METROPOLIS – Urban Airspace Designhomepage.tudelft.nl/7p97s/Metropolis/downloads/... · 2015. 5. 26. · (Metropolis) METROPOLIS – Urban Airspace Design Work Package 3: Development

This project has received funding from the

European Union’s Seventh Framework Programme

for research, technological development and

demonstration under grant number no 341508

(Metropolis)

METROPOLIS – Urban Airspace Design

Work Package 3: Development & Metrics Definition (D3.2)

Document author(s) D. Delahaye (ENAC), A. Vidosavljevic (ENAC), E. Sunil (TUD), J. Hoekstra (TUD), J. Ellerbroek (TUD), and Roalt Aalmoes (NLR)

Responsible Partner ENAC

Dissemination Level

PU Public

PP Restricted to other programme participants (including the Commission Services)

RE Restricted to a group specified by the consortium (including the Commission Services)

CO Confidential, only for members of the consortium (including the Commission Services) X

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Document information table

Contract number: ACP3-GA-2013-341508

Project title: METROPOLIS

Project Co-ordinator: Delft University of Technology

Document Responsible Partner: D. Delahaye [email protected]

Document Type: Report

Document Title: Metropolis WP3 Report

Document ID: D3.2 Version: 1.0

Contractual Date of Delivery: Nov 2014 Actual Date of Delivery: Nov 2014

Filename: WP3 Report on Development & Metrics Definition.docx

Status: Final

Cover illustration:

“Megalopolis”, (http://forwallpaper.com)

Preface

This publication only reflects the view of the METROPOLIS Consortium or selected participants

thereof. Whilst care has been taken to ensure that this information is accurate, it may be out of date

or incomplete. Neither the METROPOLIS Consortium participants nor the European Community are

liable for any use that may be made of the information contained herein.

This document is published in the interest of the exchange of information and it may be copied in

whole or in part providing that this disclaimer is included in every reproduction or part thereof as

some of the technologies and concepts predicted in this document may be subject to protection by

patent, design right or other application for protection, and all the rights of the owners are reserved.

The information contained in this document may not be modified or used for any commercial

purpose without prior written permission of the owners and any request for such additional

permissions should be addressed to the METROPOLIS co-ordinator. (Prof.dr.ir. Jacco Hoekstra, Delft

University of Technology, Faculty of Aerospace Engineering, Kluyverweg 1, NL-2629HS, Delft, The

Netherlands)

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Revision table

Version Date Modified

Page/Sections Author Comments

0.1 21/07/2014 Initial version D.Delahaye Setup of document structure

0.2 22/09/2014 First draft All involved partners

Every partners contribute in the report by the work on chapter that is responsible.

0.3 30/09/2014 First draft - commented

A. Roalt, A. Vidosavljevic

Comments added, Environment metrics update

1.0 07/11/2014 Final draft All involved partners

Report review based on previous discussions and comments

Partners involved in the document

No Member name Short

name

Check if

involved

1 Technical University of Delft TUD X

2 National Aerospace Laboratory NLR X

3 École Nationale de l’Aviation Civile ENAC X

4 Deutsches Zentrum für Luft- und Raumfahrt e.V. DLR

Executive summary

This document contains the results of work package 3 Development & Metrics Definition. It provides

review of the metrics that are used as performance indicator for evaluation of concepts for urban

airspace design. Based on defined metrics and batch simulation (WP4), all proposed urban airspace

concepts of Metropolis (WP2) will be evaluated under different scenarios of Metropolis growth

(WP1) and the results will be essential for comparing concept performance, and to discover the

limits of a particular concept.

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Table of Contents

1 Introduction .................................................................................................................................... 9

2 Critical feature of transportation system ..................................................................................... 10

2.1 Background ........................................................................................................................... 12

2.2 Categories of performance metrics ...................................................................................... 13

2.3 Concept evaluation process .................................................................................................. 14

3 Organization (complexity) metrics ................................................................................................ 15

3.1 Scope and definition ............................................................................................................. 15

3.2 Metrics .................................................................................................................................. 16

3.2.1 Geometrical approaches .............................................................................................. 16

3.3 Measurement and evaluation ............................................................................................... 19

3.3.1 Proximity calculation ..................................................................................................... 19

3.3.2 Convergence calculation ............................................................................................... 20

3.3.3 Robust extension of the metrics ................................................................................... 21

3.3.4 Concept evaluation ....................................................................................................... 23

4 Operational Metrics ...................................................................................................................... 26

4.1 Safety .................................................................................................................................... 26

4.1.1 Loss of Separations ....................................................................................................... 26

4.1.2 Conflicts ......................................................................................................................... 28

4.1.3 Summary of Safety Related Metrics ............................................................................. 29

4.2 Stability ................................................................................................................................. 30

4.1.1 Domino Effect Parameter ............................................................................................. 30

4.3 Efficiency ............................................................................................................................... 31

4.3.1 Route Efficiency ............................................................................................................ 31

4.3.2 Relative Delay Absorption Capability ............................................................................ 31

4.3.3 Departure Delay ............................................................................................................ 32

4.3.4 Arrival Sequencing ........................................................................................................ 32

4.3.5 Summary of Efficiency Related Metrics ........................................................................ 32

4.4 Capacity ................................................................................................................................. 33

4.4.1 Influence of Safety and Efficiency Metrics .................................................................... 33

4.4.2 Traffic Density vs. Traffic Demand ................................................................................ 34

5 Environmental metrics .................................................................................................................. 35

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5.1 Introduction .......................................................................................................................... 35

5.2 Scope and definition ............................................................................................................. 36

5.3 Third party risk metric ........................................................................................................... 36

5.3.1 Introduction .................................................................................................................. 36

5.3.2 Assumptions and considerations for TPR model .......................................................... 37

5.3.3 Model restrictions ......................................................................................................... 38

5.3.4 Input from simulation ................................................................................................... 39

5.3.5 Output from metrics ..................................................................................................... 39

5.4 Third party risk model design ............................................................................................... 39

5.4.1 Probability and location of an accident ........................................................................ 39

5.4.2 Location of the crash area............................................................................................. 41

5.4.3 Lethality ......................................................................................................................... 44

5.5 Energy usage metrics ............................................................................................................ 45

5.5.1 Energy usage based upon aircraft weight and flight hours .......................................... 45

5.5.2 Energy usage based upon thrust and distance travelled .............................................. 45



5.6 Noise pollution metrics ......................................................................................................... 45

5.6.1 Simplified aircraft noise metrics for METROPOLIS ....................................................... 46



6 Bibliography [All: Insert references] ............................................................................................. 47

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1 Introduction

This document contains the results of work package 3 Development & Metrics Definition. It provides

review of the metrics that are used as performance indicator for evaluation of concepts for urban

airspace design of Metropolis.

Such urban environment is characterised by high density and variety of air vehicles that requires a

structure that differs significantly from what happens today in order to accommodate traffic in a

safe and efficient manner. To have better understanding of alternatives, four extreme concepts that

has been design in WP2 are evaluated under different scenarios of Metropolis growth (WP1). To

discover the limits of concepts and to compare them in term of effectiveness of the delivered

services, a set of performance indicators (metrics) has been defined.

This document is based on a review of literature of existing and research of new possible metrics

adapted to high traffic volume in urban airspace. There are many metrics available, which might be

applicable for future urban air traffic. Extensive research of such metrics has been performed. Some

metrics are refined/combined to fit the requirements and some new are investigated.

The following sub-categories of performance metrics have been identified:

• Organization (complexity) metrics,

• Efficiency metrics,

• Environmental metrics,

• Safety metrics,

• etc.

In WP3 the metrics that are relevant to determine the quality of the different urban airspace

concepts are defined and elaborated. Societal demand as output of the WP1 and concept definition

as output of the WP2, are used as reference for metric definition. The metrics are described in a

common way, which captures all strong and weak points of the individual concepts and also enables

direct concept comparison. The metrics will be implemented in the batch simulations (WP 4).

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2 Critical feature of transportation system

The focus of Metropolis research project is to investigate future urban transport organization on the

city 50+ years into the future. Future urban environment is characterised by high density and variety

of air vehicles that requires a structure that differs significantly from what happens today in order to

accommodate traffic in a safe and efficient manner. To have better understanding of alternatives,

four extreme concepts that have been design in WP2 [1] (Figure 2.1) are evaluated under different

scenarios of Metropolis growth (WP1) [2].

Proposed concepts of urban airspace design differ in the terms of structure and control involved. To

discover the limits of concepts and to compare them in term of effectiveness of the delivered

services, a set of performance indicators (metrics) has been defined.

In this chapter, performance review process of the current ATM system will be discussed followed by

the different approaches to measure performance of the future urban transportation.

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Full mix

In this design, all vehicles share the same airspace, without any structure or non-physical constraints, in which via a prescribed airborne separation assurance algorithm, supported by automation, the vehicles avoid each other while flying an optimal route. UAVs and PAVs are mixed. This is a static airspace design, which does not require adjustments based on demand.

