Changelog

2026-02-23 — Initial publication.

What is T-100 Traffic Data?

T-100 data represents the most granular publicly available dataset on U.S. airline operations. Unlike Form 41 financial data (which is aggregated at the carrier level), T-100 data breaks down airline operations to the individual flight segment level: each row of a T-100 dataset represents a single origin-destination pair for a specific airline in a specific month. This route-level data reveals passenger flows, capacity deployment, competitive dynamics, and operational metrics that are invisible in aggregate financial reporting.

T-100 data is collected by the Bureau of Transportation Statistics (BTS) under 14 CFR Part 234, the same regulation that governs Form 41 financial reporting. Certificated U.S. airlines and foreign carriers with U.S. service file monthly T-100 reports using standardized forms. The data has been collected in its current form since the 1970s, making it valuable for long-term trend analysis spanning multiple decades.

The term "T-100" originates from the FAA's historical form designation, and the data is now split into two primary categories: domestic traffic (28DS data bank) and international traffic (28IS data bank). Each category contains identical data fields but represents different geographic markets and regulatory requirements.

Domestic vs. International T-100 Data Banks

T-100 data is split into two separate data banks reflecting different regulatory and reporting requirements.

Domestic T-100 (28DS — Data Bank 28 Domestic Segment)

The 28DS data bank contains all monthly flights operated by U.S. certificated carriers on domestic routes (U.S. city pairs, including Alaska and Hawaii). Each record represents a single origin airport, destination airport, and carrier combination for a calendar month. The 28DS database is the largest and most frequently queried of the T-100 data banks, containing approximately 20,000–25,000 unique city pairs annually and spanning dozens of airlines.

Domestic T-100 data is released monthly by BTS with a lag of approximately 30–45 days after month-end. The data is final and not revised once published (unlike some international aviation statistics that are revised as foreign governments report data).

International T-100 (28IS — Data Bank 28 International Segment)

The 28IS data bank contains all flights by U.S. certificated carriers on international routes (U.S. city to foreign city) and flights by foreign carriers with service to the United States. The 28IS database is smaller than 28DS, with approximately 2,000–3,000 unique city pairs and fewer carriers (primarily foreign legacy carriers and U.S. carriers' international operations).

International T-100 data is more complex because it includes foreign carrier reporting requirements and foreign government reciprocity agreements. Data is released with a longer lag (45–60 days) and may be revised if foreign governments submit corrected data. For analysis of U.S. carrier international competitiveness and foreign carrier penetration into U.S. markets, 28IS data is essential.

T-100 Data Fields: What Each Column Means

T-100 data contains approximately 20 standardized fields for each origin-destination-carrier-month record. Understanding these fields is essential for analysis.

Geographic and Carrier Identifiers

• Origin and Destination Airports — Three-letter IATA codes (ATL for Atlanta Hartsfield, LAX for Los Angeles International, etc.). The TranStats query system allows filtering by airport, city, or state.
• Carrier Code — Two or three-letter IATA airline code (AA for American, UA for United, DL for Delta, SW for Southwest, etc.). Foreign carriers also have codes (BA for British Airways, AF for Air France).
• Aircraft Type — Equipment flown on the route, using IATA/ICAO aircraft type codes (73G for Boeing 737-700, A321 for Airbus A321, etc.). This reveals capacity and fuel efficiency by route.

Passenger Traffic

• Revenue Passengers — Total paying passengers enplaned on the route in the month. This is the primary metric for demand measurement.
• Available Seat Miles (ASMs) — Total seats available multiplied by distance. If an airline operates two daily flights of a 150-seat aircraft on a 500-mile route, that's 2 × 150 × 500 × 30 days = 4.5 million ASMs for the month.
• Revenue Passenger Miles (RPMs) — Total paid passenger miles (passengers × distance). A 500-mile flight with 100 passengers = 50,000 RPMs.
• Load Factor — Percentage of seats filled: RPMs ÷ ASMs. If RPMs = 50,000 and ASMs = 75,000 (for example), load factor = 66.7%.

Freight and Mail

• Freight Tons — Cargo (merchandise) enplaned in tons.
• Mail Tons — USPS mail enplaned in tons.
• Freight Available Ton Miles (ATMs) — Cargo capacity in ton-miles.

