The Aviation Industry as a Graph problem
The Graph Series, part 1
Overview
In this article, we introduce a project that models the global aviation industry as a graph-theory problem. Airports are represented as nodes and direct flight routes as edges, forming a large, sparse, and highly non-uniform network.
The objective of this project is to explore how common aviation questions - such as route reachability, optimal paths between airports, and network-level efficiency - can be expressed as graph computations. As the project evolves, we will progressively introduce increasingly realistic constraints and cost functions, and examine how these affect both correctness and computational performance.
This article serves as the foundation for The Graph Series. Subsequent articles will build on this model to investigate optimisation techniques for graph traversal and path-finding, including algorithmic trade-offs, memoisation strategies, and performance improvements at scale.
The Aviation Industry
The aviation industry is broad and never still. On any given day, tens of thousands of commercial flights operate worldwide, connecting several thousand passenger airports and transporting millions of people across the globe. Beyond leisure travel, aviation underpins a wide range of other sectors — corporate travel, cargo and retail logistics, emergency services, and military operations, to name a few.
Despite this breadth, the scope of this series is intentionally narrow. For the purposes of this project, the focus is limited to leisure-oriented passenger flights, and specifically to the structure of the global flight network itself. Cargo operations, private aviation, and military routes are treated as out of scope.
Even within commercial passenger aviation, there is an overwhelming number of variables that could be modelled. Airlines operate different fleets with varying ranges and capacities, routes are constrained by aircraft performance and permitted airspace, and hub airports play an outsized role in shaping global connectivity. Additional factors such as weather, crew availability, regulatory constraints, and geopolitical considerations further complicate the picture.
This project does not attempt to model all of these factors upfront. Instead, it treats the aviation industry as a layered system: starting with the existence of routes between airports, then progressively introducing additional dimensions such as airline-specific routes, aircraft constraints, and — later in the series — temporal availability and scheduling. This allows individual modelling choices to be examined in isolation, while still grounding the work in a recognisably real-world system.
By clearly defining which parts of the aviation industry are being simulated, and which are intentionally ignored, we can focus on the graph problems themselves without losing sight of the domain they are inspired by.
Methodology
For this project, I have picked Python as the language of choice. The graph itself is implemented using NetworkX, and the underlying data is sourced from OpenFlights, which provides publicly available datasets covering airports, airlines, and direct flight routes.
The ingestion process focuses on building a clean and extensible representation of the aviation network. Airports are mapped to graph nodes, while direct routes between airports are represented as directed edges. At this stage, the emphasis is on establishing a structurally correct graph that can be easily extended with additional attributes and constraints in later iterations of the project.
Whilst the project is in active development, I plan to interact directly with the graph through a simple __init__.py entry point that exposes the graph as a first-class object. This allows for quick experimentation, ad-hoc inspection, and iterative refinement during development. Once the project reaches the benchmarking stages, interaction with the graph will shift to scripted, reproducible workflows designed to generate consistent and comparable performance measurements over time.
Alongside the core graph construction, a lightweight visualisation setup is introduced to generate visual representations of the network. These visualisations are not intended to be exhaustive or perfectly scaled, but rather to provide intuition around graph structure, connectivity, and the emergence of hubs within the aviation network. They also serve as a useful sanity check during development and a visual aid when discussing results later in the series.
This methodological foundation is intentionally kept simple. As the series progresses, the same setup will be reused to explore alternative cost functions, traversal strategies, and optimisation techniques, without changing the underlying data source or tooling.
Assumptions & Simplifications
It goes without saying that the aviation industry is far more complex than the features implemented in this project. This is a deliberately simplified, real-world-inspired example that allows us to explore graph representations, calculations, and optimisation techniques in a concrete setting.
The following assumptions and simplifications are made as part of this exercise:
Direct flights only
Only direct flight routes are represented. Multi-leg journeys are expressed implicitly through graph traversal.Single edge per route (initially)
Routes between two airports are represented by a single directed edge, independent of airline or aircraft. Later in the project, this will be expanded into a multi-edge model to capture airline, aircraft, and other route-level properties.No temporal dimension (initially)
The initial graph is static. Routes represent existence, not schedule or availability. Temporal constraints and availability will be introduced after the transition to multi-edge routes.Uniform edge behaviour
All edges are treated equivalently at this stage. Attributes such as cost, duration, or reliability are deferred to later cost functions.No capacity or congestion modelling
Airports and routes are assumed to have unlimited capacity. Operational constraints such as congestion or delays are out of scope.Airports as atomic nodes
Each airport is represented as a single node, without modelling internal structure.Dataset limitations
Route data is sourced from OpenFlights, which relies on a third-party provider that ceased updates in June 2014. As a result, the routes dataset is of historical value only. The other datasets (airports, airlines) appear to be maintained and are treated as current for the purposes of this project.
As of June 2014, the routes dataset contains 67,663 routes connecting 3,321 airports across 548 airlines worldwide, which is sufficient for structural analysis and optimisation experiments.
These assumptions define the baseline model used in the early stages of the series and will be relaxed incrementally as additional complexity is introduced.
What’s next
Following on from this project abstract, we will look into the initial project’s setup including the necessary data ingestion and modelling, followed by implementing the essential algorithms we will be experimenting with.
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