Layers

In this design, every altitude band corresponds to a heading range in a repeating pattern. The aim is to allow maximum freedom of routing while lowering the relative speeds, facilitating the separation and increasing the safety. A limit to the ceiling of UAVs will be an option on this design. This is a static airspace design, which does not require adjustments based on demand.

Zones

Based on the principle of airspace design today, different zones for different types of vehicles, speed ranges as well as global directions have been defined to aid the separation by the structure of the airspace. UAVs and PAVs each have their own zones and are mostly, if not completely, separated. A dynamic adjustment of zones based on demand or observed densities, is an option with this design.

Tubes As a maximum of structuring of airspace, tubes have been defined to provide a fixed, but dense, route structure. Different directions, speeds and vehicle types will use different tubes ensuring safety by separating potentially conflicting traffic. UAVs and PAVs each have their own tubes and are completely separated. A dynamic adjustment of zones based on demand or observed densities, is an option with this design.

Figure 2.1: Airspace Concept Designs [1]

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2.1 Background

In order to ensure the effective management of the European Air traffic management system,

Performance Review Commission (PRC) has been established in 1998 by the EUROCONTROL

Member States. PRC with support of Performance Review Unit (PRU) every year produce

Performance Review Report (PRR) as an assessment of Air Traffic Management in the Europe during

given calendar year. PRR evaluate performance and assesses to what extent agreed target are met

including recommendations for the improvement of the performance in the future.

In the 2000, based on the research and analytical work of PRC [3], International Civil Aviation

Organization (ICAO) realized the need to create a performance framework for the purpose of

enhancing safety and efficiency in the air navigation system. Following performance areas have been

identified: safety and security, capacity, predictability, flexibility, efficiency, access and equity, cost

effectiveness, global Interoperability, environment. Figure 2.2 shows some of the currently

monitored performance areas.

Figure 2.2: Key performance areas [3]

Safety. In the context of aviation, safety is defined as “the state in which the possibility of harm to

persons or of property damage is reduced to, and maintained at or below, an acceptable level

through a continuing process of hazard identification and safety risk management. [4]”

Aviation is very complex system involving many different actors, technologies and procedures.

Humans and human-built systems are prone to the errors and no system can be absolutely safe.

Therefore safety must be continuously monitored and assess in order to managed and mitigate

identified risks. Today, safety is measured by the number of accidents, serious incidents and

incidents categorised by the cause and contributing factors.

Capacity. The objective of the ATS is to “provide sufficient capacity to accommodate the demand in

typical busy hour periods without imposing significant operational, economic or environmental

penalties under normal circumstances. [5]” To effectively determine future capacity requirements, it

is necessary to monitor current capacity performance. Key indicator of capacity performance is

average ATFM delay per flight, that is calculated as ratio between the total ATFM delay and the

number of flights in a defined area over a defined period of time [3].

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Efficiency. It addresses the operational and economic cost-effectiveness of a flight from a single-

flight perspective. Therefore it may be represented by the ratio of the cost of ideal flight to the cost

of procedurally constrained flight or as measure of deviation from ideal route (user preferred route).

Today horizontal route inefficiency is measured as a ratio between lengths of the flight trajectory to

the corresponding Great Circle distance. At airport level, inbound flight inefficiency is measured as

additional delay in ASMA1 area due to airborne holding, metering and sequencing of arrival flights;

and pre-departure delay, additional taxi-out time as a measure of outbound flight inefficiency.

Environment. Future ATS has to be environmentally sustainable. Increase of the system capacity in

respond to future growth, has to go along with corresponding increase of efficiency, flexibility and

predictability to ensure that there is no adverse impacts on the environment. Today environmental

impact is measured mainly through CO2 emission.

Cost-effectiveness. Every improvement in the service quality or performance of the ATM involves

additional costs to airspace users. To be cost-effective this costs need to be balanced with interests

of the community. It is measured as a number of controlled flights per Air traffic control officer’s

work hour (ATCo-hour productivity), en-route ANS costs per service unit, etc.

2.2 Categories of performance metrics

Future urban transport, as today air transport system, has to ensure safe, efficient and expeditious

movement of air vehicles. Due to high risk involved in urban transport, safety is a main issue.

However, from the user perspective, flight efficiency is the most important as every deviation from

user preferred routes will involve additional costs to the users. The Air Traffic Management (ATM)

community has been recently faced with a new goal: reducing the impact of ATM on the

environment. Having in mind that the number of vehicles in the future urban transport will be

significantly higher than today’s volume of air transport, this issue will be more critical.

In order to compare proposed concepts of the future urban airspace design, following categories of

performance metrics have been identified.

Organizational metrics. This category of performance metrics aims to identify how the structure

involved in the concept of urban airspace design influence the complexity of the traffic situation.

Comparing complexity of traffic situations, we can implicitly compare how difficult is to control a

given situation which is crucial in the safety-critical system. Measuring the robustness, that is

strongly related with traffic complexity, will allows us to determine how much system is invariant to

changes in the initial conditions and also external influences. .

Operational metrics. From the airspace users and passenger perspective the most important is the

impact of concept on the operational performance of the flights. In the future urban transport

system, most of the people will drive their private air vehicles and safety, punctuality, efficiency, and

the cost of the flight would be equally important for them. Safety metrics focus on the ability of an

airspace concept to maintain safe separation between aircraft. Efficiency is measure of deviation

from the user preferred route in space and time.

1 ASMA (Arrival Sequencing and Metering Area) is the airspace within a radius of 40NM around an airport. [3]

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Environmental metrics. As already mentioned, impact of aircraft on the environment becomes

increasingly important and will be the main restricting factor for the future growth of the airline

industry. The main environmental issues related to traditional aircraft, that we have today, such as

emissions/pollution, noise pollution, and third party risk will remain equally or more important for

the future urban transport. The new concept of future urban transport, new vehicles, high traffic in

city areas will raise a new issues such as shadow flickering (similar to wind turbines) or light pollution

in case of nightly operations, privacy concerns, distraction, and effects on the biotope.

2.3 Concept evaluation process

Although the identified metrics could be used to evaluate future urban transportation system, the

main goal of Metropolis project is to compare different concepts of urban airspace design in term of

effectiveness of delivered services. Therefore, the analysis will focus on comparing and illustrating

how good or bad given concepts performs regarding each metric; and not comparing metrics results

against given targets. Quality of metrics results, as well as, form of the results (qualitative or

quantitative form) may influence the use of given metrics in concept evaluation. Furthermore,

different concepts may score the same way for a given metrics, making it irrelevant for final

evaluation of the concepts, as they have little influence on the specific metrics.

The different results, measured by metric, against different concepts will demonstrate which

concept performs better and suggest directions for further research to investigate the reasons and

measures that could improve results.

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3 Organization (complexity) metrics

This chapter describes organization (complexity) metrics that are used to compare different

concepts of urban airspace design. Different concepts use different ATM strategies, ranging from

central to fully autonomous control (human operator or an automatic process); therefore intrinsic

measurement of the complexity that is independent of the system in charge of the traffic is

considered here. Future urban transport, like today’s air transport, is a safety critical system and it is

required to be robust. Robustness is strongly related with traffic complexity and it will be analysed in

this chapter.

3.1 Scope and definition

Future urban transport is a safety critical system and it aims to provide safe flow of air vehicles

before making it efficient and expeditious. Maintaining safe separation between vehicles and with

other obstacles is imperative for the system. When a future conflict is detected, a resolution process

is launched which, in certain situations, may generate new conflicts. This interdependency between

conflicts is linked to the level of mixing between trajectories. In addition, uncertainty with respect to

positions and speeds increases the difficulty of predicting future trajectories. The difficulty to control

a system depends on both its sensitivity to initial conditions and interdependency of conflicts. As an

example, Figure 3.1 presents three traffic situations classed according to increasing level of difficulty

as a function of the level of predictability and of inter-dependency between trajectories.

Figure 3.1: Three traffic situations classed by increasing order of complexity

Situation on the left in Figure 3.1, do not present any difficulties as the relative distance between the

aircraft will be maintained, at least for the immediate future. The middle situation, which presents a

significant risk of conflict, is easy to manage as the same direction order must simply be given to all

of the aircraft in order to place them into safe roundabout trajectories. Finally, the most difficult

situation is presented on the right because it is not ease predictable and have a high level of

interdependency between trajectories.

Aim of this sub-category of performance metrics is to measure traffic complexity as an intrinsic

measurement associated with a traffic situation. This measure has to be independent of the system

in charge of the traffic and to solely dependent on the geometry of trajectories. Measuring and

comparing complexity of the resulting traffic situations of different airspace concepts we can

implicitly compare how difficult is to control given system. Measuring the robustness, that is strongly

Low sensitivity

No conflict

Easy situation

High sensitivity

Conflicts with no interaction between solutions

Average situation

High sensitivity

Potential conflicts with

interaction between solutions

Difficult situation

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related with traffic complexity, allows us to determine how well changes in the initial and

environmental conditions can be absorbed by the structure of the airspace concept or how much

system is invariant to those changes.

3.2 Metrics

Research into air traffic complexity metrics has attracted considerable attention in recent years.

Proposed models can be grouped into two groups: the first one focused on the air traffic control

officer (ATCo) workload, and the second one focus on traffic complexity using automatic conflict

resolution algorithms.

