Demand Assessment and Integration Feasibility Analysis for Advanced and Urban Air Mobility in Illinois

Featured Application

This research framework provides a transferable methodology for state and regional transportation agencies to evaluate the feasibility and potential impacts of Advanced Air Mobility (AAM) integration into their existing transportation systems. The specific application demonstrated here—assessing AAM viability for the State of Illinois—can be adapted by other jurisdictions seeking evidence-based guidance for AAM planning and policy development. The framework combines demand estimation models, transportation network simulation, and regulatory analysis to quantify market potential across multiple AAM use cases, with particular focus on passenger air taxi services. Transportation planners can apply this methodology to achieve the following: (i) estimate location-specific demand for AAM services under varying cost and operational scenarios; (ii) assess how AAM introduction would affect existing ground transportation mode shares and network performance; (iii) identify infrastructure requirements, including necessary AAM and UAM infrastructure (i.e., vertiport) placement and capacity needs; and (iv) develop adaptive regulatory strategies aligned with evolving federal aviation policies. The approach could be considered as an evaluation tool by entities, public or private, considering whether to invest in AAM-enabling infrastructure, as it provides quantitative projections of adoption rates, identifies cost thresholds for market viability, and highlights operational constraints that could limit service effectiveness.

Abstract

Advanced and Urban Air Mobility (AAM and UAM) represent emerging transportation concepts that involve the use of novel aircraft technologies to transport passengers and cargo within urban, regional, and intra-regional environments. These systems may include Electric Vertical Take-off and Landing (eVTOL) aircraft, Short Take-off and Landing (STOL) aircraft, and unmanned aerial vehicles (UAVs), which are being considered for a range of applications including passenger transport, cargo delivery, and other specialized operations. This study introduced a state-specific analytical framework that integrates different methodologies and data to enable a more precise evaluation of AAM viability in the State of Illinois, compared to generic national or global assessments, capturing the state’s unique mobility patterns, infrastructure constraints, and demographic distributions. One of the main goals is to provide a comprehensive evaluation of the potential implications—both challenges and opportunities—associated with AAM and UAM operations. The analysis examines potential impacts on mobility, infrastructure, economic development, and public services, with particular emphasis on identifying key considerations for policy development. The research framework categorizes use cases into two broad types: AAM for the transportation of people and cargo, and AAM for functional applications such as emergency response, agriculture, and infrastructure monitoring. The study provides a detailed quantitative assessment of passenger air taxi services, including demand estimation, business model feasibility analysis, integration effects on existing transportation systems, and infrastructure requirements. For other AAM applications, the analysis identifies operational considerations, regulatory implications, and potential barriers to implementation, establishing a foundation for future detailed evaluation.

1. Introduction

Advanced Air Mobility is an overarching term used to describe the use of emerging innovative aircraft technology to transport passengers and cargo with the use of (in the vast majority) electric power for Vertical or Short Take-Off and Landing (eVTOL and STOL) aircraft. Urban Air Mobility is an additional subcomponent of the larger AAM ecosystem, focused on incorporating these aircraft into urban settings. For passenger-oriented services, market analyses have estimated a wide range of possible revenues, ranging from $3.1 billion up to $624 billion (adjusted to 2025 U.S. dollars from 2018 baseline values [1]), depending on key assumptions and constraints. This substantial variability reflects the inherent uncertainty in forecasting demand and lifecycle costs for a novel transportation mode that relies on emerging technologies and requires new infrastructure development. For passenger transportation applications, early-stage operating costs on a per-mile basis are estimated to be similar to existing limousine or helicopter services, and significantly more expensive than other ground transportation modes [1]. This study focuses on AAM evaluation by developing an Illinois-specific analytical framework that integrates statewide demographic, travel behavior, and infrastructure data to capture the distinct urban–regional dynamics of the state. Unlike general AAM assessments, it combines calibrated multimodal travel modeling with aviation system integration and policy-aligned scenario design, providing evidence-based, jurisdiction-specific insights to inform Illinois’s transportation and aviation planning.

Multiple AAM use cases have emerged, with passenger air taxi services representing one of the most anticipated applications. Shorter travel times due to higher vehicle speeds and path flexibility make such a service competitive with ground transportation for longer distances. However, the need for first/last mile travel and the time required for loading and unloading may render air taxis less attractive for shorter trips, which could be more efficiently handled by ground transportation [2]. To provide an effective multimodal air taxi service, combining ground modes for access and egress, operators must ensure minimal delays caused by waiting, boarding, and alighting to maintain a high standard of service and achieve significant reductions in travel time [3].

This study aims to provide a thorough assessment of the potential for UAM and broader AAM services in Illinois, examining both opportunities and challenges related to mobility, economic development, and infrastructure integration, focusing primarily on passenger travel. It includes identifying and evaluating technological advancements, with a detailed analysis of relevant application domains and use cases specific to Illinois, as well as the potential impacts and challenges associated with AAM deployment. The findings intend to inform policy considerations and guide the future development of AAM, while identifying areas where regulatory intervention may be necessary.

The main research questions that this work attempts to address are the following:

• How can the State of Illinois identify and quantify the potential for AAM services in terms of passenger transportation, and the opportunities it might provide for mobility and economic development in the state?
• What combination of travel demand modeling and aviation analysis methods can be employed to estimate AAM opportunities and challenges?
• Which are the initial and main steps towards a regulatory framework that can sufficiently cover AAM operations within the State of Illinois, while integrating emerging federal regulations?

The overarching goal of this study is to conduct a comprehensive feasibility assessment of AAM deployment in Illinois, providing state transportation authorities with evidence-based insights to inform strategic planning and policy development. Rather than advocating for a specific service model or prioritizing particular applications, this research adopts an evaluative approach that systematically examines AAM across multiple dimensions to determine whether, how, and under what conditions AAM could be viably integrated into Illinois’s transportation ecosystem.

The analysis encompasses three interconnected objectives. First, the potential market demand and economic viability of AAM services in Illinois are being assessed, identifying which use cases demonstrate the most promising feasibility given current technological and economic constraints. Second, how AAM integration would affect existing ground transportation systems is being analyzed, via quantifying the potential impacts on traffic patterns, mode choice behavior, and multimodal connectivity to understand both the beneficial and adverse effects on current infrastructure. Third, the broader societal implications, including infrastructure requirements, regulatory frameworks, economic development opportunities, and accessibility considerations, are being studied, with the recognition that successful AAM deployment depends on addressing systemic challenges beyond purely technical or economic factors.

This multi-dimensional approach reflects the reality that AAM represents not merely a new transportation option, but a complex sociotechnical system requiring the coordinated consideration of technological capabilities, market dynamics, infrastructure investments, regulatory frameworks, and societal impacts. The study’s purpose is, therefore, to provide decision-makers with a holistic assessment that identifies the most viable pathways for AAM development while highlighting critical barriers and policy needs that must be addressed for successful implementation. By examining these dimensions comprehensively, this work establishes a foundation for informed policy decisions regarding whether and how to pursue AAM development in the state.

Figure 1 presents the workflow through the considered task areas, while Figure 2 below summarizes the developed framework that combined qualitative and quantitative analyses to provide a holistic and collective understanding of AAM and UAM in Illinois.

