Airports That Keep Showing Up on Cancellation Risk Lists

Some destinations don’t just experience occasional disruption — they recur. Here’s what persistent risk looks like in U.S. flight data.

If you follow airline news, disruption often feels random — a snowstorm here, an air traffic slowdown there. But when you look at route-level cancellation data over time, a different pattern emerges: some airports appear on high-cancellation lists over and over again.

This isn’t about declaring certain airports “bad.” It’s about understanding structural fragility — the places where weather exposure, airspace complexity, schedule density, and connection traffic combine to make disruption more likely to cluster.

Using U.S. DOT BTS cancellation data (Nov 2024–Nov 2025 high-risk route subset), we looked for destinations that repeatedly entered the high-cancellation threshold across multiple months.

The result: persistence is real.

The airports that recur

Persistence = number of months a destination appears in the high-cancellation subset.

Several major connection airports appeared in all 12 months of the dataset. Examples include:

These are not niche airports. They’re core infrastructure for the domestic network — which is precisely why they show up repeatedly.

When a large hub experiences disruption, the impact spreads across many routes, increasing the likelihood that multiple route-carrier combinations cross the high-cancellation threshold in the same month.

Weighted cancellation rates within the high-risk subset illustrate the scale of these recurring appearances:

Those figures don’t represent overall airport cancellation probability — they reflect behavior once a route is already experiencing elevated disruption. In other words: when problems happen, they happen repeatedly in the same places.

Why persistence happens

Persistent appearance does not mean an airport is poorly run. It usually reflects structural characteristics:

Network centrality. Major hubs handle large volumes of connecting passengers. Small disruptions propagate quickly.

Airspace complexity. Northeast corridor airports operate in constrained airspace with heavy traffic coordination.

Weather variability. Airports like DEN combine hub density with volatile winter conditions.

Schedule tightness. High utilization leaves less slack for recovery.

This combination creates a pattern familiar to operations researchers: fragility clustering — systems that function efficiently most of the time but experience repeated threshold failures under stress.

The difference between “risky once” and “risky often”

A single cancellation spike can be bad luck. Recurrence suggests something structural.

In the dataset, many routes appeared once or twice. But a smaller set appeared six, nine, or even all twelve months — indicating that disruption exposure isn’t evenly distributed across the network.

This concentration mirrors a broader finding in transportation systems: a minority of nodes drive a disproportionate share of operational instability.

For travelers, that doesn’t mean avoiding these airports. It means understanding context. A connection through a highly central hub offers more schedule options — but also more ways for disruption to propagate.

What this means for trip planning

Persistent-risk airports often have the best recovery capacity. More flights mean more rebooking options. The trade-off is exposure: when disruption happens, you’re more likely to encounter it there.

A few practical implications:

In other words, persistence isn’t a warning — it’s a planning variable.

A quieter pattern behind travel stress

One of the more surprising takeaways from route-level analysis is how calm the averages look compared to the clustering beneath them.

Air travel reliability hasn’t collapsed. But disruption has become more concentrated in specific routes, corridors, and hub environments.

That concentration makes disruption feel unpredictable to individual travelers, even when the system-level statistics look stable.

Understanding persistence helps bridge that gap.

Before booking, check whether your route shows recurring disruption patterns. FlightCancelRisk’s route calculator surfaces routes that repeatedly cross elevated cancellation thresholds — not just one-off spikes.

Check your route →

Methodology note

This analysis examines route-carrier combinations that entered a high-cancellation threshold between November 2024 and November 2025 using U.S. DOT BTS data. Findings describe patterns within elevated-risk conditions rather than overall national cancellation rates.

Future pieces will explore route concentration, thin-route failure dynamics, and corridor-level clustering — all of which help explain why some trips feel unlucky while others rarely encounter disruption.

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