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Review ArticleOperational Considerations regarding On-Demand Air
Mobility:A Literature Review and Research Challenges
Xiaoqian Sun ,1,2 Sebastian Wandelt ,1,2 Michael Husemann,3 and
Eike Stumpf3
1School of Electronic and Information Engineering, Beihang
University, 100191 Beijing, China2National Engineering Laboratory
for Multi-Modal Transportation Big Data, 100191 Beijing,
China3Institute of Aerospace Systems, RWTH Aachen University, 52062
Aachen, Germany
Correspondence should be addressed to Sebastian Wandelt;
[email protected]
Received 8 September 2019; Revised 25 November 2020; Accepted 22
January 2021; Published 5 February 2021
Academic Editor: Dongjoo Park
Copyright © 2021 Xiaoqian Sun et al. 0is is an open access
article distributed under the Creative Commons Attribution
License,which permits unrestricted use, distribution, and
reproduction in any medium, provided the original work is properly
cited.
0e idea and development of on-demand air mobility (ODAM)
services are revolutionizing our urban/regional
transportationsector by exploring the third dimension: vertical
airspace. 0e fundamental concept of on-demand air taxi operations
is not new,but advances in aircraft design and battery/engine
technology plus massive problems with congestion and increased
traveldemands around the world have recently led to a large number
of studies which aim to explore the potential benefits of
ODAM.Unfortunately, given the lack of an established, formal
problem definition, missing reference nomenclature for ODAM
research,and a multitude of publication venues, the research
development is not focused and, thus, does not tap the full
potential of theworkforce engaged in this topic. 0is study
synthesizes the recently published literature on operational
aspects of ODAM. Ourcontribution consists of two major parts. 0e
first part dissects previous studies and performs cross-comparison
of report results.We cover five main categories: demand estimation
methodology, infrastructure/port design/location problem,
operationalplanning problem, operational constraints’
identification, and competitiveness with other transportation
modes. 0e second partcomplements the report of aggregated findings
by proposing a list of challenges as a future agenda for ODAM
research. Mostimportantly, we see a need for a formal problem
definition of ODAM operational planning processes, standard open
datasets forcomparing multiple performance dimensions, and a
universal, multimodal transportation demand model.
1. Introduction
0e rise of megacities/agglomerations together with a tre-mendous
increase in travel demands leads to regulartransportation gridlocks
in many urban areas all over theworld, e.g., in New York, Beijing,
Sao Paolo, and Mumbai[1, 2]. Footage of congestions in these
regions is well known;operators and governments try to find
solutions towards abetter transportation future. 0e cost of
congestion alone inthe US is quantified with 305 billion USD for
the year 2016.Increasing the capacities further is not considered a
viableoption in many cities any longer [3].
0e vision of opening the third dimension, i.e., altitude,for
urban/regional transportation has gained substantialinterest in the
last 5–10 years, with significant efforts toexplore the so-called
on-demand air mobility (ODAM) [4].0e key idea behind ODAM is to use
eVTOL (electric
vertical takeoff and landing) vehicles for inter- and
intracitycommutes. 0ese small vehicles (usually anticipated to
fly1–4 people) are envisioned to provide line-by-sight flights
ataffordable prices, while providing a safe and enjoyable
flightexperience. 0eir ability to perform vertical
takeoff/landingenables them to be operated on small-size port
infrastruc-ture. A successful implementation of ODAM requires
notonly the development of new technologies, including
vehicledesign and advances in engine/propulsion technology, butalso
considerations of novel operational patterns, con-cerning its
on-demand characteristic. ODAM has the po-tential to radically
change our view of urban/regionalmobility and saves time in
people’s daily commutes as well ason regional thin-haul connections
[5, 6].
Industry and academia spend a large amount of re-sources on the
development of ODAM. Uber, for instance, ispioneering to push mass
adoption of VTOL services for
HindawiJournal of Advanced TransportationVolume 2021, Article ID
3591034, 20 pageshttps://doi.org/10.1155/2021/3591034
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urban air transportation. See [7] for a detailed analysis onVTOL
market feasibility across four dimensions: vehicle,infrastructure
and operations, rider experience, and eco-nomics. Uber plans to
launch the air taxi service in 2023,starting in Dallas, Texas, and
Los Angeles, California [8].Lilium, a German startup company [9],
has completed a testflight in 2017 and plans to provide a public
air taxi service forcity transportation in 2025. Kitty Hawk, a
flying-car startupcompany [10], also expects to bring air taxi
services topassengers in 2023. Partnerships with the two major
aircraftmanufacturer giants, Boeing and Airbus, further
acceleratethe progress towards urban air mobility. Airbus is
alsodeveloping its own air taxi City Airbus [11], which is
em-bedded in the EU-initiative Urban Air Mobility. Boeingrecently
acquired an aviation taxi developer, Aurora FlightSciences, which
is developing a passenger VTOL vehicle.RWTHAachenUniversity
develops a regional air taxi [12] incooperation with the University
of Applied Sciences,Aachen, and the expected entry to service of
the Silent AirTaxi is 2024. Future pandemics, such as COVID-19,
couldbecome particular drivers of on-demand (personal) airmobility
in the near future [13, 14]. While many companiesare racing to
build flying prototypes, the scientific literatureon ODAM becomes
fuzzy. 0ere is a rich body of researchfocusing on the technical
feasibility of ODAM, such asvehicle design or propulsion system,
with several recentreviews on ODAM vehicle design [15], current
technologyfor electronic VTOL drones [16], distributed
electronicpropulsion technology [17], and design of
next-generationurban air mobility vehicles. Research on the
operationalaspects of ODAM, however, is tremendously scattered,
withmany studies published in different venues, using
differentnomenclature, and investigating highly similar, yet
distinctresearch problems.
In order to fill in the gap in the operational ODAM (Notethat
our study subsumes urban air mobility (UAM), bydistinguishing urban
ODAM from regional ODAM.) liter-ature, this study makes a twofold
contribution. First, weprovide a comprehensive literature review on
the opera-tional aspects of ODAM. We intentionally leave out
bothtopics vehicle design and propulsion technology as there
areexcellent surveys on these two topics, see above. Our
reviewcovers five main categories: demand estimation methodol-ogy,
infrastructure/port design/location problem, opera-tional planning
problem, operational constraints’identification, and
competitiveness with other trans-portation modes.0e second part of
this study complementsthe report of aggregated findings by
proposing a list ofchallenges as a future agenda for ODAM research.
Onemajor challenge is to ensure reproducibility of previousODAM
studies. We argue that there is an urgent need fordefining and
agreeing upon concise research questions forODAM research,
accompanied by benchmark/referencedatasets and methodology. 0e wide
range of distinct resultson various regions in the world makes it
necessary to de-velop a universal, multimodal ODAM framework,
including
competition and cooperation with other modalities. Fur-thermore,
we see a lot of potential for studies includinguncertainties into
ODAM and regarding shared, profitableoperations.
0e remainder of this paper is organized as follows.Section 2
provides an overview of the state-of-the-art op-erational aspects
of ODAM, followed by an analysis section.Section 3 discusses future
research directions exclusively,and Section 4 concludes the
study.
2. State-of-the-Art OperationalAspects of ODAM
0is section provides an overview of the operational aspectsof
ODAM as reported in the literature. 0e overall goal is
tosummarize/compare commonalities and differences amongprevious
studies. Research on the operational aspects ofODAM, however, has
appeared in many different venues,using different nomenclature and
investigating highlysimilar, yet distinct research problems. 0is
section aims toderive a consistent classification and unified
terminology.Accordingly, we build a classification which is
inspired bytraditional transportation systems wherever
appropriate.Wecategorize the main results of research studies
according tofive major categories: demand estimation
methodology(Section 2.1), infrastructure/port design/location
problem(Section 2.2), operational planning problem (Section
2.3),operational constraints’ identification (Section 2.4),
andcompetitiveness with other transportation modes (Section2.5).
Section 2.6 summarizes the major conclusions obtainedin this
section and lays the foundations to identify oppor-tunities for the
operational aspects of ODAM in Section 3.
2.1. ODAM Demand Estimation Methodology. As a newemerging
transportation mode, demand is the fundamentaldriver for the
development of ODAM, and it is critical forthe market orientation
for ODAM operators at a strategiclevel. Table 1 provides an
overview of the ODAM demandestimation, including the region under
study, time frame,urban or regional scope, data usage and their
availability,and a short summary. 0e studies are sorted by
operationalscales (urban, regional, and urban + regional) and then
byregion.
Regarding urban ODAM, we review the work for theregion in Europe
and U. S. separately. Fu et al. [18] con-ducted a stated preference
survey with 248 respondents fromMunich, Germany, investigating the
mode choice behavioramong four transportation alternatives: public
trans-portation, private car, autonomous taxi, and autonomous
airtaxi. Results show that travel time, travel cost, and safety
arethe critical determinants for the adoption of
autonomoustransportation modes. Moreover, higher value of time
andhigher income also favor the use of urban air mobility. For
atypical European city, Decker et al. [19] used a substitutionrate
of 10% of car traffic by a personal air vehicle, assuming
2 Journal of Advanced Transportation
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Table 1: An overview of the ODAM demand estimation.