This work builds upon a comprehensive assessment of AAM operations [4], which was conducted for the Illinois Department of Transportation (IDOT) by members of the author team, among other collaborators. The existing AAM literature was reviewed across multiple dimensions, including demand estimation methodologies, operational concepts, infrastructure requirements, regulatory frameworks, use case applications (passenger transport, cargo delivery, agriculture, and emergency services), and societal and environmental impacts. The present study focuses on specific dimensions, drawing from and extending the demand estimation framework established in that broader assessment.

Studying the demand potential and understanding its implications for AAM are essential to accurately determine the possible scope and scale of AAM operations. With limited existing services resembling AAM, demand estimation is challenging. Common approaches involve either comparing AAM to similar services [2,5,6,7,8] or conducting surveys of potential AAM users [9,10,11]. Demand estimates vary, with studies projecting hundreds of daily operations to potentially serving tens of thousands of people within an urban setting [7,12].

Interest in potential UAM services has increased substantially in recent years, particularly in terms of personalized passenger air travel applications. This trend is underscored by the involvement of major ride-hailing firms exploring entry into the sector, as highlighted in several studies [3,13]. Surveys of prospective users aim to capture preferences, behaviors, and decision-making factors. In the early stages, per-mile air taxi operating costs are expected to be closer to those of a limousine or helicopter, and considerably higher than ground taxis [1]. The U.S. market was estimated to face a demand of approximately 82,000 passengers daily (55,000 flights), supported by roughly 4000 aircraft. If challenges related to weather, time-of-day, capacity, and infrastructure were addressed in the long term, demand could reach higher volumes of passengers daily, requiring nearly 700,000 aircraft [14,15]. Challenges highlighted include noise concerns, airspace restrictions, and competition from telecommuting.

The air ambulance market, already utilizing helicopter technology, also shows potential for AAM. Bulusu and Sengupta [2] found eVTOL air ambulances to have similar costs ($11,200) to existing helicopters ($12,500) per trip. However, a major constraint for eVTOL aircraft is battery recharge time, which is significantly longer than refueling rotary-wing aircraft, reducing aircraft availability. Hybrid vehicles, battery swapping, or faster charging times are suggested as solutions to improve turnaround and efficiency.

2. Methodological Framework

This section examines AAM applications relevant to Illinois across two broad categories: passenger transportation services (air taxis for daily commuting, airport access, and regional travel) and functional applications (agriculture, package delivery, emergency response, and infrastructure inspection). The analysis begins with an overview of functional applications before focusing primarily on passenger air taxi demand estimation, which represents the most commercially significant near-term use case.

2.1. Overview of AAM Applications in Illinois

Multiple AAM applications and use cases have been identified for use in the State of Illinois (Figure 3). In agricultural contexts, low-altitude UAV operations promise to improve farm management through crop health monitoring, livestock observation, irrigation systems oversight, and more precise pesticide distribution, thereby lowering operational expenses and boosting productivity. UAV-based parcel delivery could optimize logistics operations for packages under 5–10 pounds, though current battery technology limits payload capacity and the Federal Aviation Administration (FAA) regulations restrict operations during precipitation, high winds (>25 mph), and low visibility conditions, which are common during Illinois winters. Emergency AAM applications are positioned to improve healthcare access through expedited medical transportation, offering particular advantages during severe weather events and flooding scenarios, with potential expansions encompassing automated patient transport and organ transplant conveyance. UAVs are additionally anticipated for infrastructure inspection applications, offering risk reduction, temporal efficiency, and cost advantages compared to conventional inspection approaches.

2.2. Air Taxi Demand Estimation Framework

Regarding air taxi operations, three principal trip categories require demand estimation: inter-regional air taxi journeys, airport access/egress trips, and daily commuting patterns. Extended daily commutes are expected to represent a larger share of air taxi demand, given their frequency, as time reductions from air taxis can compete effectively against surface transportation. Research indicates that below specific ground transportation travel duration thresholds (typically journeys exceeding 30 min, or more precisely, trips within 50–55 min), air taxi operations would likely prove economically unviable due to comparatively elevated costs and proportionally modest travel time reductions [16]. Prior research has applied diverse methodologies for estimating air taxi demand from such journeys, generally emphasizing travel time reductions and cost competitiveness [7,17,18].

The methodology builds on Goyal et al.’s [1] framework, using 2022 National Household Travel Survey (NHTS) data to capture Illinois-specific travel patterns and demographic characteristics. Through the comparison of travel characteristics and assumed air taxi service parameters against existing surface transportation modes, a logistic mode choice framework was applied using variable coefficients obtained from the estimated logistic models of the Chicago Metropolitan Agency for Planning (CMAP) [19]. Unconstrained demand projections were first derived and subsequently adjusted through the application of the willingness-to-pay constraint. Supply-side network constraints were subsequently incorporated with unconstrained demand estimates. Critical variables including precise AAM service pricing, average aircraft cruise velocity, vertiport waiting durations, and vertiport access/egress times remain undefined yet directly affect air mobility service levels. Assumed value ranges for these variables were established (

Table 1. Assumed cost, speed, and time values.

	Pessimistic Scenario	Intermediate Scenario	Optimistic Scenario
Cost, in $USD/pax-mile	11.2	8.7	5.3
Aircraft Speed, mph (kts)	100 (87)	140 (122)	180 (156)
Wait Time (minutes)	15	10	5
Access/Egress Time (minutes)	30	20	10

) and applied throughout the demand estimation methodology.

Air taxi pricing was based on estimated per passenger-mile costs. Cost values were inflation-adjusted from Goyal et al. [1] 2018 baseline to 2025 USD for consistency. Aircraft average cruise velocities determined inter-vertiport travel durations, with the assumed speeds derived from prototype air taxi platforms currently under development [20], accounting for reduced operational velocities during the departure and arrival phases. Wait times and access/egress times ranged from 5 to 15 min (Al Haddad et al. assumed five-minute average waiting [21]), with access/egress times doubled to encompass both first and last mile distances. Figure 4 depicts the travel time reductions for AAM aircraft, calculated by comparing existing surface transportation journeys to projected air taxi operations. Travel time savings were calculated for each individual trip record in the Illinois NHTS subsample, through direct comparison of ground transportation durations against estimated air taxi travel times. For each trip exceeding the 30 min viability threshold, the ground transportation travel time was taken directly from the NHTS-reported trip duration, while the air taxi travel time was calculated using the straight-line distance between the trip origin and destination coordinates, the assumed cruise speed from scenario-specific parameters, vertiport waiting time (5–15 min depending on scenario), and first/last mile ground travel to and from vertiports (i.e., access and egress times). Time savings exhibit geographic clustering due to urban density variations, corridor effects, and distance bands. High-density Chicago metropolitan trips show different patterns than rural, downstate journeys.

The analysis examined ground transportation-based journeys exceeding 30 min, considering that threshold for competitive air taxi viability reasons (based on [16]). Within this segment, close to 90% of trips demonstrated potential travel time reductions when using air taxis. Median time savings for such journeys reached 25%, translating to 15 min or less for most trips, generally comparable to surface transportation durations. Additionally, a quarter of long-distance trips achieved at least a 50% travel time reduction, identifying a market segment where air taxi operations offer substantial time savings and enhanced user appeal. For time valuation, $20.60/h was applied (adjusted to 2025 U.S. dollars from the baseline values in [22,23]), and it was also used to evaluate potential UAM users’ willingness-to-pay, combined with per-trip time savings.

