A Modern Approach to Real-Time Air Traffic Management System
Priyank Vaidya, Vedansh Kamdar
TL;DR
The paper tackles real-time air traffic management by designing a streaming flight-data pipeline that ingests, processes, and visualizes large-scale flight information. It employs Apache Kafka for data ingestion, PySpark Streaming for real-time processing, Elasticsearch for storage/indexing, Kibana for live visualization, and Power BI for historical analysis of December 2023 US flights. Key findings include robust throughput and timely insights, with an on-time rate around 83.43% and delays mainly driven by NAS, weather, and security factors, alongside observed regional and airline patterns. The work demonstrates the feasibility and practical impact of real-time analytics in ATM, enabling proactive decision-making and planning, with future work aiming to add predictive capabilities through machine learning.
Abstract
Air traffic analytics systems are pivotal for ensuring safety, efficiency, and predictability in air travel. However, traditional systems struggle to handle the increasing volume and complexity of air traffic data. This project explores the application of real-time big data processing frameworks like Apache Spark, HDFS, and Spark Streaming for developing a new robust system. By reviewing existing research on real-time systems and analyzing the challenges and opportunities presented by big data technologies, we propose an architecture for a real-time system. Our project pipeline involves real-time data collection from flight information sources through flight API's, ingestion into Kafka, and transmission to Elasticsearch for visualization using Kibana. Additionally, we present a dashboard of U.S. airlines on PowerBI, demonstrating the potential of real-time analytics in revolutionizing air traffic management.