengers might even have to wait for the next train. This problem should therefore be assessed in this research and an eventual solution should be designed.
This chapter contains a comprehensive explanation of the research approach. First, the scientific relevance of researching the problem stated in the former chapter is briefly discussed and a research objective is formulated that should be met to sufficiently solve the system problem (Section 2.1). The research objective is then transformed in a main research question and a set of supportive research questions that will function as a framework (Section 2.2). Thereafter, the scientific methods to analyse the system and design, test and validate alternatives is discussed in Section 2.3. The last section (2.4) of this chapter contains the scope & boundaries of the system.
2.1
Research Relevance & Objective
As was briefly explained in the introduction, APM utilisation is increasing at large airports, but operations are still far from optimal. Although APMs have been around for almost 50 years, not so much has changed from a technical perspective. There have been some new concepts such as hovering vehicles, cable pulled systems and more complex track lay-outs, but vehicles have remained nearly identical and operations are unchanged. For example, the APM system at Capital Airport in Beijing was built only recently in 2008, but still makes use of the Bombardier Innovia CX-100 vehicle, which was introduced back in 1970 at Miami International Airport (Elliott and Norton, 1999).
It is not completely clear why airports have never required a more adaptive operation, but most likely this is due to the manufacturers that provide a homogeneous product. Thereby, APM system design is done by a only a couple of parties that compete in a market that is dominated by Lea+Elliot. When assessing the commercial solutions and scientific literature available, it is clear that the application of adaptive operations for APMs is not actively researched. No scientific papers could be found for any form of demand driven APM control and only a single manufacturer (Siemens) acknowledges that it can supply systems with some adaptive capability for train frequency only (?), but only in situations that the normal schedule cannot be operated (i.e. system failures).
A probable reason that research is still limited, is that technological enablers for effective demand measurements and/or capacity adaptive control have not been around for a long time. However, technological developments on both aspects have been extensive in the last decade and as a result, some initial research on the opportunities of adaptive control is done for similar guided vehicle transit systems such as metro and common rail networks (Wang et al., 2010, Lin and Sheu, 2011, Sheu and Lin, 2012).
The recent introduction of for instance Communication Based Train Control (CBTC) has strongly increased the operational capabilities. Although CBTC is a generic concept that is used in a va-riety of guided vehicle transit systems, it can be fitted with Automatic Train Protection (ATP), Automatic Train Operation (ATO) and Automatic Train Supervision (ATS) functions (Schifers and Hans, 2000). The overall goal of CBTC is to constantly update the system to a set timetable and adapt to it accordingly. Simply put, CBTC has the capability of running fully automated,
on system policies and demand measurements. In combination with new airport improvements such as Collaborative Decision Making (CDM) and advanced high resolution sensor systems that use WiFir, BluetoothTM, infra-red and/or CCTV technology, the technological means are
however available to design such a control logic for an APM system in an airport environment (Eriksen, 2002, Malinovskiy et al., 2012, Kim et al., 2008, Woodman and Harle, 2008).
The objective of this research is to analyse the ’simple’ scheduled control system of an APM and design a new adaptive control systems, further referred to as ACS, that seeks an optimum system state to adapt capacity to real-time demand. The primary goal is to determine if an ACS will result in a better optimum system state than a conventional system.
What an optimum system state is, depends on what the system owner (i.e. airport) wants. This will most likely be driven by the economical aspect of the project, but due the global perception on sustainability and growing competition between airports, other important factors are energy use and passenger comfort.
A minor aspect to the design of an ACS is that it would be favourable if for the implementation of a solution, characteristics can be shared with current systems. It should however not be seen as a restricting factor if a design can only be obtained with a change of technology.
The whole research objective is summarised as:
Determine the feasibility of adapting Automated People Mover capacity to real-time demand, by taking economic, sustainable, passenger comfort and implementation aspects into consideration.
2.2
Research Questions
To achieve the stated research objective, a main research question is defined:
What are the value drivers for the design of a control system to adapt Automated People Mover system capacity to real-time demand?