Five scenarios were constructed by combining operational parameter assumptions with network size variations. Three primary scenarios—pessimistic (low-demand), intermediate, and optimistic (best-case)—represent different combinations of cost, speed, and access time parameters from Table 1. Additionally, two network-specific scenarios vary vertiport accessibility: the small network assumes limited vertiport coverage (30% of trips accessible), while the large network assumes extensive coverage (70% of trips accessible), with both using intermediate operational parameters. Projected daily demand in the Chicago metropolitan area spans from 2800 trips (0.007% mode share) in the low-demand scenario to 260,000 trips (0.65%) in the best-case scenario. The intermediate case projects 44,000 daily trips (0.11%), while small and large network scenarios estimate 16,500 (0.04%) and 125,000 (0.31%) daily trips, respectively. The number of scenarios was determined based on the combination of key operational variables (cost per passenger mile, aircraft speed, and vertiport accessibility) that strongly influence AAM demand. These three parameters were each assigned representative pessimistic, intermediate, and optimistic values drawn from existing literature and prototype performance data, producing a set of scenarios that captures the realistic operational spectrum between conservative and optimistic system assumptions.

2.3. Infrastructure and Airspace Integration Requirements

Projected unconstrained demand for day-to-day AAM trips, exhibits considerable variation, spanning from several thousand to tens of thousands of daily trips. Notably, the highest demand projections derive from optimistic scenarios assuming brief wait durations, conveniently located vertiports, and competitive pricing structures. Beyond demand estimation, successful air taxi deployment faces operational challenges and will necessitate substantial modifications to existing aviation infrastructure and protocols. Integrating high-frequency, low-altitude AAM operations into airspace currently managed for conventional commercial and private aviation will require extensive air traffic control system adaptations, potentially including automated traffic management platforms specifically engineered for urban air mobility contexts. Furthermore, the on-demand characteristics of air taxi operations will demand novel dispatch protocols fundamentally different from scheduled commercial aviation procedures, merging traditional air traffic management elements with dynamic, real-time routing characteristics of surface-based ride-hailing platforms. These operational complexities introduce additional regulatory and coordination challenges requiring resolution for successful AAM deployment.

Unmanned aerial vehicles (UAVs), commonly termed drones, constitute a distinct aircraft system category. While this study primarily emphasizes passenger-focused AAM operations utilizing piloted eVTOL and STOL aircraft, UAVs remain relevant to the broader AAM ecosystem, particularly for small-package delivery, infrastructure inspection, and emergency response applications where onboard human presence proves unnecessary. Regulatory and airspace management considerations for UAV operations, though distinct from crewed aircraft requirements, remain pertinent to comprehensive AAM integration planning.

2.4. Limitations and Key Assumptions

The limitations of this study mostly stem from various uncertainties and areas requiring additional research, along with the absence of existing operational data and well-established regulations. These areas include the demand side, Air Traffic Management (ATM), and potential business models related to AAM operations. There are different unknowns that can directly influence the level of service of AAM operations, including variables such as the cost of the service, average aircraft operating speed, and the access, wait, and egress times at designated AAM infrastructures (e.g., vertiports/skyports [1]).

On the demand side, understanding user perceptions, adoption patterns, and the factors influencing these behaviors is important, yet these factors are not considered in this work. These include identifying potential barriers to adoption and outlining clear pathways for integrating AAM aircraft and its associated infrastructure into existing urban networks. An additional operational constraint for air taxi services involves luggage and cargo accommodation. Current eVTOL designs typically feature limited passenger capacity (4–6 seats in most configurations) with restricted cargo space. This constraint can have implications for business model viability; if a four-passenger vehicle must operate with only two or three passengers to allow for baggage, the per-passenger cost increases proportionally, potentially pushing prices beyond market acceptance thresholds.

In the ATM realm, it is paramount to examine how airspace congestion evolves under various management and routing strategies. Building on existing work on airspace congestion [24,25,26,27,28], a thorough exploration of airspace congestability is essential. Evaluating the scalability and adaptability of ATM strategies as AAM and UAM expand is essential. In this work, air congestion was not considered, along with potential ground congestion, due to the introduction of an UAM service in an urban setting. The notion of induced demand, i.e., the increase in demand due to the introduction of UAM, is also important.

Assumptions stemming from the existing regulatory framework around AAM operations were also made. The Federal Aviation Administration (FAA) is envisioned to be the primary regulatory body responsible for certifying, regulating, and providing air navigation services for AAM operations, as it currently does for commercial aviation, and other existing aviation operations. Various concepts of operation documents have been published by the FAA [29,30,31,32], and while these are not formal policy statements they are interconnected and provide valuable insights into the evolving perspective on AAM/UAM operations and their assessment. Developed collaboratively by the FAA, NASA, and industry stakeholders, these documents outline conceptual elements such as aircraft, infrastructure, supporting systems, air traffic operations, and their integration into the National Airspace System (NAS).

3. Results

This section presents a demand estimation analysis of potential AAM and UAM passenger transportation operations, along with an estimation framework of potential impacts that the introduction of an UAM service could have in existing ground transportation operations.

3.1. Quantitative Demand Estimation

Three primary use cases are examined: air taxi services for daily urban travel, airport access trips, and inter-regional connections. For each, trip characteristics were analyzed, mode choice probabilities were estimated, and operational requirements were assessed.

3.1.1. Air Taxi

Trip characteristic analysis identifies the most viable air taxi applications. Approximately 53% of prospective AAM trips serve work-business purposes, despite work trips comprising only 25.4% of total statewide travel, a 2:1 overrepresentation reflecting business travelers’ higher willingness to pay premium fares. Commuters and business travelers typically demonstrate greater willingness to cover longer distances and accept higher costs compared to those travelling for leisure and, in general, non-work-related travelers. Air taxis deliver maximum time savings on extended journeys, though at premium prices [1,2], positioning business travel as an attractive and viable application.

The temporal distribution of projected unconstrained daily air taxi demand appears in Figure 5. The time-of-day distribution of projected air taxi demand was derived from the temporal attributes of trip records that satisfied the mode choice model selection criteria. All trips exceeding a 30 min travel time threshold were evaluated through a binary logistic mode choice model. For each trip $i$, the probability of air taxi selection was calculated based on Equations (1) and (2):

$$P_{i_{air}} = {\displaystyle \frac{1}{\left(1 + exp\left[-\kappa \right]\right)}}$$

(1)

$$\kappa =\left({\beta }_{time} \mathit{\Delta }{t}_i+{\beta }_{cost}\mathit{\Delta }{c}_i+AS{C}_{air}\right)$$

(2)

where the utility incorporates travel time savings ($\mathit{\Delta }{t}_i$), cost difference between ground and air ($\mathit{\Delta }{c}_i$), travel time coefficient (${\beta }_{time}$), cost coefficient (${\beta }_{cost}$), and air taxi alternative specific constant ($AS{C}_{air}$), which were based on parameters estimated by the Chicago Metropolitan Agency for Planning (based on model coefficients described in [19]). While these coefficients were not estimated specifically for air mobility services, they represent behavioral tradeoffs between time and cost that are expected to remain consistent across modes. The application of these coefficients to UAM represents an approximation, given the absence of empirical UAM choice data, though it likely understates the additional barriers to adoption associated with novel technology concerns, safety perceptions, and unfamiliarity. UAM-specific characteristics were incorporated through the alternative specific constant and through the appropriate specification of UAM service attributes (travel time, cost, and access requirements) that reflect the unique operational profile of air taxi services. This probability calculation was performed independently for each of the qualifying trips, generating a trip-specific likelihood of air taxi selection ranging from near-zero (for trips where air taxi offers minimal time savings at substantial cost premium) to near-unity (for extended trips where time savings are significant and willingness-to-pay is satisfied).

