Total Flights
544,003
Jan 2026
Airlines
13
Mainline + regional
Airports Served
342
Origin + destination
Active Routes
5,812
Unique O–D pairs
On-Time Performance
75.3%
Arrivals within 15 min
Delayed Flights (>15m)
19.8%
107,475 flights
Cancelled
25,635
4.7% of schedule
Diverted
1,146
0.21% of schedule
Avg Departure Delay
13.1 min
Fleet-wide mean
Avg Arrival Delay
6.4 min
Fleet-wide mean
Total Delay Minutes
8.69M
Departure delay, min
Avg Taxi-Out
19.4 min
Gate to wheels-up
Avg Taxi-In
8.8 min
Touchdown to gate
Flights Delayed >15m
107,475
Arrival-side threshold
Daily Flight Volume & Delay Trend
Scheduled volume vs. average departure delay across January 2026
Delay Status Breakdown
Share of all scheduled flights
Departure vs Arrival Delay by Airline
Average minutes, top 8 carriers by volume
Delay Cause Breakdown
Total delay-minutes attributed by cause (delayed flights only)
Top 10 Airlines by Flight Volume
Ranked by scheduled departures in January 2026
◈ Executive Insights
- On-time performance sits at 75.3%, meaning roughly 1 in 5 flights arrives more than 15 minutes late — in line with typical U.S. domestic winter operations.
- Late Aircraft Delay and Carrier Delay are the two largest controllable causes, together accounting for the majority of attributed delay-minutes — pointing to turnaround efficiency and crew/maintenance scheduling as the highest-leverage fix.
- Cancellations (4.7%) are notably elevated for a single month, consistent with winter weather disruption; this warrants a dedicated contingency review (see Page 5).
- Average taxi-out (19.4 min) is more than double taxi-in (8.8 min), suggesting departure-side congestion — runway queuing or gate-hold practices — is a bigger lever than arrival-side flow.
- Southwest, Delta and American operate the largest schedules and therefore disproportionately influence system-wide OTP; small percentage-point gains at these three carriers move the needle furthest.
Delay Rate >15min
19.76%
Of all flights
Median Departure Delay
-2 min
Most flights depart early/on-time
Worst Single Delay
1,000+ min
Extreme tail event
Peak Risk Hour
18:00–19:00
Evening congestion window
Delay Trend by Hour of Day
Average departure delay & % flights delayed, by scheduled departure hour
Delay Trend by Day of Week
1=Monday … 7=Sunday
Departure Time Block Heat Strip
% of flights delayed >15min by scheduled departure window — darker = riskier
Delay Probability Matrix
Day of week × delay rate, sized by flight volume
▲ Risk Insights
- Delay risk builds through the day and peaks in the evening (17:00–20:00 block), a classic cascading-delay pattern: aircraft running late in the morning stay late for every subsequent leg.
- Early morning departures (05:00–07:00) are consistently the most reliable window — aircraft haven't yet accumulated network delay, making this the safest slot for time-critical travel.
- Delay rate varies less by day-of-week than by hour-of-day, indicating that schedule density and turnaround buffers matter more than which day a flight operates.
- Recommendation: build larger schedule buffers into evening-block flights, and prioritize on-time performance monitoring most heavily after 15:00.
On-Time % by Airline
Ranked best → worst, arrivals within 15 minutes
Flight Volume Share
Treemap-style share of total schedule
Cancellation Rate vs Diversion Rate
% of each carrier's schedule, bubble sized by flight volume
Airline Scorecard
Full metric table, sorted by volume
✈ Airline Insights
Top 15 Origin Airports by Volume
Busiest departure hubs in the network
Highest-Delay Airports
Avg departure delay, airports with 500+ flights
Top 15 Routes by Volume
Highest-frequency origin–destination pairs
Most Delayed Routes
Avg arrival delay, routes with 200+ flights
◎ Airport & Route Insights
Delay-Minutes by Cause
Total minutes attributed, all delayed flights
Cause Share (%)
Proportional contribution to total delay
Pareto: Cumulative Contribution
80/20 view — which causes drive the bulk of delay
◐ Cause Insights
- Carrier Delay (37.3%) and Late Aircraft Delay (35.0%) together account for over 72% of all attributed delay-minutes — both are within airline operational control, not external factors.
- NAS (National Airspace System) Delay contributes 18.0% — traffic control, weather-related airspace restrictions, and volume constraints outside any single carrier's control.
- Weather Delay is only 9.6% directly attributed, but likely understates true weather impact since much of it cascades into Late Aircraft and NAS delay categories.
- Security Delay is negligible (0.1%) and not a meaningful lever for improvement.
- Executive takeaway: the single highest-leverage investment is turnaround efficiency (reduces Late Aircraft Delay) followed by crew/schedule buffer optimization (reduces Carrier Delay).