Ref. Region Time frame Scale Data type Dataavail. Short
summary
[18] Munich, Germany (248respondents)
Feb.–Apr.2018 (survey
time)U Stated preference survey No
Travel time, travel cost, and safety arethe most critical
determinants for the
adoption of autonomoustransportation modes. Moreover,
higher value of time and higher incomealso favor the use of
urban air mobility.
[19] European cities Future U Scenario assumption No
For a typical European city, asubstitution rate of 10% of car
traffic bya personal air vehicle is used, assumingthat daily car
traffic is approximately300,000. Several requirements are noteasy
to be met, and a broad range of
uncertainty remains.
[20] New York, U. S. Future U New York City taxi records No
Certain locations including largefacilities and smaller stops
for New
York City are suggested. 0epercentage of time savings and
willingness to fly did not impact thelocation decision
significantly, while
the on-road travel limit does.
[21]Northern California andWashington-Baltimore
Region, U. S.Future U
National household travelsurvey, LODES, ACS, and
StreetLight dataNo
0e demand is very sensitive to thepricing structure, and the
costs have tobe kept between $1 per passenger mileand $1.25 per
passenger mile to achievea potential market share of 0.5–4%,where a
4% market share represents
320,000 trips per day.
[22]
U. S. (2500 workers in 5cities: Atlanta, Boston,
Dallas, San Francisco, andLos Angeles
Apr.–Jun. 2018(survey time) U Stated preference survey No
No results from the survey wereprovided.
[23] U. S. (four focus groups) May 2017(survey time) U Stated
preference survey No
Only survey for one focus group (6participants) was conducted
with somedescriptive feedbacks. Results from twoonline platforms
(Amazon MechanicalTurk and Qualtrics) were compared,and Qualtrics
is recommended to use
in the future.
[24]
U. S. (1405 workers in 5cities: Atlanta, Boston,
Dallas, San Francisco, andLos Angeles
Mar.–May2019 (survey
time)U Stated preference survey No
0e survey settings are very similar to[22]. No results from the
survey were
provided.
[25] Germany 2030 R 50 OD pairs’ samples inGermany No
With the ODAM door-to-door travelspeeds of 80–200 km/h, a
willingness-to-pay value of 0.5–0.8€/km (monetaryvalue in 2015) for
the year 2030 isderived for the German market.
[26] U. S. (3091 counties) 1995–2030 R DB1B and OAG Partial
More than 600 million on-demandsmall aircraft trips would
compete withautomobiles in cost per passenger mile;such number of
operations would have
negative impacts on the nationalairspace system, such as
airport
congestion.
[27]A hypothetical network
with 5 nodes and 1–10 dailytrip demands
Future R Cirrus SR22 aircraft Yes
In order to maximize profit, expected57.7% of the daily demand
should be
served/accepted, and an averageacceptance rate of approx. 90%
mightstill allow for profitable operations.
Journal of Advanced Transportation 3
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that daily car traffic is approximately 300,000.0e total
rangefor this reference VTOL vehicle is 100 km, with maximumtwo
seats and cruise speed 150–200 km/h. It was reportedthat several
requirements are not easy to be met, and a broadrange of
uncertainty remains. With New York City in theU. S. as a case
study, Rajendran and Zack [20] estimated thepotential air taxi
demand based on a subset of the currentregular taxi demand, i.e.,
approximately 300 million indi-vidual yellow and green taxi ride
records from January 2014to December 2015. An air taxi service is
eligible only if thetravel time of the air taxi is at least 40%
shorter than that ofthe regular taxi, with the assumption that the
maximum airtaxi travel distance is 120 miles (193 km) and with
cruisespeed 170mph (273 km/h). With Northern California
andWashington-Baltimore region as case studies, Syed et al.
[21]proposed an integrated approach to estimate the ODAMdemand
using the C-logit model, taking into account so-cioeconomic
information, life cycle cost, fare structures,route constraints,
and landing site requirements. Prelimi-nary results show that the
demand is very sensitive to thepricing structure, and it is
estimated that the ODAM costshave to be kept between $1 per
passenger mile and $1.25 perpassenger mile to achieve a potential
market share of0.5–4%, where a 4% market share represents 320,000
tripsper day. A 4-passenger-seat aircraft is subject to the
as-sumption that an average of 2.4 passengers per aircraftwould
generate 133,000 flights per day, even without takingreposition
flights into consideration. Note that weatherconditions, public
acceptance of a fully autonomous flyingvehicle, and its
certification costs were not considered in thisstudy. In summary,
existing work often assumes ODAM
demand comes from traditional ground transportation, andamong
several uncertainty factors, travel time and travel costare the
most critical for the profitability of ODAMoperations.
Furthermore, stated preference surveys were designed toask
potential travelers in the U. S. which transportationmodes they
would take under hypothetical situations. Binderet al. [22]
presented details of a survey for estimatingcommuters’ willingness
to pay for eVTOL flights in urbanareas in the U. S., collecting
responses from approximately2,500 high-income workers with an
average one-waycommuting time of 45 minutes or more by the
individual.0ree typical modes (transit, car, and ride-share)
arecompared with eVTOLs, where travel cost by typical
groundtransportation modes ranges from $2.5 to $20, and the
travelcost by eVTOLs ranges from $5 to $45, depending on thetravel
distance. Maximum travel time is two hours for non-eVTOL modes.
Access/egress/waiting times, transfer, andride guarantee
availability of different modes are included.Personality, lifestyle
characteristics, sociodemographics, andsocioeconomic information of
the respondents are includedas well. Garrow et al. [23] used an
online survey to identifyODAM demand segments and quantify
willingness to payfor different ODAM alternatives; two online
platforms,Amazon Mechanical Turk and Qualtrics, were used toconduct
the surveys. Prior trip characteristics, traveleritinerary
characteristics, and sociodemographics were alsoconsidered. 0ey
also conducted a second survey with ap-proximately 100 questions
[24], and the survey settings werevery similar to [22]. Four
commuting modes, transit, tra-ditional car, self-driving car, and
piloted air taxi, are
Table 1: Continued.
Ref. Region Time frame Scale Data type Dataavail. Short
summary
[28] Germany Future U+R
Jeppesen airport databaseand census 2011 dataprovided by
German
Federal Statistical Office
No
A market share of 19% or 235 milliontrips are estimated for
Germany,
assuming passenger-specific costs of0.4€/km for ODAM services,
0.3€/km
for cars, and 0.32€/km for thecontemporary commercial CS-25
aircraft.
[29] Zurich, Switzerland Future U+R Simulation data No
Vehicle parameters (cruising speed,liftoff time, access time,
and price) havea substantial impact on demand and
turnover.
[30] U. S. 2030 U+ROAG, American travel
survey, DB1B, FAA airportdatabase, and BADA
No
0e annual county-to-county personround trips for very light
jets,
commercial airline, and automobile atone-year interval from 1995
to 2030
were predicted.
[31] Worldwide (4435 cities) 2042 U+R Forecast based on ADI 2012
No
26 potential markets were identified,regarding large population,
limitedurban mobility, economic city, and
high passenger demand.U� urban; R� regional.
4 Journal of Advanced Transportation
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compared. No results from the survey were provided. Notethat all
travel costs for these four modes in the survey wereassumed as $5.
We can observe that findings from statedpreference surveys are
rather constrained, and the impli-cations for ODAM operations are
limited.
Regarding regional ODAM, Kreimeier et al. [25] per-formed an
economical assessment of ODAM for the Germanmarket for the year
2030, with a door-to-door travel speed of80–200 km/h, and a
willingness-to-pay value of 0.5–0.8€/km(monetary value in 2015) is
derived. Smith et al. [26] ana-lyzed the potential impacts of
autonomous, electric on-demand small aircrafts on the national
airspace system for3,091 counties in the U. S. based on the
projected demand forthe year 2030 by Baik et al. [30]. It was found
that a largenumber of on-demand small aircraft trips would
competewith automobiles in cost per passenger mile; such number
ofoperations would have tremendous negative impacts on thenational
airspace system, especially congestion around air-ports. Using a
hypothetical network example with five nodesand 1–10 daily trip
demands, Mane and Crossley [27]evaluated the impact of accepting
different percentages ofpassenger-demanded trips and fleet size on
the potentialprofitability of ODAM services. It was shown that, in
orderto maximize profit, expected 57.7% of the daily demandshould
be served/accepted; and an average acceptance rate ofapproximately
90% might still allow for profitableoperations.
Regarding urban and regional ODAM, Kreimeier et al.[28]
estimated the potential market size of thin-haul ODAMservices in
Germany, with a comprehensive analysis of theentire German
population and their linear spatial distancesto feasible airfields.
It was shown that a market share of 19%or 235 million trips are
estimated for Germany, assumingpassenger-specific costs of 0.4€/km
for ODAM services,0.3€/km for cars, and 0.32€/km for the
contemporarycommercial CS-25 aircraft. Balac et al. [29] presented
amethodology for demand estimation of personal aerial ve-hicles in
urban settings, with the area of 30 km radius aroundthe city center
of Zurich, Switzerland, as a case study. 0eminimum number of
vehicles needed to serve all trips wasformulated as a minimum cost
network flow problem andsolved by a Gurobi Optimizer. It was shown
that the vehicleparameters (cruising speed, liftoff time, access
time, andprice) have a substantial impact on the ODAM demand
andturnover. However, in order to reduce computationalburden, only
10% of the population in the area of 30 kmradius circle around the
Zurich city center was sampled. Itwas assumed that vehicles travel
in a straight line, withoutconsidering the constraints of
infrastructure (landing plat-forms or parking locations); the
capacity of each vehicle wasset to be one passenger. Baik et al.