Trips were then included in the projected demand distribution if they satisfied two criteria designed to represent both modal competitiveness and household economic constraints. First, the mode choice probability had to exceed a scenario-specific threshold that varied across demand scenarios to reflect different degrees of market penetration and consumer acceptance. The best-case scenario represented aggressive early adoption and high service acceptance, the intermediate scenario used moderate adoption assumptions with balanced consideration, and the low-demand scenario applied conservative adoption assumptions with strong mode preference. Second, a willingness-to-pay constraint had to be satisfied (i.e., the time savings in minutes multiplied by the value of time per hour should be greater or equal than the air taxi fare minus the ground transportation cost), ensuring that only trips where monetized time savings justified the cost premium were included. Each selected trip was then assigned an expansion weight, defined as an expansion factor, which scales the individual survey response to represent the Illinois population, multiplied by the probability of choosing an air taxi. This approach generates fractional trip counts that aggregate to produce the corresponding demand projections.

Finally, each selected trip’s original reported departure time was preserved without modification or smoothing, and weighted trip counts were aggregated into one-hour bins spanning the 24 h day, with each bin representing the sum of expansion-weighted trips departing during that time window. Peak usage concentrates on two windows, 6:00–10:00 a.m. and 4:00–6:00 p.m., with demand volumes reaching twice (or more) the levels observed during off-peak hours. These patterns suggest air taxi networks will face concentrated demand during morning and evening peaks, with extended periods of minimal activity in between (Figure 5).

Regional distribution of potential air taxi usage across Illinois shows a marked geographic concentration. The Chicago region accounts for 87.6% of projected daily air taxi operations, compared to 68.5% of total travel statewide. This concentration reflects both Chicago’s substantial trip volume and the prevalent congestion-related delays throughout the metropolitan area. Beyond Chicago, central Illinois represents the primary secondary market (10% of projected air taxi trips versus 24.9% of all statewide trips), with the St. Louis region showing more limited activity (2.4% of projected air taxi trips versus 6.6% of total trips). Figure 6 visualizes the geographic distribution of projected AAM demand across Illinois counties, with color coding and hatch patterns distinguishing the three primary demand regions and illustrating the marked concentration in the Chicago metropolitan area.

The substantial price differential between air taxi and ground transportation suggests that demand will concentrate among higher-income households, aligning with findings from comparable research [11,12]. Price sensitivity remains a decisive factor influencing travelers’ choices between air taxi and conventional transport options.

3.1.2. Airport Trips

Airport access represents a distinct AAM/UAM application, addressing first-and-last-mile connectivity challenges for intercity air travel between residences or workplaces and airports offering long-haul services. Conventional travel surveys typically undercount airport trips due to their sporadic nature and the frequency of non-resident travelers. Consequently, this analysis applies an estimation approach adapted from Goyal et al. [14].

Airport passenger volumes are derived from T100 market data [33], excluding connecting passengers from the totals. Demand is spatially distributed across census tracts within a 75-mile airport radius proportional to population, generating a trip dataset. Service characteristics for both ground (automobile) and air taxi modes are calculated using travel time, cost, access duration, and waiting period variables. The air taxi operational parameters from Table 1 inform the estimation of fares and travel durations. A logistic, mode choice framework compares the resulting service attributes across three demand scenarios: conservative, moderate, and optimistic projections. These scenarios yield unconstrained demand estimates of approximately 1500 daily trips (1.11% mode share), 4100 trips (3.06% mode share), and 13,600 trips (10.26% mode share), respectively. These demand scenarios are operationalized through systematic variation in the air taxi alternative specific constant, with holding time and cost coefficients fixed, representing different degrees of traveler familiarity, trust, regulatory maturity, and adoption. The low-demand scenario employed mode preferences where travelers exhibited substantial resistance to air taxi adoption even when time-cost tradeoffs are favorable.

This parameterization was chosen to reflect potential early-stage technology adoption issues, along with issues of limited public familiarity, safety concerns, and regulatory uncertainties that suppress demand below the levels predicted by purely instrumental service attributes. The intermediate scenario represented moderate adoption barriers consistent with mid-stage market development, where air taxi services have established safety records and operational track histories but have not yet achieved mass market normalization. The best-case scenario represented the wide acceptance and minimal adoption resistance characteristics of mature market conditions, where air taxis could be viewed as routine transportation options comparable to conventional modes.

Airport trip projections indicate air taxi services may achieve a higher mode share for airport access compared to general commuting purposes. This difference likely stems from airport trips’ typically longer distances and often higher costs when factoring in parking or taxi expenses. While several thousand potential airport trips represent a viable market opportunity, air taxis would still serve a minority of total airport ground access trips, primarily due to their anticipatedcost.

Demand concentration emerges at two facilities: Chicago O’Hare (ORD) and Chicago Midway (MDW), which handle the majority of commercial passenger traffic at Illinois airports. Figure 7 displays the projected air taxi demand at airports exceeding ten trips daily. The two major Chicago airports could generate up to 2000 daily passengers, while other Illinois facilities may serve only several dozen passengers per day.

3.1.3. Regional Air Taxi

Short-haul commercial aviation currently serves inter-regional travel demand, particularly connections between smaller regional facilities and major hub airports. Smaller, more efficient air mobility platforms may offer advantages for this market segment. AAM vehicles operating within a 150-mile range could compete with regard to travel duration, pricing, and departure frequency. This analysis examines current short-haul commercial aviation data alongside air mobility vehicle cost performance, relative to conventional aircraft, to project the future mode share for these platforms.

The State of Illinois has limited commercial aviation routes under 150 miles. Figure 8 maps these connections, with the line thickness representing passenger volume. All routes connect to one of the following three primary hub facilities: Chicago O’Hare (ORD), Chicago Midway (MDW), and St. Louis Lambert (STL). Other regional airports shown in the figure include Marion (MWA), Decatur (DEC), Champaign (CMI), Bloomington (BMI), Peoria (PIA), Freeport (FIA), Moline (MLI), Dubuque (DBQ), Madison (MSN), Milwaukee (MKE), South Bend (SBN), Kalamazoo (AZO), Grand Rapids (GRR), and Muskegon (MKG). These represent the main regional nodes within and around Illinois that currently support short-haul commercial operations. Figure 9 illustrates the distribution of approximately 6600 daily passengers across these routes, with the Chicago O’Hare facility dominating short-haul traffic. These figures include originating, terminating, and connecting passengers.