The first group of models has objective to model the control workload associated with given traffic

situations. The main approaches are as follows. In model based on traffic level [6], the workload is

defined as the proportion of control time (duration of control actions taken to resolve conflicts) over

an hour. Queue-based model [7] consider control sector as a system supplying service and queuing

theory is used to determine a maximum acceptable arrival rate for a sector. Model based on

airspace structure [8] estimate the capacity of a sector is based solely on its structure (flight levels,

routes, route intersections, etc.). In the context of operational control, the ideal would be to find a

metric which precisely measures the level of mental effort of the controller that is needed to

manage a certain situations. In NASA a set of traffic characteristics (number of changes in direction,

changes in speed, changes in altitude, etc.) and the workload experienced by a controller have been

studied, and Dynamic density model [9] [10] [11] is built as a weighted sum of traffic complexity

factors. However listed models are not generalized and are linked to studied sector structure and

sensitive to controllers used to infer the model.

Other approaches [12] [13] model the complexity of a traffic situation using automatic conflict

resolution algorithms, for which we measure the number of trajectory modifications required in

processing a given situation. In the same way as before, these methods are highly dependent on the

type of algorithm used to resolve conflicts.

The goal of WP3 of the Metropolis project is to compare developed concepts for the future urban

airspace design. The concepts proposed in WP2 of the project differs in the level of structure and

way how system is managed and controlled. For this reason, previously listed approaches to

measure traffic complexity are not suitable as it is necessary to use an intrinsic traffic complexity

metric that is only linked to trajectory structure, and not to the system used to process them. In the

rest of this chapter, some of existing complexity metrics linked to trajectory structure are studied.

3.2.1 Geometrical approaches [14]

These metrics are calculated at a given instant using the positions and speed vectors of airplanes

present in the chosen geographical zone. Each of these geometrical metrics exhibits a particular

characteristic associated with the complexity of the situation.

3.2.1.1 Proximity metric

Proximity metric is used to characterize the geographical distribution of aircraft in the given volume

of airspace. It allows us to identify spatial zones with high levels of aggregation in relation to their

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volume. Thus, for a constant number of airplanes in a sector, proximity is used to distinguish

whether these aircraft are distributed homogeneously or in the form of clusters. In the example in

Figure 3.2, on the left five airplanes are well distributed across the sector, while in the situation

represented on the right, the same five airplanes are aggregated in a reduced spatial zone.

Figure 3.2: Two situations of spatial distribution of airplanes

Example in Figure 3.3 presents an artificial traffic situation with four groups of eight aircraft placed

on a square. Top left in the figure shows pure conflict situation where all airplanes converge to the

same point. In the top right and bottom left situation airplanes are organized in the roundabout-like

pattern and parallel tracks respectively. Finally, bottom right figure represent completely random

situation. For each point in the space, the average level of proximity is calculated, considering the

airplanes in the vicinity of the point, and the map of proximity is shown in Figure 3.3. The result

shows that proximity indicator is able to identify areas where airplanes aggregate, but is unable to

distinguish between situations using speed vectors. The two situations at the bottom of the figure

are represented in the same manner, despite the fact that the situation on the right is much more

difficult to manage.

Figure 3.3: Proximity map

Using proximity metric, one can easily (in quantitative manner) identify moments of low traffic

density and moments of clustering. However proximity does not take into account orientation of the

Sector

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speed vectors and therefore not take into account evolution of the current situation. This

consideration led to develop a convergence indicator.

3.2.1.2 Convergence metric

The convergence indicator is used to quantify the geometric structure of the speed vectors of

airplanes present in a sector. Thus, for identical proximity values, the convergence indicator allows

us to distinguish between converging and diverging aircraft.

When a dense zone has been identified, the zone may be characterized using the rate of

convergence of the aircraft present in this area. This indicator is higher the closer the aircraft and the

faster the convergence. Thus, in the example shown in Figure 3.4, the convergence indicator is used

to provide an unambiguous classification of the eight situations. Each situation corresponds to two

aircraft, for which the relative distance is constant (higher in the top four cases) and the relative

speed varies from strong divergence to strong convergence (from left to right). In the case of

divergence, the indicator will be null, and for convergences, it will be increasingly high as the relative

distance diminishes and the relative speed increases.

Figure 3.4: Converging/Diverging flights example

Same example with the artificial situation involving four groups of eight aircraft (as for proximity

map) is used for the test and resulting convergence map is shown in Figure 3.5. From this figure, it is

clear that only the two non-organized situations (pure conflict and random situation) are identified

by the indicator.

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Figure 3.5: Convergence map

3.3 Measurement and evaluation

Complexity metrics using previous geometrical approaches can be computed during post-processing

for every concept of the urban airspace design. To do so, it is necessary to log trajectories of all flight

in given simulation scenarios including information of vehicle type (UAV, PAV, etc.).

Trajectories of the future flying vehicles, as airplane trajectories, are objects belonging to the space

with infinitive dimension. It is represented as a mappings from a time interval [a, b] to a state space

E with E either ℜ3 or ℜ6 depending on whether speed is assumed to be part of flying vehicle state or

not. Traditional way for representing trajectories is to use order list of trajectory samples in the 4D

space. Each trajectory sample or position vector contains information of trajectory position (3D) and

time (T). As additional information, each vector position may be associated with velocity vector as a

vehicle intention. However, this information can be extracted from position vectors assuming

uniform motion between two consecutive vehicle’s positions. This assumption is correct if time

discretization step is small enough (5-10 seconds) and the given model represents good

approximation of the real situation.

Smaller time discretization step induces a higher precision of trajectory representation.

Unfortunately small time discretization requires a lot of memory to log the data and highly depends

on size of simulated scenario. As in previous case, we can use approximate tools using linear uniform

motion between two consecutive trajectory positions.

Having position and velocity vectors for all time steps and all flight in the given scenario, complexity

metrics can be computed using the following methods.

3.3.1 Proximity calculation

For the given time and for each vehicle under consideration, we open a spatial weighting window

centred on that vehicle, making it the reference vehicle. We then calculate the relative distances of

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the other vehicle from the reference vehicle in order to calculate weighting coefficients using a

spatial window using following formula:

�� = �� (3.1)

where � is a parameter fixed by the user and �� is the normalized distance separating vehicle � from vehicle �. Normalized distance is used as the measurement of relative distances between pairs

of vehicle due to the fact that separation norms are not the same in the horizontal and vertical

planes and depend on the type of air vehicles. Consequently a classical Euclidian notion of distance is

not suitable and normalized distance between two vehicles � and � is calculated:

��,� = �� − ��,� = �� − �� + �� − �� + �� − ��ℎ� (3.2)

where �� and �� are the positions of two vehicles � and � in a local earth referential, � is the

horizontal separation distance and ℎ is the vertical separation distance.

Adding together factors of all pairs of vehicles in the reference spatial window, we can compute the

proximity factor linked to the reference vehicles !"�#: !"�# = $�� %

�&' (3.3)

where ( is the number of vehicles for consideration at instant ). Due to exponential function used,

distant vehicles are of less importance than nearby vehicles.

The proximity of a spatial zone is then calculated using the sum of the proximities of the vehicles

present in that zone for the given time. Depending on the distribution of vehicles in a sector, the

value of this metric varies from ( when traffic is uniformly distributed to (� when all of the vehicles

are aggregated at the same point.

3.3.2 Convergence calculation

Let us take two moving points � and � (see Figure 3.6); the level of variation of their relative distance

is given by:

*�� = ++) �� (3.4)

where �� is their normalized distance. Thus, a pair of vehicles converges if, and only if, this level of

variation is negative; convergence becomes increasingly rapid as the absolute value of this level

increases.

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Figure 3.6: The variation of the relative distance between two vehicles

Let �� be the normalized relative position vector and ,��the normalized relative speed vector:

�� = --�� − �� − �� − ��ℎ

,�� = --,. − ,.��,/ − ,/��,0 − ,0�ℎ

(3.5)

*�� is thus given by:

*�� = ++) �� = ++) 1�� ∙ �� = �� ∙ ,�� (3.6)

In reality, the risk associated with the convergence of a pair of vehicle also depends on the relative

distance between vehicles. We must therefore simultaneously account for the speeds and relative

distances of each pair of vehicle. One possible form of a complexity metric using convergence

indicator associated with a vehicle � is given below:

3,"�# = λ $ −*�� ∙ �5∙�� /7� 89 (3.7)

where λ and α are weighting coefficients.

The complexity of the given traffic situation at a given time is then calculated using the sum of the

convergence of the vehicle present in that zone for the given time.

3.3.3 Robust extension of the metrics

The approaches presented so far use noiseless observations, allowing us to generate instantaneous

metrics. Due to possible change in initial conditions (delay) and external issues (wind, disruptions,

regulations, etc.), the stochastic aspect of observations need to be taken into account in order to

generate reliable (robust) metrics. To do this, trajectory observations, computed through simulation

using a set of flight plans, are affected by noise, particularly in the temporal dimension.

