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arxiv: 2606.31142 · v1 · pith:TIIQUJW5new · submitted 2026-06-30 · 🧮 math.OC

Augmenting airline networks using airside-to-airside buses to strengthen system resilience under disruptions

Micah M. Borrero , Max Z. Li This is my paper

Pith reviewed 2026-07-01 05:05 UTC · model grok-4.3

classification 🧮 math.OC
keywords airline network resilienceairside-to-airside busesdisruption managementagent-based simulationmultimodal transportationnetwork augmentationpassenger delaysoperational costs
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The pith

Converting 10 regional air routes to airside buses cuts average passenger delays by 8% on disrupted days under a $10 million budget.

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

The paper examines how airline networks can be made more resilient to disruptions by replacing some short regional flights with airside-to-airside bus service. It builds a model to add these bus lines to the existing air network and tests the results in an agent-based simulation that tracks passenger delays under both normal operations and disruption scenarios. The central finding is that a $10 million investment to convert 10 routes produces measurable reductions in delays while also lowering overall operational costs. This approach is presented as a proactive alternative to reactive measures that respond only after disruptions occur.

Core claim

The paper claims that augmenting an airline network by converting 10 regional routes from air service to airside-to-airside bus service, subject to a $10 million investment constraint, reduces average hourly passenger delays by 8% on disrupted days and 6% on nominal days, as measured in agent-based simulations, while also decreasing operational costs relative to the historic air-only network.

What carries the argument

A network construction model that augments the air transportation network with airside-to-airside bus lines, evaluated through agent-based simulation of average hourly passenger delays under nominal and disrupted conditions.

If this is right

  • The augmented network decreases operational costs compared to the historic air-only network.
  • Continuously expanding bus parameters such as range and investment budget produces diminishing returns in delay mitigation.
  • The modeling approach supplies a decision-support tool for integrating multimodal strategies into disruption management policies.
  • Sensitivity analysis identifies practical limits to scaling the bus services.

Where Pith is reading between the lines

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

  • The same modeling approach could be applied to evaluate bus augmentation at specific high-disruption hub airports to check simulation accuracy against local data.
  • Airport planners might incorporate dedicated airside bus infrastructure when designing future terminals or ground transport links.
  • The observed diminishing returns point to an optimal investment level that future studies could locate more precisely.

Load-bearing premise

The agent-based simulation correctly captures passenger transfer behavior, bus reliability, and the effects of disruptions on the original network without missing real costs or operational limits.

What would settle it

Real-world data from an airport or airline that implements airside bus service on 10 comparable routes showing no reduction or an increase in average passenger delays on disrupted days would falsify the claimed resilience benefit.

Figures

Figures reproduced from arXiv: 2606.31142 by Max Z. Li, Micah M. Borrero.