Air taxi services replacing short-haul commercial aviation could offer competitive advantages in the following three dimensions: travel duration, pricing, and service frequency. Figure 10 contrasts current gate-to-gate commercial flight durations on short-haul routes with projected air taxi travel times, assuming a 150 mph cruise speed. Each data point represents an airport pair, with the point size proportional to passenger volume. Results indicate that regional air taxis would reduce travel time on most routes compared to current commercial services, likely through more direct routing and reduced ground operations time. However, time savings typically remain within 15 min, which may lack practical significance for many travelers.

Comparing potential air taxi operations with current short-haul service in terms of cost and frequency dimensions presents additional considerations. Air taxi pricing remains undefined, though cost projections fluctuate with market conditions, and longer routes offer some cost efficiency advantages. Industry analyses suggest mature regional air taxi operations could achieve $2.8 to $6.2 per passenger mile [1,14]. Air taxi services could also provide frequent departures throughout operating hours, potentially offering numerous daily flights in high-demand corridors.

Figure 11 displays price per passenger mile versus flight frequency for current Illinois short-haul aviation services. Each data point represents a route, with the point size indicating passenger volume. These routes average approximately six daily flights. While lower than potential air taxi frequency, this suggests the existing service already provides reasonable availability. The aircraft envisioned for regional air taxi operations would carry substantially fewer passengers than current short-haul aircraft, creating potential viability challenges. Increased frequency combined with superior fuel/energy efficiency could provide one pathway to achieve competitive operations.

This analysis excludes potential demand for new short-haul connections, particularly between smaller facilities, as well as dedicated airport shuttle operations handling access and egress trips. Available data does not support the assessment of induced demand should a regional air taxi service launch between these secondary airports.

3.1.4. Supply Constraints Impacts on Demand

Supply-side limitations significantly affect achievable demand, including vertiport accessibility from trip endpoints, total system throughput (encompassing both vehicle and vertiport capacity), operational hours (generally not 24 h service), and meteorological restrictions (limiting operations to visual flight rules (VFR) conditions with adequate visibility).

Competitive air taxi operations require vertiport locations proximate to trip origins and destinations. The fraction of trips within convenient vertiport access depends on network density and site selection. This analysis models accessible trip shares of 30%, 50%, and 70% representing small, medium, and large network configurations.

Operating hours and weather represent additional supply constraints. Air taxi operations are typically conceived for daylight hours, operating from 6 a.m. to 10 p.m. This operational window would accommodate 91% of unconstrained demand. Operations are further restricted to VFR conditions with adequate visibility, which occur approximately 95% of daytime hours in Illinois.

System throughput capacity represents a critical supply constraint, determined by vehicle and vertiport characteristics. Vehicle configurations typically accommodate one to five passengers, while vertiports are projected to process 20 to 100 operations hourly, yielding throughput of 20 to 500 passengers per hour [1,15,16].

Air taxi network research suggests deployments ranging from twelve to over one hundred vertiports, producing capacities from several hundred to 50,000 passengers per hour [6,17]. Helicopter operations provide relevant precedent; Silverstone heliport achieved approximately six hundred movements hourly [34], while Denver International Airport recorded 298 helicopter movements per hour. eVTOL projections vary widely—Uber estimates 338 hourly vertiport operations [34], while other analyses assume up to 1250 hourly operations [35]. Alternative studies suggest 45 to 53 movements per vertiport hour [8,36,37], defining a movement as a single flight. An estimate of 32 operations per vertiport hour could reflect initial operational years for these transport modes, considering their performance characteristics, economics, and demand patterns. Air taxi network capacity calculations, assuming three passengers per vehicle trip, vary with vertiport quantity and hourly operation rate. A twenty-five vertiport network handles 1500, 4500, or 7500 passengers hourly at 20, 60, or 100 operations per hour. Expanding to fifty vertiports increases capacities to 3000, 9000, and 15,000 passengers per hour, while a one hundred vertiport network accommodates 6000 to 30,000 passengers hourly across the same operational intensity range.

3.2. Impacts of AAM and UAM on Existing Transportation Systems

Having established demand projections across different use cases, the analysis now examines how UAM integration would affect existing transportation systems and mode choice behavior in the Chicago metropolitan area.

3.2.1. Mode Choice Simulation Framework

Multiple research efforts have examined the interaction between AAM and surface traffic congestion [38,39,40]. AAM’s influence on transportation networks depends on operational characteristics, including empty repositioning flights, solo versus shared ridership, and journey length. Simulation research [39] demonstrates that while AAM may alleviate congestion under certain conditions, it can redistribute traffic to alternative network segments through mechanisms such as ground access/egress movements to vertiports, potentially increasing total vehicle unit distance traveled [40].

This section’s analysis employed simulation methodology centered on the Chicago metropolitan area to project mode selection patterns following UAM service introduction. Initially, a transit assignment platform modeled Chicago’s existing bus and rail infrastructure [41], establishing baseline public transportation performance benchmarks. Subsequently, dynamic traffic network assignment methodology [42] simulated private automobile movements, capturing temporal traffic flow variations and congestion dynamics across the network. These elements were then unified through discrete choice modeling to forecast traveler mode preferences. The mode choice framework and calibrated coefficients derived from CMAP’s regional modeling framework [19]. The analyzed demand profile (930,026 individual journeys) utilized household travel survey data for trip generation combined with sociodemographic information from Census and American Community Survey sources. The simulation framework models UAM as theoretically available for all origin–destination pairs to isolate the effects of cost, travel time, and service quality on mode choice behavior, producing upper-bound demand estimates. This, however, is as a limitation of the analysis. Section 3.1.4 adjusts these estimates by applying supply constraints that limit accessibility to 30%, 50%, or 70% of trips based on vertiport network density, providing more realistic near-term projections.

Available modes in the simulation included walking, private automobile, combined transit and walking (TRW), and park-and-ride (PNR—driving to a transit facility followed by transit use). Figure 12 displays mode shares before UAM service introduction.

The introduction of UAM as a hypothetical alternative was modeled through an incremental logit model, enabling new option integration into existing choice frameworks while preserving underlying behavioral consistency. This technique proves particularly suitable for novel transport modes with limited empirical evidence. The UAM utility specification drew upon existing modes with comparable attributes. Specifically, UAM was benchmarked against PNR and private automobile modes through the following logic:

• Like PNR, UAM necessitates access transportation to the departure facility (vertiport), requiring multimodal journey coordination.
• Private automobile travel provides the nearest analog regarding on-demand availability, privacy levels, comfort characteristics, and relatively elevated costs.
• Given the anticipated elevated UAM pricing during early deployment, probable initial users would likely possess private vehicle access.

Consequently, PNR and private automobile utility specifications provided the foundation for UAM utility construction, adjusted for UAM-specific operational characteristics. Journey distances were computed using known origin–destination coordinates for each traveler’s O–D pair. Travel durations were calculated per trip using assumed aircraft velocities, incorporating a UAM travel time reduction coefficient. The analysis assumed ground journeys requiring 59–85 min would translate to 15–25 min UAM trips (encompassing flight, departure, and arrival operations) [43]. Pre-boarding waiting, boarding procedures, and alighting were assigned a 10 min “cost”, representing moderate waiting time assumptions (following Al Haddad et al. [21]). Using utility specifications for each available mode and calibrated beta coefficients for cost, walking duration, in-vehicle and out-of-vehicle travel times, and mode transfers (where applicable), individual traveler utilities across all modes were estimated. These enabled choice probability calculations and subsequent mode share projections. Sensitivity analysis across multiple parameters assessed result robustness and identified critical UAM adoption drivers. The analysis evaluated elasticities for the following:

• In-vehicle travel durations (reflecting aircraft velocity variations).
• Cost per passenger mile (pax mile cost).
• Out-of-vehicle durations (including waiting, boarding, and alighting elements).

