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arxiv: 2605.25456 · v1 · pith:LI7PBEAEnew · submitted 2026-05-25 · 📡 eess.SY · cs.SY

Aircraft and Fleet Sizing for Regional Air Mobility: College Town Case Studies

Jung Ho Park , Changyeob Lee , Shangqing Cao , Raja Sengupta , Mark Hansen , Pavan Yedavalli This is my paper

Pith reviewed 2026-06-29 21:00 UTC · model grok-4.3

classification 📡 eess.SY cs.SY
keywords regional air mobilityaircraft sizingfleet sizingdiscrete choice modelcollege town corridorsprofitabilitytask assignmentmarket share optimization
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The pith

Larger aircraft and fleets do not always improve profitability in regional air mobility for college towns.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper applies a joint optimization framework to aircraft seat configuration and fleet size in regional air mobility services connecting college towns. It integrates passenger mode choice between air service and driving into the scheduling decisions and evaluates performance across three U.S. corridors for 4-, 6-, and 8-seat aircraft at varying costs and fleet sizes. The central finding is that bigger planes and larger fleets do not raise profits in every setting; instead 4-seat aircraft perform best where demand is directionally imbalanced and 6-seat aircraft perform best where demand is balanced or dense. This result matters because it shows operators how to match capacity to market conditions rather than defaulting to scale. The analysis traces performance differences to pricing power, operating costs, and revenue under the integrated choice and assignment model.

Core claim

Using a joint supply-demand optimization framework that integrates a binary logit discrete choice model into a task assignment formulation, the study simultaneously determines market share, fare, and flight schedule for regional air mobility. Across three U.S. college town corridors and configurations from 4 to 8 seats with fleet sizes from 12 to 30, larger aircraft and fleets do not improve profitability universally. Larger aircraft are preferred only where economies of scale are favorable and demand is sufficient and directionally balanced. In these case studies the 4-seat configuration is best in imbalanced markets while the 6-seat configuration is best in balanced or dense markets.

What carries the argument

Joint supply-demand optimization framework that integrates a binary logit discrete choice model into a task assignment formulation to set market share, fare, and schedule together.

If this is right

  • Larger aircraft improve profitability only when demand is sufficient and directionally balanced.
  • The 4-seat configuration yields higher profits than larger options in imbalanced markets.
  • The 6-seat configuration yields higher profits than other options in balanced or dense markets.
  • Fleet size interacts with aircraft configuration so that simply adding aircraft does not raise profits across all markets.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Operators could first measure directional demand balance in a corridor before selecting aircraft size rather than assuming scale always pays.
  • The same optimization structure could be tested on other short-haul markets with similar imbalance patterns to see whether the 4-seat preference generalizes.
  • Adding time-varying factors such as weather or maintenance into the task assignment step might alter the preferred fleet sizes even in balanced markets.

Load-bearing premise

The binary logit discrete choice model accurately captures passengers' mode choice between regional air mobility and driving across the origin-destination pairs in the three corridors.

What would settle it

If observed ridership and mode shares from the three corridors diverge from the shares predicted by the binary logit model under the same fares and schedules, the profitability rankings by aircraft size would shift.

Figures

Figures reproduced from arXiv: 2605.25456 by Changyeob Lee, Jung Ho Park, Mark Hansen, Pavan Yedavalli, Raja Sengupta, Shangqing Cao.

Figure 1
Figure 1. Figure 1: Vertiport networks for the three case-study corridors. [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Average operating cost per mile by aircraft configuration in each [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Daily operating profit across the three case-study markets as a function of fleet size, seat configuration, and cost scale. [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Daily passenger throughput at cost scale 0.6, by market, seat configuration, and fleet size. [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Per-mile fare distribution by market and seat configuration at cost [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Revenue per Available Seat Mile (RASM) as a function of cost scale, by market and seat configuration, at fleet size 12. [PITH_FULL_IMAGE:figures/full_fig_p007_6.png] view at source ↗
read the original abstract

We examine how aircraft seat configuration interacts with daily operation in Regional Air Mobility by applying a joint supply-demand optimization framework that simultaneously determines market share, fare, and flight schedule. The framework integrates a binary logit discrete choice model into a task assignment formulation, capturing passengers' mode choice between Regional Air Mobility and driving across spatiotemporal origin-destination pairs. We evaluate three U.S. college town corridors under 4-, 6-, and 8-seat configurations across cost scales from 0.4 to 1.0 and fleet sizes from 12 to 30 aircraft. Profitability and throughput serve as primary performance metrics, and we analyze pricing power, operating cost, and revenue to explain performance variation across markets. We find that larger aircraft configurations and fleet sizes do not improve profitability universally. Larger aircraft are preferred where economies of scale are favorable and demand is sufficient and directionally balanced. The best configuration in these case studies is the 4-seat in imbalanced markets and the 6-seat in balanced or dense markets.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper claims that a joint supply-demand optimization framework integrating a binary logit discrete choice model for mode choice between Regional Air Mobility and driving shows that larger aircraft seat configurations (6- or 8-seat) and fleet sizes (12-30) do not improve profitability universally across three U.S. college town corridors; instead, 4-seat aircraft are preferred in imbalanced markets while 6-seat are better in balanced or dense markets, with profitability and throughput evaluated over cost scales 0.4-1.0.