To answer this main research question, the content is broken up into smaller supportive questions that act as a framework for this thesis. Question 1 is important to allow any comparison of solutions, whereas question 2, 3 and 4 answer how the ACS should conceptually translate demand into an appropriate capacity (considering the current technology available). Question 5 results in a set of alternatives for the ACS, which in turn should be tested and compared with the current situation to answer question 6.
1. What measures the performance of an Automated People Mover system at an Airport? 2. What is the current approach to control an Automated People Mover system at an Airport? 3. What determines capacity and how can capacity be changed?
4. What determines demand and how can demand be measured? 5. How can capacity be adapted to demand?
6. Does an adaptive control systems alternative significantly improve the performance of an Automated People Mover system compared to a conventional control system?
2.3
Research Methodology: Engineering Design
Paragraph Summary: The theory of Engineering Design will function as a framework for the research approach. In the conceptualisation of the ACS logic, a single or set of On-Off controller(s), Proportional controller(s), Proportional, Integral, Derivative (PID) controller(s) and/or Model-Based Predictive controllers (MPC) will be used in either a single-agent, distributed or hierarchical structure. The ACS will be tested with two test cases for which the process is first analysed with a flow chart and thereafter modelled and simulated in the Arena/SIMAN environment. A Cost-benefit Analysis is used as primary alternative comparison method and incorporates all monetary output parameters from the simulation. If any non-monetary results are measured, these should be prepared for a Saaty and/or Regime analysis. The latter two analyses should only be performed if reliable weights can be appointed to the different output parameters.
A robust research framework is needed to come from an initial problem to a comprehensive conclusion. As the focus of this research is to design an ACS, the Engineering Design theory is chosen as the framework for the overall project approach. Engineering Design is used to systematically transform a problem into a task and eventual product or theory in a stepwise process (Ertas and Jones, 1996, Pahl and Beitz, 2013).
It is important in any design research, to first of all determine the objective and state a set of supportive research questions (Sections 2.1 and 2.2). While these are just the initial steps of the research, Verschuren and Doorewaard (2007) explain that the objective should be clearly formulated (what should be solved) and already contain a rough outline on what aspects of the system should be considered. Setting an outline in an early stage of the research could limit the solution space, but is a necessity to obtain a comprehensive result within the the time and scope available for a typical thesis research.
The next step is to analyse the available literature on and actual use of the design product (APMs, control theories, etcetera), after which the design is conceptualised. This conceptualisation is on a high level and is needed to find the probable feasibility/implementability of the concept and set the minimum requirements that the design should have. This is followed by a more detailed design that can be tested and evaluated.
The last steps of the Engineering Design process are the production design and actual imple-mentation of the system. These steps are however beyond the scope of this research in both terms of complexity and time.
This section contains a further explanation of the methods to perform the individual steps. This includes the control theory on which to conceptualise a design and the methods to define, test and evaluate a detailed design.
2.3.1
Conceptual Design: Controller Types
The basic idea of a control system is that some sort of control entity (e.g. a human or a computer) can perform an action u to adjust the state x of a system. A particular distinction can be made between open and closed loop control systems.
In an open loop control system, a single action is executed by the controller after which the system state changes. The drawback of this logic is that the system cannot be adjusted for disturbances. A closed loop control system should be used instead, as it includes a feedback mechanism (Kuo, 1981). By means of measurements y the system state is checked after a controller action is made. It can then readjust the system based on the additional information if the system state has diverted from what was expected due to a disturbance d (figure 2.1). As the goal of the ACS is to control and adjust the system based on changing demand (i.e. disturbance), only closed feedback loop logics will be considered.
Figure 2.1: Basic Control System principle (Negenborn and Hellendoorn, 2010)
The action taken by a controller depends on the logic it uses. This logic can differ from very simple calculations to complex model-based decisions. The possible controller types are therefore summarised in this section to give the reader a better understanding of the methods available. Which of these controllers should eventually be included in adaptive control system is depended on the system requirements and are determined at a later stage in the conceptualisation phase.
