Urban Air Mobility (UAM): A Model Proposal based on Agents using

Netlogo

Felipe Desiglo Ferrare

, Derick Moreira Baum

, Jorge Rady de Almeida Júnior

João Batista Camargo Júnior

and Paulo Sérgio Cugnasca

Polytechnic School, University of São Paulo, São Paulo, Brazil

Keywords: Netlogo, Multi-Agent, Unmanaged Air Vehicles.

Abstract: Netlogo is a tool for creating MAS (Multi-Agent Simulations), and it is used to create simulations for multiple

areas and scenarios. With the advent of the use of manned and unmanned aerial vehicles, considering electric

vehicles known as eVTOL (electric Vertical Take-Off and Landing), multiple problems in the urban

environment appear. Also, as multiple vehicles are expected to be used to obtain urban mobility in dense

metropolitan regions around the world, the concept of UAM (Urban Air Mobility) emerges as a way to assure

environment air control. To not compromise the future of UAM, researchers were faced with the challenge of

structuring the airspace with specific air traffic rules, with separations between vehicles lower than those

currently applied, without reducing the aviation required safety levels. As testing in a real scenario is not

practical, simulation is a form to gather data and define parameters for this new system. This work aims to

present a computational tool that uses multiple agents to generate different UAM scenarios, being possible to

analyse the impact that simulation input parameters variation will cause in the safety indicators proposed in

the model.

1 INTRODUCTION

Every day, many hours are wasted by people on roads

around the world. People spend a considerable part of

their time commuting between work and home,

resulting in millions of unproductive hours every day

(Holden; Goel, 2016).

One of the biggest challenges for authorities

around the world is urban mobility. Over the years, to

make it possible to reduce travel time, especially in

metropolitan regions where congestion has become a

major problem, considerable investments have been

made in inland transport infrastructure (Patterson;

Antcliff; Kohlman, 2018).

However, with the constant increase in population

and the consequent increase in demand for transport,

in recent years the industry and scientific

communities have invested resources to create new

https://orcid.org/0000-0003-3896-8650

https://orcid.org/0000-0003-1988-6991

https://orcid.org/0000-0003-3839-4570

https://orcid.org/0000-0001-5098-6769

https://orcid.org/0000-0002-5675-4667

ideas to improve the performance of urban transport

(Neto et al., 2019). As a consequence, the conception

of an intelligent air transport system (UAM)

(Thipphavong et al., 2018) has the objective to

provide air transport services within a metropolitan

area to overcome the increase in surface congestion.

Types of operations include emergency medical

evacuations, rescue, humanitarian missions,

newsgathering, land flow assessment, weather

monitoring, cash deliveries, and passenger

transportation (Thipphavong et al., 2018).

Several projects are under development for

application at UAM. An example is eVTOL (electric

Vertical Take-Off and Landing), considerably

cheaper when compared to helicopters, which can be

used for inspection, transportation of valuables and

people, with a market potential of US$ 74 billion and

23,000 units by 2035 (PORSCHE, 2018). The

forecasts are that, with the use of 4,000 eVTOL,

352

Ferrare, F., Baum, D., Almeida Júnior, J., Camargo Júnior, J. and Cugnasca, P.

Urban Air Mobility (UAM): A Model Proposal based on Agents using Netlogo.

DOI: 10.5220/0010557203520359

In Proceedings of the 11th International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH 2021), pages 352-359

ISBN: 978-989-758-528-9

55,000 air taxi flights will take place every day in the

United States (Booz Allen Hamilton, 2018).

To this market not be compromised in the future,

some barriers must be overcome. Some examples are

aircraft certification, noise impacts caused in urban

areas, cybersecurity protection, and the creation of an

air traffic system with characteristics different from

those displayed on conventional airspace.

The air traffic system required by UAM should be

able to handle aircraft demands far superior to those

currently existing in general aviation. Landings and

take-offs will be possible in most varied places,

increasing the complexity of managing this type of

traffic. It is evident that there is a need for new criteria

for separation between aircraft and for new vehicle

performance both in landing and on take-off

procedures and in cruising (level) procedures. In

order not to compromise the required safety indices,

new models of airspace complexity and airspace

capacity should be presented.

As the scenarios using only one or a few

coordinated eVTOLs are a majority in the literature,

this works, however, not covers real case scenarios

where we have multiple eVTOLs, not from the same

organization.