In the context of stochastic process theory, this phenomenon is known as clock shifting: “the

trajectory continues to conform to the flight plan in the spatial dimension, but the position of the

vehicles on the trajectory may be subject to significant deviations in the temporal dimension [14]”.

iP

jP

Vi

Vj

ijd

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As shown in the Figure 3.7, vehicle trajectories are extended in the future by using uncertainty

segment.

Figure 3.7: Trajectory time shifting

Using the distribution of these time shifts it is possible to produce realistic sets of observations and

to compute convergence indicator for these trajectory segments (not considering time difference).

Finally, resulting complexity metrics values for this trajectory segments can be aggregated in order

to obtain robust metric.

To include robustness when calculating convergence metric, for the given time and for the given

reference vehicle, we open a spatiotemporal window centred on that vehicle (Figure 3.8).

Figure 3.8: Spatiotemporal window for the reference vehicle ;<===> and time ? In the given spatiotemporal window we compute the convergence indicator between reference

vehicle and some other vehicle taking into account all possible pair of observations of those vehicles.

Red lines in the Figure 3.8 indicate all possible pair of observations between vehicles � and �. Convergence indicator associated with a vehicle � with respect to vehicle � at a given time ) is

computed as an time averaging of the convergence over all pairs of observations () − ∆) A B A ) +∆)) and is given by:

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23

3,"�#� = $ $ −*��C�C ∙ �5∙�� D�D �EF∆EC &E∆E/7� D�D 89

EF∆EC�&E∆E $ $ 1EF∆E

C &E∆EEF∆E

C�&E∆EH (3.8)

where *��C�C and ��C�C represent variation of relative distance and normalized distance of vehicle � at

the time B� and vehicle � at the time B�.

In the same manner as before, convergence indicator associated with a vehicle � is computed as the

sum over all pairs of vehicles in the spatiotemporal window:

3,"�# = λ$3,"�#�� (3.9)

3.3.4 Concept evaluation

The presented method is used to compute complexity of the traffic situation at a given instant of

time t, and it may be used to compare complexity of different traffic situations. However, the goal of

organizational metrics is to evaluate and compare complexity of different concepts of urban airspace

design in Metropolis. It is therefore necessary to compare the overall complexity of the resulting

traffic situations using given concepts.

Using presented method, it is possible to compute the evolution of the traffic situation complexity

for the whole simulation period. The graph in Figure 3.9 allows us to quantify the level of complexity

as a function of time, and identify easily the moments of low complexity (the night or beginning of

simulated period) and the moments of high complexity.

Figure 3.9: Evolution of traffic complexity as a function of time

However, comparison of complexity evolution for different scenarios, is generally not easy task.

Figure 3.10 presents an easy situation, which undoubtedly shows that one of compared concepts

performs better than other one in the terms of traffic complexity.

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Figure 3.10: Comparison of concept’s traffic complexity – easy situation

Contrary, Figure 3.11 presents a situation where it is not possible to give simple answer which

concept is better without further examination.

Figure 3.11: Comparison of concept’s traffic complexity – hard situation

Several individual indicators can be used to compare concept complexity. Table 3.1 shows resulting

values of some indicators for two concepts presented in Figure 3.11. The maximum value of traffic

complexity over time, taken as indicator of concept complexity, is not suitable as it doesn’t take into

account time distribution of complexity. Using single maximum value of complexity, orange concept

performs worse in this example. The average value of traffic complexity over time, on the other

hand, might favour concepts with high complexity for a smaller time periods than the concepts

which result in traffic situations of moderate complexity for longer time periods. Using average

complexity value only, blue concept in the example performs worse compared to orange concept

(although both concepts have similar average complexity value). Sum of complexity over time

individually might not be sufficient as well. For that reasons, the concept’s complexity in the

Metropolis project is compared using weighted sum of maximum complexity value and sum of

complexity values over time (3.10).

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3, = α ∙$3,")#E + I ∙ maxE 3,")# (3.10)

Table 3.1: Concept evaluation results

Max. value Avg. value Sum Complexity

(α= 0.1, β=0.5 )

Blue concept 18 10.2 153 24.3

Orange concept 28 9.7 145 28.5

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4 Operational Metrics

This chapter describes metrics that aim to measure the operational differences between the

airspace concepts considered in the Metropolis project. Here, operational metrics are defined in

terms of:

1. Safety

2. Stability

3. Efficiency

4. Capacity

It should be noted that all the metrics described in this chapter can be computed separately for PAV-PAV, PAV-UAV and UAV-UAV cases (if required). Furthermore, safety and stability related metrics are to be logged/computed only in the experiment area (green trapezium) of the city (to alleviate CPU load during the simulation), while the efficiency and capacity related metrics are computed for the entire simulation area.

4.1 Safety

In this project, four concepts are compared as a means of providing separation through different

airspace structures. Hence, safety related metrics focus on the ability of an airspace concept to

maintain safe separation between aircraft. Separation assurance performance can be measured in

terms of loss of separations and conflicts. Loss of separations, also termed as intrusions, occur if the

minimum separation requirements are violated, i.e., if an ‘intruder’ aircraft enters the protected

zone of an ‘ownship’ aircraft. On the other hand, conflicts are defined as predicted intrusions, i.e., if

the track of an intruder is expected to pass through the ownship protected zone when both aircraft

trajectories are extrapolated over a pre-defined ‘look-ahead’ time. In the following subsections,

metrics associated with these two safety related occurrences are explained.

4.1.1 Loss of Separations

Consider the hypothetical scenario in Figure 4.1 where the path of an intruder aircraft (red dashed

line) through the protected zone of the ownship (grey area) is pictured. Here, the intruder

approaches the ownship with a very low minimum vertical distance (point A) and a very low

minimum horizontal distance (point B). But as these two extremes do not occur simultaneously, it

can be concluded that the example displayed below represents relatively low safety risk to the

ownship.

From the above discussion, it is clear that both the number as well as the severity of protected zone

intrusions must be taken into account when analyzing loss of separations.

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Figure 4.1: Vertical cut-out view (Z-Y axis) of a loss of separation. The red dashed line indicates the path of an intruder

through the ownship protected zone (grey area). Note that the horizontal and vertical intrusions, MNand MO, are

measured from the boundary of the ownship protection zone.

Severity of Loss of Separations As explained above, the severity of a loss of separation/intrusion is dependent on the path flown by

an intruder aircraft through the ownship protection zone. This severity can be computed using the

following expression:

PQRSTUT7�E/ = maxEWEX Ymin\]�")#, ]U")#_` (4.1)

Here, ]ab and ]ac represents the horizontal and vertical intrusions that are normalized with respect to

the horizontal and vertical protection zone dimensions2, while )9 and )d are the start and end times

of an intrusion. In eq. 4.1, the part within the square brackets implies that the extent of intrusion for

each point flown within the ownship protection zone is equal to the minimum of the instantaneous ]ab and ]ac. From this, the total severity can be determined as the maximum intrusion of all the

individual points that make up the complete intrusion trajectory. Using this logic, the loss of

separation severity for the intrusion trajectory pictured in Figure 4.1 is equal to the normalized

horizontal intrusion at point A.

PQReTUT7�E/ is a metric that is computed during post-processing for each loss of separation that is

experienced during a simulation run. To do so, the following data needs to be logged during a loss of

separation with high a frequency (e.g. 0.5 [Hz]):

1. Scenario file and concept id 2. Runtime 3. Aircraft id 1 4. Aircraft id 2 5. Aircraft type 1 (PAV, UAV, etc.) 6. Aircraft type 2 7. Current horizontal distance [NM] (absolute value) 8. Current vertical distance [NM] (absolute value)

In addition to computing PQReTUT7�E/, recording the above data set for each loss of separation allows

for a graphical comparison of the intrusion paths for the different airspace concepts, see Figure 4.2.

2 Note that intrusions are measured from the boundary of the ownship protection zone, see Figure

4.1

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Such comparisons may reveal qualitative trends in the trajectories used by different airspace concepts to avoid collisions.

Figure 4.2: Four example trajectories through the protection zone. Such graphical comparisons may reveal qualitative

trends that are not easily visible through numeric metrics.

As mentioned earlier, PQReTUT7�E/ is computed separately for each loss of separation incident.

However, an aggregate metric is needed such that different airspace concepts can be compared for a particular traffic volume scenario. For this purpose, the average loss of separation severity, PQRfffffeTUT7�E/, can be computed during post-processing as: PQRfffffeTUT7�E/ = ∑PQReTUT7�E/hije (4.2)

Here, hije is the total number of loss of separations during a simulation run. This can be logged as a ‘running total’ every time a loss of separation is encountered during the run.

Rogue Aircraft Rogue aircraft are used within the Metropolis project to test the robustness of the different airspace

concepts to non-nominal situations. Rogue aircraft fly over the city without respecting the airspace

structure, nor do they try to avoid other traffic. They can be considered synonymous to small

stochastically moving ‘no-go’ areas that other aircraft have to avoid. Therefore, the number and the

average severity (eq. 4.2) of the loss of separations experienced by the rogue aircraft is a good

indicator of the ability of an airspace concept to absorb unexpected events.