Post-COVID, cargo data became increasingly important as airlines filled capacity with cargo revenue when passenger demand was depressed. Cargo margins are lower than passenger margins, but cargo provides crucial revenue stability.

Operations

• Departures Performed — Number of flights completed on the route in the month (scheduled departures, not including cancellations).
• Seats Available — Total capacity (seats) deployed in the month: departures × seats per aircraft type.

How to Download T-100 Data from TranStats

Accessing T-100 data is free and straightforward. Here is a step-by-step guide.

Step 1: Navigate to TranStats

Go to https://www.transtats.bts.gov/. On the left sidebar, under "Resources," click "Data Finder" → "Aviation."

Step 2: Select T-100 Data Bank

In the Aviation Data Library, scroll to find "Air Carrier Statistics (T-100)." You will see options for:

• Data Bank 28DS (T-100 Domestic Segment Data) — For U.S. domestic routes
• Data Bank 28IS (T-100 International Segment Data) — For U.S.-international routes
• Data Bank 28DM (T-100 Domestic Market Data) — Market-level aggregation (all carriers combined for a city pair)

Select "Data Bank 28DS" for most analyses. Click the link to open the query interface.

Step 3: Configure Query Parameters

The TranStats query builder allows filtering by:

• Year/Month — Select the month and year. Recent data (2024, 2025) is available; historical data back to the 1970s is archived.
• Carrier — Select all carriers or specific airlines (American, United, Delta, Southwest, etc.).
• Origin and Destination Airports — Filter by specific airports (e.g., LAX to JFK), all routes from a specific airport, or specific city pairs.
• Distance Category (optional) — Filter by flight length (short-haul, long-haul, etc.).
• Data Format — Choose CSV, Excel, or XML.

Step 4: Download and Analyze

Click "Download" or "Get Data." TranStats will generate a file with all matching records. Most downloads complete within seconds. For large queries (e.g., all airlines, all domestic routes, full-year data), downloads may take 30–60 seconds.

Example Query: To analyze competitive dynamics on the LAX-JFK route, set: - Data Bank: 28DS - Year/Month: January 2025 - Origin: LAX - Destination: JFK - Download as CSV The result is a spreadsheet with one row per airline operating LAX-JFK in January 2025, showing passengers, ASMs, load factors, and departures for each carrier.

Alternative: Direct BTS Database Access

For users preferring raw database queries, BTS provides SQL-like access to T-100 data at:

https://datahub.transportation.gov/

This interface allows more advanced filtering and bulk downloading of years of data. However, the interface is less intuitive than TranStats, and queries may return very large files.

T-100 Analysis Techniques: From Data to Insights

T-100 data enables multiple layers of analysis for airport finance professionals, airline analysts, and competitive researchers.

Load Factor by Route

Load factor reveals route-level demand and pricing power. High load factors (above 80%) indicate strong demand or capacity discipline. Low load factors (below 65%) indicate weak demand, excess capacity, or pricing that fails to stimulate demand.

Example: If American Airlines' LAX-JFK flight has an 85% load factor while United's LAX-JFK has a 72% load factor, American's pricing, schedule, or product is more aligned with demand. United may be over-investing in that route or may need to adjust pricing/schedule to fill seats.

Trend analysis of load factor over time reveals whether a carrier is maintaining capacity discipline or loading/offloading capacity in response to demand. Carriers with rising load factors are tightening capacity (bullish signal); carriers with falling load factors are adding capacity (bearish signal for pricing).

Competitive Overlap Analysis

T-100 data reveals which carriers compete on which routes. For a given city pair (e.g., Boston-to-Miami), T-100 shows:

• Which airlines operate the route
• How many weekly flights each carrier operates
• Passenger volume and market share for each carrier
• Load factors, indicating pricing/demand alignment

This is essential for understanding airline competition, particularly in the context of airport rate negotiations. If two airlines dominate a route and both are profitable, they have less incentive to accept high airport charges. If five carriers compete on the same route, each with lower load factors, the route is competitive and price-sensitive to airport charges.

