=Paper=
{{Paper
|id=Vol-2706/paper2
|storemode=property
|title=An Agent-based Simulator for Urban Air Mobility Scenarios
|pdfUrl=https://ceur-ws.org/Vol-2706/paper3.pdf
|volume=Vol-2706
|authors=Maria Nadia Postorino,Francesco Alessandro Sarné,Giuseppe M.L. Sarné
|dblpUrl=https://dblp.org/rec/conf/woa/PostorinoSS20
}}
==An Agent-based Simulator for Urban Air Mobility Scenarios==
https://ceur-ws.org/Vol-2706/paper3.pdf
An Agent-based Simulator for Urban Air Mobility
Scenarios
Maria Nadia Postorinoa , Francesco A. Sarnéb and Giuseppe M. L. Sarnéc
a
Department DICAM, Alma Mater Studiorum, University of Bologna, Viale Risorgimento 2, 40136 Bologna, Italy
b
Politecnico of Milan, P.za Leonardo da Vinci, 32 20133 Milano, Italy
c
Department DICEAM, University Mediterranea of Reggio Calabria, Loc. Feo di Vito, 89122 Reggio Calabria, Italy
Abstract
In the next years, flying cars are expected to become a real opportunity to realize Urban Air Mobility
(UAM) systems. Most of the appeal is given by the opportunity of avoiding congestion, gaining time and
reducing environmental impacts with respect to conventional mobility. However, UAM implementation
is not trivial as it has several implications in manifold areas like safety, security, traffic control, legal
issues and urban design among the others. To investigate on the impacts of UAM, a dedicated agent-based
framework has been designed. The results of some preliminary tests carried out to verify the capabilities
of this simulator are presented.
Keywords
Flying cars, Simulator, Software agents, Transportation network, Urban Air Mobility
1. Introduction
In the wake of Icarus, thanks to recent technological advancements, Personal Aerial Vehicles
(PAV) and Passengers Unmanned Aerial Vehicles (PUAV) moving on both land and aerial
modalities, also known as “flying cars”, make it real the opportunity to realize an Urban Air
Mobility (UAM) for point-to-point connections. To this aim, a growing number of flying cars is
being developed or tested all over the world also by commercial companies like Uber [1], which
is planning to start with aerial services [2] when technical, urban, legal and economic criticisms
will be solved. Indeed, until now UAM requirements have not been considered neither in urban
planning policies (e.g., landing and take-off spaces for transition from ground to aerial mode
and vice versa) nor from laws and regulations point of view (e.g., safety, security and privacy
issues due to flights over or close to buildings have not been considered yet).
Consequences on urban transportation contexts and economic convenience of UAM scenarios
are not fully understood and, therefore, there is the need to investigate about them. To this aim,
the state of a transportation network [3], where conventional vehicles coexist with flying cars,
has to be simulated. In particular, interactions and decision processes not taken into account in
WOA 2020: Workshop “From Objects to Agents”, September 14–16, 2020, Bologna, Italy
" marianadia.postorino@unibo.it (M. N. Postorino); francescoalessandro.sarne@polimi.it (F. A. Sarné);
sarne@unirc.it (G. M. L. Sarné)
~ https://www.unibo.it/sitoweb/marianadia.postorino/ (M. N. Postorino);
https://www.unirc.it/scheda_persona.php?id=50234 (G. M. L. Sarné)
0000-0002-6609-4818 (M. N. Postorino); 0000-0003-3753-6020 (G. M. L. Sarné)
© 2020 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073
CEUR Workshop Proceedings (CEUR-WS.org)
2
Maria Nadia Postorino et al. 2–14
usual traffic simulations (e.g., interactions of flying cars with other vehicles and obstacles or
criteria adopted to choose of moving in aerial or ground modality) have to be considered for
evaluating their effects in UAM scenarios.
Intelligent software agent technology (from here on only agent) has been extensively applied
to simulate and manage different aspects, at different level of detail, of a wide variety of
transportation systems [4, 5, 6, 7]. In transportation systems, agents can play different roles
(e.g., travelers, vehicles, signals, etc.). Many studies have explored the opportunity of taking
advantage from the autonomous, adaptive, learning, pro-active and social abilities of agents [8]
as well as their capabilities to work in large, centralized or distributed contexts also in presence
of uncertainty or dynamic behaviors [9, 10].
