AI Integrated Flight Delay Prediction and Passenger Rerouting

AI-Driven Flight Delay Prediction and Passenger Rerouting

1. Data Collection

1.1. Historical Flight Data

Collect historical data on flight schedules, delays, and cancellations from various airlines.

1.2. Real-Time Data Acquisition

Utilize APIs such as FlightAware or FlightStats to gather real-time flight status updates.

1.3. External Factors

Integrate weather data from sources like OpenWeatherMap or NOAA to assess potential impacts on flight schedules.

2. Data Processing and Analysis

2.1. Data Cleaning

Employ tools like Python’s Pandas library to clean and preprocess the collected data for analysis.

2.2. Predictive Modeling

Utilize machine learning algorithms via platforms such as TensorFlow or Scikit-learn to develop predictive models for flight delays.

2.3. Model Training and Validation

Train the predictive models using historical data and validate their accuracy with metrics such as RMSE and accuracy score.

3. Delay Prediction

3.1. Real-Time Prediction

Implement the trained model to continuously analyze incoming data and predict potential flight delays.

3.2. Alert System

Develop an alert system using tools like Twilio or Slack API to notify stakeholders (airlines, passengers) of predicted delays.

4. Passenger Rerouting

4.1. Impact Assessment

Evaluate the impact of predicted delays on affected passengers using the airline’s booking system.

4.2. Rerouting Options

Utilize AI-driven tools like Amadeus or Sabre to identify alternative flights and routes for affected passengers.

4.3. Automated Communication

Implement automated messaging systems to inform passengers of their rerouting options and assist with rebooking.

5. Feedback Loop

5.1. Data Collection for Continuous Improvement

Gather feedback from passengers and airlines regarding the rerouting process and overall satisfaction.

5.2. Model Refinement

Use the feedback and new data to refine predictive models and improve the accuracy of future predictions.

6. Reporting and Analytics

6.1. Performance Metrics

Generate reports using BI tools like Tableau or Power BI to analyze the effectiveness of the delay prediction and rerouting process.

6.2. Stakeholder Review

Conduct regular reviews with stakeholders to assess performance and identify areas for further enhancement.