On-Off Controller
The simplest control structure is an on-off or “bang-bang” controller that generates a Boolean action u(t)minor u(t)max. The error e(t) that actuates the control is defined as the
difference between the measurement y(t) and the set point (desired value) hsp (Bequette,
2003). The resulting algorithm is shown in equation 2.1, in which the factor δ represents a threshold value compared to set point.
if y(t) > hsp+ δ, then u(t) = u(t)min
if y(t) 6 hsp− δ, then u(t) = u(t)max
if hsp− δ < y(t) < hsp+ δ, then u(t) = current value(u(t))
(2.1)
An on-off controller is far from optimal, especially for transport systems. For example, one would be reluctant to step in a train that can only fully engaged or disengage its propulsion or brakes.
Proportional Controller
The proportional controller is a more advanced control logic that executes an action u(t) that is proportional to the error e(t). u(t) can take a value from a continuous range that is equal to the standard output required in a steady state system, also known as the bias term b, combined with the error that is multiplied with the proportional gain kp.
While this control logic is more adaptable than the on-off controller, it still has some difficulties in calculating an optimal action.
Proportional-Integral-Derivative (PID) controller
The PID controller is the most commonly used controller application in industrial systems. It incorporates the aforementioned proportional control and combines this with a integral component and a derivative component. As Araki (2002) explains, the proportional con-trol only assesses current measurements to make decisions, whereas the PID concon-trol also considers earlier decisions and can to some point predict future decisions. This is possible as the integral part of the error will increase if the former action made by the controller is to small or large, thereby tuning the action over time. The differential part thereby corrects the following action based on the rate of change in the error, which will approach 0. The resulting formula is given in equation 2.3, where kc is equal to kp and τI and τD
are the ratio of the integral gain factor kI
kp and the derivative gain factor kd kp. u(k) = kc e(t) + 1 τI Z t 0 e(t)dt + τD de(t) dt (2.3)
Model-Based Predictive Controller (MPC)
The shortcoming of the aforementioned control logics is that they solemnly rely on feedback measurements. This is viable in systems that are quickly adaptable or encounter only limited excessive disturbances, but are less optimal for systems that show more capricious behaviour.
The model-based predictive control is therefore developed, which incorporates the fun-damental feedback logic to measure current state x(t) as is used in the former control solutions and combines this with an applicable model C with which it makes a prognosis of the future systems states x(t + n) and thereby determines an action u(t) (equation 2.4) (Morari and Lee, 1999).
u(t) = C(y(t))) (2.4)
The value n is case specific and the appropriate action u(t) is determined by n objective function J that considers all future actions u(t + n). this objective function J is shown in equation 2.5 and consists of one or more sub-objectives which should meet an appropriate value (e.g. minimisation or maximisation). The constant α gives a weight to the respective sub-objective. J (y(t + n), u(t)) = N X x=1 α ∗ Jx(yx(t + n), ux(t)) (2.5)
2.3.2
Conceptual Design: Controller Structures
A system can be controlled by a single controller or a combination of multiple controllers. The three controller structure types explained below are commonly used in theory and practice (Ne-genborn and Hellendoorn, 2010):
• Single-agent controller: In this control structure there is only one control agent, which controls all the actions and receives all the information from the system. In this structure
is necessary. However, the computational requirements are high, as it has to process all measurements and actions simultaneously. Thereby, it is not robust to failures, which will shut down the whole system.
• Multi-agent single layer (distributed): In a multi-agent single layer controller there are multiple agents that each control their own part of the system. These agents only control the actions, and receive information from their own system. In this control structure it is possible that the systems can or cannot communicate with each other. It becomes more difficult to achieve optimal performance when multiple agents are involved, as each of the different systems might behave differently and if agents optimise there own part of the system, this could negatively affect the overall system performance.
• Multi-layer control structure (hierarchical): In this control structure there are mul-tiple agents that operate at different hierarchical layers. For instance, at a lower layer in the hierarchy the systems are controlled by controllers with a distributed control struc-ture, these controllers try to optimize their own performance. The controller at a higher layer influences the controllers at the lower level, and this controller tries to optimize the performance of the whole system.
Similar to the controller types, the correct structure is depended on the system requirements and is determined at a later stage in the conceptualisation phase.