Such agent features well fit with the need to simulate autonomous vehicles, their motion
on transportation networks and their choice processes. Therefore, the agent technology has
been adopted also to implement an UAM simulator by associating an agent with each moving
flying or ground vehicle that, similarly to Connected Automated Vehicles (CAVs), has been
assumed to be fully automated. By using this UAM simulator, we want to investigate on the
potential advantages, in terms of travel times, deriving by the possible, future realization of
UAM scenarios but without to simulate other aspects which depend from laws and regulations
that at the moment are not defined. To this aim, some test transportation networks of different
size have been considered, in order to have comparable scenarios. In fact, it is expected that
UAM scenarios in existing urban contexts of different size, to which transportation networks
refer, will be affected by the urban features, such as location of spaces for landing and take-off,
urban structure, building height and specific vertical obstacles among the others, which will
result in specific requirements for each tested real network. Then, to avoid specific-feature
effects and provide appropriate comparisons, in this study modular test transportation networks
have been used, which are based on the aggregation of suitable, unitary modules and refer to the
same urban features. The preliminary campaign of experiments carried out on these modular
transportation networks of different size has allowed to calibrate the agent-based simulator,
including agents’ behaviors.
To compare UAM scenarios, the index called “Travel Time Advantage” (TTA) has been
introduced, which is the ratio between travel times computed when both ground and flying
mobility are allowed on the examined transportation network and travel times computed when
only ground mobility is admitted.
The paper is organized as follows. In Section 2 some of the main characteristics of flying cars
and some scenarios are presented. In Section 3 the agent-based UAM model is described and in
Section 4 the UAM agent-based simulator is presented and discussed. Section 5 some related
work are described and, finally, in Section 6 some conclusions are drawn.
2. Urban Air Mobility (UAM)
In this Section the main features characterizing (prototype) flying cars and UAM scenarios will
be shortly introduced.
The main flying car characteristics can be summarized in:
1. Vehicle architecture. Shape and size of vehicles must be compatible with both flying
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Maria Nadia Postorino et al. 2–14
(e.g., aerodynamic) and land (e.g., road lanes width, take-off, landing and parking spaces)
constraints [11]. Vehicles mainly differ for take-off and landing (TOL) operations, which
can be Conventional (CTOL) or Vertical (VTOL). In urban contexts, VTOL vehicles are
expected to be preferred to CTOL ones for the smaller TOL spaces required and the higher
maneuverability [12].
2. Operability. Different aspects can influence the vehicle operability [13, 14, 15, 16], which
is usually defined in terms of:
a) Range - the maximum flight distance, measured on the ground, traveled for the
maximum fuel/charge capacity;
b) Endurance - the maximum flight time with respect to the maximum fuel/charge
capacity;
c) Speed - with respect to both “on-the-road” and “in-flight” modalities.
3. Vertical position and main flight rules. The vertical position of flying objects in low level
space may be identified by the following vertical distances, namely:
a) Height - measured from the Above Ground Level (AGL);
b) Altitude - measured from the Mean Sea Level (MSL);
Flying conditions [17] currently operating are:
a) Visual Flight Rules (VFR), for Visual Meteorological Conditions (VMC), permitted
until 3000 𝑓 𝑡 from the ground or sea level;
b) Instrument Flight Rules (IFR), applying to Instrument Meteorological Conditions
(IMC) [18].
Flying car VFR conditions are expected to be realized at a Very Low Level (VLL) airspace,
i.e. 0 − 500 𝑓 𝑡 AGL, where cabin pressure plant does not need.
4. Automation level. Flying-cars can have different automation/autonomy and flight assis-
tance degrees, depending on the on-board driving systems [19, 20] and communication
features, e.g. FANET [21]. Note that autonomous vehicles are expected to be driverless
and fully automated (e.g., they monitor the environment around them to adapt their
positions/behaviors).
