Building a Machine Learning Model to Predict Flight Delays

August 10, 2024

Flight delays can be a major inconvenience for passengers and a logistical headache for airlines. By leveraging machine learning, we can build a model to predict flight delays based on various factors such as weather conditions, flight status, and historical data. This blog “Machine Learning Model to Predict Flight Delays” will provide an overview of the practical aspects of building such a model and offer pseudocode to guide the implementation.

Data Collection

To build a robust model, we need diverse and comprehensive datasets. Key data sources might include:

Historical Flight Data: Information about past flight schedules, delays, and statuses.
Weather Data: Historical and forecasted weather conditions that can affect flight schedules.
Flight Status Data: Real-time updates on flight statuses.

These datasets can be collected from various APIs such as:

SMHI OpenData API: Provides historical weather data.
Zyla Flight Status API: Offers historical flight data.
SMHI Forecast API: Delivers weather forecasts.
Swedavia Flight API: Supplies flight schedule and status data.

Data Pipeline

To manage and process the data efficiently, we can set up a data pipeline that handles data ingestion, transformation, and storage. The pipeline can be broken down into several components:

Historical Feature Pipeline: Collects and processes historical data.
Daily Feature Pipeline: Collects and processes daily updated data.
Daily Inference Pipeline: Processes incoming real-time data for making predictions.

Pseudocode for Data Pipeline

Here is the pseudocode for setting up a data pipeline:

python

# Import necessary libraries import requests import pandas as pd from datetime import datetime, timedelta # Define API endpoints and parameters SMHI_HISTORICAL_API = ‘https://api.smhi.se/open/historical-weather’ ZYLA_FLIGHT_API = ‘https://api.zyla-flightstatus.com/historical-flights’ SMHI_FORECAST_API = ‘https://api.smhi.se/open/weather-forecast’ SWEDAVIA_FLIGHT_API = ‘https://api.swedavia.com/flight-schedules’ # Function to fetch historical weather data def fetch_historical_weather(start_date, end_date): response = requests.get(SMHI_HISTORICAL_API, params={ ‘start_date’: start_date, ‘end_date’: end_date }) return pd.DataFrame(response.json()) # Function to fetch historical flight data def fetch_historical_flights(start_date, end_date): response = requests.get(ZYLA_FLIGHT_API, params={ ‘start_date’: start_date, ‘end_date’: end_date }) return pd.DataFrame(response.json()) # Function to fetch weather forecast data def fetch_weather_forecast(): response = requests.get(SMHI_FORECAST_API) return pd.DataFrame(response.json()) # Function to fetch flight status data def fetch_flight_status(date): response = requests.get(SWEDAVIA_FLIGHT_API, params={‘date’: date}) return pd.DataFrame(response.json()) # Fetch data for historical feature pipeline start_date = ‘2023-01-01’ end_date = ‘2023-12-31’ historical_weather = fetch_historical_weather(start_date, end_date) historical_flights = fetch_historical_flights(start_date, end_date) # Fetch data for daily feature pipeline daily_weather_forecast = fetch_weather_forecast() daily_flight_status = fetch_flight_status(datetime.now().strftime(‘%Y-%m-%d’)) # Combine and process data combined_data = pd.merge(historical_flights, historical_weather, on=’date’) combined_data = pd.merge(combined_data, daily_weather_forecast, on=’date’, how=’left’) combined_data = pd.merge(combined_data, daily_flight_status, on=’flight_id’, how=’left’)

# Save processed data for training combined_data.to_csv(‘processed_flight_data.csv’, index=False)

Feature Engineering

Feature engineering is crucial for enhancing the performance of the machine learning model. Relevant features for predicting flight delays may include:

Weather Conditions: Temperature, wind speed, precipitation, visibility, etc.
Flight Information: Scheduled departure and arrival times, actual departure and arrival times, airline, flight number, etc.
Temporal Features: Day of the week, time of day, season, holidays, etc.

Model Training

With the processed data and engineered features, we can now train a machine learning model. Commonly used algorithms for predicting flight delays include:

Logistic Regression
Random Forest
Gradient Boosting Machines
Neural Networks

Pseudocode for Model Training

Here is the pseudocode for training a machine learning model:

python

# Import necessary libraries for machine learning from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import accuracy_score, confusion_matrix # Load processed data data = pd.read_csv(‘processed_flight_data.csv’) # Define features and target variable features = [‘temperature’, ‘wind_speed’, ‘precipitation’, ‘visibility’, ‘scheduled_departure’, ‘scheduled_arrival’, ‘airline’, ‘day_of_week’, ‘season’] target = ‘delay’ # Split data into training and testing sets X_train, X_test, y_train, y_test = train_test_split(data[features], data[target], test_size=0.2, random_state=42) # Train a Random Forest model model = RandomForestClassifier(n_estimators=100, random_state=42) model.fit(X_train, y_train) # Make predictions y_pred = model.predict(X_test)

# Evaluate the model accuracy = accuracy_score(y_test, y_pred) conf_matrix = confusion_matrix(y_test, y_pred) print(f’Accuracy: {accuracy}’) print(f’Confusion Matrix:\n{conf_matrix}’)

Model Evaluation and Selection

Evaluating the model’s performance is essential to ensure it accurately predicts flight delays. Common evaluation metrics include:

Accuracy: The percentage of correctly predicted instances out of all instances.
Precision and Recall: Measures of a model’s ability to correctly predict positive instances and the balance between precision and completeness.
F1 Score: The harmonic mean of precision and recall, providing a single metric that balances both.

Pseudocode for Model Evaluation

Here is the pseudocode for evaluating the model:

python

from sklearn.metrics import classification_report # Evaluate the model accuracy = accuracy_score(y_test, y_pred) report = classification_report(y_test, y_pred) print(f’Accuracy: {accuracy}’) print(f’Classification Report:\n{report}’)

Inference Pipeline

Once the model is trained and evaluated, we can set up an inference pipeline to make real-time predictions. This involves processing incoming data, making predictions using the trained model, and returning the results.

Pseudocode for Inference Pipeline

Here is the pseudocode for setting up an inference pipeline:

python

# Function to preprocess incoming data def preprocess_data(raw_data): # Extract relevant features features = [‘temperature’, ‘wind_speed’, ‘precipitation’, ‘visibility’, ‘scheduled_departure’, ‘scheduled_arrival’, ‘airline’, ‘day_of_week’, ‘season’] processed_data = raw_data[features] return processed_data # Function to make predictions def predict_delay(processed_data): predictions = model.predict(processed_data) return predictions # Example of using the inference pipeline new_data = fetch_flight_status(datetime.now().strftime(‘%Y-%m-%d’)) processed_data = preprocess_data(new_data) delay_predictions = predict_delay(processed_data) print(f’Delay Predictions:\n{delay_predictions}’)

Conclusion

Predicting flight delays using machine learning involves collecting and processing diverse datasets, engineering relevant features, training and evaluating models, and setting up an inference pipeline for real-time predictions. By following the practical aspects and pseudocode provided in this guide, you can build a robust model to help predict flight delays and improve the overall passenger experience.