Significance. If the mode-choice model is valid for the corridors, the results provide context-specific guidance on aircraft and fleet sizing for Regional Air Mobility, emphasizing demand balance and economies of scale. The joint optimization approach that simultaneously solves for market share, fares, and schedules is a methodological contribution that could inform similar supply-demand problems in transportation systems.

major comments (2)
  1. [Model Formulation and Integration] The binary logit discrete choice model is described as integrated into the task assignment formulation to generate market shares across spatiotemporal OD pairs, yet the manuscript provides no parameter values, estimation procedure, data sources for the college-town corridors, or any goodness-of-fit/validation metrics. This assumption is load-bearing for the central claim because all reported profitability rankings, pricing power, and configuration preferences (4-seat vs. 6-seat) are downstream of the resulting choice probabilities.
  2. [Results and Discussion] The abstract and results sections state that profitability and throughput are evaluated across stated ranges of cost scales and fleet sizes, but supply no sensitivity analysis or error bounds on the logit-driven outputs; without this, it is unclear whether the reported preference ordering (4-seat in imbalanced markets, 6-seat in balanced/dense) is robust to plausible variation in value-of-time or unobserved heterogeneity parameters.
minor comments (2)
  1. [Introduction and Case Studies] The three corridors are referred to only generically in the abstract; naming them and providing basic demand statistics (e.g., daily OD volumes or directional balance metrics) in §2 or a table would improve reproducibility.
  2. [Methodology] Notation for the integrated optimization (e.g., decision variables for task assignment and logit utilities) should be defined in a single table or appendix to aid readers.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments, which highlight important aspects of model transparency and robustness. We address each major comment below and will revise the manuscript to incorporate the requested details and analyses.

read point-by-point responses

  1. Referee: The binary logit discrete choice model is described as integrated into the task assignment formulation to generate market shares across spatiotemporal OD pairs, yet the manuscript provides no parameter values, estimation procedure, data sources for the college-town corridors, or any goodness-of-fit/validation metrics. This assumption is load-bearing for the central claim because all reported profitability rankings, pricing power, and configuration preferences (4-seat vs. 6-seat) are downstream of the resulting choice probabilities.

    Authors: We agree that additional detail on the binary logit model is necessary for reproducibility and to substantiate the downstream results. In the revised manuscript, we will include the specific parameter values, describe the estimation procedure and data sources used for the college-town corridors, and report goodness-of-fit and validation metrics for the choice model. revision: yes

  2. Referee: The abstract and results sections state that profitability and throughput are evaluated across stated ranges of cost scales and fleet sizes, but supply no sensitivity analysis or error bounds on the logit-driven outputs; without this, it is unclear whether the reported preference ordering (4-seat in imbalanced markets, 6-seat in balanced/dense) is robust to plausible variation in value-of-time or unobserved heterogeneity parameters.

    Authors: We concur that sensitivity analysis is needed to assess robustness. The revised manuscript will add sensitivity analyses varying value-of-time and unobserved heterogeneity parameters, along with associated error bounds or confidence intervals on the logit outputs, to confirm the stability of the reported configuration preferences. revision: yes

Circularity Check

0 steps flagged

No significant circularity; results derive from explicit optimization under stated model assumptions.

full rationale

The paper applies a joint supply-demand optimization embedding a binary logit mode-choice model to compute market shares, fares, and schedules for varying aircraft sizes and fleet sizes, then reports profitability and throughput metrics. No quoted step shows a prediction or result that reduces by construction to its own inputs (e.g., no parameter fitted to a data subset then re-predicted as a related output, no self-definitional loop, no load-bearing self-citation chain, and no renaming of known results). The logit integration is presented as a modeling framework whose parameters and data sources are not detailed here, but this is a standard modeling assumption rather than a tautological reduction; outputs depend on inputs in the usual forward-computation sense. The derivation chain remains self-contained as a simulation study and does not meet any enumerated circularity pattern.

Axiom & Free-Parameter Ledger

2 free parameters · 1 axioms · 0 invented entities

The framework rests on standard discrete-choice assumptions and optimization formulations whose parameters are not shown to be derived from first principles or external benchmarks within the abstract.

free parameters (2)
  • cost scales (0.4 to 1.0)
    Ranges supplied for operating cost variation; treated as exogenous inputs to the optimization.
  • fleet sizes (12 to 30)
    Tested fleet sizes chosen for the case studies.
axioms (1)
  • domain assumption Binary logit model accurately represents mode choice between RAM and driving
    The model is integrated into the task assignment without reported calibration or validation against observed data for the corridors.

pith-pipeline@v0.9.1-grok · 5723 in / 1332 out tokens · 36038 ms · 2026-06-29T21:00:32.206294+00:00 · methodology

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