4.1.2 Conflicts

The second set of safety related metrics relates to the number of conflicts. The number of conflicts is

mainly affected by the traffic scenario, however, airspace concepts with a low level of structure are

expected to lead to more secondary conflicts. Furthermore, the number of conflicts is a good

indication of the load on the tactical conflict resolution algorithm (MVP) for each concept.

Similar to loss of separation metrics, it is necessary to evaluate the severity of a conflict. As conflicts

reflect a lower safety risk than loss of separations, it has been decided to classify the severity of a

conflict into two categories that are based on the predicted time to closest point of approach, )klm:

1. )klm < 60[r] 2. )klm < 30[r]

In TMX, the first conflict category is denoted as an ‘amber alert’, which requires the aircraft with

lower priority in a conflict pair to maneuver to solve the conflict. The second conflict category is

denoted as a ‘red alert’, and requires both aircraft in a conflict pair to maneuver to solve the conflict.

Percentage of Aircraft in Conflict

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The percentage of aircraft experiencing a conflict can be computed online as a measure of the

conflict density within the experiment area:

3uh�v�w)xT7yTzE�{T = hy|zd}�yEh��7y7�dE ∙ 100 (4.3)

This metric can be computed separately for both conflict categories (amber and red alerts) with a

frequency of 1 [Hz]. At the end of a run, the average conflict percentage for that run can be used as

an aggregate metric to compare the different concepts in terms of conflict density. To compute this

average, a running total of the conflict percentages (eq. 4.4) and the number of conflict percentages

(eq. 4.5) logged (for which hy|zd}�yE ≠ 0) needs to be computed online at the same frequency of 1

[Hz]:

$3uh�v�w)xT7yTzE�{T =�' $3uh�v�w)xT7yTzE�{T�'

' + 3uh�v�w)xT7yTzE�{T� (4.4)

hy|zd}�yE�� =hy|zd}�yE�� + 1; ��hy|zd}�yE ≠ 0 (4.5)

The average conflict percentage can then be computed during post-processing using eq. 4.6 below:

3uh�v�w)fffffffffffxT7yTzE�{T = ∑ 3uh�v�w)xT7yTzE�{Tz'hy|zd}�yE�� (4.6)

3uh�v�w)fffffffffffxT7yTzE�{T can be computed separately for amber and red alert conflicts to capture the

severity of each conflict type.

4.1.3 Summary of Safety Related Metrics

Table 4.1 below summarizes the safety related metrics proposed for Metropolis:

Table 4.1: Summary of safety related metrics

Metric Description Implementation

Loss of

Separations

hije Running total of the number of loss of separations

Online

PQRSTUT7�E/ Severity of each individual loss of separation

Post-processing

PQRfffffeTUT7�E/ Average severity of all loss of separations during a run

Post-processing

Conflicts

3uh�v�w)xT7yTzE�{T

Instantaneous percentage of aircraft in conflict computed separately for red and amber alerts

Online

$3uh�v�w)xT7yTzE�{T�'

Running total of conflict percentage computed separately for red and amber alerts

Online

hy|zd}�yE��

Running total of the number of conflict percentages logged for which hy|zd}�yES ≠ 0, computed

separately for red and amber

Online

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alerts.

3uh�v�w)fffffffffffxT7yTzE�{T

Average conflict percentage during a run. Equivalent to average number of conflicts per aircraft.

Post-processing

Metrics that can be computed during post-processing require data to be logged during the

simulation. See sections 4.1.1 and 4.1.2 for more details.

4.2 Stability

Conflict resolution using airborne CD&R methods (as is the case for Full Mix, Layers and Zones

concepts) increases the distance travelled by aircraft. As a consequence, resolution maneuvers may

trigger additional conflicts. At high traffic volumes, this effect may destabilize the airspace such that

conflicts propagate to a point where a large number of aircraft exist simultaneously in a conflicted

state with each other, with little maneuvering room to solve these conflicts. The stability of the

airspace as a direct result of conflict resolution maneuvers has been measured in literature using the

so called Domino Effect Parameter (DEP).

4.1.1 Domino Effect Parameter

The DEP measures airspace stability by comparing the number of conflicts with and without conflict

resolution for identical simulation scenarios. This can be visualized from the Venn diagram below:

Figure 4.3: The Domino Effect Parameter relates the additional conflicts caused by resolution maneuvers to airspace

stability

Here:

• R1 is all aircraft which had a conflict when was resolution off, but did not have a conflict

when resolution was on

• R2 is all aircraft which had a conflict when was resolution off, and did have a conflict when

resolution was on

• R3 is all aircraft which did not have a conflict when resolution was off, but did have a conflict

when resolution was on

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By comparing R3 with R1 for identical scenarios, the additional conflicts that were a triggered by

resolution maneuvers can be determined. Thus the DEP is defined as:

��! =�3 − �1�1 = �3�1 − 1 (4.7)

A high value of DEP indicates high airspace instability. It should be noted that the Tubes concept

uses a centralized approach to de-conflict aircraft. Thus, the DEP has no meaning for the Tubes

concept.

4.3 Efficiency

Efficiency metrics aim to compare the concepts in terms of how well the available airspace has been

utilized. In this section, four efficiency related metrics are described.

4.3.1 Route Efficiency

Route efficiency compares the great circle, or direct distance, to the actual distance flown.

Therefore, this metric is a good indicator of the fuel used, as fuel consumption has a very strong

correlation with route efficiency.

Route efficiency can be computed as:

�7|�ET = ��r)�hw��7TyE)*�w�P�h�)ℎ (4.8)

In eq. 4.8, )*�w�P�h�)ℎ is used as the denominator even though it is expected to vary between the

different concepts. This is so that a higher numerical result of eq. 4.8 indicates a higher route

efficiency. By computing �7|�ET for every flight, the average as well as the distribution of route

efficiencies for all flights can be used to compare the different airspace concepts.

To compute route efficiency during post-processing, the ��r)�hw��7TyE and the )*�w�P�h�)ℎ of

each aircraft during the logging hour needs to be known. For this purpose, aircraft in the air can use

their initial and final positions during the logging hour to compute ��r)�hw��7TyE during post-

processing. On the other hand, )*�w�P�h�)ℎ can be estimated online based on the actual positions

of an aircraft (during the logging hour) at a frequency of 0.1 [Hz] (i.e., at the same frequency as

position logging).

Note that vertical manoeuvring is not captured by the route efficiency metric. As a consequence,

vertical conflict resolution manoeuvres are considered to be optimal with respect to route efficiency,

a fact also recognized today in the analyses of conventional scenarios.

4.3.2 Relative Delay Absorption Capability

As the traffic volume increases and the airspace becomes busier, trip times are expected to increase

for all concepts. To measure the extent of traffic volume induced delays, twelve origin-destination

pairs (three per compass direction) have been manually added to all simulation scenarios. Thus by

comparing the variations in trip times between airspace concepts across increasing traffic volume

scenarios for the 12 standardized trajectories, the relative delay absorption capability of the

different concepts can be deduced. Moreover, a comparison of trip times allows for the

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determination of the relative speeds of the four concepts, i.e., which is fastest from origin to

destination.

Note that comparing travel times takes into account vertical maneuvers used conflict resolution

(unlike the route efficiency metric mentioned earlier).

4.3.3 Departure Delay

Some concepts (e.g. Tubes) ensure traffic separation by computing a conflict free route prior to take-

off. This process may require departure delays to be applied to aircraft until a conflict free route is

available. The application of this departure delay is foreseen to lead to an imbalance between

demand and airspace availability/capacity. Thus it is necessary to measure departure delays when

evaluating the overall efficiency of a concept.

Departure delay is defined as the period between an aircraft’s spawn time and its actual take-off

time:

)�T}�/ = )E��T|dd − )Sx��z (4.9)

The departure delay can be computed online for every aircraft that is spawned during the logging

hour. The sum and average of )�T}�/all departure delays can be used to compare different concepts.

4.3.4 Arrival Sequencing

Due to the complexity of simulating holding patterns and/or point-merge arrival sequences, it has

been decided not to simulate the final approach and landing of PAVs in this project. However, to

determine the effect of airspace structure on arrival management procedures, an arrival sequencing

metric has been defined. This metric considers the time interval between two successive arrivals at

each runway in the simulation area during the logging hour. For the ith arrival at a particular runway,

the arrival time interval can be computed simply as:

)�zUT7U�}� = )�77�U�}� − )�77�U�}�� ; � ∈ [2, h] (4.11)

Here n is the total number of aircraft landing at a particular runway during the logging hour.

During post-processing, the average and minimum intervals can be computed for each runway. This

data can be used to cluster runways in terms of (pre-defined) arrival interval ranges (by means of a

cluster bar chart). In this way, it is possible to investigate whether a particular airspace concept leads

to unacceptable arrival sequences that cannot be sustained in reality.

