Flight Punctuality Predictor(FPP)

Introduction

Flight Punctuality Predictor (FPP)

Flight delays present a critical issue for airlines, impacting operational efficiency and causing substantial financial repercussions. The primary goal of this project is to develop a predictive model to forecast flight punctuality, thereby enabling airlines to mitigate delays and enhance overall efficiency.

Storytelling

Flight delays cost airlines billions of dollars annually. For instance, in the United States alone, flight delays and cancellations amounted to approximately $28 billion in direct costs in 2019, according to the Federal Aviation Administration (FAA). Operational disruptions due to delays lead to cascading effects such as missed connections and scheduling conflicts. On average, over 20% of flights worldwide are delayed, causing widespread inconvenience for passengers.

Consider the case of Singapore Changi Airport, renowned for its operational efficiency and world-class service. Changi Airport meticulously times its maintenance and cleaning operations to ensure smooth transitions and minimal downtime between flights. However, even minor delays can have significant repercussions. For instance, if a single flight is delayed, it can cause a domino effect, leading to gate hold-ups, rescheduling of ground services, and increased congestion on the runways. This can disrupt the tightly coordinated operations, leading to inefficiencies and increased operational costs. Moreover, the high standards of service expected at Changi mean that delays can severely impact passenger satisfaction and the airport’s reputation for punctuality and reliability.

Customer satisfaction also takes a hit. According to a study by J.D. Power, satisfaction levels among passengers decrease by 50 points when flights are delayed by just 15 minutes. Dissatisfied passengers may choose alternative airlines for future travel, affecting revenue and market share. Moreover, flight delays tarnish the reputation of airlines, eroding trust and loyalty among passengers. High-profile delays can generate negative publicity and social media backlash, further damaging the brand image of airlines and influencing consumer perception.

To tackle this problem, I decided to create the Flight Punctuality Predictor. This innovative solution leverages advanced machine learning techniques to analyze historical flight data and various influencing factors to provide airlines with actionable insights. Unlike existing solutions that focus solely on historical data analysis or real-time flight tracking, our predictor integrates both, offering real-time predictions that empower airlines to anticipate potential delays and take proactive measures.

Technical Part

The Flight Punctuality Predictor uses a combination of Docker, Python, Pandas, scikit-learn, XGBoost, FastAPI, Uvicorn, and Nginx to deliver a robust and efficient solution.

• Docker ensures consistency and portability across different environments, making it easier to deploy and manage the application.
• Python serves as the primary programming language, utilized for data manipulation and machine learning tasks.
• Pandas is employed for data manipulation and preprocessing, cleaning and preparing the extensive historical flight data for analysis.
• scikit-learn is used to develop and train the predictive model for flight delays, while XGBoost enhances model accuracy and performance.
• FastAPI provides a robust framework for building the backend web API, and Uvicorn runs the FastAPI application, ensuring high performance.
• Nginx manages incoming traffic and ensures smooth operation of the deployed application.

How to Execute and Use It

For development and testing, I am using PyCharm as my Integrated Development Environment (IDE). PyCharm facilitates coding, debugging, and managing the project efficiently.

To run the Flight Punctuality Predictor, you need Docker installed on your system and virtualization enabled in the BIOS. Follow these steps to get the application up and running:

• Clone the Repository:
  
  git clone https://github.com/IliasMCBM/flight-punctuality-predictor.git

• Navigate to the Project Directory:

cd flight-punctuality-predictor

• Build and Start the Docker Containers:

docker-compose up --build

• Access the Application: Open your web browser and navigate to http://localhost/

The application interface is user-friendly, requiring inputs typically found on flight tickets, such as flight number, departure time, and date. Users can easily enter these details to obtain predictions.

Distribution and Results

The front-end interface of the Flight Punctuality Predictor is organized into four main sections, each designed to enhance user experience and functionality:

• Input Section (Top-Right Corner): This section allows users to enter the necessary flight information. The input fields are straightforward and user-friendly, designed to be intuitive since they require data that users typically find on a flight ticket, such as the flight number, departure time, and date.
  
• Flight Route Map (Bottom-Left Corner): In this section, users can view a map that shows the flight route. By clicking the "Show Route" button, the map will highlight the path the flight is expected to take. This feature provides a visual representation of the flight’s trajectory, making it easier for users to understand the planned journey.
  
• Prediction Results and Distance Information (Bottom-Right Corner): This area displays the results of the flight punctuality prediction along with the distance of the flight in miles. The prediction results indicate the likelihood of a delay, while the distance information gives users context about the length of the flight.
  

Each section is designed to provide clear and relevant information, ensuring that users can easily access and understand the details they need.

Conclusion

The Flight Punctuality Predictor offers airlines a powerful tool to enhance operational efficiency, reduce costs, and improve customer satisfaction by accurately predicting flight delays. By integrating advanced machine learning algorithms with real-time data, this solution provides a competitive edge in the market, helping airlines deliver more reliable and punctual services.