The main expected UAM scenarios are:
I) Point-to-point services between origin/destination prefixed points (i.e., from/to rele-
vant places to/from suitable collecting areas), by certified transportation operators with
authorized flight plans;
II) Long/medium-distance trips, with flying mode for the longer legs and ground mode
within cities, and with take-off and landing areas on external or dedicated transition roads
completely separated from ground mode operations;
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Maria Nadia Postorino et al. 2–14
Figure 1: Case C: Short/medium-distance trips within cities.
III) Short/medium-distance trips (Figure 1), where flying cars can move both between city
pairs and almost everywhere within cities, although TOL operations happen only at
dedicated areas linked to roads for only ground mode.
The agent-based simulator has been designed for the latter one, which includes the main
features of the other two cases.
3. The Agent-based UAM Model
In this Section we describe the within-city trip scenarios (case III in Section 2) for which the
agent-based UAM simulator (see Section 4) has been designed. Simulation results have been
evaluated based on the travel times required to move between origin/destination pairs. Moreover,
agents simulate flying cars assumed to be electric and autonomous (coherently with expectations
for the still-in-progress CAVs models) to i) keep separations in the three dimensions, right
trajectories and altitude (according to meteorological conditions) and ii) exchange data to be
processed on-board to avoid collisions or wake turbulence effects.
The interactions among i) flying cars, ii) flying and ground cars and iii) flying cars and ground
obstacles (e.g., buildings, cables) have been considered. Security issues have not been explicitly
simulated, while safety aspects have been considered in terms of suitable distances kept from
each type of obstacle, including other moving objects. Interactions among flying and ground
cars within the city have been allowed only at pre-fixed “transition areas” (TAs) (i) placed where
the urban structure is sufficiently dispersed, at no less than 𝑑𝑚𝑖𝑛 from each other transition
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Maria Nadia Postorino et al. 2–14
area and (ii) sufficient to provide safe conditions for entering/leaving the ground transportation
network also along the TAs [22]. Moreover, flights have been allowed only along prefixed, safe
routes. Other issues concerning rules and prescriptions for security reasons are behind the
focus of this research.
To obtain realistic simulations, we assumed that: (i) flying cars keep safe distances with
ground obstacles and other flying cars; (ii) TOL transition areas are suitably connected to
the ground transportation network; (iii) flying mode can be chosen to move between TAs by
maintaining a height suitably greater than the highest building or ground obstacle.
In detail, the agent framework has been specified as follows:
1. Vehicles are homogeneous for characteristics and equipment and each vehicle 𝑖 is associ-
ated with an agent 𝐴𝑖 .
2. For each couple (𝐴𝑖 , 𝐴𝑗 ) of agents:
a) In ground mode, each 𝐴𝑖 follows 𝐴𝑗 at a minimum distance 𝑑𝑖𝑗 = 𝑡 · 𝑣𝑔𝑖 + 𝑠𝑖𝑏 , where:
𝑡 = 1 𝑠𝑒𝑐 is the time to start braking; 𝑣𝑔𝑖 is the ground speed of 𝐴𝑖 ; 𝑠𝑖𝑏 is the braking
space at a constant deceleration.
b) In flying mode, the vertical position of 𝐴𝑖 , flying over 𝐴𝑗 , is ℎ𝑖 = 𝑑0 + 𝑛 · 𝑠𝑧 , where:
𝑑0 is the minimum height to overfly urban areas; 𝑛 is the number of agents under 𝐴𝑖
on the 𝑧 axis; 𝑠𝑧 is the minimum vertical separation between agents. Note that more
vehicles can use the same horizontal route but at different heights.
c) In flying mode, ∀𝐴𝑖 that follows 𝐴𝑗 on the same horizontal route, their minimum gap
𝑠𝑥 is constant.
d) Transition from ground/flying to flying/ground mode happens at dedicated TAs based
on a booked and confirmed time slot authorization; the time slot depends on the
estimated arrival/leaving time at the TA depending on ground and flying traffic condi-
tions.