Capacity Deployment Trends

Changes in ASMs year-over-year on a specific route reveal strategic capacity decisions. Rising ASMs indicate the airline is growing service (optimistic outlook, pricing power, or market share gains). Falling ASMs indicate the airline is contracting (pessimistic outlook, market share loss, or redeployment of capacity to more profitable routes).

Example: Southwest's ASMs on LAX-Las Vegas may increase 15% year-over-year while American's ASMs on the same route fall 8%. This suggests Southwest is gaining market share and American is redeploying capacity elsewhere.

Seasonal Patterns

T-100 data reveals strong seasonal patterns: summer leisure travel peaks (June–August), winter holidays peak (November–January), and shoulder seasons (March–May, September–October) show softer demand. By comparing month-to-month data across years, analysts can identify whether an airline is increasing seasonal intensity (more capacity growth in peak months) or smoothing demand across months.

For airports, understanding these patterns is essential for terminal capacity planning and concession revenue forecasting.

Hub Concentration Measurement

T-100 data reveals hub-and-spoke networks by showing which airports have high volumes of traffic to multiple destinations from a single carrier. A hub airport shows asymmetric traffic patterns: many flights from/to a limited set of carriers. Major hubs like Atlanta, Dallas, Chicago, and Denver show high concentration of Delta, American, United, and Southwest traffic respectively.

By measuring Herfindahl-Hirschman Index (HHI) or similar concentration metrics on T-100 data, analysts can quantify hub dependence. High-hub-dependent airports face revenue risk if a hub carrier reduces service.

Combining Form 41 and T-100 Data for Route Profitability

Form 41 data alone shows carrier-wide profitability; T-100 data shows route-level traffic. Combining them enables route-level profitability estimation, which is valuable for airport finance professionals assessing whether specific routes or airline partnerships are sustainable.

Constructing Route-Level Unit Economics

The process involves:

1. Extract T-100 data for the target route (passengers, ASMs, departures)
2. Calculate TRASM (Total Revenue Available Seat Mile) from Form 41 carrier-wide data or industry benchmarks. Use Form 41 to get total revenue and total ASMs, divide to get carrier TRASM. Example: American Airlines TRASM = $12.5 billion revenue ÷ 900 million ASMs = $0.0139 per ASM.
3. Calculate CASM (Cost per Available Seat Mile) from Form 41. Example: American Airlines CASM = $10.8 billion costs ÷ 900 million ASMs = $0.012 per ASM.
4. Estimate route-level profit margin = (TRASM – CASM) × ASMs. If the route has 50,000 ASMs and TRASM is $0.0139 while CASM is $0.012, route operating income = (0.0139 – 0.012) × 50,000 = $0.0019 × 50,000 = $95,000 monthly.

This approach assumes the route carries the carrier's average TRASM and CASM, which is a simplification (premium routes or routes with high fuel intensity may differ), but it provides a useful first-order estimate of route profitability.

Caveats and Refinements

Several caveats apply to this route-level estimation:

• Allocation of fixed costs: Not all of an airline's costs are variable by route. Headquarters, IT, customer service, loyalty programs, and corporate overhead are fixed and allocated across routes using methodologies that vary by carrier. Route-level analysis may underestimate the true economic cost.
• Hub/spoke dynamics: Connecting passengers in a hub generate higher revenue per passenger but also lower load factors on spoke routes. A route that looks unprofitable on mainline passengers alone may be profitable when connecting revenue is included.
• Strategic loss-leaders: Airlines sometimes operate low-profit or loss-making routes to maintain hub presence, connectivity, or competitive positioning. Route-level profitability estimation cannot capture these strategic trade-offs.

Despite limitations, combining Form 41 and T-100 data provides a more nuanced understanding of airline economics than either dataset alone.

Real-World Examples: T-100 Analysis in Action

Consider three hypothetical case studies using actual metrics:

Case 1: Ultra-Low-Cost Carrier Competitive Entry

Suppose Spirit Airlines (a ULCC with CASM of $0.075 per ASM) enters the Boston-to-Tampa route operated for years by Southwest (CASM $0.065) and American (CASM $0.085). T-100 data would show: - Southwest's ASMs declining as the carrier reduces frequency (loses market share to Spirit) - American's ASMs remaining stable or declining (less exposed to ULCC competition due to premium positioning) - Spirit's new entry with rapid growth (increasing passengers, high load factors due to aggressive pricing) This competitive dynamic, visible only in T-100 data month-by-month, reveals that the route is vulnerable to ULCC price competition and that premium carriers like American may hold pricing while Southwest cuts capacity.