4.3.5 Summary of Efficiency Related Metrics

Table 4.2 below summarizes the safety related metrics proposed for Metropolis:

Table 4.2: Summary of efficiency related metrics

Metric Description

Route Efficiency Ratio of direct distance and actual distance flown

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Relative Delay Absorption capability

Comparison of trip times for 12 standardized trajectories

Departure Delay Time interval between the spawning and take-off of an aircraft

Arrival Sequencing Time interval between two successive arrivals at a particular airport

Note that all efficiency metrics require parameters to be computed and logged online, using which

the aggregate metrics listed in Table 4.2 can be computed during post-processing.

4.4 Capacity

4.4.1 Influence of Safety and Efficiency Metrics

One of the main research goals of the Metropolis project is to investigate the airspace structure-

capacity relationship. Four traffic scenarios of increasing traffic demand have been defined to study

this relationship. Similar to other transportation systems, airspace capacity is difficult to define

explicitly, however, it is clear that that both safety and efficiency must be considered when

evaluating the structure-capacity relationship. Therefore, it is proposed that capacity be measured

indirectly by considering the relationship of the safety and efficiency metrics with respect to the

(prescribed) demand of the four traffic scenarios.

To better illustrate this rationale, consider the following simple fictional example where the average

conflict percentage for two airspace concepts are plotted against scenario number (i.e., against

traffic demand):

Figure 4.4: Fictional example of how capacity can be inferred indirectly from safety and efficiency metrics. Here the

relationship between the average conflict percentage and traffic demand/scenario is illustrated. The gradient with

respect to traffic demand may reveal capacity limits.

Such plots may reveal how the safety and efficiency metrics are affected by traffic demand for the

different airspace concepts. By reviewing the plots of several metrics in unison, as well as evaluating

the trends between metrics (for instance between conflict percentage and the airspace busyness

index), it may be possible to qualitatively study the effect of structure on capacity. Furthermore, by

analyzing the gradient of the safety and efficiency metrics with respect to demand, it may be

possible to detect capacity limits. For instance in Figure 4.4, the large increase of the conflict

percentage for 'concept 2' between scenarios 3 and 4 may indicate that a capacity limit exists

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between these two scenarios. However, it should be noted that the limited number of scenarios

used in this project may make it difficult to arrive a conclusive capacity limit for all concepts, as

illustrated for 'concept 1' in Figure 4.4.

4.4.2 Traffic Density vs. Traffic Demand

Another way to evaluate capacity is to measure the extent to which traffic density matches the pre-

defined traffic demand for each scenario. It is possible that for high demand scenarios, the

departure metering used to prevent conflicts during take-off may limit the maximum number

aircraft that can enter the airspace. The ratio between the number of aircraft that took-off and the

number of spawned aircraft during the logging hour can be used to measure this relation:

� = h�yE��T|ddh�y��

(4.10)

A running total of the number of 'actual' take-offs can be used to log h�y��XXduring the logging

hour. As mentioned above, the number of aircraft spawned during the simulation hour, h�y��, is

known in advance of the simulation.

If the ratio between density and demand is below a prescribed threshold (for example 75%), or if

there is a significant reduction of the ratio between two scenarios, a capacity limit may be identified.

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5 Environmental metrics

5.1 Introduction

The impact of aircraft movements on the environment has always played an important role in the

acceptance of new airports, new air routes, and new aircraft types. It is therefore expected that the

introduction of a complete new concept, such as the setting of UAVs/PAVs in a metropolis, will have

significant impact on the environment of the city and the environment involved. In Figure 5.1, an

overview is given of some of the most important concerns with respect to PAV, and UAV operations

within a city. These impacts can be split into two categories: First, the main environmental concerns

around traditional aircraft, such as emissions/pollution, noise pollution, and third party risk. These

are equally present for PAVs and UAVs. Second, other concerns may also become important such as

shadow flickering (similar to wind turbines) or light pollution in case of nightly operations, privacy

concerns, distraction, and effects on the biotope. For this latter category, the impact on the society

is still not clear and requires a timely process to assess the real impact, similar to other

environmental questions raised with the introduction of other “new” transportation technologies,

such as the train, the automobile, and the (traditional) aircraft.

For the first category, the emission concern may be of less concern in 2040 as the vehicles are

smaller than traditional aircraft, and there is significant development of sustainable energy sources

now already, that may resolve emission issues in 2040. Furthermore, the total flying time in a

scenario relates strongly with the energy use for an aircraft and can be used as indicator for the

emission/energy use as well.

Both noise pollution and third party risk is therefore the most promising candidate to measure for

the METROPOLIS scenarios. Both environmental impact relate strongly to the technology that is to

be developed, and will therefore deviate from the impact that is expected in 2040. But the

differences between the scenarios that are tested can be determined, as long as the parameters for

the model are the same. Third party risk is probably the most challenging impact to be considered,

Figure 5.1: Environmental concerns related to Personal and unmanned aerial vehicles.

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as there is little literature available for these aircraft, and therefore chosen to be measured in more

detail for METROPOLIS.

5.2 Scope and definition

For all of the metrics mentioned in this chapter, the focus is on comparing the different scenarios,

not on finding the right absolute value or score for each scenario: The future scenario of Metropolis

with its aircraft types and other assumptions are too futile to predict the right value or score, but

comparing the scenarios with each other creates an equal level playing field.

It is also assumed that traffic cannot migrate from one type of aircraft to another, as defined in the Metropolis definition report [2]. So a number of persons that plan to take a PAL-V to work cannot switch to taking the AW609 ‘bus’ instead, resulting in a decrease of PAL-V movements and an increase in AW609 movements.

5.3 Third party risk metric

5.3.1 Introduction

One of the main concerns is the safety for the people on the ground, also known as the third party

risk (TPR). In the present context, third party risk is the risk of people on the ground involuntarily

exposed to an aircraft accident. Those people on ground are defined as third parties. By definition,

people who are on board of an aircraft (air crew and passengers) and people who work within an

airfield terrain are considered as first party or second party, respectively.

As mentioned above, different futuristic aerial vehicles will operate alongside with each other. For

the personal air vehicles and unmanned aerial vehicles it is envisioned that the amount of traffic

The following metrics will not be considered for evaluating the scenarios, but are worth to

mention as they may play an important role in the acceptance of any of the Metropolis

scenarios:

• Privacy concerns: the current debate on the acceptance of civil unmanned vehicles is strongly

influenced by privacy concerns and the risk of eavesdropping by providing a camera (or

microphone) to a UAV.

• Shadows flickering: The effects of shadow flickering of wind turbines cause some of these

energy sources to be turned off during certain time of day. This effect can be equally

disturbing to civilians living or working underneath certain aircraft routes. The exact impact

of shadow flickering is not yet known as the Metropolis concept has not been adopted yet.

• Light pollution: Similar to shadow flickering is the disturbance of low-flight aircraft emitting

lights for navigational or other purposes during the night.

• Effects on biotope: although large cities are usually not considered to be national parks, there

can still be a variety of animals, mostly birds, which can be affected by the increase of low-

flying aerial movements.

• Distraction: Just as animals, people can be distracted as well by the metropolis concept.

Especially around schools, hospitals or some other areas where concentrated work is done,

this can be important to take into account.

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could be significant. With their capabilities to operate within confined air space, it is likely the traffic

takes place in or in the close proximity of built-up areas. In order to understand the magnitude of

risk that people on the ground are exposed to, there is a need to quantify the third party risk due to

the traffic of those aerial vehicles.

The NLR has done research on TPR around airports [15] and developed a TPR model that is now part

of Dutch law to determine safety zones around airports [16] in the Netherlands. The principles of

this model can be used to determine the TPR in METROPOLIS. However, the specific procedures of

METROPOLIS aircraft must be considered, and in particular the cruise phase of flight, which is

exempted from consideration in the third party risk around an airport, should be taken into account

as well. This requires a different approach in modelling third party risk and in developing a TPR

model suitable for the METROPOLIS scenarios.

5.3.2 Assumptions and considerations for TPR model

Risk factor related to (human introduced) hazards depends on the compensating benefit [17]: the

higher the societal benefit, the higher the risk that is accepted for a lethal accident. At this moment

the benefit of using drones or personal air vehicles is low and not considered beneficial, so the

safety criteria for these vehicles will be high. Perception of safety plays an important role for UAV

operations [18]. It could be expected that when the benefits of improved delivery times or reduced

travel times become more obvious, the safety criteria may reduce, but this is not certain.

The main difference between the traditional TPR model used in the determination of risk around

airports, and the TPR model in Metropolis is that for the traditional model the risk during cruise

phase of a flight is not considered while in Metropolis it should be taken into account. Because the

flights of PAV/UAV concentrate over the city area where the population density is high, the cruise

phase of flight should be included in risk modelling and determination.

Secondary effects caused by accidents such as an aircraft crashing into a gas station causing huge

fire or explosion shall not be taken into account in this model. Only the risk of direct causalities on

ground caused by aircraft crashes is considered relevant.

For TPR, the following kind of hazards can be discerned [19]:

• Mid-air collision with other aircraft;

• Other hazards, such as system failure, weather, bird strike, terrorism, human error. For

these kinds of error a specific analysis per type of hazard is considered not necessary, since a

general estimation of the total hazard per aircraft type is assumed to be sufficient.