[30] presented a modellingframework to predict the annual
county-to-county personround trips for the air taxi, commercial
airline, and auto-mobile at one-year intervals from 1995 to 2030.
Based on atraditional gravity model to estimate ODAM demand
andforecasting the air passenger demand between 4,435 set-tlements
worldwide for the year 2042, Becker et al. [31]identified 26
interurban potential markets. However, themaximum range of ODAM was
limited to 300 km.
In summary, we can observe that most studies focus onthe U. S.,
while a few focus on Germany and Switzerland, aswell as one for the
worldwide market. 0ere is no commonagreement on which market (urban
or regional or both) ismore profitable for the ODAM operators.
Often, there arefundamentally different parameter settings and
differentassumptions on flight missions depending heavily on
thecase study under consideration. Significant uncertainties
anddiscrepancies in the demand estimation have been identified,and
these study results cannot be compared in a consistentway. 0ere is
no significant amount of data available fromthe previous studies in
order to compare and understand theresults and their implications
better.
2.2. ODAMInfrastructure/Port Design and Location Problem.In the
last few years, several terminologies for ODAM in-frastructures
have been proposed, such as vertiport, skyport,skypark, and
airpark. In the following, we do refer to originalterminologies,
whenever applicable. Table 2 provides anoverview of the ODAM
infrastructure/port design and lo-cation problem, including the
region under study, vehicletype, size of the problem, data usage,
data availability, and ashort summary. 0e studies are sorted by
vehicle types(STOL, VTOL, and eVTOL) and then by region. We
discusseach of these studies in detail in the following.
Potential locations of airparks for the vehicle type STOLin two
U. S. cities, Miami and South Florida, have beenrecently studied.
Based on the FAA Obstacle Data Team’sdatabase, Robinson et al. [32]
estimated the geodensity ofairparks suitable for short takeoff and
landing ODAM op-erations. Four individual airpark construction
types wereanalyzed: vacant land construction, barge
construction,additive construction, and pre-existing airport
incorpora-tion. Preliminary results for the Miami metropolitan
areashow that an average airpark geodensity of 1.66 airparks
persquare mile can be achieved with a 300-foot long runway.Somers
et al. [33] investigated the impact of the performanceof the short
takeoff and landing vehicle (ground roll andclimb/descent gradient)
on the airpark geodensity in theSouth Florida region, taking into
account the constraints ofobstacles and weather conditions. 0e
Light Detection andRanging (LIDAR) data were used to detect the
obstaclessurrounding vacant parcels, and weather data (wind
speedsand wind directions) were taken from the National Oceanicand
Atmospheric Administration. It was shown that, inSouth Florida,
doublet runways are needed which make itcomplex to find suitable
parcels due to poor weather con-ditions, and the climb/descent
gradient has to be greaterthan six degrees.
For the vehicle type VTOL, capacity envelopes of po-tential
vertiports in the U. S. have been analyzed as well.With New York
City, U. S., as the case study, Rajendran andZack [20] proposed an
iterative constrained clustering al-gorithm with the multimodal
transportation warm-starttechnique to identify potential locations
for ODAM. 0enumber of locations varies from 10 to 85. Certain
locationsincluding large facilities and smaller stops for New York
Cityare suggested. Surprisingly, they found that the percentage
of
Journal of Advanced Transportation 5
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time savings and willingness to fly did not impact the lo-cation
decision significantly, whereas the on-road travellimit (the
maximum time for passengers spent on the road)does. Vascik and
Hansman [34] used an integer program-ming approach to derive
vertiport capacity envelopes andassess the sensitivity of ODAM
vehicle throughput to ver-tiport topology variations and
operational parameters. Fourclasses of topologies were included:
linear topology, satellitetopology, pier topology, and remote apron
topology. 0isapproach was applied to 156 different vertiports and
146different operational parameter settings. Results show thatthe
ratio of gates to touchdown and liftoff pads at a vertiportis a key
design factor. Moreover, the construction of vehiclestaging stands
is recommended. Finally, vertiports withmultiple touchdown and
liftoff pads which allow fully
independent operations or simultaneous arrivals and de-partures
would be beneficial as well. Based on Seoul Met-ropolitan Areas as
the case study, Lim and Hwang [35]selected the number of vertiports
for three major routesbased on the K-means clustering algorithm
over the com-muting data for the cases of 10–36 vertiports. Note
that nooptimization strategy was used in the selection process of
thevertiport location.
For the vehicle type eVTOL, the determination of op-timal
locations of vertiports in a given region can bemodeledas a variant
of standard hub location problems (HLPs): howto collect, transfer,
and distribute travel demands betweenorigin nodes and destination
nodes in a network, see recentreviews on network design in [39–42].
Rath and Chow [36]formulated the air taxi skyport location problem
as an
Table 2: An overview of the ODAM port design and location
problem.
Ref. Region Vehicletypes Problem size Data typeDataavail. Short
summary
[32] Miami, U. S. STOL 15,666 potentialpark locationsFAA
Obstacle DataTeam’s database No
0e Miami metropolitan area showed that anaverage airpark
geodensity of 1.66 airparks persquare mile can be achieved with a
300-foot
long runway.
[33] South Florida,U. S. STOLUp to 10,310
parcels
U. S. Light Detectionand Ranging data and
NOAAPartially
Doublet runways are needed to find suitableparcels in South
Florida due to poor weatherconditions; and the climb/descent
gradient has
to be greater than six degrees.
[20] New York, U. S. VTOL 10–85 sites Taxi records No
Certain locations including large facilities andsmaller stops
for New York City are suggested.0e percentage of time savings and
willingnessto fly did not impact the location
decisionsignificantly, while the on-road travel limit
does.
[34] U. S. VTOL 156 vertiports Simulation data No
0e ratio of gates to touchdown and liftoffpads at a vertiport is
a key design factor;vehicle staging stands are recommended;
vertiports with multiple touchdown and liftoffpads which allow
fully independent operationsor simultaneous arrivals and departures
would
be beneficial as well.
[35] Seoul, SouthKorea VTOL10–36
vertiports, 3routes
Commuting data inSeoul, Incheon, and
GyunggiYes
0e number of vertiports was based on the K-means clustering
algorithm, based on the
commuting data for three major routes in thecase study of
Seoul.
[36] New York, U. S. eVTOL 10 skyportsTaxi and Limousine
Commission records forJan. 2018
Yes
When the number of skyports p> 6, there isnot much variation
in the incoming demand;an even higher value (p> 9) is required
toachieve at least 10% market penetration.
[37] San Francisco,U. S. eVTOL 1–8 vertiportsCensus tract data
andpopulation and income
dataNo
0e objective function is to maximize thepackage demand served,
with limits on thenumber of vertiports. Increasing the time
threshold for the allowable driving time fromthe vertiport to
the customer would result in awider geographical distribution of
vertiports.
[38]San Francisco
and Los Angeles,U. S.
eVTOL 10, 20, and 40vertiportsCensus data (LODES
and ACS) No
0e objective function is to maximize thecumulative time saved in
commuting
throughout the network. Most appealing tripsare relatively
short-range trips in San
Francisco; the strongest growth is in thecentral area for Los
Angeles.
STOL� short takeoff and landing, VTOL� vertical takeoff and
landing, and eVOTL� electric vertical takeoff and landing.
6 Journal of Advanced Transportation
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uncapacitated single allocation p-hub median problem (thenumber
of hubs is p). 0e objective function is to minimizethe travel time
for each OD pair in the skyport network, withthe consideration of
air taxi transfer time and congestionfactor on the ground. Gurobi
was used to solve this problemwith up to 10 skyports for travel
demands among 144 taxizones and 3 airports. Experimental results
for New York Cityshow that when the number of skyports p> 6,
there is notmuch variation in the incoming demand; an even
highervalue (p> 9) is required to achieve at least 10%
marketpenetration. With San Francisco Bay area as the case
study,German et al. [37] examined the use of eVTOL vehicles
forsmall package delivery. 0e optimal location of the
cargovertiports was formulated as an optimization
problem:maximizing the package demand served, with limits on
thenumber of vertiports. However, only 1–8 vertiports weresolved
for this optimization. Results show that increasing thetime
threshold for the allowable driving time from thevertiport to the
customer would result in a wider geo-graphical distribution of
vertiports. 0e vertiport locationproblem was further extended by
Daskilewicz et al. [38],taking into account household population
density as well aswork locations. With San Francisco and Los
Angeles as twocase studies, the new objective function is to
maximize thecumulative time saved in the network, with three
differentnetwork sizes: 10, 20, and 40 vertiports. It was found
thatmost appealing trips are relatively short-range trips in
SanFrancisco; the strongest growth in eVTOL use is in thecentral
area for Los Angeles. Moreover, Wawrzyniak et al.[43] analyzed the
ODAM network based on complex net-work theory using real-world
cargo aircraft movement datawithin the U. S. Central Command area
of responsibility inthe year 2006.