2.3.3
Detailed Design: APM System Test Case(s)
A (set of) test case(s) is required to validate the functioning of the ACS and measure the effectiveness of real time adapting compared to conventional APM control. The goal is to first conceptualise an ACS that is generic to any (realistic) APM operation and thereafter build a detailed design test case for an APM system at an airport. As time in this project is limited, the number of test cases is 2. The representative cases should together contain a diverse set of possible APM system characteristics.
2.3.3.1 Process Analysis
As the research is focused on a process, it is good to (graphically) comprehend the complexity and therefore several methods can be used such as; the flow chart, IDEF0, UML, swim lane and value stream mapping (VSM). With changing demand and capacity as a main focus, it is important that the method allows for a decision logic and therefore the VSM method is not preferable. While the essence of Lean, of which VSM is a supportive tool, is something that should be incorporated in the overall research (i.e. standardisation, decreasing wastes, etc.), the processes assessed with VSM are generally repetitive (Womack and Jones, 2010).
UML and swim lane are possible approaches to analyse the system, but incorporate complex aspects to allow a system analysis with multiple stakeholders (Bergenti and Poggi, 2000). As there is only one stakeholder involved (i.e. only the airport), these features are however redundant and less complex methods will have the same result When choosing between the Flow Chart method and IDEF0, the former is preferred, as it is simpler but with similar effectiveness. The advantage of IDEF0 is that it allows describing decision logics that are affected by both internal and external information/support. As the system at hand concerns an automated system in an enclosed environment, all decisions are however made internally.
2.3.3.2 Modelling & Simulation
Modelling & Simulation (M&S) is needed to test the system and obtain quantitative results to determine the effectiveness of an ACS. It is therefore chosen to use discrete event modelling & simulation (DEM/S) in an Arena/SIMAN environment.
According to Ackoff (1999), it is important to match the M&S decision method to the actual system decision method. He therefore proposes 4 types of systems in which decision can be made on system level and/or parts level (Table 2.1). The system level in an APM network is for instance the scheduling of the APM vehicles, whereas the parts can be the APM vehicles on the network, aircrafts arriving and/or passengers that make use of the system.
Table 2.1: Types of Systems (Ackoff, 1999)
Parts Whole Example
Deterministic No Choice No Choice Clock Ecological Choice No Choice Nature Animate No Choice Choice Person Social Choice Choice Corporation
The ACS will represent an ’Ecological’ or ’Social’ system, as choices in the system are at least made by the parts such as, passengers walking at their own pace and aircraft arriving at a non-scheduled moment. It however depends on the type of control structure used in an alternative, if choices can be made at a system level.
The controller structure that is chosen from the former section can thus influence the system M&S method. If for instance a system is controlled with a fixed schedule, no further choices can be made on a system level and the system will be considered ’Ecological’. If on the other hand an ACS is used, choices can be made on a system level and the system becomes ’Social’. According to Verbraeck (Course lecture, 2014), it is best to use DEM/S for ’Ecological’ systems, whereas Agent Based Modelling & Simulation (ABM/S) would better suit a ’Social’ system. DEM/S represents a system by having entities endure a discrete sequence of events in time to alter their own state and/or that of the whole system (Banks et al., 1998).
In ABM/S the complexity of a true system can be much better represented as choices of an agent are not exclusively bounded to a predefined range, but they can in fact show emergent behaviour (Macal and North, 2010). As it is however undesirable to build the alternatives in different M&S environments, a choice is made to favour DEM/S over ABM/S for a couple of reasons:
1. The influence of individual agents that can show emergent behaviour (i.e. humans and aircraft) is limited;
2. Automated Agents in the APM network (i.e. controller and trains) are bound to rules to prevent emergent behaviour;
3. The researcher is more experienced with the DEM/S.
Arenar Simulation Software. The prime advantage of these two tools over the competition is
their visual modelling dashboard and the ability to represent the system via easy animation. This will not only help in explaining the model to anyone else than the builder, but also help the latter in test and tune the model appropriately.
Arenar is chosen to model the system in this research due to availability of the program,
experi-ence of the researcher and easy implementation of flow chart system descriptions. The software methodology and language is summarised in Appendix C.
2.3.3.3 Alternative Testing Framework
An alternative testing framework is needed to create a comprehensive conclusion on the effec-tiveness of the ACS, based on the results of the simulation model.