3. For a given origin/destination (𝑂/𝐷) pair [23] the following conditions hold:
a) The flying leg of a trip follows the Euclidean route. If the Euclidean distance of a trip
is greater than 𝑑𝑚𝑖𝑛 it will take place by combining ground and aerial links, otherwise
it will be only on ground mode.
b) For each 𝑂/𝐷 trip, the minimum travel time path is computed as 𝑡(𝑓𝑔 ) = 𝑙𝑔 /𝑣𝑔 +𝑙𝑓 /𝑣𝑓 .
The ground speed 𝑣𝑔 is empirically computed for urban roads as 𝑣𝑔 = 𝑐1 − 𝑐2 · 𝑓𝑔 ,
where 𝑓𝑔 is the traffic volume (i.e., the number of agents) on the ground link at a
given time, 𝑐1 = 37.5 and 𝑐2 = 8.5 · 10−6 (for 𝑣𝑔 measured in 𝐾𝑚/ℎ) are coefficients
empirically computed for averaged road features (e.g., width, slope, etc.), 𝑙𝑔 , 𝑙𝑓 and
𝑣𝑓 are respectively the length of the ground link, the length of the aerial link and the
speed on the aerial link. Note that, congestion effects have been assumed to be caused
only by ground traffic flows [24].
c) Agents are autonomous in their choices, although coordinated by a central Agency
to/from which they send/receive information about their position, those of other agents
and obstacles in their neighboring and about the status of the transportation network.
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Maria Nadia Postorino et al. 2–14
Combined ground and aerial trips will start only after agents receive information by
the Agency, in order to avoid congestion effects at the transition areas
d) All the agents adopt the same TOL procedures.
To compare UAM scenarios at increasing network size, the index “Travel Time Advantage”
(TTA) is computed as the ratio between the total “flying-ground” travel time and the total
ground travel time for “only ground” mode to move between an 𝑂/𝐷 pair over all the agents
and O/D pairs, has been considered:
∑︀ 𝐺+𝐹
𝑖 𝑇 𝑂/𝐷, 𝑖
𝑇 𝑇 𝐴 = ∑︀ 𝐺
(1)
𝑖 𝑇 𝑂/𝐷, 𝑖
where, for each 𝐴𝑖 , (i) 𝑇𝑂/𝐷,
𝐺+𝐹
𝑖 is its travel time in ground+flying mode and (ii) 𝑇𝑂/𝐷, 𝑖 is its
𝐺
travel time in only-ground mode.
4. The Agent-based UAM Simulator
This Section describes the agent-based simulator designed to implement the UAM model pre-
sented in Section 3 and the preliminary results obtained. This simulator has been written in
C++ by expanding the one developed for simulating the ground mobility, exploited in [25, 26],
and it is not equipped with a graphical user interface.
More in detail, each agent represents a vehicle and it is an object implemented by a specific
class. All the vehicles (i.e., agents) can move on both ground and flying modalities and are
assumed to be provided with homogeneous features (a reasonable assumption because it is
expected that they will have standardized features and equipment).
Agents can autonomously decide in which modality to move among O/D pairs on the basis
of the information that they mutually exchange with the other agents and with the Agency,
that acts as a Traffic Controller. Information are exchanged by messages having a simplified
JADE-like structure and implemented as objects of a dedicate class. In particular, each message
stores information about (i) sender, (ii) receiver, (iii) type of the content (e.g., Information, Route,
Action) and (iv) content (e.g., O/D pair, route, ground/flight modality flag, coordinates on the
three axes, speed, action required).
As specified in Section 1, the aim of this simulator is to investigate UAM advantages in
terms of travel times, with respect to transportation networks of different sizes. Because of
real urban transportation networks have evolved without considering UAM features (e.g., TOL
spaces for flying-cars, which should be based on standards for commercial flying cars that are
still undefined), the simulation results could not be completely comparable among them if the
simulator is applied to real contexts of different size. As introduced in Section 1, network size
plays a role in the assessment of potential benefits coming from UAM contexts. Therefore,
without loss of generality, we exploited artificial, modular transportation networks based on a
specific transportation network module object. In this way, all the transportation networks are
intended to share the same urban design, such as building heights and position, road features,
and with particular attention to the location of TAs for flying-cars transition from/to flying
mode to/from ground mode.