3.2.2. Baseline and UAM-Integrated Results

Three scenarios, optimistic, moderate, and pessimistic, were evaluated to establish plausible outcome boundaries under varying operational and market circumstances. Figure 13 presents the sensitivity analysis results across the following three parameter categories: cost per passenger mile (left column); out-of-vehicle time, including waiting and access/egress (center column); and aircraft cruise speed (right column). Each column displays five parameter values ranging from conservative to optimistic assumptions, illustrating how UAM mode shares respond to variations in each operational dimension.

Cost has the higher influence on UAM adoption, with mode share declining from 10.67% at $3.00 per passenger mile to 2.79% at $9.00—a nearly fourfold reduction. This relationship is highly non-linear, with the steepest decline occurring between $3.00 and $4.50 (10.67% to 6.92%). Out-of-vehicle time shows moderate sensitivity, with UAM mode share declining from 6.36% at 10 min total waiting/access time to 3.02% at 30 min—approximately a 50% reduction. Aircraft speed variations produce the weakest effect, with mode share increasing only from 4.25% at 100 mph to 6.12% at 180 mph, because ground-based access and egress times decrease the benefits of faster cruise speeds. Across all scenarios, personal vehicles maintain 70–76% of mode share, with diverted UAM trips primarily shifting to automobile use rather than transit alternatives. These patterns confirm that cost represents the binding constraint on UAM adoption, with roughly 3–4 times greater sensitivity than access time and 5–6 times greater sensitivity than aircraft speed, suggesting that substantial cost reductions will prove essential for achieving meaningful market penetration beyond specialized premium segments. To further assess this, a pessimistic scenario (Figure 14) was developed using an estimated cost of $6.25 per passenger mile (2025 U.S. dollars). This value was derived from a base range of $0.51–$2.6 per passenger mile [8], with an assumed industry benchmark of $6.70 per passenger mile, and supplemented by a fixed base fare of $23.20 [44], representing a conservative assumption consistent with early-stage operational conditions.

Results shown in Figure 14, combined with broader sensitivity findings, confirm that elevated service costs substantially limit adoption potential, with UAM capturing only 0.76% of mode share under pessimistic cost assumptions, nearly an order of magnitude below the competitive threshold of 5% of mode share, which is typically associated with sustainable transportation service viability. Under these cost parameters, UAM functions as a premium transportation option accessible predominantly to travelers with high time valuations (>$75/hour) and the financial capacity to justify cost premiums exceeding $150–$200 relative to personal vehicle alternatives for typical airport access trips. Personal vehicles retain dominant market position at 75.04% of mode share, while transit-based alternatives collectively serve approximately one-fifth of airport access demand, and active transportation accounts for 3.15% representing short-distance trips from airport-proximate origins. This distribution indicates that substantial cost reduction (through technological progress (improved battery energy density and reduced manufacturing costs via production scaling), operational efficiency gains (higher aircraft utilization rates and optimized vertiport placement), and regulatory maturation) will prove essential for achieving market penetration levels beyond highly specialized user segments.

4. Discussion

The demand estimates in Section 3 establish baseline market potential, but several factors will critically influence whether and how AAM materializes in Illinois. This section discusses induced demand effects, ground transportation impacts, parameter sensitivities, regulatory alignment needs, and equity considerations that shape AAM’s practical feasibility and societal implications.

4.1. Induced Demand Effects

The introduction of AAM could stimulate changes in existing activity patterns and demand flows. For example, additional travel patterns and trips could be added to the network (i.e., induced demand), and reduced travel times between Chicago’s central business district and distant suburban areas could potentially enable commuters to accept employment opportunities previously considered infeasible. Reduced travel time costs could increase trip frequency for existing origin–destination pairs. For example, business travelers might conduct more frequent in-person meetings with travel times of a full trip declining from 3 to 4 h by car (e.g., Chicago-to-Springfield or Chicago-to-Peoria) to under 90 min via AAM. Illinois’s polycentric structure, with Springfield serving as the state capital and numerous regional employment centers across central Illinois, creates potential for frequency-based induced demand on inter-regional routes.

Quantifying this effect for Illinois requires estimating the potential induced demand from reduced travel times. The transportation economics literature regarding induced demand from highway improvements and speed increases suggests that time savings often generate additional trip-making, with induced demand effects typically ranging from 10% to 50% of the direct capacity benefit, depending on market conditions and network characteristics [45]. Given AAM’s premium pricing and limited initial accessibility, induced demand effects are likely to fall toward the lower end of this range. Applying moderate assumptions to the 87.6% of AAM demand concentrated in Chicago suggests induced demand could add 10–25% to the mode-shift-based estimates in Section 3.1.1, translating to approximately 6400–16,000 additional daily trips in intermediate scenarios. However, this represents a preliminary estimate with substantial uncertainty, as empirical evidence specific to air mobility induced demand does not yet exist.

4.2. Ground Transportation Impacts from Vertiport Access

AAM operations generate ground transportation trips for vertiport access and egress that could partially or fully offset congestion benefits from mode shift away from automobile travel. The magnitude of this effect depends critically on vertiport location and access mode distribution. Vertiports located in the central business district of the Chicago Area (the Loop and Near North Side) would concentrate access/egress trips in already-congested downtown areas. If 125,000 daily AAM passengers (large network scenario from Section 2) generate 250,000 access/egress trips, with a reasonable assumption that 70% of these trips will use ride-hailing or private vehicles, this adds approximately 175,000 vehicle trips to the downtown Chicago road network. These trips would exacerbate peak period congestion on major arterials and streets, potentially offsetting much of the congestion relief from reduced automobile commuting. Vertiports at existing commuter rail stations or near major suburban employment centers would distribute access/egress trips across the regional network, likely producing lower marginal congestion impacts, since suburban roadway networks generally operate below capacity outside immediate freeway interchange areas. However, this strategy compromises door-to-door travel time benefits for downtown-oriented trips, reducing AAM attractiveness and thus limiting mode shift and congestion benefits. Vertiports integrated with major public transit stations could facilitate transit-based access, reducing vehicle trip generation.

The above findings contrast with simplified assumptions that AAM relieves congestion. The net effect depends critically on (i) vertiport location strategy and resulting access mode distribution, (ii) whether AAM primarily substitutes for automobile trips or transit/other modes, (iii) the geographic distribution of vertiport access/egress trips relative to existing congestion hotspots, and (iv) the induced demand magnitude.

