My younger brother is studying aviation and so I became curious as to how air travel had been performing in Australia since COVID-19. Based on my analysis of BITRE data, passenger movements through Australian airports reached 101.1% of pre-COVID levels (2019 levels) for the 12 months ending June 2026. However, I wanted to know how this trend differed between airports.
Analysing monthly data sourced from the Bureau of Infrastructure and Transport Research Economics (BITRE, 2026), this report analyses passenger movements through Australia’s 20 busiest airports from 2019 through to June 2026. It looks at how airports differ in terms of recovery levels, domestic vs international travel and changes over time. Each passenger movement is counted as either an arrival or departure, so domestic trips are counted at both Australian airports.
Note. Monthly points show total passenger movements over the previous 12 months as a percentage of the 2019 annual total. Australia represents the national total across all Australian airports, not just the 20 airports shown in this report. The dashed line represents the 2019 level (100%). Data sourced from Bureau of Infrastructure and Transport Research Economics (2026).
Australian airports have had mixed fortunes over the past year. Total passenger movements across Australian airports in the 12 months to June 2026 were 101.1 per cent of what they were across the whole of 2019. But passenger numbers have not recovered uniformly. Sydney Airport and Melbourne Airport—Australia’s two busiest airports—have had passenger numbers below their 2019 total, currently sitting at 96.4 per cent and 99.0 per cent respectively.
Note. Bars show the change in passenger movements (millions) from July 2025 to June 2026 compared with 2019. Data sourced from Bureau of Infrastructure and Transport Research Economics (2026).
Passenger movements can be compared even further when looking at the actual numerical changes. From July 2025 to June 2026, Perth had the greatest increase in passenger movements with roughly 2.28 million more passenger movements than in 2019. Sydney had the largest decrease with around 1.58 million fewer passenger movements. This demonstrates that although overall passenger movements have resumed to their previous levels, individual airports have experienced varying degrees of growth and loss.
Note. Only airports with 50,000 or more international passenger movements in 2019 are included, along with the national total. Data sourced from Bureau of Infrastructure and Transport Research Economics (2026).
International passenger movements have recovered to a greater extent than domestic movements have at some airports. Sydney Airport, for example, has seen international movements return to 103.3 per cent of the 2019 level, whereas domestic movements have returned to just 92.3 per cent of the 2019 level. The same is true of Melbourne Airport (international movements have reached 108.8 per cent of the 2019 level, compared to 94.7 per cent for domestic movements), although not all airports follow this trend. International recovery through Canberra Airport and Gold Coast Airport was actually weaker than domestic recovery.
Note. Monthly passenger movements are compared with the same month in 2019. Data sourced from Bureau of Infrastructure and Transport Research Economics (2026).
Monthly passenger movements show that recovery did not occur at the same rate over time. Although airport movements fell sharply in 2020 for most airports, the pathway back has not followed the same trend for all airports. Sydney, Melbourne and Canberra experienced another significant decline in movements in 2021. However, other airports including Karratha and Sunshine Coast recovered earlier and later surpassed corresponding monthly totals in 2019. Annual or rolling 12-month figures make this effect less apparent.
Note. Bubble size represents passenger movements in 2019. Data sourced from Bureau of Infrastructure and Transport Research Economics (2026).
Airports that had relatively larger passenger numbers through 2021 (compared to 2019) seem to have higher recovery levels by June 2026 (e.g. Karratha). Sydney had a larger decrease in passengers through 2021 and has stayed below the 2019 mark. There are a few exceptions to this trend, such as Perth seeing a large recovery despite dropping significantly in 2021.
This may suggest a relationship between passenger movements in 2021 and recovery levels by June 2026. However, this still does not fully explain the variation between some airports. Overall, passenger movements across Australia have recovered to pre-pandemic levels; however, some airports show different recovery patterns.
I referred to online R documentation and tutorials for more detailed coding examples, including Chapters 25 and 33 of Interactive Web-Based Data Visualization with R, plotly, and shiny (Sievert, 2019), particularly for custom tooltips and improving interactive charts. I also consulted Plotly documentation for reference lines (Plotly, n.d.), and dplyr and scales documentation for rolling calculations and number formatting (Posit Software, PBC, n.d.-a, n.d.-b). For the colour scheme, I explored aviation-related palettes on ColorAny (n.d.) and used its Family-Friendly Skies: Warmth and Trust palette as a reference.
I used ChatGPT (OpenAI, 2026) to help review, modify and simplify my existing R Markdown code while building the five interactive charts. I also asked for help with troubleshooting errors and adjusting chart formatting and interactive features, such as overlapping titles and legends, inconsistent labels, custom tooltips and bubbles being cut off.
Throughout the process, I spent time testing and adjusting the visualisations in RStudio, including their colours, layouts, labels and interactive features. I reviewed the suggested changes and made further adjustments to improve the readability and overall presentation of the charts.
Bureau of Infrastructure and Transport Research Economics. (2026). Monthly airport traffic data for top twenty airports: January 2009 to June 2026 [Data set]. https://www.bitre.gov.au/resource/aviation/airport-traffic-data
ColorAny. (n.d.). 53 airline color palettes & schemes. https://colorany.com/color-palettes/airline-color-palettes/
OpenAI. (2026). ChatGPT (Oct 27 version) [Large language model]. https://chatgpt.com/
Plotly. (n.d.). Horizontal and vertical lines and rectangles in R. https://plotly.com/r/horizontal-vertical-shapes/
Posit Software, PBC. (n.d.-a). Compute lagged or leading values. dplyr. https://dplyr.tidyverse.org/reference/lead-lag.html
Posit Software, PBC. (n.d.-b). Superseded interface to label_number()/label_comma(). scales. https://scales.r-lib.org/reference/comma.html
Sievert, C. (2019). Interactive web-based data visualization with R, plotly, and shiny. Chapman and Hall/CRC. https://plotly-r.com/