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Maria Nadia Postorino et al. 2–14
L
5
V1
4
TA
3
V2
2
V3 l
1
1 2 3 4 5
Figure 2: Baseline transportation network module (green lines represents real connections, red line
represent virtual connections with trip origin/destination red nodes 𝑉 , 𝑇 𝐴 is the transition area of the
module).
In particular, the basic transportation network module consists of a square mesh grid (see
Figure 2) of dimensions 𝐿 × 𝐿 formed by 5 × 5 nodes and two-way road links of equal capacity
and length 𝑙 = 𝐿/4. TOL procedures at the transition area 𝑇 𝐴 (represented by the yellow
circle in Figure 2) are maintained distinct for ground traffic flows entering to/exiting from it.
More in detail, each combined (i.e., ground + flying) trip: i) starts from an origin (o) node 𝑉𝑜 ;
ii) reaches the transition area 𝑇 𝐴𝑜 in ground mode; iii) takes-off from the transition area 𝑇 𝐴𝑜
and lands at the destination (d) transition area 𝑇 𝐴𝑑 in flying mode; iv) reaches in ground mode
the destination node 𝑉𝑑 where the trip ends.
The UAM simulator has been applied on two test transportation networks formed by 2 × 2
and 3 × 3 modules and by setting the length of each module to 𝐿=1600 𝑚. Moreover, the
𝑂/𝐷 trip demand has been simulated by adopting an average value of 250 vehicles/h. For each
O/D pair, the demand for time intervals of 5 minutes has been generated by using a variation
coefficient set to 0.4. The minimum flight height has been set to 50 𝑚1 , by assuming a maximum
building height of 30 𝑚. Based on the aerial link length and height, the cruise flying speed
varies in the range 80 ÷ 120 𝐾𝑚/ℎ. In principle, departure times at a transition area depend
on i) the expected ground travel time to reach the transition area from an origin node and ii)
the queue at the transition area. To avoid or minimize waiting times at the transition area (i.e.,
queues for departures and arrivals), the Agency will inform each agent (i.e., vehicle) about the
estimated times:
i) to reach, in ground mode, the transition area from an origin node by considering the current
number of agents on the path;
ii) to fly between two transition areas by considering take-off and landing procedures, cruise
1
Note that the adoption of a lower minimum flight height requires the assumption of additional conditions and
hypothesis on the vehicle equipment, the air traffic control and the urban design.
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Maria Nadia Postorino et al. 2–14
Table 1
TTA results for the tested networks of 3 x 3 and 4 x 4 modules
Scenario 3x3 4x4
𝑆0 (𝑂/𝐷 baseline) 0.38 0.35
𝑆1 (10% 𝑂/𝐷 increase) 0.45 0.39
𝑆2 (20% 𝑂/𝐷 increase) 0.66 0.57
𝑆3 (30% 𝑂/𝐷 increase) 0.73 0.65
speed and the time spent until a free slot is available, which depends on the current number
of agent on that route.
The structure of the transportation test network is coherent with conventional city organiza-
tion where only few areas could be available for transition processes, mainly for safety reasons
and urban obstacles. Moreover, we assumed that the aerial network is considered virtually not
congested because on the same route there is the opportunity of using more lanes, separated
from each other by 5 𝑚 in height (see Section 3). Note that, for short trips the travel time of
only-ground paths could be less than the one of combined ground + flying paths.
Agent moves on the transportation links according to a minimum travel time path crite-
rion [27]. The link travel times are continuously updated by considering the number of agents
that are on the links (see Section 3, point b). At transition areas, the maximum acceleration and
speed in ground modality have been set respectively to 2.5 𝑚/𝑠𝑒𝑐2 and 100 𝑘𝑚/ℎ [28].
The reference (i.e., baseline) transportation UAM scenario is 𝑆0 , with baseline 𝑂/𝐷 trip
demand level and only-ground mode. For the other scenarios, the 𝑂/𝐷 trip demand has been
increased by 10%, 20% and 30% with respect to 𝑆0 . For the 𝑆0 scenario, the value of 𝑇 𝑇 𝐴 is
1, while enabling also the flying modality the obtained results are shown in Table 1. As it can
be seen, the higher the level of demand, the more the link traffic flows increase that, in turn,
causes travel times increase according to a congested network approach2 . Finally, given that
not all individual trip origins and destinations can be reached in a ground mode, and not all
the trips are suitable for flying legs, flying and ground modes have to co-exist. However, when
ground traffic increases then travel times generally increase and the times to reach transition
areas to travel in aerial mode could not be more convenient than using only ground links.
