ML-Optimized Flight Route Planning Around Weather

Discretize the airspace between two airports into a directed graph. Create a latitude-longitude grid at cruise altitude with nodes spaced at 1° intervals (roughly 60 nautical miles). Add edges between neighboring nodes — each node connects to its 8 neighbors (cardinal + diagonal directions). Use NetworkX to build the graph structure.

For a realistic test case, use a transcontinental U.S. route (e.g., JFK to LAX). This gives roughly a 40×15 grid with ~4,800 nodes and ~30,000 edges — computationally tractable but large enough to demonstrate meaningful route deviations around weather. Include multiple flight levels (FL350, FL370, FL390) to allow altitude optimization.

Download GFS (Global Forecast System) data from NOAA's NOMADS server. Request 0.25° resolution grids for: u-wind and v-wind at cruise-level pressure surfaces (200–300 hPa), convective available potential energy (CAPE) for thunderstorm risk, turbulence diagnostics (Ellrod index or Richardson number), and tropopause height.

Use xarray and cfgrib to read the GRIB2 files. Interpolate the gridded weather data onto your graph nodes. Each node now has a weather state vector: wind components, turbulence index, and convective risk. This spatial matching between the weather grid and your routing graph is a critical engineering step.

Calculate baseline edge costs using physics. For each edge, compute: ground speed (true airspeed ± wind component along the edge direction), fuel burn (proportional to time = distance/ground_speed for a given thrust setting), and turbulence penalty (scaled by the Ellrod index at the edge midpoint).

The wind effect is crucial and non-trivial: a 100-knot jet stream headwind can increase fuel burn by 20%, while a tailwind saves the same amount. This is why eastbound transatlantic flights are typically faster than westbound. Compute these costs analytically first — this gives you a benchmark for the ML model.

Build a small feedforward neural network in PyTorch that predicts edge traversal cost from a feature vector: wind components (u, v), turbulence index, CAPE, temperature, edge direction, and edge length. Train it on historical flight data where the "true cost" is derived from actual fuel burn records (or, for this project, from your physics-based model with added stochastic weather uncertainty).

The ML model's advantage over the physics model is its ability to learn non-linear interactions between weather variables and capture patterns that simple linear cost functions miss — for example, the interaction between wind shear and convective instability. Use an 80/20 train/test split on historical weather days and validate that the ML model matches physics-based costs within 5% on average.

Implement A* search on your weather-weighted graph using the haversine distance as the heuristic (admissible because great-circle is the shortest possible path). The algorithm explores nodes in order of estimated total cost (cost-so-far + heuristic-to-goal), efficiently finding the optimal route without exploring the entire graph.

Run the optimizer on several weather scenarios: a day with a strong jet stream (route should deviate to ride the tailwind), a day with a line of thunderstorms (route should deviate around convection), and a calm day (route should approximate the great circle). Visualize each route on a map using cartopy or folium with the weather field overlaid.

For each test case, compare the optimized route against the great-circle route. Report: fuel savings (gallons or percentage), time difference (minutes), distance added (nautical miles), and turbulence exposure reduction (integrated turbulence index along the path). On strong jet-stream days, optimized routes can save 5–10% fuel on transcontinental flights.

Run the comparison across 30+ historical weather days to compute statistics. What's the average fuel saving? On how many days does the optimizer find a route significantly different from great-circle? Create a scatter plot of fuel savings vs. jet stream strength — the relationship should be clear and positive.

Investigate how the system behaves under different conditions. Vary the turbulence avoidance weight in the cost function: a high weight produces safer but longer routes, a low weight saves fuel but exposes passengers to rougher air. Plot the Pareto frontier of fuel cost vs. turbulence exposure — this trade-off curve is exactly what airline dispatchers navigate daily.

Document the complete system architecture, validation results, and computational performance. A research-quality write-up should include: problem formulation, data sources, methodology, results, comparison with baselines, and honest discussion of limitations (grid resolution, simplified fuel model, single-altitude optimization). This project is publishable at conferences like AIAA Aviation or AMS Weather and Forecasting.