In summary, similar with the case of demand estimation,most
previous studies cover the U. S., except one study forSeoul, South
Korea [35]. 0erefore, the universal applica-bility of these results
to other regions in the world is verylimited since different
metropolitan areas have distinctsocio-economic-demographic
characteristics. 0e vehicletype covers STOL, VTOL, and eVTOL, among
which themajority of studies in the literature focuses on VTOL.
0eproblem size for the ODAM infrastructure design and lo-cation
problem is rather limited in the literature because ofthe
computational complexity.0ere are some studies whichcompute optimal
solutions for very small size of theproblem, for instance, less
than 10 skyports/vertiports[36, 37], while a few studies consider
more than 10,000potential locations [32, 33] using heuristics and
rules ofthumb to obtain the solutions. Tradeoffs between the level
ofaccuracy and the computational complexity need to befurther
explored. Moreover, there is no well-defined refer-ence benchmark
dataset as well as solution techniques forthe ODAM infrastructure
location problem; therefore, it isdifficult to reproduce the
results and perform comparativeanalysis and further extensions.
2.3. ODAM Operational Planning Problem. 0is sectionsummarizes
the literature on operational planning ofODAM. Table 3 provides an
overview of the ODAM
operational planning problem, including the region understudy,
vehicle type, size of the problem, mathematical for-mulation,
solution technique, and a short summary. 0estudies are sorted by
vehicle types (general vehicles, small jet,very light jet, and
eVTOL) and then by region.
Traditionally, there are three horizons in the airlineplanning
process for scheduled air transportation: long-termstrategic
planning, midterm tactical planning, and opera-tional planning
[54]. Strategic planning involves customerdemand forecasting,
market segmentation, and brandingand marketing strategies; tactical
planning addresses flightscheduling (which determines the optimal
daily schedule offlight operations) and fleet assignment (where an
aircrafttype is assigned to each flight in the schedule);
operationalplanning deals with aircraft scheduling (which decides
asequence of flight legs to an individual aircraft, and it is
oftendecomposed into aircraft routing and tail assignment) andcrew
scheduling (the pairing of crew resources according tothe flight
schedules). 0e traditional aircraft schedulingproblem has always
been the main focus of the airline in-dustry since fuel
consumption, crew salaries, and aircraft-related cost typically
represent the largest expense for anairline. For large commercial
airlines, this problem is usuallycombined with flight scheduling
[55, 56], fleet assignment[57], and crew scheduling [58]. A wide
range of research hasbeen proposed to tackle this problem such as
the classicflight string model [59].
0e business model of ODAM shares large similaritieswith existing
on-demand aviation services, especially frac-tional management
companies. Among on-demand aviationservices, there are several
types of programs: fractionownership, timeshare, and joint
ownership [60]. Whilefraction ownership allows different
stakeholders to useaircraft resources for a fraction of time at
different levels,much research has been done for the scheduling of
thefractional operation business model [61–64]. See [65] for
anexcellent review on the vehicle routing problem with pickupand
delivery, with four areas of applications, the dial-a-rideproblem,
the urban courier service problem, the dial-a-flightproblem, and
the emergency vehicle dispatch problem, fo-cusing on the problem
formulation and solution algorithms.
Few studies investigate the operation planning problemfor
general vehicles. Van der Zwan et al. [44] adopted the setpartition
model [61] and provided a detailed description ofthe aircraft
routing problem for per-aircraft air taxi operator.Contrary to the
column generation solution methodologyapplied in [61], they used a
k-shortest path algorithm togenerate feasible routing pools for the
set partition. With anumber of 19–25 air taxis for 225 airports
over the 72-hourtime horizon, CPLEXwas applied to solve the
set-partitioningmodel. However, only virtual test environment and
virtual airtaxi operator data were used due to the lack of real air
taxioperational data. Kohlman and Patterson [45] presented anurban
air mobility network model for rapid simulation,consisting of four
parts: network definition and missionmodel, vertiport model,
vehicle model, and demand model.0e concurrent on-demand vehicle
design and vehicle lo-cation problem has been investigated as well.
In order to solveconcurrent aircraft design and operation planning
problems,
Journal of Advanced Transportation 7
-
Table 3: An overview of the ODAM operational planning
problem.
Ref. Region Vehicletype Problem sizeMathematicalformulation
Solution technique Short summary
[44] Fly Aeolus,Belgium General
225 airports, 19–25vehicles, 7–15 flightdaily requests, and
15closest trips for a route
Set-partitioningmodel
Exact solution withCPLEX
0e developed aircraft routingsystem consisted of model
creator,CPLEX, and the solution module.0e number of closest trips
that areappended to a trip to a route is 3–15,and this parameter
has a large effect
on the feasible routes and thecomputational time.
[45] Hypothetical General N/A N/A N/A
An urban air mobility networkmodel for rapid simulation was
presented, consisting of four parts:network definition and
missionmodel, vertiport model, vehiclemodel, and demand model.
[46] Hypothetical General 10 airports Integer program Exact
solution withCPLEX
Concurrent aircraft design andoperation planning problems
werecombined together; its integerprogramming model
entailedrevenue-generating trips, non-revenue-generating trips,
and
charter flights.
[47] FlySmart,Norway Small jet12 airports, 10 vehicles,and 200
daily bookings
Dial-a-flightproblem model
Heuristics(insertion + local
search)
0e required air taxi fleet to handleapproximately 100
bookingrequests is close to 15, andconfirming the pickup times
immediately to the customers canprovide as good solutions as
postponing the confirmation thenight before.
[48] Etirc Aviation,EuropeVery
light jet10 airports, 38 routes,and 270 weekly flights
Dial-a-rideproblem model
Dynamicprogramming
An average of 5.5% cost advantageis achieved on the cost of
emptylegs; the addition of a two-hour timewindow in operations
increases thecustomer acceptance rate with
2–3%.
[49] NorthCarolina, U. S.Very
light jet
5 airports, 10 routes,and 50–250 weeklypassengers per route
Discrete-eventmodel and flow
modelGradient algorithm
An application of these two modelson air taxi pricing showed
that theflow model could be used for a
pricing application.
[50] U. S. Verylight jet12–75 vehicles (test
scenario) N/A N/A
A fast-time simulation tool wasdeveloped to understand
thecomplex dynamics of air taxi
networks.
[51] Hypothetical eVTOL 3 vertiports, 20 vehicles,and 6
routes
Mixed-integerquadraticprogram
Gurobi solver (planto use)
0ree variants of basic formulationmodels for different types
of
ODAM services were presented.However, these models were not
being implemented, and nonumerical experiment results were
provided.
[52] Hypothetical eVTOL N/A N/A N/A
An open-source multiagenttransportation simulationframework was
developed,
considering the effects of variationsin vehicles as well as
infrastructure
locations.
8 Journal of Advanced Transportation
-
Mane and Crossley [46] combined aircraft allocation andaircraft
design together; its integer programming modelentailed
revenue-generating trips, non-revenue-generatingtrips, and charter
flights. Due to the model complexity, aninstance of 10 city nodes
was tested through direct CPLEXimplementation.
For the small jet vehicle type, Fagerholt et al. [47]presented a
simulation methodology to support strategicdecisions for an air
taxi company in Norway (FlySmart),including fleet size, price
differentiation strategies, andbooking confirmation policies. A
heuristic with insertionand local search was used to optimally
distribute waitingtime along a single aircraft schedule. A fleet of
ten aircraftswith approximately 200 daily bookings and 1–4
passengersfor each booking were used as case studies.
For the very light jet vehicle type, de Jong [48] modeledthe air
taxi dispatch problem (which is defined as a time-scheduled trip
being part of a single aircraft route) as a dial-a-ride problem
based on a case study of ten airports, 38routes, and 270 flight
requests per week, representing the airtaxi network of Etirc
Aviation (a company which operatespersonalized business flights in
Europe and Russia). Oneselected case study showed that an average
of 5.5% costadvantage is achieved on the cost of empty legs, and
theaddition of a two-hour time window in operations increasesthe
customer acceptance rate by 2–3%. Note that thestandard dial-a-ride
problem assumes a homogeneous fleetwith a fixed size. A
single-provider air taxi service model wasused to characterize the
week-to-week flow of passengersand the aircraft [49] for the case
of 5 airports, 10 routes, and50–250 weekly passengers per route
[66]. Results show thatthe flow model could be used for a pricing
application.Bonnefoy [50] built a fast-time simulation tool to
under-stand the complex dynamics of air taxi networks,
includingdemand modelling with the gravity model, trip
generationbased on Monte Carlo simulation, aircraft routing, and
pilotassignment, as well as unscheduled maintenance events.
For the eVTOL vehicle type, based on the simulatedODAM market
demand, Shihab et al. [51] formulated threebasic models of
scheduling types, fleet dispatching, and fleetplanning, with the
goal to maximize profit. 0ree types ofservices were compared as
well: on-demand service,scheduled service, and amix of both
services. However, thesemodels were not implemented, and no
numerical experi-ment results were provided. Rothfeld et al. [52]
presented anopen-source multiagent transportation simulation
frame-work, MATSim, with the extension of considering the effectsof
variations in personal aerial vehicles as well as dedicated
infrastructure locations. Kleinbekman et al. [53] proposed
asequencing and scheduling algorithm for ODAM arrivals,and
experiment results showed that their algorithm can besolved within
79 seconds for up to 40 incoming vehicles. Anovel concept of
airborne trajectory management for highODAM traffic densities in a
congested urban area has beenproposed as well [67].
