Orbital Traffic Management
Model satellite constellation density, automated collision-avoidance maneuvers and space-traffic policy compliance in crowded LEO shells.
Why this matters
Model satellite constellation density, automated collision-avoidance maneuvers and space-traffic policy compliance in crowded LEO shells.
This model is adapted from an internal scenario-planning tool, distilled here into three linked calculations that mirror how real operators, engineers and analysts reason about the system.
How the model works
- Satellites in constellation (ratio) — Satellite density per orbital shell (fixed at 6 shells) — higher density shrinks the average spacing between neighbouring satellites.
- Maneuver automation (%) — Share of collision-avoidance maneuvers handled autonomously (68 conjunctions/day baseline) — automation cuts human reaction latency.
- STM policy compliance (%) — Space Traffic Management rating combining regulatory compliance with deorbit-plan timeliness (5-year baseline).
Reading the results
Each control drives one of three underlying formulas taken from the source engineering model. Moving a slider recomputes its metric instantly and updates the 3D bar in the simulation — taller, brighter bars mean the system is closer to its optimum operating envelope. Try pushing each parameter to its extreme to see where the model breaks down or saturates.
Frequently Asked Questions
What is orbital traffic management used for?
Model satellite constellation density, automated collision-avoidance maneuvers and space-traffic policy compliance in crowded LEO shells.
Is this a real-world engineering model or a toy?
The underlying formulas are simplified versions of real planning heuristics used in this domain — accurate enough to show the right trends and trade-offs, but not a substitute for full engineering simulation software.
Can I use my own numbers?
Yes — every slider in the simulation maps directly onto one of the model's input variables, so you can explore scenarios well outside the defaults shown here.
Why does the bar height saturate at the extremes?
Each metric is normalised to a 0–1 range against a realistic reference ceiling from the source model, so very large inputs will visually cap out even though the underlying number keeps growing.