The general approach to compare alternatives in infrastructural projects, is to perform a Cost Benefit Analysis (CBA) (Vickerman, 2007, Layard et al., 1994). The CBA method compares the cost and benefits of a project and thereby determines the most profitable (or least costly) solution. All factors that are not expressed in a monetary unit, are converted in the relative value (e.g. meter toAC).
As the costs are generally assumed too low, overspending is a large problem with infrastructural projects. To make the results more realistic, Salling and Banister (2009) propose an adjusted method known as the CBA-DK, which corrects the expected costs with statistical distributions of factor costs, based on other infrastructural projects.
Nonetheless, CBA or CBA-DK are limited and less reliable when more soft criteria (i.e. non-monetary) are included in the research. Vreeker et al. (2002) therefore propose a framework that combines CBA and Multi Criteria Decision Analysis (MCDA) to better comprehend the solution space. They state that especially airport developments and expansions require such an approach, as airports are projects where costs are not necessarily the prime criteria. Airports act as the entrance to a city or country and therefore soft criteria such as aesthetics, passenger comfort and/or sustainability are of importance too.
Non-monetary criteria are considered aside from a normal CBA and are included by using a proper MCDA method, notably Regime method, Saaty’s AHP method and the flag method (graphically explained in figure 2.2). Any MCDA method is based on (subjective) weights and can only be effectively used if these are reliable. Generally, the weights are set by a system owner and should be a trade-off of the importance of different output parameters.
It should be noted that if no reliable weights can be determined for a test case within the research time frame, it is of no use to conduct a MCDA. The non-monetary output parameters should in this case only be prepared for a MCDA, so that a comparison of the different alternatives can be made at a latter stage.
Figure 2.2: Multi criteria decision support methodology (Vreeker et al., 2002)
2.4
Research Scope & Boundaries
Paragraph Summary: A conceptual design for an APM car is used as reference case in the research, for which the characteristics are determine with a benchmark study. No other APTS are considered in terms of physical characteristics, but the adaptive control logic of the PRT will be further researched. The test case APM system should only be accessible to passengers and honour the ’must-ride’ principle (i.e. only mode of transit between two locations), which makes it possible to create reliable demand patterns based on aircraft movements. The test case airports will be Amsterdam Schiphol International Airport (AMS) and Shenzhen Bao’an International Airport (SZX) that together contain a broad range of typical APM system characteristics. Furthermore, it is assumed that the automated parts of the system will endure no failures, the system should cope with peak demand for the day with the 30th annual peak hour and that a representation of a fixed schedule will only distinguish a different capacity for day and night.
To obtain a scientific relevant result, it is important that the research is made concise. A set of boundaries to the system is therefore set to limit the scope.
2.4.1
Automated People Movers
There is a large variety of APTS, which is sorted and summarised in table 2.2. An extensive explanation of APMs has already been introduced in chapter 1 and the characteristics of all other APTS are found in Appendix A.
Table 2.2: Airport Passenger Transit Systems
Mode Availability Continuous Discrete
Control Manual Walking Train, Metro, Bus, taxi Automated Travelator APM, Metro, PRT
Table 2.3: Differences Between APM and PRT systems (Furman et al., 2014)
APM PRT
Operates like an automated bus: fixed route, vehicle may have multiple stops and starts from origin to destination, and stations may be on or off the main line (but are typically on the main line)
Operates like an automated taxi: no fixed route, vehicle travels non-stop from origin sta-tion to destinasta-tion stasta-tion, and stasta-tions are located off the main line
Passengers gather in groups with strangers Passengers can travel alone or with chosen companions
Passengers must wait for a vehicle on a fixed schedule
Passengers may schedule vehicles at their con-venience
As it is not the goal of this research to make a comparison of APTS, only APM systems are considered in this research. The reason for this choices is summarised in this section.
Discrete System
In essence, all systems could be assessed and tested (except for walking), as some sort of schedul-ing is required. Nonetheless, it is deemed unnecessary to consider a continuous system such as a travelator, as due to its continuous availability, an ACS is simple and many commercial solu-tions are already available. Adaptive travelators are widely used and can instantly start or stop operations when detecting passengers that enter the system (Mitsubishi Electric, 2015). Instead, discrete systems impose a larger challenge as activating capacity requires considerable effort (personnel availability, vehicle availability, vehicle location, etcetera).
