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February 2025
4 min read

Real-time Flight Tracking and Airspace Visualizer | Real Time Analytics

A Real-Time Flight Tracking Dashboard With Airspace Visualization Using Kafka and Superset

Demo video

The Problem: Beyond Traditional ATC

Traditional ATC systems often lack the granular, real-time data and visualization capabilities needed for optimal decision-making in a dynamic airport environment. BLR faced specific challenges:

  • Situational Awareness: ATC needed a complete, real-time picture of the airspace, including precise aircraft locations, statuses, and projected paths.
  • Resource Allocation: Efficiently managing runways, gates, and ground personnel requires accurate arrival/departure predictions and proactive identification of potential delays.
  • Delay Mitigation: Minimizing the cascading effects of delays requires real-time insights into their causes and extent.
  • Communication & Coordination: A centralized platform was needed to improve communication between ATC, ground crews, and other stakeholders.

The Solution: A Real-Time Dashboard

The project delivered a powerful dashboard, built using a modern data stack, that addresses these challenges head-on. Here’s how it works:

  1. Data Acquisition: The system pulls real-time and static flight data from the Aviation Edge Flight API. This API provides a wealth of information, including:

    • Real-time flight tracking (location, altitude, speed, heading)
    • Flight schedules (planned departure/arrival times)
    • Airline and airport information
    • Aircraft details
  2. Data Transformation & Storage: The raw data, which arrives in JSON format, is processed and transformed using Python and Pandas. This involves:

    • Parsing & Normalization: The JSON data is parsed and flattened into a tabular format.
    • Dynamic Table Creation: Custom PostgreSQL functions automatically create database tables based on the structure of the incoming data.
    • Data Loading: Pandas efficiently loads the data into the PostgreSQL database. Real-time data is appended continuously, while static data is updated strategically.
  3. Data Streaming (Kafka): The processed data is streamed through Kafka, using a producer-consumer model, to the postgresql database.

  4. Visualization & Analysis (Apache Superset): The heart of the solution is the interactive dashboard built using Apache Superset. Superset provides a user-friendly interface for visualizing the data and performing ad-hoc analysis. Key features of the dashboard include:

    • Real-time Flight Tracking Map: A map centered on BLR displays the current location of all inbound and outbound flights.
    • Key Metrics: Prominent displays show critical information like:
      • Number of Delayed Flights
      • Average Delay Time
      • Number of Grounded Flights
      • Detailed Flight Schedules
    • Interactive Tables & Charts: Users can drill down into detailed flight information, filter data, and explore trends. This includes breakdowns by airline, origin/destination airport, and flight status.
    • Inbound & Outbound Flight Analysis: Dedicated sections provide detailed views of inbound and outbound flights, including distribution charts and maps.

Key Technologies

  • Aviation Edge API: Provides comprehensive flight data.
  • Python & Pandas: Used for data acquisition, processing, and transformation.
  • PostgreSQL: The relational database used to store and manage the flight data.
  • Psycopg2: PostgreSQL Connection
  • Apache Kafka: A distributed streaming platform for handling real-time data feeds.
  • Apache Superset: An open-source data exploration and visualization platform.

Impact & Benefits

This real-time flight tracking dashboard provides significant benefits to BLR’s ATC operations:

  • Enhanced Situational Awareness: ATC personnel have a complete, real-time view of the airspace.
  • Improved Resource Allocation: Accurate arrival/departure predictions enable better management of resources.
  • Proactive Delay Management: Real-time insights into delays allow for quicker mitigation efforts.
  • Better Communication & Coordination: A centralized platform facilitates seamless information sharing.
  • Increased Efficiency & Safety: The overall result is a more efficient and safer airport operation.

Future Enhancements

The system’s flexible and scalable architecture allows for future expansion. Potential enhancements include:

  • Integration with Weather Data: Adding weather information would provide valuable context for flight operations.
  • Runway Management System Integration: Connecting to runway management systems would further optimize resource allocation.
  • Predictive Analytics: Implementing machine learning models could predict potential delays and other operational challenges.

This project demonstrates the power of data-driven solutions to address complex operational challenges in the aviation industry. By providing real-time insights and a user-friendly interface, the dashboard empowers ATC personnel to make informed decisions, leading to a safer and more efficient airport experience for everyone.