An organization that tries to be competitive and

operating multiple vehicles in the limited air space

needs to try to create the best rules to ensure fair use

of the resources in a safe way.

In this work, to simulate different UAM

scenarios, a model developed using a computational

tool based on agents (Multi-Agent System – MAS),

called Netlogo, will be presented. During the

execution of this model, based on the several

parameters used, it is possible to change the

behaviour of the system. The outputs generated,

including the number of conflicts and possible

collisions between vehicles, represent the safety

indicators for each scenario, making it possible to

establish a relationship between the inputs and the

outputs in UAM scenarios. In this modelling, all

vehicles will be considered unmanned.

Netlogo is used in a way that has a stable and

powerful simulation tool, then it is possible in an

easy way to develop the model and obtain the

problem solution and results. The system will

receive inputs (parameters) from the user and the

model developed will execute the simulation and

show the results.

We will make the development of this text as

follows. Section 2 presents the tools used in this

work: MAS and Netlogo. In Section 3 the UAM is

conceptualized. Section 4 presents the model

developed for the simulations and the criteria used

and some example results. And finally, in Sections

5 and 6, the conclusions and future work are

presented, respectively.

2 MAS AND NETLOGO

Some problems existing in the current approaches are

the difficulty to validate scenarios with more than one

vehicle and put more than one variable in an

experiment and analyse the impact of individual

variables in the whole system. And as there is no real

system in operation, the simulation is the only

feasible alternative to gather data before a real system

could be implemented.

The approach that we used, in this case, is to

provide a simulation that includes multiple intelligent

vehicles, create a model using the characteristics that

we want in a way that we could collect data close to

the real. Then, for the reasons described, it is possible

to see why this approach has been considered the best

way to tackle this problem.

MAS (Multi-Agent System) is a paradigm for the

development of intelligent systems, being a subarea

of distributed artificial intelligence, widely used in

several areas to solve complex problems in a

decentralized manner, with or without coordination

between agents. The agent-oriented paradigm is one

of the main ones used in artificial intelligence, with

the ability for multiple agents to act autonomously in

a defined environment. Several applications are

presented in the literature, including those that require

high reliability, such as aviation (Wooldridge, 2002).

In MAS multiple agents are interacting, requiring

the exchange of information between all,

collaborating to achieve, together, the same objective.

In other cases, they will work, with a certain

collaboration, to achieve individual goals, which can

sometimes even be conflicting (Wooldridge, 2002).

Netlogo is an agent-based language developed by

Wilensky (1999) that presents simple structures to

facilitate its use. Nevertheless, it is a language that

makes it possible to build several programs, with

results of different levels of complexity.

Netlogo, with its ability to be multi-agent, adds

even more simulation features while maintaining the

simplicity of the Logo with versions with 2D and 3D

simulation capabilities. Also, Netlogo has been used

in several academic works in the construction of

MAS models for different areas such as Physics and

Social Sciences (Wilensky, 2015).

However, we can find some relevant works with

agent-based approaches applied to UAM, as is the

case with eVTOLs. Most use an agent-based

Urban Air Mobility (UAM): A Model Proposal based on Agents using Netlogo

353

approach, forming a group (swarm) in which several

agents coordinate for the execution of a task (Cooley;

Wolf; Borowczak, 2019).

In some examples in the literature more complex

problems are presented, where the agents are not used

to achieve the objective in a coordinated way, with

each agent having different objectives, often

conflicting with each other. An example of this

approach is found in Liao et al. (2017), where

eVTOLs must maintain their separations in-flight

using MAS.

Alvarez-Munoz et al. (2019) use MAS to decide

whether an aircraft should use the collaboration of

other vehicles to determine its trajectories. Despite

this, it also considers the decision process based on

consensus between the aircraft and not in a

competitive scenario,

Postorino; Sarné (2020) proposed the use of MAS

to measure the impact of mobility of people,

simulating transport by air and land vehicles. This

simulation was used to measure the impacts on urban

mobility and the capacity to transport people.

Kitajima et al. (2019) use MAS to simulate the

integration between autonomous and non-

autonomous land vehicles with a focus on validating

reliability in certain scenarios, considering cases of

conflicts, cases of accidents, as well as other

parameters. It is a proposal similar to the one

presented in this work but applied to other types of

autonomous vehicles.