The risk of mid-air collision is described in other metrics, See section 4.1.1 on page 26,, and

therefore not specifically considered for the third party risk. Instead of analysing the mid-air

collision risk, an estimated mid-air collision risk shall be taken into account when calculating the risk

in cruise phase.

The following factors have influence on the TPR model:

• Probability of an accident during landing or take-off. This probability is expressed per flight

stage.

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• Probability of an accident during cruise and the chance. This probability is expressed per

flight hour.

• Location of the crash area after malfunction (it depends on whether the aircraft is fixed wing

or not). There should be some estimation of the location of the crash area. Also the chance

that an aircraft crashes into a building and leads to casualties in the building or on the

ground beneath should be taken into account.

• Counter measures to prevent crashing (e.g. different routes, back-up systems, etc.)

The routes that the aircraft take are influenced by:

• The airspace concept used and the routes that it proposes

• The take-off/landing zones

• The scenario where traffic demand appears

For the impact of the crash, the following factors play a role:

• Consequence area in terms of aircraft size expressed in maximum (take-off) weight. This is

the area in which the people on ground could receive fatal injury should an aircraft accident

occur.

• Density of the population

• Shielding of population by buildings [21]

• Commuters/traffic on the street

• The time of day in relation of the number of people in the area.

The model should include the lethality of a crash, which is determined as the ratio of the number of

people killed in the crash area and the number of people present in that area. A separate method

can be used to determine the number of people in the crash area at a certain time of day.

A summation of all traffic shall be provided, but it is expected that the TPR of the smallest aircraft

type (the microdrone) will have a negligent impact on the resulting, total TPR, because of the limited

number of microdrone movements. As such, the inclusion of microdrone in the model is still crucial

for the overall concept and for the determination of the level of risk acceptable for the population in

the city. The same is true of aircraft movements over the lower density areas of the city (outer ring),

that will have a relative small contribution to the total risk.

5.3.3 Model restrictions

The third party risk model makes use of some assumed values, based on existing aircraft accident

data. This is not representative for operations of PAVs/UAVs in a dense city as there is not enough

data available for accidents of this kind. For this reason, the data should only be compared between

the different scenarios. For this reason, the full-mix concept is chosen as the reference concept for

Third party risk. The third party risk of all other three concepts will be related to the safety of this

scenario. For instance, the layers concept can score a certain percentage better or worse than the

full-mix concept.

The data can also be used to determine target level of safety for future PAV/UAV operations, but this

is not part of the goal of the Metropolis project.

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The TPR model implementation can be computational intensive due to the number of tracks that are

being processed, the length of the simulation, and/or the chosen model parameters. For this reason,

further simplifications to the metrics calculation may need to be chosen, so the metrics can still be

calculated. Examples of simplification can be:

• Reduction of interpolation of the tracks or grid size or density of the simulation area

• Reduction of number of tracks or simulation time that is considered for this metrics

• Reduction of aircraft types, so only aircraft types that have a significant influence on the TPR

are taken into account.

5.3.4 Input from simulation

• Aircraft tracks for each aircraft in the simulation.

• Population density for the city.

5.3.5 Output from metrics

• Percentage of Third Party risk compared to the reference concept (full-mix).

5.4 Third party risk model design

The model shall make use aircraft tracks and iterate over the given tracks. For each of the aircraft

tracks, the risk is calculated by the combination of:

1. The location and the chance of an accident of the aircraft.

2. The location of the crash area, in case of an accident.

3. The lethality chance based upon the aircraft and the location.

5.4.1 Probability and location of an accident

To calculate the probability of an accident of one flight, this flight will be split up in a take-off phase,

a cruise phase, and a landing phase. For each of these phases, prior research exists on accidents in

these phases, and a model can be made that calculate the risk on the ground for the take-off, cruise,

and landing phase. After that, these risks can be summed up for the total risk of the whole flight on

the ground.

Take-off and landing phases

For the take-off and landing phases, the probability of an accident can be calculated based upon

prior research of similar aircraft types. There is extensive data available that includes both the total

movements, and the kind and number of accidents during start or landing. However, such approach

in data research is not feasible for the given aircraft types in METROPOLIS, as it is impossible to find

any historic data at this moment. Therefore, similar aircraft types must be used, such as other rotor-

based aircraft. The probability that a landing or a take-off results in an accident will be a parameter

in the model. A Weibull distribution [22], as used in the NLR TPR model [16] to describe the aircraft

accident locations, will be used to determine where the accident will take place. The specific

parameters of the Weibull distribution shall be based on prior data research and engineering

judgment.

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Specific take-off and landing failure rates for rotor-craft aircraft can be found [23], but they explicitly

exclude data from tilt-rotor aircraft. However, a Congressional study indicates that a typical tilt-rotor

aircraft such as the V-22 Osprey has similar failure rates [24]. Therefore, for the aircraft in

METROPOLIS, either the single engine or dual engine turbine failure rates are taken. As there is no

data available for the microdrone UAV, the worst-case values are.

Table 5.1: METROPOLIS Aircraft types matched on helicopter types from Third Party Risk studies

Assumed type

PAL-V One Piston/single turbine

Terrafugia TF-X Twin turbine engine

AgustaWestland 609 Twin turbine engine

Microdrone MD4-3000 Piston/single turbine

To determine potential impact area of an accident during take-off or landing, the flight track needs

to be available from take-off to a certain distance where the Weibull distribution can be considered

1. For landing, the track should be reversed from touch-down back to the point where the Weibull

distribution is valid for this phase of flight. For these calculations, iteration along the route should

take place based on fixed distance steps.

Two modifications are proposed compared to the NLR TPR model. First, in the NLR TPR model, an

aircraft is considered to be in cruise over 500 feet [23]. For METROPOLIS is can be expected that

some aircraft, in particular UAVs, will have a more vertical than horizontal flight path. For this

reason, instead of using the height of 500 feet as cut-off, the total flown distance of 1000 metres is

taken as limit what part of the flight path is considered part of the start or landing. Second, it could

be that there is still some risk left on the Weibull curve during the take-off or landing phase at the

moment of cut-off. For an airport-based TPR model, this risk is negligible, as it concerns areas further

away from the airport. In Metropolis, this residual risk will be evenly distributed over the trajectory

in which the aircraft within the 1000 metres.

Cruise phase

Table 5.2: METROPOLIS Aircraft cruising speed

Cruise speed [kts] Cruise speed [m/s]

PAL-V One 75 39

Terrafugia TF-X 160 82

AgustaWestland 609 200 103

Microdrone MD4-3000 31 16

For the cruise-phase, the probability of an accident will be calculated based upon flight-hours. In

[25], an accident rate of 5.6x10-5 per flight hour is given based upon data from the National

Transportation Safety Board (NTSB) that leads to an accident resulting in a fatality on the ground to

1.48x10-7. For METROPOLIS, as no extensive data is available on PAV/UAV accident rates, the

decision to compare the concepts in relation with each other can be justified. For this reason, this

number is only used to balance the relation between the accident risk in cruise phase and the

accident risk in start/landing phase.

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41

As the risk is based upon flight-hours, the speed of the aircraft shall be taken into account. To

determine the risk for the cruise-phase, the model needs to iterate through the given route, and

calculate the risk for each iteration step. The time step (in seconds) of this iteration speed will be a

parameter in the model.

If the aircraft route is given as a list of waypoints, an approximation must be made for the iteration

step between the waypoints. It is expected that this list of waypoints is not too far away and the

iteration can be considered to be a straight line. Alternative methods are the use of interpolation

techniques like splines3, but these methods are more computational intensive and are not needed

for a simple model set-up described in this context. It is also assumed that the aircraft speed is

known for each of the given waypoints, and therefore, the average speed between the previous and

next waypoint is used to calculate the aircraft speed during the interpolation.

Mid-air collision risk

The risk on mid-air collision for a specific number of aircraft within a volume of airspace can be

translated into certain chance on a mid-air collision per flight hour [26]. For this reason, an

additional factor can be added to the risk that is found in cruise-phase.

5.4.2 Location of the crash area

Figure 5.2: Accident chance and impact area of a single track

A grid will be defined that will contain the operating area of the aircraft. Each grid cell will receive a

risk value based upon the offered number of tracks and the model that calculates the risk of this cell.

For obvious reasons, simplicity and consistency, the size of the grid cells are considered to be square.

The number of grid cells and the size of the grid cell will be a parameter in the model. The smaller

3 http://en.wikipedia.org/wiki/Spline_(mathematics)

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the grid size, the more precise the Third Party risk can be calculated, but the more complex the

calculation that must be done. The same is true for the previously mentioned iteration speed.

Potential impact area

In case of an accident, a footprint can be calculated based upon aircraft type, aircraft weight, speed,

direction, etc., which determine altogether the potential impact area on the ground. This footprint,

as displayed in yellow in Figure 5.2 will cover one or more grid cells on the ground. For each of the

grid cells, the probability is calculated for the aircraft impact in this cell. In this figure, a hypothetical

ellipse is used as potential impact area.

An estimation must be made about which grid cells will be taken into account and what kind of

probability distribution are used for the potential impact area. For instance, cells on the outside of

the potential impact area could have a smaller probability of impact than the cells lying more

towards the centre of the impact area.