In summary, we can observe that there are a few studiesconducted
from the air taxi operator’s point of view, in-cluding a Belgian
air taxi company Fly Aeolus [44], aNorwegian air taxi company
FlySmart [47], and the Lux-embourg-based air taxi company Etirc
Aviation [48], whilemost studies used hypothetical network
operators. Very lightjet and electronic vertical takeoff and
landing (eVTOL)vehicles are the chosen types. 0e size of the
operationalplanning problem is very limited, including the number
ofvehicles, the number of airports (3 vertiports [51] and 5airports
[49]), and the number of served routes (10 routes[49]). 0e scale of
the problem in existing research is farfrom the envisioned
operational scale of ODAM, with thepredicted trip demand in a
magnitude of million [26, 28].Furthermore, different mathematical
models have beenformulated with distinctive solution techniques;
for verysmall-scale problems, the exact solution with CPLEX
wasoften used [44, 46, 53], while heuristics are frequently used
tosolve medium-size problems [47]. Similar with the case ofthe ODAM
infrastructure/port design and location problem,there are no
well-defined reference benchmark datasets aswell as solution
techniques for the ODAM operationalplanning problem, and thus, it
is extremely difficult to re-produce and understand the results
from existing work.
2.4. ODAM Operational Constraints’ Identification.Although there
is a large body of research focusing on theassessment of the
technical feasibility of ODAM, operationalconstraints during the
implementation process of theODAM network at large scale are the
bottleneck forachieving market success. Table 4 provides an
overview ofidentified operational constraints for ODAM, including
theregion under study, vehicle type, fleet size of ODAM ve-hicles,
operational scale (urban vs. regional), data usage andtheir
availability, and a short summary. 0e studies aresorted by vehicle
types (VTOL, personal air vehicle, andgeneral vehicles) and then by
region.
For the VTOL vehicle type, Rothfeld et al. [68] analyzedthe
operation performance of a potential ODAM imple-mentation with the
artificial Sioux Falls Network using a
Table 3: Continued.
Ref. Region Vehicletype Problem sizeMathematicalformulation
Solution technique Short summary
[53] Hypothetical eVTOL 10–40 vehicles Mixed-integerlinear
programExact solution with
CPLEX
A sequencing and schedulingalgorithm for ODAM arrivals
wasproposed, and it can be solvedwithin 79 seconds for up to 40
incoming vehicles.eVOTL� electric vertical takeoff and
landing.
Journal of Advanced Transportation 9
-
multiagent simulation tool, MATSim [52]. Varying pa-rameters
have been tested: vehicle cruise speed (50–450 km/h), vehicle
takeoff and landing speed (5–20m/s), ground-based process time
(0.5–20min), passenger capacity (1–12),fleet size (50–300), and the
number of vertiports (4–10).Simulation results show that the
adoption of ODAM isstrongly influenced by the potential travel time
reduction.However, only homogeneous vehicles are simulated, and
theprice of using ODAM is assumed to be three times the
carprice.
For personal air vehicles, Liu et al. [69] provided anoverview
of research activities with the focus on the U. S. andEurope. It
was found that, despite dramatic technologyinnovation, several
challenges still remain in the ultimateapplication of personal air
vehicles, especially safety, in-frastructure availability, and
public acceptance.
Most studies on the identification of the ODAM oper-ational
constraints are for general vehicles. With the LosAngeles Basin as
the case study, Vascik and Hansman [70]identified several critical
operational constraints for ODAMservices: noise and public
acceptance, accessibility of takeoffand landing sites, interaction
with air traffic control, ground
infrastructure, and flight density. 0ey also discussed
po-tential mitigation strategies against the first three key
op-erational constraints. 0e authors further investigated
threemajor cities in the U. S. (Los Angeles, Boston, and
Dallas)[71]. With 32 reference missions within these three cities,
itwas found that the three most stringent constraints are
noiseacceptance, availability of takeoff and landing sites,
andscalability of air traffic control.
0e routes and procedures for urban air mobility basedon the
existing helicopter routes have been investigated aswell. With
Dallas-Fort Worth area as the case study, Vermaet al. [72]
investigated routes and procedures for urban airmobility based on
the existing helicopter routes along withdifferent communication
procedures. 0ree different levelsof traffic were evaluated: low
(115 flights), medium (167flights), and high (225 flights). Field
test data showed thathelicopter routes and communication procedures
on thecurrent day can support near-term urban air mobility
op-erations, but may not be scalable due to extra workload forair
traffic controllers.
Moreover, the integration of the urban air mobility withthe city
transportation has been studied recently.
Table 4: An overview of the ODAM operational constraints’
identification.
Ref. Region Vehicletype Fleet size Scale Data typeDataavail.
Short summary
[68] Artificial Sioux FallsNetwork VTOL 50–300 U Simulation data
No0e adoption of ODAM is strongly influenced
by the potential travel time reduction.
[69] U. S. and Europe Personal airvehicle N/AU
and R N/A N/A
It was found that, despite the dramatictechnology innovation,
several challenges stillremain in the ultimate application of
personalair vehicles, especially safety, infrastructure
availability, and public acceptance.
[70] Los Angeles General N/A U FAA 5010database, etc. No
Identified several critical operationalconstraints for ODAM
services: noise and
public acceptance, accessibility of takeoff andlanding areas,
interaction with air trafficcontrol, ground infrastructure, and
flight
density; potential mitigation strategies againstthe first three
key operational constraints were
discussed.
[71]3 cities in the U. S.
(Los Angeles,Boston, and Dallas)
General N/A UCensus data,
consumer wealthdata, etc.
No
With 32 reference missions within these threecities, it was
found that the three most
stringent constraints are noise acceptance,availability of
takeoff and landing areas, and
scalability of air traffic control.
[72] Dallas-Fort Wortharea General115–225flights U
Field experiment/test data No
It was found that helicopter routes andcommunication procedures
in the current daycan support near-term urban air mobility
operations, but may not be scalable.
[73] General General N/A U N/A N/A
Several relevant factors regarding theintegration of ODAM to the
urban city
transportation environment were discussed,such as ownership
structure, adaptability of
schedules, and demand drivers.
[74] Munich, Germany General N/A U N/A N/A
Preliminary results for the case study ofMunich show that rents
in all city centers
increase, while the household’s average utilitydecreases.
U� urban; R� regional.
10 Journal of Advanced Transportation
-
Straubinger and Raoul [73] discussed several relevant
factorsregarding the integration of urban air mobility to the
citytransportation environment, such as ownership structure(private
vs. rental), adaptability of schedules (scheduled vs.on-demand),
and demand drivers (job vs. housing). Fur-thermore, the authors
developed an urban spatial com-putable general equilibrium model to
assess the welfarechanges for the urban air mobility integration
[74]. Pre-liminary results for the case study of Munich show that
rentsin all city centers increase, while the household’s
averageutility decreases.
In summary, there seems to be a consensus that thepublic
acceptance, infrastructure availability, and scalabilityof ODAM
operations are critical operational constraints.However, different
problem definitions have been consid-ered in the literature, for
instance, personal air vehiclesindicating the ownership of the
vehicle itself [69] and urbanair mobility being part of the city
transportation system[73, 74], inducing different operational
concepts and busi-ness models. Furthermore, the projects in the
field of ODAMresearch have not been commonly planned or
coordinated,which makes the accessibility to the in-depth findings
andanalysis results difficult.
2.5. Competitiveness of ODAM with Existing TransportationModes.
As a new emerging transportation mode, ODAMhas the potential to
relieve the grid-locked ground trans-portation in megacities by
leveraging the third spatial di-mension. Table 5 provides an
overview of thecompetitiveness of ODAM compared with existing
trans-portation modes, including the region under study,
oper-ational scale (urban vs. regional), data usage and
theiravailability, competition modes, evaluation criteria, and
ashort summary. 0e studies are sorted by scale (urban andurban +
regional) and then by region.
Most studies focus on urban ODAM; stated preferencesurveys were
often used to identify the mode choice behavioramong different
transportation modes. Fu et al. [18] con-ducted a stated preference
survey with 248 respondents inMunich, Germany, regarding the mode
choice behavioramong four transportation alternatives: public
trans-portation, private car, autonomous taxi, and autonomous
airtaxi. Five different criteria were applied: total travel
time,total travel cost, safety, inconvenience, and
multitaskingpossibility. Results show that the first three are the
mostcritical determinants for the adoption of
autonomoustransportation modes. Moreover, higher value of time
andhigher income also favor the use of urban air mobility.Moreover,
there are some studies on the U. S. regions.Binder et al. [22]
presented details of a survey for estimatingcommuters’ willingness
to pay for eVTOL in 5 cities,Atlanta, Boston, Dallas, San
Francisco, and Los Angeles inthe U. S., collecting responses from
2,500 high-incomeworkers whose average one-way commuting time is
45minutes or more by the individual. Four transportationmodes are
compared, transit, traditional personal car, self-driving car, and
piloted air taxi, regarding multiple criteria:travel time, travel
cost, access/egress/waiting times, transfer,
and ride guarantee availability of different modes.
Fur-thermore, Garrow et al. [24] conducted a second surveyamong
1,405 respondents in the same five cities in the U. S.as in [22],
and the survey settings with approximately 100questions are very
similar to [22], so as the same fortransportation mode alternatives
and evaluation criteria.However, no results from both surveys
mentioned abovewere provided.