Case 2: Route Contraction During Economic Downturn

During an economic recession, T-100 data would show declining passengers and declining ASMs as carriers reduce frequencies. However, the pace of cutback varies by carrier: - Legacy carriers (United, American, Delta) might maintain more capacity on core routes due to fixed gate/slot commitments - Low-cost carriers (Southwest, Frontier) might cut capacity more aggressively, prioritizing profitability over market share - Regional carriers might exit routes entirely T-100 data reveals these dynamics route-by-route, helping airports anticipate service reductions and adjust terminal staffing/costs accordingly.

Case 3: Seasonal Capacity Deployment Strategy

An airport analyzing summer peak season (June-August) T-100 data might observe: - Southwest adding 30% more flights to leisure destinations (Las Vegas, Cancun, Maui) during summer - American adding 15% more flights on business routes (New York, Los Angeles, Chicago) - Frontier adding capacity on low-cost leisure routes Each carrier's seasonal strategy reveals pricing power and market positioning. Southwest's large summer ramp-up indicates strength in leisure pricing; American's smaller ramp suggests business travel is less seasonal or that premium positioning limits seasonal elasticity.

Limitations and Caveats of T-100 Data

While T-100 is invaluable, analysts should understand its constraints.

Passenger Composition Unknown

T-100 shows aggregate passenger volumes but not passenger type (business vs. leisure, first-class vs. economy, connecting vs. non-stop). This obscures yield (revenue per passenger) on each route. Two routes with identical T-100 passengers but different yield (due to different passenger mixes) will have different profitability, but T-100 alone cannot reveal this.

Connecting Passengers Ambiguous

T-100 records passengers by origin-destination segment, but does not distinguish connecting passengers (who generated revenue on a prior segment and will generate revenue on a subsequent segment) from non-stop passengers. In hub-and-spoke analysis, this ambiguity complicates understanding of hub economics.

Aircraft Retirement and Entry Ambiguity

T-100 shows aircraft type by route and month, but does not show the specific tail number (aircraft instance). This makes it difficult to track individual aircraft utilization or age. Analysts needing aircraft-specific data must cross-reference T-100 with FAA aircraft registration databases (N-number registrations).

Reporting Lag

T-100 data is published with approximately 30–45 day lag, making it unsuitable for real-time market monitoring. Industry participants and investors rely on more current data sources (airline seating capacity databases like Sabre/Amadeus, real-time flight tracking) for tactical decision-making.

Accessing Historical T-100 Data

BTS maintains T-100 archives back to 1973, making it one of the longest-running aviation datasets. To access historical data:

1. Visit https://www.transtats.bts.gov/ and select "Data Bank 28DS" or "28IS"
2. Configure queries for historical year/month ranges
3. Download in bulk (yearly files) or by carrier/route
4. For analysis older than 1995, consult archived databases or academic sources like MIT's Airline Data Project

Researchers analyzing multi-decade trends (e.g., "how has airline capacity in domestic markets changed since 1980?") can construct datasets spanning 40+ years, enabling analysis of deregulation impacts, technology adoption (aircraft fleet evolution), and market concentration trends.

Integration with Airport Finance Decision-Making

Airport finance professionals use T-100 data for several critical functions:

Airline Service Planning: T-100 data reveals capacity trends by carrier, informing gate allocation, terminal planning, and capacity expansion decisions. If a carrier is consistently growing ASMs, investing in additional gates serves the carrier and generates terminal rental revenue for the airport.

Revenue Forecasting: T-100 passenger growth trends inform passenger fee revenue projections. If domestic routes are growing 5% year-over-year but international routes are declining, the airport can adjust concession and passenger facility charge (PFC) revenue assumptions accordingly.

Rate Competitiveness Assessment: By analyzing which carriers grow at particular airports versus peer airports, airports can assess whether their rate structure attracts or repels growth. Sustained passenger growth while competitor airports see declines suggests competitive rates; the reverse suggests rates may be above market.