The size of the potential impact area in this TPR model depends on the flight and accident

characteristics of the aircraft. The glide angle of the aircraft involved is a parameter to determine the

outer limits of this area. For the aircraft that are used in the METROPOLIS project, the expected glide

angles are determined through the application of helicopter flight knowledge and engineering

estimates. Table 5.3 shows the results.

Table 5.3 Glide angles of METROPOLIS aircraft based upon available data and engineering judgment

Aircraft Estimated glide angle

Helicopter/gyrocopter configuration Aircraft configuration

PAL-V One 1:5 to 1:6 Not Applicable

AW609 1:3.5 to 1:4.0 1:8

Terrafugia TF-X VTOL 1:5 1:10

Microdrone MD4-3000 1:24 Not Applicable

Bivariate normal distributed potential impact area

According to Clothier [19], a bivariate (2D) normal distribution is proposed for the potential impact

area, where 99% of the impacts will occur within the maximum glide distance. The variance

orthogonal to the track heading is considered half that of the variance in the heading of the track. An

exception is made for the quad-copter Microdrone that does not have a dedicated flying direction,

so the variance of the width is considered the same as the length.

Discrimination between mid-air collision risk and system failure risk is not made for the potential

impact area in this model, as it is assumed that this distribution functions applies for both types of

accidents.

Consequence area

4 No data available, so assumed by Author

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The consequence area is the size of the affected area if a crash happens at that location. Within this

area a third party could be fatally injured due to this crash. The consequence area can either be:

1. smaller than the cell size, and thus only affect this cell;

2. Or it can be larger than this cell and other cells are affected as well.

For simplicity of the model, the distribution of the accident consequence due to the aircraft ground

impact is considered uniform within the consequence area. If part of the cell (or part of a

neighbouring cell) is affected, the percentage of overlap is used as a factor to determine the lethality

of the cell. This will be discussed more in the next section.

If the consequence area for all the different aircraft types and routes is smaller than the cell size,

then the model can be made more simple: the consequence of an impact on neighbouring cells can

be considered zero and the consequence area need not be distributed to neighbouring cells, as is

done in the original NLR TPR model [16]. This simplification requires that the grid size is not chosen

too small, and at least larger than size of the impact area of the aircraft with largest impact area size.

According to NLR’s research on helicopter crashes and third party risk for inland heliports [23], the

consequence area can be given by the following formula:

3�"�# = 230ln"�# + 330

(5.1)

where x represents the helicopter MTOW given in metric tonnes and CA the consequence area given

in square metres. This relation is valid for a range from 500 to 12,000 kg.

The Microdrone UAV cannot use this formula as its mass with 15kg is well below the minimum of

500kg required by equation 5.1. As an alternative, Equation 5.2 in [20] represents the worst-case

consequence area of an aircraft in horizontal glide will be used with the following values:

• Height of the person (Hp) of 2.0 metres and radius (Rp) of 0.5 metres

• Dimensions of microdrone MD4-3000 aircraft of length (Lua) = 2.052 metres and width (ωua)

of 1.888 metres

• Glide angle according to 1:2 glide ratio (Table 5.3)

Αib� = "�� + 2�x#"P�� + �xtan + 2�x#

(5.2)

This leads to a consequence area of 21 m2 for vehicle with the diameters of the microdrone.

5 Weight is based on the similar Terrafugia Transition, See http://en.wikipedia.org/wiki/Terrafugia_Transition

Table 5.4: The aircraft types in METROPOLIS and their considered maximum take-off weight

Aircraft type MTOW (in kg)

Microdrone MD4-3000 15 PAL-V One 910 Terrafugia TF-X VTOL PAV 6505 AgustaWestland AW609 7620

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44

According to Table 5.4, a grid size of at least 30 by 30 meters (equals 900 m2) will fit the

consequence area for the METROPOLIS aircraft types.

Figure 5.3: The green box demonstrates the simplification in the model by not rotating the affected grid cell.

A second simplification made is by not rotating the grid cells for which the impact chance is

calculated, See Figure 5.3. By not rotating the cell, the calculation of the impact chance for that cell

can be calculated easier, reducing processing time. It is expected that the deviation from the actual

value is small, as the area surface for the impact is still the same.

5.4.3 Lethality

In case of a crash on an impact area, consequently the lethality must be known. The lethality could

be determined by among others the energy of impact, the density of the population in the area and

the shielding of building, vehicles, etc. in that area. For Metropolis, a grid cell consists of a

combination of buildings and roads. For each of these infrastructure items, in combination with the

population density and the shield, an average density can be calculated that is used in the model.

As an alternative for using the energy level to calculate the lethality, in [23] an estimate is made.

The estimated value is 17% chance that a third party could be killed in a helicopter crash within the

consequence area (as defined by 5.1). It should be noted that this value was based on the empirical

data available for helicopter crashes investigated. So it is conjectured that the use of this value for

lethality is appropriate for the aircraft types used in Metropolis.

As the way the consequence area is determined is different for the microdrone UAV than for the

other aircraft types, the lethality percentage for the microdrone should be examined separately. We

would expect that impact energy of the microdrone is much lower than for other aircraft, but still

high enough for unshielded people within the consequence area to be killed. Therefore, we can

expect that at least for people on the streets (12%), the lethality is closed to 1, while the lethality for

people in the buildings will be much lower, due to shielding.

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45

5.5 Energy usage metrics

Aircraft pollution and aircraft emission is dependent on the total use of the aircraft. Considering the

rise of green propulsion system, such as electronic engines, the calculation of the expected pollution

can be difficult or may not be an issue at all. For this reason, instead of measuring pollution or

emission, the total energy use of each of the scenario is estimated. Two approaches are taken, that

will be described in the following two sections.

5.5.1 Energy usage based upon aircraft weight and flight hours

To reduce the complexity of the calculation of the energy use, the total operating hours for each

aircraft in a scenario is calculated. This simplification can be justified as all aircraft work with a

rotorcraft-based propulsion system and the cruise-phase where the tilt-rotors will operate only

limited time in horizontal configuration. The weight of each aircraft type shall be taken into account

to accumulate the total energy usage for each scenario. To further reduce complexity, the average

weight, and not the actual weight will be used to make the estimation.

5.5.2 Energy usage based upon thrust and distance travelled

For the calculation of the thrust, the simulation model cannot be used: the thrust and fuel usage are

not simulated for the aircraft models of METROPOLIS. For this reason, a separate energy model will

be developed that calculated the required thrust based upon the flight track information that is

available after the simulation run. This model will be developed as part of WP4.


• Total flying/operating hours per simulation and aircraft type.

• Average weight of each aircraft type in the simulation.

• Flight tracks logging, including time, position, altitude, aircraft speed, climb/descent angle,

mode of operation (for tilt-rotors).


• Total estimated energy usage for each simulation in:

o Kilogram flight hours.

o Joules, based upon thrust calculation of flight tracks.

5.6 Noise pollution metrics

Aircraft noise around airports is one of the main obstacles to growth. For airports, aircraft

movements are concentrated through the use of fixed runways and fixed routes. The weather, the

amount of (peak) traffic, maintenance, etc., makes the optimization for reduction of aircraft noise

for the dwellings of the nearby city difficult. In Metropolis, the number of ‘runways’ are much more

than at an airport and the distance of the surrounding building is also much closer than at an airport.

Some of the aircraft types in Metropolis (Microdrone, PAL-V) will generate less noise than the

average aircraft at the airport, but a larger aircraft like the AgustaWestland AW609 can create a

significant portion of noise that can be a challenge for future concepts.

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46

As the number of starts and landings for the different scenarios will be the same, it is expected that

the same number of starts and landings will take place for each of these scenarios. For the smaller

aircraft, including the Terrafugia TF-X, it is therefore assumed that the noise impact will be similar.

But the larger AW609 can also have an impact during cruise phase. For these reasons, only the noise

of the AW609 will be taken into account for the calculation of the noise pollution. Other aircraft will

hardly be audible on higher cruise altitudes.

5.6.1 Simplified aircraft noise metrics for METROPOLIS

Most current noise models do not have sufficient data to measure the noise of a tilt-rotor aircraft. As

an alternative, a similar-sized helicopter can be taken as an alternative. Considering the limited

scope of the noise pollution, and the high uncertainty of the actual sound levels of the AW609

aircraft, another simplification can be used to compare the noise level between the different

scenarios: the time flying below a certain altitude can be taken, as demonstrated in the following

table:

Flying zones \ Concept Concept 1 Concept 2 Concept 3 Concept 4

Flying time below 2000ft

Flying time between 2000ft-4000ft

Flying time between 4000ft-6500ft

The more time the aircraft spends in the lower altitude, the more nuisances are generated on the

ground. The noise pollution will exponentially decrease by the distance, so the noise generation in

the higher levels will have a lower impact on the total noise of all the movements. The relative

scoring between the different zones shall be determined before the measured simulations. This

metrics can be incorporated in the simulation, without the need of extensive noise calculation post-

processing afterwards.


• Flown aircraft tracks for AW609 per simulation.


• Total noise level for each simulation per zone of the city.

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