Figure 1
Figure 1. Figure 1: Airport clusters across the continental U.S. for 𝑘 = 4. M. M. Borrero and M. Z. Li Page 6 of 28 [PITH_FULL_IMAGE:figures/full_fig_p007_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Classified active routes across the Northeastern U.S. Note, as we use 𝑘-means clustering on historic node degree and passenger flow, traditionally large airports (e.g., PHL, LGA, EWR, JFK) are classified as medium hubs due to their lower domestic through-connectivity compared to airports such as ATL, DEN, and DFW. M. M. Borrero and M. Z. Li Page 7 of 28 [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Visualization of passenger flow for a given set of airports 𝑖, 𝑙, 𝑗 ∈ 𝑉𝐴 . all intermediary cities, the total fraction of flow serviced by all stopover cities must be feasible. This is visualized as routes such as those represented by the purple edges in [PITH_FULL_IMAGE:figures/full_fig_p011_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Optimization results for an augmented network obtained using the baseline parameter values in Tab. 1. M. M. Borrero and M. Z. Li Page 11 of 28 [PITH_FULL_IMAGE:figures/full_fig_p012_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Average hourly passenger delay for both the original and augmented New England network(s) for the nominal case on July 21st, 2023. 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 Hour of Day 0.5 1.0 1.5 2.0 2.5 Average Hourly Delay (hrs) Average Hourly Passenger Delay - Day 14 Original: 1.49 hours Augmented - Base: 1.38 hours Augmented - Increased Range: 1.38 hours [PITH_FULL_IMAGE:figures/full_fig_p016_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Average hourly passenger delay for both the original and augmented New England network(s) for the disrupted case on July 14th, 2023. both [PITH_FULL_IMAGE:figures/full_fig_p016_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Sensitivity with respect to objective value (total estimated cost for a given operational day). Pivoting to the operational impacts of these parameter variations, we examine the simulated schedule discussed in Sec. 5.2.3. In particular, we simulate the schedule for the case where the maximum bus range is increased to 300 mi, producing the previously mentioned network visualized in [PITH_FULL_IMAGE:figures… view at source ↗
Figure 8
Figure 8. Figure 8: Optimization results for an augmented network considering buses with an increased range of 300 mi. various input parameters—such as bus range, or the overall investment budget—can lower estimated operational costs, these modifications may yield diminishing returns with regard to passenger delay mitigation. Although not all parameter variations were discussed in this analysis, further investigation is requi… view at source ↗
Figure 9
Figure 9. Figure 9: Landline Company partnership with American Airlines (Hartley, 2025). (a) Screenshot of American Airlines booking interface for a PHL–ACY itinerary on Sept 30, 2025 (captured on Sept 23, 2025) (American Airlines, 2025). (b) Screenshot of Air Canada booking interface for a JFK–YHM itinerary on Sept 30, 2025 (captured on Sept 23, 2025) (Air Canada, 2025) [PITH_FULL_IMAGE:figures/full_fig_p020_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Sample screenshots of airline booking options on buses. designed to facilitate connections on broader, longer-haul trips rather than serving as standalone regional links. For instance, in Fig. 10a, the targeted route for service by American Airlines between Philadelphia (PHL) and Atlantic City (ACY) is effectively priced out as an independent segment, limiting its accessibility for passengers seeking shor… view at source ↗
Figure 11
Figure 11. Figure 11: Average hourly passenger delay for both the original and augmented New England network(s) for the nominal case on July 22nd, 2023. 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 Hour of Day 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 Average Hourly Delay (hrs) Average Hourly Passenger Delay - Day 23 Original: 0.58 hours Augmented - Base: 0.55 hours Augmented - Increased Range: 0.54 hours [PITH_FULL_IMAGE:fi… view at source ↗
Figure 12
Figure 12. Figure 12: Average hourly passenger delay for both the original and augmented New England network(s) for the nominal case on July 23rd, 2023. M. M. Borrero and M. Z. Li Page 23 of 28 [PITH_FULL_IMAGE:figures/full_fig_p024_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Average hourly passenger delay for both the original and augmented New England network(s) for the nominal case on July 15th, 2023. 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 Hour of Day 0.5 1.0 1.5 2.0 2.5 Average Hourly Delay (hrs) Average Hourly Passenger Delay - Day 16 Original: 1.33 hours Augmented - Base: 1.18 hours Augmented - Increased Range: 1.19 hours [PITH_FULL_IMAGE:figures/full_fig_p025… view at source ↗
Figure 14
Figure 14. Figure 14: Average hourly passenger delay for both the original and augmented New England network(s) for the nominal case on July 16th, 2023. M. M. Borrero and M. Z. Li Page 24 of 28 [PITH_FULL_IMAGE:figures/full_fig_p025_14.png] view at source ↗
read the original abstract

Each year, disruptions in the air transportation network strand millions of passengers and cost airlines billions in revenue. Airline networks prioritize operational and cost efficiency through hub-and-spoke structures that maximize revenue; however, these hubs also act as critical choke points during disruptions. Previous studies have focused on reactionary measures in response to air transportation network disruptions, whereas this work proposes a proactive strategy to improve resilience by reconfiguring the network's topology. Specifically, we consider airside-to-airside bus lines as a low-cost, frequent alternative to short, regional flights, offering service that can circumvent air traffic-related delays. We develop a network construction model that augments the existing air transportation network with these bus lines. The augmented networks are analyzed through an agent-based simulation, where increased resilience is measured in terms of decreased average hourly passenger delays under both nominal and disrupted conditions. Our results demonstrate that converting 10 regional routes from air service to airside-to-airside bus service, for a baseline scenario that is constrained by a $10 million investment budget, can reduce passenger delays by an average of 8% on disrupted days and 6% on nominal days. Furthermore, through a sensitivity analysis, we show that while augmenting the system using these buses decreases operational costs compared to the historic air-only network, continuously expanding bus parameters (i.e., range and investment budget) yields diminishing returns in delay mitigation. Finally, we discuss real-world precedents alongside regulatory and political hurdles to implementation. The proposed framework offers airlines, airports, and regulators a decision-support tool for integrating multimodal strategies into future disruption management policies.

