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HACK TRENT 2024

Name: Karan Majotra

Inspiration

Flight landing and takeoff optimization, particularly focusing on runway and air traffic management, was chosen as the project’s theme. Airport operations face immense challenges in managing runway congestion, especially during peak hours or adverse weather. This congestion leads to delays, increased fuel consumption, and safety concerns. The idea of optimizing runway usage and managing air traffic more efficiently inspired the development of this solution.

What it does

The project aims to optimize the usage of runways and airspace to address the problem of congestion, delays, and inefficient fuel use. By developing a dynamic runway scheduling and slot optimization algorithm, this project efficiently manages the allocation of time slots for takeoff and landing based on urgency, real-time conditions, and historical data. It uses priority queues, graph-based airspace management, and scheduling algorithms to ensure smooth operations for airports.

How we built it

The system was built using a combination of the following concepts and algorithms:

• Dynamic Slot Allocation: A priority queue was implemented to dynamically allocate time slots for takeoff and landing based on urgency (fuel levels, flight schedules, etc.).
• Graph-Based Airspace Management: We modeled the airspace and runway as a graph, using shortest path algorithms like Dijkstra’s to determine the optimal route for planes, reducing congestion.
• Runway Scheduling Algorithm: We incorporated scheduling algorithms such as Round-Robin and Shortest Job Next (SJN) to ensure efficient runway usage, reducing delays.
• Collision Avoidance: AVL trees and interval scheduling algorithms were used to prevent collision risks by ensuring no two planes were assigned to the same runway or airspace segment simultaneously.
• Real-Time Monitoring: A heap structure was implemented to allow real-time monitoring and adjustments to the queue, ensuring quick responses to changing conditions such as delays or emergencies.

Challenges we ran into

During the development of this project, we encountered several challenges:

• Data Integration: Collecting and integrating real-time data from airport systems and flight schedules proved difficult, as we had to simulate this data for testing.
• Dynamic Adjustments: Managing dynamic adjustments to flight queues in real-time, especially during unexpected events like emergencies, required careful design of the scheduling algorithms.
• Complexity of Algorithms: Implementing algorithms such as Dijkstra’s and AVL trees required a deeper understanding and efficient coding practices to avoid performance bottlenecks.
• Testing Under Real-World Conditions: It was challenging to test the system under real-world constraints, such as weather changes and fluctuating flight volumes, without access to actual flight data.

What we learned

This project provided valuable insights into:

• The intricacies of air traffic management and how algorithms can improve airport operations.
• How to implement and combine advanced data structures like priority queues, AVL trees, and heaps to optimize scheduling systems.
• The importance of real-time data and system flexibility in managing complex processes such as flight landings and takeoffs.

What's next for Runway and Air Traffic Management Optimization

In the future, the system can be further developed to:

• Integrate real-time data feeds from airport systems to enhance scheduling accuracy and efficiency.
• Incorporate machine learning models to predict peak times and optimize slot allocation based on weather conditions, historical data, and flight patterns.
• Expand the system to include international airport networks, enabling cross-airport flight coordination and efficiency improvements on a larger scale.

Built With

• python