5. Related Work
Decision processes underlying planning and management activities require the knowledge of
the state of a system under different conditions and constraints, which can be obtained by using
simulation tools to test hypotheses and architectures [3]. To this aim, the agent technology
is widely adopted for its advantages, particularly the opportunity of providing agents with
different degrees of intelligence, autonomy, learning, adaptive, time-persistent and pro-active
capabilities [4, 6]. In the transportation field, agent-based simulations are mainly carried out at
2
Agent’s path choices change according to link travel times, which in turn depend on agents on the link, thus
producing a traffic flow distribution on the network [29, 30].
9
Maria Nadia Postorino et al. 2–14
a microscopic level [31], but there exist also a significant amount of both macroscopic (usually
less competitive in terms of design and use of computational/storage resources) and mesoscopic
(combining micro and macro aspects) agent-based tools for simulations [32, 33].
Agents have been exploited to study almost all the different aspects involved in usual trans-
portation systems like, among the others, network management [34], transit [35], car-sharing
and car-pooling [36, 37], vehicle emissions [38, 39], pedestrian mobility [40], flight recommender
[41]. However, given the overwhelming body of researches presented in the literature and the
impossibility to provide the interested readers with a comprehensive summary, they could refer
to the many existing survey as [4, 42, 43, 44]
In the latter years, an increasing number of research dealt with different aspects involved in
UAM and, also in this case, agent-based simulation have been widely exploited to study the
opportunities offered by this new promising type of mobility [45]. For instance, high-dense
traffic UAM scenarios have been considered in [46, 47] by adopting several scheduling horizons,
in [48] airspace integration approaches have been investigated on air vehicle separation issues
and in [49] autonomous vehicles, driven by an algorithm with collision avoidance capability,
have been simulated on three free-flight scenarios. Other studies have simulated an UAM service
on the Sioux Falls area to evaluate several parameter sets and contexts in [50] or by analyzing
three case studies to identify possible constraints for UAM services on the basis of mission types
or environments in [51].
Finally, communications play an important role for automated/autonomous vehicles and
software agents are frequently adopted to simulate communication architecture, routing pro-
tocols and the coverage range of the ground infrastructure in complex urban environments.
For ground and flying vehicles, Vehicular and Flying Ad hoc Networks (i.e., VANET [52] and
FANET [53]) have been respectively proposed to improve the safety of vehicles and prevent
collision accidents. In particular, [54] highlights as usual Air Traffic Control (ATC) systems, in
presence of high UAM traffic levels and complex urban environments, might fail in monitoring
and supporting the vehicle safety and this requires that vehicles should be provided with high
levels of autonomous driving.
6. Conclusions
This paper presented an agent-based simulator designed to simulate UAM by considering vehicle
interactions (when they are in ground and aerial modality), transition processes, security and
air traffic control issues. It allows to evaluate the benefits deriving from UAM, with the desired
level of detail, on simulated transportation networks, by means of the value of TTA measure.
Forthcoming researches will test this simulator on different UAM contexts represented by
transportation networks of different size and with different demand levels also to evaluate the
potential advantage given by UAM with respect to the demand level, flight distance and location
of transition nodes. However, note that current regulations do not admit private flights over the
city at low altitudes, except some specific, authorized cases and, therefore, before UAM becomes
a reality the whole regulatory framework should be changed/adapted to meet some specific
requirements.
Finally, further advancements will include the simulation of aerial congestion phenomenon,
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Maria Nadia Postorino et al. 2–14
the optimization of taking-off and landing processes under specific conditions and the effects
due to the location of transition nodes.
Acknowledgments
This study has been supported by the Network and Complex Systems (NeCS) Laboratory at
the University Mediterranea of Reggio Calabria, Department of Civil, Energy and Materials
Engineering (DICEAM).
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