4.3. Demand Estimation Parameters Discussion

Demand estimates rely on assumed operational parameters whose values remain uncertain in the absence of existing and mature AAM markets. Three categories of assumed parameters drive demand estimation outcomes: cost parameters (per passenger mile pricing), operational performance parameters (aircraft cruise speed and vertiport throughput capacity), and user experience parameters (waiting times and access/egress times). Cost per passenger mile exhibits the strongest influence on demand across all trip categories, representing the binding constraint on AAM adoption in Illinois. For daily air taxi trips, cost varying produces demand estimates ranging from approximately 260,000 trips daily (0.65% of mode share) to 2800 trips daily (0.01% of mode share), a 95-fold variation. Airport trips demonstrate similar cost elasticity, with mode shares ranging from 1.11% to 10.26% across the cost scenarios. Notably, even optimistic cost assumptions yield moderate modal shares, suggesting substantial cost reductions below current industry projections would be necessary for mass market adoption. The relationship between cost and demand is non-linear. Demand remains relatively stable at lower cost levels before declining sharply as the cost increases, indicating a threshold beyond which AAM becomes prohibitively expensive for most travelers.

Aircraft cruise speed variations produce modest demand effects compared to cost. Increasing speed from 100 to 180 mph improves time savings, but demand increases only approximately 2–3 fold, since ground access/egress times remain constant regardless of cruise speed, diluting the benefit of faster flight times, and many Illinois trips already achieve substantial time savings, even at lower speeds, due to ground traffic congestion. Regional air taxi routes show particularly weak sensitivity to speed variations because most routes are short (under 150 miles), resulting in flight time differences in only 10–20 min across the speed range. This explains why regional air taxi demand remains limited even under optimistic assumptions—the absolute time savings are insufficient to justify premium pricing regardless of aircraft performance.

Waiting and access/egress times demonstrate moderate sensitivity. Reducing combined waiting and access/egress times from 60 min (pessimistic scenario: 15 min wait + 30 min access + 15 min egress) to 20 min (optimistic scenario: 5 min wait + 10 min access + 5 min egress) increases demand approximately 4–7 fold for daily air taxi trips. Supply constraint parameters create additional uncertainty in achievable demand. Vertiport network size (modeled at 30%, 50%, and 70% trip accessibility representing small, medium, and large networks) directly scales achievable demand proportionally—a small network captures only 30% of unconstrained demand regardless of cost or performance assumptions. Vertiport throughput influences peak period operations most critically. Given that most of Illinois’s potential demand for AAM concentrates in Chicago, and peak period demand reaches double off-peak levels, vertiport capacity constraints could create bottlenecks even if demand materializes. A 25 vertiport Chicago network operating at 60 operations per hour per facility could process 4500 passengers hourly, or approximately 18,000 passengers during a four-hour morning peak, which is potentially insufficient for best-case demand scenarios (260,000 daily trips implies approximately 106,500 during combined morning and evening peaks, or 26,500 per four-hour peak period).

4.4. Regulatory Context and Considerations for Illinois

The FAA continues to evolve its AAM operational and regulatory framework, and Illinois policy should incorporate flexibility to align with future federal regulatory developments. Given uncertain AAM and UAM demand trajectories and adoption patterns, Illinois should develop adaptable policy frameworks accommodating variable demand scenarios. Integrating AAM aircraft into existing aviation networks and systems represents a potentially significant strategic approach. Furthermore, the Illinois Aviation System Plan (IASP) should undergo comprehensive updates to fully incorporate such aircraft operations, transitioning from treating these as speculative future possibilities to actively accommodating their operational requirements and supporting infrastructure. While the IASP comprehensively addresses the state’s aviation system, AAM operations receive only limited mention in selected sections. Current treatment includes elements such as AAM vehicle technology, infrastructure needs, and prospective air traffic scenarios requiring thorough evaluation.

Different state-level policy measures could be considered to facilitate AAM and its smooth integration in Illinois. First, the state should establish clear guidelines for vertiport location and zoning requirements, providing municipalities with standardized frameworks that balance local land-use authority with statewide connectivity objectives. This would ensure consistency across jurisdictions while supporting equitable access to AAM services. Second, Illinois could develop targeted incentive programs to attract AAM manufacturers, operators, and supporting industries to the state. Such initiatives may include tax incentives for vertiport development in designated zones, workforce training programs aligned with AAM industry skill needs, and research partnerships between state universities and AAM companies to promote innovation and job creation.

In addition, the state should create protocols for AAM involvement in emergency medical services, disaster response, and public safety operations. These protocols would define operational authorities, liability frameworks, and coordination mechanisms among key agencies such as the Illinois Emergency Management Agency and the Illinois Department of Public Health, ensuring the safe and efficient integration of AAM assets in critical response scenarios. IDOT should also incorporate AAM considerations into regional transportation planning processes to ensure alignment with existing surface transportation investments and public transit operations. Metropolitan planning organizations should be directed to evaluate AAM impacts and opportunities within long-range transportation plans to achieve a cohesive multimodal network.

Expanding the IASP to address system-wide vulnerabilities represents another key priority. Illinois should proactively identify and mitigate infrastructure vulnerabilities such as cybersecurity risks, electrical grid reliability, and natural disaster resilience. This includes establishing cybersecurity standards for AAM operations that exceed federal minimums, developing grid resilience strategies for vertiport networks, and implementing weather contingency protocols tailored to Illinois’s climate conditions.

Finally, Illinois should adopt a phased regulatory approach to AAM deployment. Initial efforts could focus on demonstration projects within controlled environments, such as designated corridors between O’Hare International Airport and downtown Chicago. These pilot projects would then expand to limited commercial operations in high-demand urban markets, with statewide deployment proceeding only after the technology demonstrates consistent safety, reliability, and operational performance.

4.5. Equity and Accessibility Considerations

The projected cost structure of air taxi services, ranging from $5.30 to $11.20 per passenger mile, raises significant equity concerns, at least during the first years of UAM operations. A typical 20-mile trip would cost $106–$224 for an air taxi service compared to $15–$45 for ride-hailing, or negligible marginal cost for personal vehicle users, effectively limiting access to high-income travelers. This premium pricing structure means AAM would function primarily as a luxury service rather than a broad mobility solution, with limited benefits for transportation-disadvantaged populations who face the greatest mobility challenges. Future policy discussions should consider whether public investments in AAM infrastructure can be justified given this limited accessibility, or whether alternative pricing mechanisms (subsidies, shared-ride models) could broaden access.

Environmental implications of AAM deployment require careful consideration. While eVTOL aircraft produce zero direct emissions, offering advantages over helicopter services, their full lifecycle environmental impact depends on electricity grid composition. Noise represents a potentially more immediate concern; although eVTOL aircraft are projected to be significantly quieter than helicopters (60–70 dB versus 80–90 dB at 500 feet [4]), concentrated vertiport operations could generate community opposition similar to heliport noise complaints. The projected air taxi demand of several thousand to tens of thousands of daily operations represents a significant increase in low-altitude aircraft activity, suggesting noise impacts could become a significant barrier to public acceptance and regulatory approval.

5. Conclusions

This section synthesizes the study’s key findings, discusses their implications for AAM deployment in Illinois, acknowledges analytical limitations, and identifies priority areas for future research.