3 URBAN AIR MOBILITY (UAM)

Due to concerns about the time spent by people

travelling by land, mainly in places with high

population density, researchers, industry, and

authorities are looking for innovative solutions to this

problem. Therefore, in recent years there has been a

growing interest in the integration of urban air

mobility (UAM) operations around the world,

leveraging the development of new technologies and

types of aircraft and thus requiring changes in the way

of using and managing airspace (Bosson; Lauderdale,

2018; Lascara et al., 2019).

Urban Air Mobility (UAM) is a term used to

describe a system that allows air transport services on

demand, with a high level of automation, for the

transport of passengers or cargo. The predominant

vehicle in the UAM environment is the VTOL,

enabling vertical landings and take-offs in small

areas, called “vertiports”. Among the direct benefits

of replacing, when possible, land transport employing

air transport is the possibility of more direct routes

and the increase in vehicle speed. These benefits can

reduce the travel time of passengers in the current

transport system, considering the door-to-door

displacement (Patterson; Antcliff; Kohlman, 2018).

Considering the advantages presented for UAM

(traffic decongestion, improved mobility, reduced

travel time, reduced accidents, etc.), it is expected that

there will be a considerable increase in demand for

this type of service. However, for this to be possible,

in the UAM environment vehicles with different

ascending, descending, and cruising performances

are required, as well as new parameters for separation

between aircraft, new air traffic rules, and a new air

traffic control system that does not compromise

current security levels.

Companies like UBER, interested in this market

and partnership with industry and researchers, have

made vehicles with differentiated performance

possible, in addition to noise reduction and lower fuel

consumption expenses. These vehicles are called

eVTOL.

Due to the density of traffic in the UAM

environment, current ATC (Air Traffic Control)

procedures are expected to be insufficient to deal with

a large number of new UAM aircraft. This is due to

the limitations of the air traffic controllers´ workload

and the minimum necessary separation distances

between the aircraft. Likewise, the design of airspace

and current flight routes restricts UAM's access to

significant parts of airspace above metropolitan areas

(Vascik; Hansman, 2018).

Thus, some needs so that the future of UAM is not

compromised are the availability of land

infrastructure geographically distributed in areas

where there is demand from customers, the

integration of urban air transport operations with Air

Traffic Control (ATC), and the potential need for a

new automated ATC system to manage airspace

below 3,000 feet (Vascik; Hansman, 2017),

integrating airspace in a UAM environment with

other existing airspaces.

This integration should under no circumstances

compromise the levels of security expected in

aviation as a whole.

Researchers around the world are aware of the

challenges presented by UAM. The characteristics of

the aircraft, the landing and take-off locations, as well

as the demand projections, which create highly dense

air spaces, compel the aeronautical community, in

particular researchers and air traffic authorities in all

countries, to propose new separation criteria, new air

traffic rules and new models of complexity and

airspace capacity.

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In Neto et al. (2019) a computer simulation tool

was presented to measure the safety and effectiveness

of trajectory-based UAM operations, considering the

presence of manned and unmanned eVTOL vehicles.

In Baum et al. (2018) it was presented the concept

called TML (Technology Maturity Level) related to

the familiarization of ATCo (Air Traffic Controller)

with unmanned aerial vehicles. From the simulation

of different scenarios with the gradual increase of

unmanned aircraft, it was proposed to gradually

increase the ATCo's familiarity with these unmanned

aircraft. Although the VTOL can be manned or

unmanned, all concepts presented in this work will be

based on unmanned aircraft.

The airspace where the UAM takes place in

particular, and the concepts currently applied in air

traffic can compromise the future of this market, such

as, for example, the minimum separation applied

between the aircraft can make it not viable. But

knowing the relationship between the demand for

eVTOL, its performances, and the safety indexes

applied in aviation, we can try to define the number

of aircraft that will be able to fly simultaneously

safely, which is still a desire and a challenge for all

researchers.

4 DEVELOPED MODEL

For the development of the model, Netlogo 3D

version 6.1.1 was used. The proposal was to enable

the creation of numerous UAM scenarios with the

possibility of varying many proposed inputs. Thus, it

will be possible to establish the relationship between

the variation of a given "input" with the "outputs"

resulting in the simulation.