Automated
As is shown in table 2.2, there is a variety of discrete systems in use at airports, but for this research the system is bounded only to automated systems. The reasoning hereof is that whether the vehicle is controlled by human or computer, both have to follow strict rules and policies when executing actions. Any sort of control should therefore have the same result (e.g. a train starts moving or stops at a station). With the prime goal of this research in mind it is important to have as many factors kept the same, thus favouring automated systems that have a much higher repeatability.
The choice is made to consider the physical infrastructure and train characteristics of APM system for this research and use the currently used control approach as reference case. The reason for this choice is that the usage of APMs at airports is higher than either a metro or PRT.
It should be noted that an analysis of the control logic of PRT systems is beneficial to this research. A comparison of PRTs and APMs (table 2.3) shows that whereas APMs use a fixed operation, PRTs are well capable of operating adaptively.
APM Reference Case
As it is not the goal of this thesis to determine the differences between different APM system and/or favour one over the other, the APM characteristics are taken generic, albeit based on available systems.
To allow for free movement through the network (i.e. forwards, backwards and between tracks), a rubber-tired system is the only available solution. The APMs that have such a feature are build by either Mitsubishi (Crystal Mover), Bombardier (Innovia APM 100/200/300) and Siemens (AirVal) and all share primary characteristics. A benchmark research is done (Appendix B) on these systems and the resulting mean characteristics are summarised below.
A single car is guided by a central rail and is roughly 12.00 metres in length, 2.80 metres in width and 3.40 metres in height. One car can transport some 60 passengers, based on the of 0.36 m2/pax, which is adequate for a short transit in peak periods (Lea + Elliot, 2009). Energy and
environmental information is based on the most recent APM developed by Bombardier (Innovia APM 300), which consumes 2,56 kWh/km, expels 1470 gr/km CO2(Europe Average) and has a
life cycle of 30 years (Bombardier, 2015). The acquisition value of an APM is $2.4 million, which corresponds with a single car cost for the Innovia APM CX-100 system (Kimley Horn, 2014). The speed and acceleration that a vehicle can attain varies per system. It is hereby important to distinguish operational speed and maximum design speed, which can differ substantially. The maximum design speed of an APM vehicle is mostly 80 kph (Bombardier, Siemens, Mitsubishi), but due to the distinctively short distances of an APM system, the operational speed is generally around 50 kph. For the acceleration and deceleration of the vehicles, the assumption is made that both are 1ms2. A render of the reference vehicle is shown in Figure 2.3.
Figure 2.3: Test Case APM vehicle
2.4.2
APM Airport Function
The demand that is generated for an APM system depends on the function that it has at an airport and the locations that are served. Based on a thorough survey of current APM systems (Appendix B) it is determined that APMs are used for 4 distinct functions:
1. Inter-terminal transit; 2. Intra-terminal transit; 3. Terminal-to-parking transit;
(a) Departure
(b) Arrival
Figure 2.4: Available actions between APM and Aircraft movement
An important aspect of these functions is the must-ride principle that entails the necessity for people to make use of the APM system. A terminal to satellite connection is an example of such a must-ride as all passengers that fly in and out from the satellite will make use of the APM (e.g. Atlanta & Zurich). It is therefore possible to set the APM demand equal to the satellite passenger throughput. If the APM is however used as an additional means of transportation and passengers can also walk to their destination, only part of the passengers will use it (e.g. Detroit).
In coherence with the must-ride principle, a distinction should also be made between APMs operating on landside, airside or both. The reason for this is that demand on airside is only dictated by passenger flows, whereas landside systems can be used by anyone (e.g. meeters, greeters, employees or business). This means that the most apparent driver for airport APMs, the arrival and departure of an aircraft, is not necessarily the only reason for transport demand in all APM cases. Other driving forces could for instance be employment, auxiliary businesses, shopping and plane spotting. For this research, only systems are analysed that contain the must-ride principle and can only be accessed by passengers, so that demand patterns can effectively be deducted from available aircraft movement data.