Travel time, specifically door-to-door travel time, is themost
critical factor for the competitiveness of differenttransportation
modes. 0ere are two studies regarding thecompetitiveness of ODAM:
one for the U. S. and the otherone for Europe. Wei et al. [75]
proposed a methodology toanalyze the door-to-door commuting time of
short takeoffand landing vehicles, compared with personal cars. A
total of4,529 different commutes between 1,035 unique censustracts
in South Florida were used as the case study. Asensitivity analysis
of the door-to-door travel time againstthe vehicles’ takeoff
distance, cruise speed, ground handlingtime, climb rate, and cruise
altitude was performed. It wasfound that takeoff distance and
cruise speed are the twomostimportant parameters for the benefits
of ODAM. Reducingthe takeoff distance is important in order to be
able to find asufficient number of vertiport locations. For South
Florida,ODAM operations should target for ground commutesmore than
45min, and the vehicles should be able to take offfrom runways of
500 ft, with cruising speeds over 160 kts.Note that commutes
shorter than 30min are filtered outbecause of lack of incentives to
switch to an ODAM service.
For the scale of urban and regional ODAM, Sun et al.[76]
designed and implemented a door-to-door travel timeestimation
framework to analyze the potential competi-tiveness of ODAM when
competing with existing trans-portationmodes (car, railway, and
aircraft), and 29 countriesin Europe were used as case studies.
Results show that whilethe major competitor for ODAM in Europe is
railway, thecompetitiveness of air taxi service, however, largely
dependson the region. 0ese 29 European countries have a
largepotential to benefit from air taxi service up to 450 km
be-cause the periphery of Europe has the largest potential forODAM
services.
Although travel time is appealing for the adoption ofODAM,
travel cost is also one decisive factor for thecompetitiveness of
ODAM. Uber estimated that the price forpersonal air vehicles is at
the level of Uber Black or opti-mistically as low as its cheapest
service UberX price [77].Regarding the potential market size of
thin-haul ODAMservices in Germany, Kreimeier et al. [28] estimated
amarketshare of 19% or 235 million ODAM trips, with the as-sumption
that passenger-specific costs will be 0.4€/km forODAM services,
0.3€/km for cars, and 0.32€/km for con-temporary commercial CS-25
aircrafts. With NorthernCalifornia and Washington-Baltimore region,
U. S., as twocase studies, Syed et al. [21] estimated that the ODAM
costshave to be kept between $1 per pax mile and $1.25 per paxmile
to achieve a potential market share of 0.5–4%, where a4% market
share represents 320,000 trips per day. ODAM isused for different
range classes, and this alone leads toseemingly inconsistent
results (e.g., urban air mobility $2 per
Journal of Advanced Transportation 11
-
pax km and regional air mobility less than $0.65 per pax km).See
[78] for vehicle configurations up to 100 km, as well asthe Silent
Air Taxi and Lilium for ranges beyond 100 km.
In summary, it is instrumental to compare the com-petitiveness
with existing transportation modes upon theintroduction of the ODAM
in order to predict the mode
choice behavior of travelers more accurately and align themarket
position for the ODAM service more realistically inthe future. From
the existing literature, we can observe thatin comparison with
traditional transportation modes, onemajor advantage of ODAM is its
travel time, both for thecase of the U. S. and Europe. However,
travel costs estimated
Table 5: An overview of the competitiveness of ODAM with
existing transportation modes.
Ref. Region Scale Data type Dataavail. Competitive modes
Evaluation criteria Short summary
[18] Munich,Germany UStated preference
survey No
Public transport,private car,
autonomous taxi,and autonomous
air taxi
Total travel time, totaltravel cost,
inconvenience, safety,and multitasking
possibility
Travel time, travel cost, andsafety are the most critical
determinants for theadoption of autonomoustransportation
modes.
Moreover, higher value oftime and higher incomealso favor the
use of urban
air mobility.
[22]
U. S. (2500workers in 5cities: Atlanta,Boston, Dallas,San
Francisco,
and Los Angeles)
U Stated preferencesurvey NoTransit, traditionalpersonal car,
andride-share car
Travel time, travel cost,access/egress/waiting
times, transfer, and rideguarantee availability of
different modes
No results from the surveywere provided.
[24]
U. S. (1405workers in 5cities: Atlanta,Boston, Dallas,San
Francisco,
and Los Angeles)
U Stated preferencesurvey NoTransit, traditionalpersonal car,
andself-driving car
Travel time, travel cost,access/egress/waiting
times, transfer, and rideguarantee availability of
different modes
No results from the surveywere provided.
[75] South Florida,U. S. U
Censustransportation
planning products of2000 (survey data)
No Personal car Door-to-door traveltime
Takeoff distance and cruisespeed are the two mostimportant
parameters forthe benefits of ODAM. Forthe South Florida area,
ODAM operations shouldtarget for ground
commutes more than45min, and the vehiclesshould be able to
takeofffrom runways of 500 ft,with cruising speeds over
160 kts.
[76] 29 countries inEurope U+R
OpenStreetMap,openAIP, and
gridded populationof the world
YesConventional
aircraft, rail, andpersonal car
Door-to-door traveltime
0e major competitor forODAM in Europe israilway, and the
competitiveness of air taxiservice largely depends onthe region.
Furthermore,
these 29 Europeancountries have a large
potential to benefit fromair taxi service up to
450 km, and the peripheryof Europe has the largestpotential for
ODAM
services.U� urban; R� regional.
12 Journal of Advanced Transportation
-
for ODAM with different range classes (urban vs. regional)seem
completely inconsistent; also, market shares werederived
incomparably. 0ese inconsistencies make it ex-tremely difficult to
judge the profitability of ODAM oper-ations. 0e unavailability of
the large amount of data makesthe comparative analysis and
assessment even more difficultand less reliable.
2.6. Discussion. We have reviewed recent literature on
op-erational aspects of ODAM, covering five major categories:demand
estimation methodology, infrastructure/port de-sign/location
problem, operational planning problem, op-erational constraints’
identification, and competitivenesswith other transportation modes.
Research on the opera-tional aspects of ODAM is widely dispersed
and published indifferent scientific venues, using different
nomenclature andinvestigating highly similar, yet distinct research
problems.0e major findings of our survey are summarized in
thefollowing.
2.6.1. Lack of Formal Problem Definition. While small air-crafts
have been used and operated privately for businesspurposes (general
aviation) for decades, the idea of a broadand ubiquitous air taxi
network as a mobility service hasbeen promoted, especially since
the emergence of new de-sign concepts and propulsion technologies.
With this hype,many recent research projects aim at analyzing and
opti-mizing a part of air taxi flight operations, however,
withoutdefining a fully established and standardized
nomenclaturefor research projects first. For instance, some studies
con-sider the design and planning of ODAM flight operationsfrom the
perspective of typical routing and planningproblems and therefore
adopt the corresponding vocabularyfrom standard planning problems
of ground traffic (e.g., see[20]). In other studies, on the
contrary, assumptions arebased on already known general aviation
procedures, whichis why terms and definitions from this area are
used (e.g., see[44]). Furthermore, the aircrafts used for such
flight servicesare sometimes referred to as “air taxis” (e.g., see
[27]), butelsewhere also as “personal air vehicles” (e.g., see
[5]),suggesting ownership by the passenger himself, and thus
acompletely different operational concept. 0is lack ofestablished
phraseology, the misuse of language, and, aboveall, the lack of a
real, uniform problem definition often leadto confusion and make it
considerably more difficult tounderstand the current state of air
taxi research, let alone torecognize similarities and
differences.
2.6.2. Coverage and Inconsistency. Analogous to the non-uniform
formulation of the actual research problem, ODAMliterature makes
different assumptions regarding flightmissions, especially
depending on the case study (i.e., initialconditions of the area of
investigation) and individualproject focus. An important
distinction is, for example, theintended use and the associated
range capacity of the ODAMaircraft. 0is fundamental difference in
initial mission as-sumptions alone seems to lead to inconsistent
study results
in terms of operating costs per passenger kilometer, a
keymeasure for comparing study results. In studies with
urbanapplications (“urban air mobility”), flight costs of
approx.2$/pax/km were calculated; for regional air mobility with
aflight distance of up to 500 km, however, less than 0.65$/Pax/km
were calculated (see [28]). In addition, despite manyserious
efforts of recent ODAM studies to estimate accuratedemand in
several regions of the world, we have identified acollection of
significant uncertainties and discrepanciesbetween the results of
these studies. For example, Kreimeieret al. [28] estimated a market
share of 19% or 235 milliontrips per year for the potential market
size of ODAM thin-haul services in Germany, assuming
passenger-specific costsof 0.4€/km for ODAM services, 0.3€/km for
cars, and 0.32€/km for modern commercial CS-25 aircrafts. In
contrast,Decker et al. [19] used a substitution rate of 10% of car
trafficby ODAM for a typical, average European city
instead,assuming that daily car traffic is around 300,000 trips.
Syedet al. [21] estimated that ODAM costs have to be kept be-tween
$1 per pax mile and $1.25 per pax mile to reach apotential market
share of 0.5–4%, with a market share of 4%representing 320,000
trips per day for Northern Californiaand the Washington-Baltimore
region in the U. S. as casestudies. As particular projects aim at
completely differentstudy areas with heterogeneous characteristics,
it is im-portant for the comparison of the study results to have a
listof initial study parameters. As mentioned before,
individualassumptions of the passenger and range capacity of the
studyvehicles result in different substitute transport capacities
andthus diverging information regarding a possible transportdemand.
