Australia relies on domestic aviation to connect people with work, study, family, tourism, health care and regional services. When flights are delayed or cancelled, the impact is not only inconvenience: it can mean missed work, disrupted study, lost tourism income, extra accommodation costs and reduced access for regional communities. These five interactive charts use BITRE airline on-time performance data to examine how flight reliability differs across airlines, routes, route types and months.
On-time arrivals, departures and cancellations across monitored domestic routes.
National flight reliability varies noticeably month by month. Separating on-time performance from cancellations shows that disruption is not only about late flights, but also about services that do not operate at all.
Latest 12 months: on-time arrivals, cancellations and service volume.
Airline reliability differs across the domestic network. The strongest position is the upper-left of the chart, where an airline combines higher on-time arrival performance with a lower cancellation rate. Bubble size shows that reliability should also be interpreted alongside the volume of scheduled services.
Latest 12 months: cancelled flights plus delayed arrivals.
Route-level reliability shows that disruption is not evenly distributed across the domestic network. Some routes expose passengers to a higher combined risk of cancellation or late arrival, which matters for people relying on flights for work, study, family commitments, tourism and regional access.
Latest 12 months: capital-city routes compared with regional and leisure connections.
Grouping routes shows that disruption is not only a capital-city problem. Most routes sit within a similar disruption range, but some regional and leisure connections show higher disruption. This matters because passengers on these routes may have fewer alternative travel options when flights are delayed or cancelled.
Latest 12 months: delayed arrivals plus cancellations as a share of scheduled services.
The heatmap shows that disruption is not spread evenly across time or airlines. Darker cells mark months where a higher share of scheduled flights were either cancelled or arrived late, helping passengers see when reliability problems cluster rather than treating disruption as random.
Bureau of Infrastructure and Transport Research Economics (BITRE). Airline On Time Performance Time Series, April 2026.
I used ChatGPT as a support tool for limited brainstorming, wording refinement and troubleshooting during the development of this data story. The final topic selection, dataset choice, visualisation decisions, interpretation and submission review were completed by me.
Bureau of Infrastructure and Transport Research Economics. (2026). Airline on time performance time series: April 2026 [Data set]. Australian Government Department of Infrastructure, Transport, Regional Development, Communications and the Arts.
OpenAI. (2026). ChatGPT [Generative AI tool]. https://chat.openai.com/