In figure 2.4a and 2.4b the relation between an aircraft movement and an APM movement of a single passenger is visualised. There is a variety of actions that could be made in the meantime, which dictate the time shift between a passenger being in the aircraft and being in the APM. Such a time shift can be represented by a stochastic distribution.
2.4.3
Test Case Airport Systems
Amsterdam International Airport Schiphol (AMS) and Shenzhen Bao’an International airport (SZX) are chosen for the test cases. Due to the time limitation of this research, a mix is sought in two airports that together contain as many typical characteristics of airports. This will ensure a good understanding of the effectiveness and generality of the ACS.
Neither airport has an APM in operation, but both have considered the implementation of one. NACO has been involved in both designs and adequate information is thus available on the ’would-be’ characteristics of the APM systems (size, length, stations, track lay-out, etcetera). The individual characteristics are shortly summarized in table 2.4. Especially the difference in
Table 2.4: Test Case Airports
Characteristic AMS SZX
Annual Passengers (2013) 52.6 million 32.2 million Annual Passengers (2020) 54.9 million –
Annual Passengers (2040) – 67.0 million
Region Western Europe Far East Asia
Design configuration Single Terminal Two terminals + Satellite
Transfer rate 40% <2%
Domestic/International* 33%/67% 98%/2%
APM system length 0.8 km 2.7 km
Stations 3 3
Track Lay-out Pinched Loop Pinched Loop *AMS differentiates Schengen/Non Schengen
movement types (transfer/origin-destination and domestic/international) generate very different demand patterns, which are of interest to the research. It should be noted that the annual passenger values given in Table 2.4 are not representative for the research. Both APM systems are considered for their respective design year, which is 2022 for AMS and 2040 for SZX. Forecasts models for both passengers and aircraft movements are made by the respective airports and these are assumed to be adequate to generate a concise future demand.
2.4.4
0% System Failures
Failures such as car or network break downs in the system can heavily influence the operation, which can result in delayed or cancelled APMs. As Ledoux1 (personal communication, 2015)
explains, the Automatic Train Supervision (ATS) is the governing control system that will ensure an optimal routing plan in such an event. It is therefore deemed unnecessary to pay further attention to system failures in this thesis, as the ACS will replace the normal system operation and it can thus be assumed that any back up plan executed by an ATS will still function in an adaptive situation. Thereby, APM systems show extreme reliability due to there enclosed environment and automated operations (ACRP, 2012a).
2.4.5
System Design Capacity
Airports experience demand fluctuations on a day, week, month and annual base and it is therefore not advisable to design system capacity for the absolute peak demand. If an airport would decide to do so, it will experience excessive overcapacity.
There are numerous methods to determine the maximum demand that the system should handle (Reichmuth et al., 2011). The methods are intrinsically based on client requirement, but the most common approach is to take the day that includes the 30th peak hour of the year (Wang
and Pitfield, 1999), which is in line with the 95% certainty of appropriate service provided, that is used for airport systems designs in general (ACRP, 2012a, Sloboda, 2009).
2.4.6
Basic System Schedule Design
All APM systems use a fixed schedule with a high frequency service during the day and a low frequency service during down times (e.g. at night). While it is possible to prepare the system with a changing schedule for a day, the assumption is made for this research that only a day and a night operation is used. The day frequency is a usually a couple of minutes and is determined based on client requirements, whereas the night frequency is assumed to be 15 minutes to offer minimal service.
2.5
Key Performance Indicators
It is important to have measurable criteria, also known as Key Performance Indicators (KPIs), to quantify and compare any alternative with a reference (current) APM operation. A distinction can be made of three KPIs:
• Passenger experience; • System Cost;
• External effect.
The KPIs give an overall indication of the respective system aspects and are combinations of several supporting Performance Indicators (PIs).
2.5.1
Measuring Passenger Experience
The overall passenger experience is measured by means of three PIs. The most basic PI is that passengers are transported in a reasonable time that is composed of a dwell time and a transit time. However, the assumption is made that trains run at 100% certainty, which automatically means that transit times will never differ. Therefore, only platform dwell time should be measured in this research to test the effective transit of passengers. The period of the acceptable waiting time is case specific and relies heavily on the customer requirements. The two other Passenger Experience PIs concern the space that passengers have during their APM transit period on the platform while waiting and in the APM. By means of the IATA/Fruin standards it is possible to determine the level of service (LOS) in the respective areas with which a ranking can be made.
