In the world of data science, there is an old axiom that holds truer in aviation than perhaps any other industry: Garbage In, Garbage Out.
If you are building a predictive model, whether it’s to forecast flight delays, optimize supply chain logistics, or power a dynamic pricing engine, the architecture of your machine learning (ML) model is secondary to the quality of your training data. And aviation data is notoriously "dirty."
It is a chaotic ecosystem of changing call signs, shifting time zones, complex codeshare agreements, and operational irregularities. If you feed raw, unprocessed JSON directly into a neural network or a regression model, your predictions will fail.
This guide explores the specific data engineering pipeline required to normalize historical airline flight data. We will move beyond basic cleaning and discuss how to structure your datasets to build models that actually reflect the reality of the skies.
The First Hurdle: The "Codeshare Trap"
The most common mistake developers make when ingesting aviation data is treating every flight number as a unique physical event.
In the airline industry, a single physical aircraft flying from New York (JFK) to London (LHR) might carry five different flight numbers simultaneously (e.g., AA100, BA1500, IB4000). This is known as a Codeshare.
Why this breaks Machine Learning:
If your dataset treats these as five separate rows, your ML model perceives five airplanes landing at the exact same second. This artificially inflates your volume metrics and skews your capacity analysis. It creates "phantom congestion" that doesn't exist.
The Normalization Fix:
You must normalize your data based on the Operating Carrier, not the Marketing Carrier.
- Step 1: Ingest the raw data.
- Step 2: Filter for the flag
is_codeshare: false. - Step 3: Master the data to the "Operating" flight number.
By isolating the "metal", the actual aluminum tube flying through the air, rather than the tickets sold, you ensure your training data reflects physical reality.
Feature Enrichment with an Airport Data API
Historical flight data usually gives you the event (Takeoff, Landing, Delay Minutes). However, to predict why a delay happened, you need to understand the environment.
This is where integrating an Airport data API becomes critical for feature engineering. A delay of 30 minutes at a small regional airport means something very different than a 30-minute delay at a massive international hub.
Out-of-the-Box Idea: Runway & Terminal Weighting
Don't just train your model on the string "LHR" (London Heathrow). That is just a label. Instead, use an API to fetch the metadata for that airport and enrich your dataset with new columns:
-
number_of_runways -
airport_elevation -
terminal_complexity_score
The Logic: A single closed runway at an airport with only two runways (cutting capacity by 50%) is a catastrophic event. The same closure at an airport with five runways is a minor inconvenience. By enriching your historical flight data with these infrastructure features, your ML model learns to weigh the resilience of the airport, leading to far more accurate delay predictions.
Time Normalization: The UTC vs. Local Paradox
Aviation operates on Coordinated Universal Time (UTC/Zulu). However, passenger behavior, airport staffing, and rush hour traffic operate on Local Time.
The Challenge:
If you only use UTC, your model won't understand "Rush Hour." 17:00 UTC is late afternoon in London, but it is morning in Los Angeles. If you try to train a model to predict delays caused by evening congestion using only UTC, the patterns will be washed out.
The Strategy:
You need to normalize your timestamps into two distinct feature sets:
- Sequential Time (UTC): Use this for calculating flight duration, turnaround times, and linking chronological events.
- Cyclical Time (Local): Convert the arrival/departure times to the local IANA timezone (e.g.,
America/New_York) provided by your API.
Use the local time to extract "human" features, such as hour_of_day (0-23) and day_of_week. This allows your model to learn patterns like "Flights departing JFK on Fridays between 16:00 and 19:00 local time have a high probability of taxi-out delays."
Handling Edge Cases: Diversions and "Ghost Flights"
In standard datasets, a diverted flight can look like a data error. The flight was scheduled to land in Chicago (ORD) but the data says it arrived in Indianapolis (IND).
The Fix:
Create a route_integrity boolean flag during your preprocessing phase.
Compare arrival_airport_scheduled against arrival_airport_actual.
- If they match:
True. - If they differ:
False(Diverted).
Depending on your goal, you should either exclude diversions (if you are training a standard schedule reliability model) or segregate them into a specialized "Anomaly Detection" dataset. Training a general model on diverted flight paths will introduce noise that degrades overall accuracy.
The "Turnaround" Feature: Chaining the Tail Number
Most basic flight trackers look at flights in isolation. But in reality, a flight is just one link in a chain.
To build a truly predictive model using historical airline flight data, you need to stitch the data together using the Aircraft Registration (Tail Number).
The "Chain Reaction" Feature:
By querying the history of a specific tail number, you can engineer a feature called incoming_delay_buffer.
- Scenario: Flight B is scheduled to depart at 14:00.
- Data: The aircraft for Flight B is currently flying Flight A, which is delayed and won't land until 13:50.
- Calculation: 10 minutes is not enough time to deplane, clean, and board.
Even if the weather is perfect and the crew is ready, Flight B will be delayed. By normalizing your data to track the specific aircraft rather than just the route, you capture these "knock-on" effects that standard models miss.
Choosing Your Data Source: Top APIs for Developers**
To build this pipeline, you need a data provider that offers both robust real-time streams and deep historical archives in a machine-readable JSON format. Here are the top contenders:
Aviationstack:
Widely considered the most developer-friendly option on the market. Aviationstack is built for scale, offering a free tier that is perfect for testing your normalization scripts. It provides real-time flight tracking and extensive historical data that includes the granular details needed for ML, such as terminal data, gate information, and specific aircraft codes. Its API structure is clean, requiring minimal "munging" before ingestion into Python/Pandas.FlightAware (AeroAPI):
A strong contender for enterprise-level logistics, offering a "Firehose" stream for heavy data users.OAG:
Best known for schedule data and definitive On-Time Performance (OTP) records, often used for post-flight analysis and auditing.
From Raw Logs to Predictive Intelligence**
The difference between a dashboard that looks nice and a dashboard that drives business decisions lies in data engineering.
Raw aviation data is a record of what happened. But when you normalize that data, stripping away codeshares, contextualizing it with an Airport data API, and chaining aircraft histories, it becomes a map of what will happen.
By following these normalization steps, you ensure that your machine learning models are training on clear signals, not noise.
Frequently Asked Questions
Q1: How far back should my historical data go for training?
For most delay prediction models, a "sliding window" of 1 to 3 years is ideal. This captures seasonal trends (winter snow vs. summer storms) without including outdated operational data from a decade ago that no longer reflects current airline schedules.
Q2: How do I handle missing data points in historical logs?
Aviation data often has gaps (e.g., missing "actual runway time"). Do not fill these with zero. Use imputation techniques based on the "Scheduled" times plus the average delay for that specific carrier on that specific route for that month.
Q3: Can I use this data for sustainability reporting?
Yes. By normalizing via the specific aircraft tail number (available in Aviationstack), you can identify the exact aircraft model used (e.g., A320neo vs. A320ceo) to calculate precise CO2 emissions rather than using generic averages.
Recommended Resources
- Get Your Free API Key: Start building your dataset today. Sign up for Aviationstack to access real-time and historical flight data for your ML models.
- Automate Your Workflow: Want to streamline your data ingestion? Check out this step-by-step guide on how to use Aviationstack with Zapier to automate flight data collection in 2025.
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