MyFlight

How we built it

We built MyFlight using a modular pipeline approach:

Data ingestion parses synthetic flight planning data (routes, waypoints, timing).

Trajectory analysis identifies overlaps, density clusters, and conflict-prone segments.

Insight engine scores conflicts based on severity, cost impact, and efficiency loss.

Visualization layer presents hotspots and recommendations in a planner-friendly way.

The system was designed to be extensible, so additional constraints (fuel burn, emissions, capacity limits) can be integrated easily.

Challenges we ran into

Defining meaningful conflicts: Not all overlaps are equally problematic, so we had to design scoring logic that reflects operational relevance.

Balancing simplicity and realism: The data is synthetic, but the insights still needed to feel realistic and useful.

Performance vs. clarity: We iterated on algorithms to ensure analysis remained fast while still producing interpretable results.

Accomplishments that we're proud of

Building an end-to-end insight pipeline in a limited timeframe

Translating raw trajectory data into clear, actionable recommendations

Designing a solution that could realistically scale to more complex, real-world constraints

What we learned

Insight-driven tools are far more valuable than dashboards that only visualize data

Even simple heuristics can provide strong operational value when well-designed

Aviation planning problems benefit greatly from modular, explainable systems

What's next for MyFlight

Next steps include:

Incorporating cost models (fuel, delay propagation, emissions)

Adding what-if simulations for planners to test alternative scenarios

Enhancing visualizations for time-based congestion analysis

Exploring integration with real-world planning data (outside hackathon use)

Built With

• css
• python