Calendar Heatmap — Daily Delay Intensity
Avg departure delay by day of month; darker = more delayed
Flight Volume by Departure Time Block
Scheduling density across the day
Delay Rate by Distance Group
Distance group 1 = shortest hops, 11 = longest hauls
◷ Time Insights
- Peak congestion falls in the 1600–2000 departure blocks, where the highest flight density overlaps with the highest delay rate — a compounding risk window.
- Short-haul, high-frequency routes (low distance group) show more schedule variability, as tighter turnarounds leave less buffer to absorb upstream delay.
- Weekday-to-weekday delay variance is modest, reinforcing that time-of-day — not day-of-week — is the dominant scheduling lever.
Total Delay Minutes
8.69M
Departure-side, Jan 2026
Est. Aircraft-Hours Lost
~144,880
Delay minutes ÷ 60
Cancellation Impact
25,635 flights
~4.7% of capacity pulled
Diversion Impact
1,146 flights
Unplanned landings
Avg Turnaround Tax
28.2 min
Taxi-out + taxi-in combined
Gate Occupancy Proxy
+13.1 min
Avg dep delay extends gate hold
Delay Waterfall — From Scheduled to Actual
How on-time departures erode into arrival delay across the network
Disruption Funnel
Scheduled flights → completed → on-time
Taxi Congestion — Out vs In
Average minutes by phase; taxi-out is the dominant ground-delay contributor
⚙ Operational Insights
- ~144,880 aircraft-hours were lost to delay in a single month — at typical utilization economics, this represents a substantial recoverable-efficiency opportunity.
- Taxi-out (19.4 min) more than doubles taxi-in (8.8 min), pointing to gate-hold and runway-queue policies as a more actionable lever than arrival-side flow.
- Cancellations removed 4.7% of scheduled capacity — at network scale this cascades into missed connections and downstream aircraft repositioning costs well beyond the cancelled flights themselves.
- Recommendation: prioritize gate/ramp resourcing during the 1600–2000 window (Page 6) where congestion and delay both peak simultaneously.
Risk Score Formula
Business-rule weighted score (0–100), built from four historical reliability signals known before departure
Risk Score = 0.30 × Airline Historical Delay Rate
+ 0.30 × Route Historical Delay Rate
+ 0.25 × Departure-Hour Delay Rate
+ 0.15 × Day-of-Week Delay Rate (×100)
+ 0.30 × Route Historical Delay Rate
+ 0.25 × Departure-Hour Delay Rate
+ 0.15 × Day-of-Week Delay Rate (×100)
Low Risk
Medium Risk
High Risk
Critical Risk
Risk Bucket Distribution
Flights classified into four risk tiers
Model Validation — Predicted Risk vs Actual Delay Rate
Actual observed delay rate (DEP_DEL15) within each predicted bucket
Highest Risk-Score Flights (Sample)
Top-scored flights this period and their actual outcome
◆ Predictive Insights
- The model is well-calibrated: Critical-risk flights actually delayed 55.1% of the time vs just 4.2% for Low-risk flights — a 13x separation confirms the scoring logic captures real signal.
- Most volume sits in Medium/High tiers, meaning most flights carry some baseline risk from route or airline history rather than being purely random events.
- Operational use case: flag Critical/High risk flights automatically each morning for proactive passenger notification, extra ground crew staging, and gate-priority handling.
- Next iteration: incorporate live METAR/TAF weather feeds and real-time upstream aircraft status to move from historical-pattern scoring to true same-day prediction.
1. Optimize schedules to cut Carrier DelayCarrier Delay is the largest controllable cause (37.3%). Rebuild block times for the routes and hours identified on Pages 2 & 6.
2. Improve aircraft turnaround efficiencyLate Aircraft Delay (35.0%) cascades from earlier legs — tighter, monitored turnaround SLAs at hub airports directly reduce it.
3. Increase staffing during peak departure windowsThe 1600–2000 block shows the highest simultaneous volume and delay rate (Pages 2 & 6) — resourcing should scale with it.
4. Strengthen weather contingency planningCancellations ran at 4.7% this month; a dedicated winter-ops playbook would reduce reactive, last-minute cancellations.
5. Prioritize high-risk routes for operational monitoringUse the Most-Delayed-Routes list (Page 4) to assign dedicated ops oversight to chronic underperformers.
6. Optimize gate allocation to reduce taxi congestionTaxi-out (19.4 min) is more than double taxi-in (8.8 min) — gate/ramp sequencing is the highest-leverage ground-ops fix.
7. Improve maintenance scheduling for frequently delayed aircraftCross-reference tail numbers with repeat Late Aircraft Delay incidents to target proactive maintenance windows.
8. Deploy the Flight Risk Score operationallyThe validated model (Page 8) separates Critical (55% actual delay) from Low risk (4%) — use it to proactively notify passengers and stage ground crews.
9. Monitor airport congestion in real timeReal-time dashboards at the highest-delay airports (Page 4) help catch cascading delay before it spreads network-wide.
10. Track airline KPIs continuously, not just monthlyStanding up this dashboard as a live, refreshing Power BI report turns this one-time analysis into an ongoing decision-support tool.