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

1 major / 1 minor

Summary. The manuscript proposes augmenting airline networks with airside-to-airside bus lines as a low-cost alternative to short regional flights to improve resilience against disruptions. It develops a network construction model under a budget constraint and evaluates the augmented networks via agent-based simulation, reporting that converting 10 regional routes to bus service under a $10 million budget reduces average passenger delays by 8% on disrupted days and 6% on nominal days. The work also includes sensitivity analysis on bus parameters showing diminishing returns and discusses real-world implementation issues.

Significance. If validated, the proactive multimodal augmentation framework and the reported delay reductions could provide airlines and regulators with a practical decision-support tool for resilience planning. The sensitivity analysis on investment budget and bus range is a positive element that strengthens the analysis by identifying limits to scaling. However, the absence of any reported calibration, validation against historical data, or robustness checks on the simulation assumptions substantially reduces the current significance of the quantitative claims.

major comments (1)
  1. [Abstract] Abstract: The headline quantitative result (8% disrupted-day and 6% nominal-day delay reductions from converting 10 routes under a $10M budget) is produced exclusively by the agent-based simulation, yet the abstract supplies no description of how the simulation was calibrated, validated against real disruption records, or tested for sensitivity to assumptions on passenger transfer behavior, bus reliability, or disruption propagation. This is load-bearing for the central claim, as the reported percentages cannot be assessed without such information.
minor comments (1)
  1. [Abstract] The abstract refers to 'average hourly passenger delays' as the resilience metric but does not define how delays are aggregated or computed within the agent-based model.

Simulated Author's Rebuttal

1 responses · 0 unresolved

Thank you for the opportunity to respond to the referee's report. We address the major comment below.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The headline quantitative result (8% disrupted-day and 6% nominal-day delay reductions from converting 10 routes under a $10M budget) is produced exclusively by the agent-based simulation, yet the abstract supplies no description of how the simulation was calibrated, validated against real disruption records, or tested for sensitivity to assumptions on passenger transfer behavior, bus reliability, or disruption propagation. This is load-bearing for the central claim, as the reported percentages cannot be assessed without such information.

    Authors: We agree that the abstract, constrained by length, omits key details on the simulation. The full manuscript presents the agent-based simulation framework, including modeling of passenger itineraries, transfer behaviors, and disruption scenarios, along with sensitivity analysis on investment budget and bus range. We will revise the abstract to add a concise clause summarizing the simulation approach, noting that parameters are drawn from the literature, that sensitivity testing on bus parameters is included, and that full methodological details appear in the main text. We will also clarify that the simulation relies on synthetic scenarios and literature-based assumptions rather than direct calibration or validation against historical disruption records, as such granular data were not incorporated; this limitation will be noted explicitly. revision: yes

Circularity Check

0 steps flagged

No circularity: delay reductions are forward simulation outputs

full rationale

The paper constructs augmented networks via an optimization model under a budget constraint, then evaluates resilience via agent-based simulation of passenger delays under nominal and disrupted scenarios. The 8%/6% reductions are direct outputs of that simulation applied to the constructed networks; no equations, fitted parameters, or self-citations are shown that would make these outputs equivalent to the inputs by construction. The derivation chain is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Only the abstract is available; no explicit free parameters, axioms, or invented entities are stated. The model implicitly assumes that bus operations can be added without altering air traffic control rules or incurring unbudgeted ground costs.

pith-pipeline@v0.9.1-grok · 5823 in / 1137 out tokens · 39389 ms · 2026-07-01T05:05:00.180141+00:00 · methodology

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