In addition to technical parameters, standardizedboundary
conditions must also take into account opera-tional factors such as
mission time, mission start and endlocation, possible connections,
pricing systems, and acces-sibility when setting the mission
studies.
2.6.3. Applicability of Research Results. As mentioned be-fore,
many research projects base their studies on a specificstudy area
(urban or regional areas), adopt its specificcharacteristics as
input values for parameter studies, andalign the ODAM network
accordingly. However, mostprevious ODAM research projects focused
on large U. S.metropolitan areas (e.g., Los Angeles as in [22] or
[70] or SanFrancisco as in [37]) so that the universal
applicability ofthese results is very limited. Different
metropolitan areas allover the world with their demographic,
geographical, ortopographical characteristics, different demand
patterns forair taxi services, and air traffic management systems
facedifferent operational challenges. A systematic,
comparativeanalysis of hypothetical ODAM operations in
representativeregions of the world, which highlights essential
differences,is therefore lacking.
2.6.4. Optimality vs. Scalability. Analogous to
conventionalproblems with the design and optimization of
networks,ODAM optimization is inherently difficult. With
increasingscenario size and additional parameters as the input,
thecomputational complexity and thus the computation time
Journal of Advanced Transportation 13
-
also increase significantly so that large computer clusters
arerequired. 0is often results in a compromise between thelevel of
detail/accuracy and handling/calculation time.Hence, previous
studies can generally be assigned to twogroups:
(a) Studies that calculate optimal solutions/assignmentsfor very
small study areas with a small fleet (3 routes[35]) and only a few
requirements, for instance, lessthan 10
vertiports/skyports/airports [36, 37, 46, 51].0e level of detail
and significance is to be regardedas low, but the calculation time
is comparativelyshort.
(b) Studies that consider many heuristics and carry outrules of
thumb for large input problems, for example,more than 10,000
potential locations [32, 33]. 0isincreases the significance; thus,
the study results canbe classified as comparatively reliable.
Handling, onthe contrary, is very demanding and requires
suffi-cient computational power and time.
A particular problem here is the fact that there is almostno
favorable, useful transition or overlap between the twotypes of
studies as they are often carried out and published atdifferent
institutions with different intentions, e.g., in op-erational
research or air traffic management, consideringdifferent
publication criteria.
3. Future Research Directions
As our literature review shows, there are enormous effortsmade
to understand the potential of ODAM and to use it torevolutionize
the way we think about transportation. Nev-ertheless, based on the
above results, we see a whole set of linesfor future research on
the operational aspects of ODAM.0ese proposals for research are
discussed in the following.
3.1. Formulation of a Common Lexicon and Adequate ODAMResearch
Problems. Due to the comparably young field ofresearch, ODAM
research problems are loosely defined,especially in comparison to
well-established counterparts inground traffic and commercial
(scheduled) air trans-portation. 0is makes it rather challenging to
qualitativelycapture and evaluate the contributions of specific
studies,let alone compare studies and their results on a larger
scale.0erefore, we believe that future ODAM projects will
benefitsignificantly from a number of specially aligned
ODAMresearch problems. Established studies can inspire some ofthese
problems, but the focus must be on key differences
andpeculiarities. In light of the studies reviewed as part of
thissurvey, we propose to use the basic terminology ODAM
taxi(covering air taxi, personal air vehicle, etc.) and ODAM
port(covering vertiport, skyport, and airfield). Moreover,
wepropose the following ODAM operational research prob-lems; while
these problems interact with each other, they arelisted in a
chronological order.
3.1.1. ODAM Demand Estimation. Estimate the demandpotential for
ODAM operations in a given region. Tradi-tional models, such as
gravity or radiation model, are good
for estimating the large demands. Accordingly, out of thebox,
these models have limited applicability since ODAMoperations cannot
be assumed to fully capture such esti-mates. ODAM, by design, is
targeting special trips, oftenwith lower demand (premium
passengers, urgent trans-portation requests, or those without any
reasonable trans-portation alternatives). Accordingly, there is a
need fordemand estimation including (a) uncertainty, (b) presenceof
existing transportation infrastructure, and (c) temporalinformation
(time of day).
3.1.2. ODAM Port Location Problem. Optimize the locationsof ODAM
ports in a given region. 0is problem has to relyon accurate
estimations from the previous stage (ODAMdemand estimation). 0e
optimization function for theODAM port location problem cannot
simply be trans-portation time or cost. Instead, this is naturally
multi-objective: it needs to further include ground access time
forpassengers, environmental costs, and other societal costs.
3.1.3. ODAM Scheduling. Design schedules for ODAMtaxis. 0ree
types of problems are conceivable: first, a simplemodel where all
ODAM taxis are grounded until there is atransportation request. In
this case, there is no special needfor scheduling between ODAM
ports, second, a modelwhere all ODAM taxis are airborne in
anticipation of triprequests (as induced by ODAM demand estimation)
inorder to reduce air access time and maximize vehicle in-usetime,
and third, a model combining both previous modelsinto a general
one.
3.1.4. ODAM Dispatching. Assign actual trips to ODAMtaxis. 0is
case is specifically interesting for ODAMscheduling operations,
where some ODAM taxis aregrounded and others are in flight.
Choosing an appropriateODAM vehicle (or, in an extreme case,
rejecting thetransportation request) involves decisions regarding
ODAMtaxi capabilities, estimated profit, passenger perception,
andmore.
3.1.5. ODAM Routing. Assign actual routes to ODAM taxis.0is
includes trajectory planning and collision avoidance, allunder
safety considerations and within operational limits.0is stage has
potential interactions with ODAM dis-patching, in case of
in-flight, on-demand requests, whichlead to changes in planned
trajectories.
Future research studies should carefully distinguishODAM
operations from traditional airline scheduling. Forexample, in
scheduled air transportation, flight rotationsconsist mainly of
revenue-generating trips between airports,whereas commercial
airlines often analyze aircraft sched-uling and other related
optimization issues one or moremonths in advance. 0e ODAM service
features door-to-door travel patterns, resulting in much more
dynamic andcomplex operations. In addition, nonrevenue trips due
torepositioning flights must also be taken into account inplanning
to meet dynamic demand, an event that is more
14 Journal of Advanced Transportation
-
likely to be avoided in conventional scheduled air
transport.Since the demand mechanisms for ODAM services are
oftenshort term, less predictable, and less reproducible,
theexisting conventional air transportation planning process
forODAM services must be adapted [44], taking into accountdifferent
variants of delay [79] and disruptions [80].Moreover, most studies
in the literature so far focus onODAM taxis with space for only one
passenger. 0e first realbusiness model directly linked to ODAM
operation istherefore likely to be a single-seater model, covering
thetransport needs of a single person. As a direct extension,
avariant is conceivable in which more than one passengershare an
ODAM taxi for a flight on the same route and thushead for the same
departure and destination airfield/station.0e problem with such
operations, however, is that there is acompromise between the
passenger pickup and the demandsituation (on-demandness): in order
to maximize the loadfactor of an ODAM taxi, the operator is tempted
to wait forfurther requests, while additional waiting times let
thepassengers lose the benefits of on-demand operation.
0iscompromise and the possibility of rejecting
unprofitabletransport requests are largely unexplored in the
ODAMliterature and deserve further attention. In this context,
thebehavior, aspirations, and acceptance of passengers shouldalso
be examined in detail. Studies that formally define andverify such
standard problem formulations in trans-portation, e.g., in terms of
traffic assignment [81], facilitylocation [82], and location
routing [83], are among the mostfrequently cited papers in their
fields.
3.2. Creating Reference Benchmark Datasets for ODAM.When
formulating appropriate ODAM research problems,there is a need to
evaluate and compare required solutiontechniques, design decisions,
and heuristics. 0is impliesthat once such research problems are
clear and standardized,it must be possible to understand the state
of the art, which ismuch easier when the same terminology is used.
Otherresearch areas, for example, have benefited massively fromthe
establishment of reference datasets. For example, theexistence of
small-scale databases such as CAB [84] in hublocation research
[85], the equivalent of vertiport locationproblems in ODAM research
projects, led to fair perfor-mance evaluations among the exact
solution techniques andis considered the indispensable gold
standard for a wholerange of hub location problem types. Although
the networkconsists of only 25 nodes, it has advanced this area
further asits optimal solutions are well known to the community
andeasily accessible at all times. Over time, the desire to
solveeven larger problems has led to the development of addi-tional
standard datasets of increasing size, e.g., Turkishpostal system
dataset [86] and Australian postal dataset [87].
A starting point for ODAM datasets is to build onexisting ground
transport datasets, which usually containinformation about
locations (often in zones) as well asdistances and requirements
between these locations. Forexample, Transportation Networks for
Research [88]maintains a collection of such datasets for several
regions ofthe world. Table 6 summarizes the network data for
some
selected datasets. It becomes evident that these networkscover a
broad spectrum of regions and scales. Together withpublicly
available reference contracts and jointly agreedbenchmark measures
(gap, execution time, loss of profit,etc.), we expect that such and
other benchmark datasets,such as [89], will further shift the
boundaries of successfulODAM research.