2.5.2
Measuring Costs
A large infrastructural project like an APM system affects the airport owner financially and it is therefore important to measure and compare the financial implications of the alternatives. This financial impact is partly based on capital costs and partly on operational costs.
Capital Cost Performance Indicators
The capital cost factors that are applicable to an APM system are:
• Tunnel system • Guidance network • Switch systems • Control System • Platforms • Platform access
• Passenger Sensor systems • APM Vehicles
Not all capital cost factors should be considered in this research, because the networks are considered to be the same in all alternatives and only the operations are affected by the ACS. Those factors that will differ and thus function as a PI, are the amount of passenger sensor systems and the required amount of APM vehicles. With the assumption that no failures occur, the amount of vehicles will not consider a surplus for maintenance and/or backup. To determine the costs, the PIs should be multiplied with the respective costunit value.
Operational Cost Performance Indicators
Human labour cost will not be considered as PI. The APM is an automated system and only a handful of employees reside in the control room to supervise the system, which will be the same for all alternatives.
Operational costs are instead dictated by the usage of the system, which is expressed in vehicle energy cost and vehicle maintenance cost. Both factors are PIs for this research and can be measured with the run distance or run time.
2.5.3
Measuring External Influences
The last aspect that should be measured is the external influence of the system, which is in this case the pollution of the system. This PI is calculated by multiplying the energy consumption with the average CO2pollution of energy production that is characteristic for the airport region.
Pollution can be expressed in more emission types, such as NO2and PM(x), but these emissions
are just as CO2directly proportional to the run distance. It is therefore chosen to only monitor
System for Automated People Movers in an
Airport Environment
Chapter Summary The control of current APM operations attempts to replicate a pre-defined schedule by constantly measuring parts of the system (i.e. trains) and take actions to diminish any errors. A known example of a system that does adapt capacity to demand is Personal Rapid Transit, but its control logic is purely reactive, which is undesirable for higher capacity systems such as APMs. However, the introduction of Communication Based Train Control (CBTC) and its supporting systems (ATO, ATS, ATC) for APMs allows for a system that changes states according to a flexible schedule generated by a proactive Model-Base Predictive Controller (MPC). This MPC is part of a hierarchical control structure in which it only defines the (future) time and location of when and where a train is required with a certain amount of cars. All local decisions on i.e. transit progress is done by hier-archically lower controllers in the trains, the track changes and the supportive Automatic Train Supervision (ATS).
The MPC can adjust train scheduling and train composition based on changes in demand. The primary action of the controller is to add a 1-car train to the schedule in response to the initial creation of demand, such that the first passenger has to wait a maximum acceptable waiting time (client requirement). If the demand for a scheduled train exceeds the available capacity, there are two approaches to increase capacity further; an additional train can be scheduled before or after the first train and effectively increase frequency, or the train can be extended by an extra car. If in either approach a maximum is reached (no more trains able to be scheduled or no more cars to be added), the other approach should be used to further expand capacity. This results in two concept ACS designs which can be tested.
This chapter contains a comprehensive explanation of the Adaptive Control System (ACS). The current application of control systems for APMs is discussed first in Section 3.1, followed by an applicable control structure for the ACS in Section 3.2. The conceptual decision logic of the ACS is thereafter explained in 3.3.
3.1
Available Control Systems
Control systems are used to guide, check and adapt the trains on the network. According to Lott et al. (2009), two types of control systems are used for guided APM systems, which are Fixed Block Train Control (FBTC) and Communication Based Train Control (CBTC). The latter is a relatively new innovation which is slowly being introduced in APM systems (Little and Ross, 2013). For example, CBTC has become standard for the Siemens Airval and optional for Bombardier systems with the CityFlo650 control system or the Mitsubishi Crystal Mover with SelTrac (Drolet and Jadhav, 2005, Siemens, 2014, Thales, 2015).