5.1. Summary of Findings

This study evaluated the potential for Advanced Air Mobility (AAM) and Urban Air Mobility (UAM) services in Illinois through an analytical framework addressing demand estimation, business model viability, transportation system impacts, and regulatory requirements. The findings reveal that AAM faces significant market constraints that limit near-term deployment viability. AAM services in Illinois would serve a narrow market segment, namely business travelers in the Chicago metropolitan area willing to pay premium fares for time savings on longer commutes. The analysis identifies Chicago as capturing 87.6% of projected air taxi demand, driven by substantial trip volumes and congestion-induced delays that create favorable conditions for time-competitive operations. Beyond Chicago, central Illinois represents only 10% of projected demand, with the St. Louis region accounting for 2.4%, indicating its limited viability outside the primary metropolitan area. Within viable trip segments, air taxi services show the strongest potential for work-related trips exceeding 30 min of ground travel time, particularly with regard to the 22% of longer trips achieving at least 50% travel time reduction. Temporal demand patterns concentrate heavily during peak commute hours (6:00–10:00 a.m. and 4:00–6:00 p.m.), with volumes reaching double or more compared to off-peak periods, creating operational challenges for fleet utilization and infrastructure efficiency.

Cost represents the dominant barrier to AAM adoption, exhibiting 3–4 times greater influence on mode choice than access time and 5–6 times greater sensitivity than aircraft speed. Under pessimistic but realistic cost scenarios ($6.00–$9.00 per passenger mile, 2025 USD), UAM captures only 0.76–2.79% of mode share—well below the 5% threshold typically associated with sustainable transportation service viability. Even optimistic cost projections ($3.00 per passenger mile) yield only 10.67% of mode share, indicating that substantial cost reductions beyond current industry projections would be necessary for mass market adoption. The cost–demand relationship exhibits strong non-linearity, with the steepest decline occurring between $3.00 and $4.50 per passenger mile, suggesting a critical threshold beyond which AAM becomes prohibitively expensive for most travelers. These findings indicate that AAM would function primarily as a premium service accessible to high-income travelers (likely top 10–20% of income distribution) and business users with employer reimbursement, raising questions about public investment justification given limited accessibility benefits.

While cost dominates adoption decisions, other operational factors demonstrate secondary but meaningful effects. Out-of-vehicle time (waiting and access/egress) shows moderate sensitivity, with combined times increasing from 20 to 60 min, reducing mode share by approximately 50%. This underscores the critical importance of vertiport network density and convenient locations that minimize ground access times. In contrast, aircraft cruise speed variations (100–180 mph) produce surprisingly weak effects, improving mode share only by 44% in relative terms (4.25% to 6.12% absolute) because ground-based access and egress times constitute significant portions of total trip time regardless of flight speed. These patterns indicate that infrastructure planning and network design prove nearly as important as aircraft technological performance for achieving market viability.

Given the current cost projections and limited market potential, Illinois should adopt a phased, evidence-based approach to AAM rather than pursuing immediate large-scale infrastructure investments. Deployment should proceed only when specific market maturity indicators are demonstrated: (i) eVTOL operational safety records established through at least 1 million flight hours across multiple markets; (ii) FAA regulatory frameworks finalized for routine commercial operations beyond experimental authorizations; (iii) business models proven financially sustainable without requiring excessive public subsidization; and (iv) operational costs declining to the $4.00–$6.00 per passenger mile range where mode share projections suggest potential viability. Until these conditions materialize, Illinois resources would likely yield greater mobility benefits through investments in transit improvements, first/last mile connectivity enhancement, and conventional congestion mitigation strategies that serve broader population segments.

Beyond direct mode shift effects, AAM integration presents complex secondary impacts requiring careful consideration. Induced demand from reduced travel times could add 10–25% to mode-shift-based projections, though substantial uncertainty surrounds these estimates given the absence of empirical air mobility data. Ground transportation impacts from vertiport access and egress trips could partially or fully offset congestion benefits, with effects depending critically on vertiport location strategies and resulting access mode distributions. Environmental considerations (including lifecycle emissions dependent on electricity grid composition and noise impacts from concentrated vertiport operations) require mitigation strategies to maintain community acceptance. Equity implications remain significant, as premium pricing structures would limit AAM primarily to affluent travelers, raising questions about public infrastructure investment priorities when transportation-disadvantaged populations derive minimal benefits.

5.2. Summary of Limitations

This study’s limitations stem primarily from uncertainties inherent to emerging AAM technology and the absence of operational data from mature markets. Key limitations are as follows. Critical operational parameters (including service costs, aircraft speeds, and vertiport access/wait/egress times) remain undefined, requiring assumed value ranges that influence demand projections. User perceptions, adoption patterns, and behavioral factors influencing AAM acceptance were not empirically assessed. A UAM service was modeled as uniformly available across all origin–destination pairs to isolate behavioral responses to service characteristics. This simplification produces upper-bound mode share estimates that overstate near-term achievable adoption, while existing vertiport networks would serve only subsets of OD pairs.

Airspace congestion under various air traffic management strategies was not modeled, despite being critical for evaluating system scalability. Ground congestion effects from vertiport access/egress trips and induced demand from AAM introduction receive limited quantitative treatment, though a qualitative analysis of these phenomena is provided. The framework assumes that the FAA will serve as the primary regulatory body for AAM certification and air navigation services, consistent with current aviation governance. However, actual regulatory frameworks remain under development, with the FAA concept of operation documents providing guidance but not constituting formal policy.

Current eVTOL designs feature limited passenger capacity (4–6 seats) with restricted cargo space due to battery weight constraints. Passenger baggage accommodation directly competes with seating capacity, potentially requiring reduced passenger loads that increase per-passenger costs.

These limitations indicate that demand estimates, particularly optimistic scenarios, should be interpreted as upper bounds rather than as precise forecasts, with actual market penetration dependent on technological maturation, cost reduction, infrastructure deployment, and regulatory framework development.

5.3. Future Steps

In the realm of AAM demand, conducting additional research is important for delving deeper into user perceptions and adoption patterns beyond the current study’s scope. Moreover, it is important to outline a clear path for integrating AAM into existing infrastructure. The identification of optimal vertiport locations balancing different objectives, such as accessibility to high-demand origin–destination pairs, minimization of ground access times, integration with existing transit infrastructure, avoidance of environmentally sensitive areas, and mitigation of noise impacts on residential communities, is another important dimension. Subsequent research will address two key areas not fully explored in this study: the operational impacts of AAM integration on existing airport infrastructure and procedures (the reader is directed to the study by Ahrenhold et al. [46]), and the application of UAM and AAM services for airport ground access transportation [47].

On the ATM side, enhancing current understanding of airspace congestion dynamics under various air management and routing strategies is critical. This includes analyzing how different air traffic management approaches affect airspace efficiency and safety, particularly their scalability and adaptability within the evolving urban air mobility ecosystem.

Furthermore, the business models associated with various verticals within the AAM domain require individual examination. Given the range of sectors, such as air taxis, agriculture, emergency services, package delivery, and others, the diversity in the business models needed for large-scale deployment needs to be acknowledged. A comprehensive evaluation of potential public–private arrangements and partnerships that can support the effective implementation and sustainability of these varied business models could also be beneficial.

Survey-based research with Illinois commuters should validate mode choice parameters for AAM services, assess willingness-to-pay distributions across income segments, and identify specific origin–destination corridors demonstrating the highest viability potential. Discrete choice experiments which systematically vary cost, travel time, access requirements, and service reliability to capture behavioral responses to AAM service attributes not observable in revealed preference data can also be insightful.