4.1 Model Description

Aircraft generation and landing and take-off locations

occur stochastically within the limits of the proposed

scenarios. Considering that 1 NM (Nautical Mile)

equals 1.852 km and 1 ft (feet) equals 0.3048 m, the

dimensions shown are 30 NM x 30 NM or 15 NM x

15 NM, from 1000 ft to 3200 ft above the landing and

take-off location. Each scenario is divided into

0.5NM x 0.5NM squares horizontally and 200 ft

vertically.

This difference in the feet and Nautical Mile is

because these are the most common distances in the

aeronautical field and is necessary a smaller distance

vertically to make the system secure, because of that

the area is not a cube with equal sides and have

different distances horizontally and vertically. We

can see this division in Figure 1.

Figure 1: Illustration of the agent environment.

The air traffic rules included in the model, such as

minimum horizontal and vertical separation, rate of

climb and descent, speed when the aircraft is on a

level (cruising speed) and altitude that it can maintain

depending on the magnetic course it will use on the

flight, as shown in Figure 2, that are presented in Neto

et al. (2019). A new rule proposed in this work is the

interdiction of the squares above the aircraft landing

and take-off location, preventing other aircraft from

crossing this air space whenever an aircraft is

performing an approach or take-off procedure

Figure 2: Division of altitude ranges by heading.

Figure 3 presents the graphical interface

developed in Netlogo, where the green “buttons” are

all the “inputs” that may be affected, making it

possible to create numerous UAM scenarios.

Figures 4 and 5 show in more detail how the input

interfaces look like. And Figure 6 shows in detail the

output interface with text items and the graphs that

show the variation during each iteration of the

simulation.

Urban Air Mobility (UAM): A Model Proposal based on Agents using Netlogo

355

Figure 3: Netlogo graphical interface.

Figure 4: Netlogo controls interface detail.

Figure 5: Netlogo velocity controls interface detail.

Figure 6: Netlogo execution interface detail.

Figure 7 shows the model in execution, with the

agents (aircraft) in 3D. The VTOLs types 1 and 2 are

presented by the different colours (yellow and red) in

the model. These vehicles could have the same or

different parameters as the number of vehicles in the

airspace and different velocities (vertical and

horizontal), as to simulate vehicles as fast small

delivery and slow big passenger vehicles.

The inputs proposed in this model are:

1) Scenarios: the scenarios describe the formats

of the trajectories, which can be classified in

parallel, perpendicular, and general (in any

direction), relative to the other planes in the

same level;

2) Simulation time: the time may vary according

to the analysis that will need to be performed;

3) The number of VTOL1 and VTOL2: in the

simulation two different types of VTOL are

possible, being at the discretion of the

researcher how the horizontal and vertical

speeds will be varied. Thus, it will be possible

to maintain aircraft with different

performances in the environment so that the

impact on the safety indices presented

analysed;

4) Aircraft generation interval: it is possible to

vary the aircraft entry interval, making air

space increasingly dense;

5) Safety parameters: it is possible to vary the

minimum horizontal and vertical separation so

that the impact on safety indices is analysed.

Although the necessary separations between

aircraft in UAM airspace are being proposed

by different entities, the values used in this

investigation consider the reference present in

the literature (Neto et al., 2019; Booz Allen

Hamilton, 2018), that is, 0.5 NM horizontally

and 200 ft vertically;

6) Vertical and horizontal speed: the vertical

speeds (rate of ascent and descent) and

horizontal of the VTOL1 and VTOL2 may be

varied to increase the complexity of the

airspace and verify the impacts on the safety

indexes.

The “outputs” proposed in this model, which can

be called security indexes, are:

1) Completed trips: traffic will be considered

complete when it takes off and lands within

the simulation time. If the environment

becomes more complex, forcing the aircraft to

wait to comply with safety parameters, the

number of traffic completed may vary;

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356

2) Collisions: traffic should, in principle, always

be kept in different squares (0.5 NM x 0.5 NM

and 200ft horizontally). However, depending

on the level of complexity generated in the

scenario, these minimums may be

compromised. If two aircraft are in the same

grid, a collision is considered;

3) Conflicts: conflicts are considered when the

aircraft does not comply with the minimum

horizontal and vertical separation presented in

the safety parameters;

4) Blocked aircraft: is the computation of the

number of aircraft that will maintain their

position for more than 10 min in order not to

enter restricted airspace.