Regarding the creation of reference datasets, approachesin the
literature on simulated data should be combined withreal-world
data. We propose Guangzhou, China, as an ex-cellent candidate for
such reference benchmarks. Guangz-hou is the capital of Guangdong
Province in southern China.First of all, Guangzhou is at the heart
of the Pearl Rivermetropolitan area, one of the largest urban
agglomerationsin the world, with an urban population of
approximately 15million in the year 2018. While Guangzhou has a
rather well-established public transportation infrastructure, with
bus/subway/taxi networks and the third largest airport in
China,yet, induced by its tremendous travel demands, it
facesregular grid locks along its highways, i.e., there is a
strongneed for novel transportation solutions. 0e government
ofGuangzhou and China are pushing towards modern tech-nologies,
which make the implementations/simulations ofODAM taxis much easier
than in conservative regions, suchas, for instance, Germany.
Moreover, EHang, the Chineseautonomous aerial vehicle company, has
tested its firstODAM taxi network in Guangzhou on August 9, 2019.
Withthis pilot program, EHang will help the Guangzhou gov-ernment
to set up a command-and-control center and tobuild up basic
infrastructures to support ODAM, and moreflight routes and ODAM
ports would be tested as well beforethe real commercial operations.
0is makes Guangzhou anexcellent testbed for preliminary scientific
studies and earlyprototypical implementation in practice.
3.3. Development of a Universal, Multimodal ODAM Trans-portation
Competition/Cooperation Model. Previous studieson ODAM services
preferably focus on a single urban/re-gional area and report on
specific results. Especially becauseregions around the world have
their own specifications, it isnot clear how universal the results
of such studies are andhow general conclusions could be transferred
to other re-gions. 0e aim should therefore be to derive a
universalmodel that, for example, provides predictions about
thecompetitiveness of ODAM services in the presence of aspecific
standardized transportation infrastructure. In thisway, general
findings can also be adapted to other regions.Also, the
understanding of the infrastructure as multimodalis decisive for
the interpretability and validity of the studies.In our review, we
found that most studies assume thatODAM services will compete with
only one mode, especiallywith cars. In reality, however, ODAM must
compete withthe sum of all transport options in a given study area,
whichincludes a well-developed public transport
infrastructure,novel means of transportation such as electric
scooters, andhigh-speed alternatives such as high-speed rail in the
re-gional case. Without taking into account all the facets of
theavailable modalities, the significance of the study results
is
Journal of Advanced Transportation 15
-
rather limited. While the results of such analysis are
highlycity-/region-specific, depending on its geography,
thereshould be the goal of producing universal conclusions thatcan
be easily transferred. Naturally, researchers have totarget a
tradeoff between being too specific and operationallytoo far from
being applicable. Clustering similar regions is astarting point for
this research.
One reason for limiting coverage in previous studies isthat
researchers often conduct studies in regions for whichdata are
available. However, given the recent progress inOpen/Linked Data,
there is an abundance of data. Moreimportantly, organizations,
researchers, and volunteersaround the world have made enormous
efforts to make dataavailable at the planetary level [90, 91]. A
few examples ofsuch datasets are shown in Table 7, covering a wide
range oftransport modes, routing services, and economic data.
Withthese data, it is possible to consistently compare the
potential
of ODAM services across the planet and identify com-monalities
and dissimilarities, with the ultimate target of auniversal
multimodal transportation model that makesODAM competition
accessible.
4. Conclusions
0e focus of our review is on the operational issues ofODAM. A
large number of studies have appeared in theliterature addressing
this topic; our survey concerns a total of100 references published
in recent years. While there areseveral other issues in ODAM, e.g.,
vehicle design or pro-pulsion system, there exist excellent reviews
in these areas,e.g., on vehicle design [15], current technology for
electronicVTOL drones [16], and propulsion technology [17].
Webelieve that our work on the operational issues in
ODAMcomplements these existing reviews, helps to advance the
Table 6: An overview of selected transportation networks for
ODAM research at
https://github.com/bstabler/TransportationNetworks.
Network Zones Links Nodes FolderAnaheim 38 914 416
TransportationNetworks/tree/master/AnaheimAustin 7388 18961 7388
TransportationNetworks/tree/master/AustinBarcelona 110 2522 1020
TransportationNetworks/tree/master/BarcelonaBerlin Center 865 28376
12981 TransportationNetworks/tree/master/Berlin-CenterBirmingham,
England 898 33937 14639
TransportationNetworks/tree/master/Birmingham-EnglandChicago-sketch
387 2950 933
TransportationNetworks/tree/master/Chicago-SketchChicago 1790 39018
12982 TransportationNetworks/tree/master/chicago-regionalEastern
Massachusetts 74 258 74
TransportationNetworks/tree/master/Eastern-MassachusettsGold Coast,
Australia 1068 11140 4807
TransportationNetworks/tree/master/GoldCoastHessen-asymmetric 245
6674 4660
TransportationNetworks/tree/master/Hessen-AsymmetricPhiladelphia
1525 40003 13389
TransportationNetworks/tree/master/PhiladelphiaSydney, Australia
3264 75379 33837
TransportationNetworks/tree/master/SydneyTerrassa-asymmetric 55
3264 1609
TransportationNetworks/tree/master/Terrassa-AsymmetricWinnipeg 147
2836 1052 TransportationNetworks/tree/master/Winnipeg
Table 7: An overview of global data sources for ODAM
research.
Data Organizations Links
Air transportation
Official Airline Guide (OAG) http://www.oag.com/International
Civil Aviation Organization (ICAO) http://www.icao.int/
Airport Council International http://www.airports.org/Sarbe
Airport Data Intelligence http://www.sabreairlinesolutions.com/
Open Flights http://openflights.org/Innovata Flight Schedules
http://www.innovata-llc.com/
Railway transportationInternational Union of Railways (UIC)
http://www.uic.org/
OpenRailwayMap https://www.openrailwaymap.org/Railway Directory
http://www.railwaydirectory.net/
Road transportation OpenStreetMap
https://www.openstreetmap.org/
Routing services
Open Source Routing Machine https://project-osrm.org/Google Maps
https://maps.Google.com/Baidu Maps https://map.baidu.comTransitland
https://transit.land/GTFS Router
https://atfutures.github.io/gtfs-router/
Population/economic data
Gridded Population of the World
https://sedac.ciesin.columbia.edu/data/collection/gpw-v4/World Bank
Open Data https://data.worldbank.org
UN World Population Prospects
https://population.un.org/wpp/International Monetary Fund
https://www.imf.org/en/data/Global Change Data Lab
https://global-change-data-lab.org/
16 Journal of Advanced Transportation
https://github.com/bstabler/TransportationNetworkshttp://www.oag.com/http://www.icao.int/http://www.airports.org/http://www.sabreairlinesolutions.com/http://openflights.org/http://www.innovata-llc.com/http://www.uic.org/https://www.openrailwaymap.org/http://www.railwaydirectory.net/https://www.openstreetmap.org/https://project-osrm.org/https://maps.Google.com/https://map.baidu.comhttps://transit.land/https://atfutures.github.io/gtfs-router/https://sedac.ciesin.columbia.edu/data/collection/gpw-v4/https://sedac.ciesin.columbia.edu/data/collection/gpw-v4/https://data.worldbank.orghttps://population.un.org/wpp/https://www.imf.org/en/data/https://global-change-data-lab.org/
-
understanding of ODAM, and paves the way towards itssuccessful
implementation.
Eventually, the three research challenges mentionedabove lead to
the overarching goal of research reproduc-ibility. With existing,
well-defined reference benchmarksand the availability of large
amounts of data, it would bepossible to reproduce and understand
results from previousstudies. A crucial prerequisite for this would
be that authorsmake their additional data and codes available to
the publicas has become common in other areas in recent years,
e.g.,health care [92], geography [93], public sector [94],
andeducation [95].0e Open Science Initiative [96] also calls
forsuch an approach by making scientific data collected directlyas
part of an experiment or indirectly as a byproduct of adownstream
analysis available to the public and online [97].In addition to
objective reproducibility, the provision ofresearch data also
benefits the overall effectiveness of sci-entific processes
[98–101], which, for instance, also results ina robust citation
advantage [102].
In addition to pure access to raw data, future projects inthe
field of ODAM research should be commonly planned,coordinated, and
carried out. 0is also implies the will-ingness to make more
in-depth findings and analyses ac-cessible, as well as voluntary
support for subsequent projects.One possibility for this is, for
example, the establishment andfounding of interest and working
groups, which are com-mitted to the ongoing observance of these
requirements andwhose members are representatives of various
researchinstitutions from different countries. At central
conventions,conferences, and association meetings, essential
progressshould be recorded and evaluated so that work steps basedon
this can be derived. We are therefore committed toremoving barriers
to the availability and reusability of sci-entific data, codes, and
study results used throughoutODAM research. 0is is crucial for
reliable and long-termdecisions about our future multimodal
transport systems.
Data Availability
No data were used as the input for this review. 0e data
asreported from other studies are subject to availability in
thedata tables and original publications.
Conflicts of Interest
0e authors declare that they have no conflicts of interest.
Acknowledgments
0is study was supported by the National Natural
ScienceFoundation of China (Grant nos. 61861136005,61851110763, and
71731001).
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