Figure 7: Running Graphical View of the 3D agent model.

Considering that the generation of the scenarios is

stochastic, it is necessary to perform a considerable

number of tests per scenario for the analyses to be

correctly performed. For this purpose, the “batch”

function was created in the model, making it possible

to define the number of experiments for the proposed

scenario, exporting the data from the multiple

simulations to CSV, making posterior analysis

possible.

4.2 Model Validation

For model validation, air traffic specialists analysed

each proposed scenario by comparing the results

obtained with the expected results. Considering that

all landing and take-off locations are stochastically

defined, to enable the analysis, the following

validation outputs were created:

1) Average Vertical Distance: average vertical

distance, of all simulation aircraft, between the

landing and take-off location and the cruising

level;

2) Average horizontal distance: average

horizontal distance of all aircraft in the

simulation, between landing and destination;

3) Average horizontal flight time: it is the

average time used by all aircraft at the cruise

level;

4) Average vertical flight time: it is the average

time used by all aircraft in the ascent and

descent procedures;

5) Total average time: is the sum of the average

time used by all aircraft;

The model used was validated and considered

ready for the simulation when it was found that the

behaviours of the agents (aircraft) and the outputs

generated were compatible with the chosen

performances, as well as with the other parameters

used.

4.3 Example Case

For example, we could have a sample execution using

only VTOL1 with 400ft/min and 90KT using

simulation, putting between 0 and 3 new vehicles

every 3 minutes, running each simulation for 600

minutes. Using a travelling environment of 15 NH x

15 NH. Executing this simulation 100 times, we could

take some median values of each execution.

In this sample, we have 220.48 finish planes per

run (with a standard deviation of 14.23). These planes

run in the median of 2593.15 ft (with a deviation of

83.82) and take 6.99 minutes in the median (with a

deviation of 0.20).

Taking this value, we can see that a plane running

in a 400ft/min for 6.99 minutes will take a distance of

2794.32 ft. We could see that our median value is

7.2% less than that because the vehicles could not use

the full velocity or have to stop and wait, this is the

loss of performance by having multiple vehicles

sharing the same airspace.

5 CONCLUSIONS

Due to the high population density in several

metropolises, researchers, industry, and authorities

are faced with the challenge of solving the problem of

urban mobility. Thus, the concept of UAM (Urban

Air Mobility) emerges and, with it, countless new

challenges for the viability of its future.

The vehicles proposed for use at UAM have

performance for vertical landings and take-offs,

called VTOL, with the electric version called eVTOL.

These vehicles have operational conditions to land

Urban Air Mobility (UAM): A Model Proposal based on Agents using Netlogo

357

and take-off in small areas, called “vertiports” in the

literature.

Conventional aviation has well-defined landing

and take-off procedures, with separations between

aircraft applied without impacting the system's

capacity and with a well-defined strategy for traffic

and airspace management. However, if we use such

concepts, the future of UAM will be compromised. It

is a consensus among researchers from all over the

world that the parameters and equipment used, for

example, in air traffic control of commercial aviation

are not applicable at UAM, making all operations

unfeasible.

New models of complexity and airspace capacity

should be developed based on the operational

characteristics of eVTOL. In this work, to simulate

UAM scenarios, a model was presented using

Netlogo. In the model it is possible to vary several

parameters ("inputs"), checking their impact on the

simulation results ("outputs"). After an exhaustive

process of checks by air traffic specialists and

successive calibrations, the model proved to be

satisfactory for simulating UAM scenarios.

Using this model, we could validate ideas from

the literature of how the system should behave and

validate all the parameters and impact in the system.

6 EXPECTED RESULTS AND

FUTURE WORK

With the presented model, it is possible to generate

several scenarios, checking what is the impact on the

results when there is a variation of the input

parameters.

The results of the simulations will be used in the

future for the development of an airspace complexity

model. The studies sought to define the relationship

between the variation of "inputs" and the increase in

the complexity of airspace and the consequent impact

on its capacity.

In the future, it will also be verified what is the

appropriate limit of minimum horizontal or vertical

separation between eVTOLs without compromising

the level of security required for aviation. This is

possible since any variation in the proposed safety

parameters changes the model's “outputs” and can be

considered as safety indicators, presented in this work

as “completed”, “collisions”, “conflicts” and

“blocked”.

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