Update on Australian transport trends (December 2023)

Mon 1 January, 2024

[Updated 29 March 2024: Capital city per-capita charts updated using estimated residential population data for June 2023]

What’s the latest data telling us about transport trends in Australia?

The Australian Bureau of Infrastructure and Transport Research Economics (BITRE) have recently published their annual yearbook full of numbers, and this post aims to turn those (plus several other data sources) into information and insights about the latest trends in Australian transport.

This is a long and comprehensive post (67 charts) covering:

I’ve been putting out similar posts in past years, and commentary in this post will mostly be around recent year trends. See other similar posts for a little more discussion around historical trends (December 2022, January 2022, December 2020, December 2019, December 2018).

Vehicle kilometres travelled

Vehicle and passenger kilometre figures were significantly impacted by COVID lockdowns in 2020 and 2021 which has impacted financial years 2019-20, 2020-21, and 2021-22. Data is now available for 2022-23, the first post-pandemic year without lock downs.

Total vehicle kilometres for 2022-23 bounced back but were still lower than 2018-19:

The biggest pandemic-related declines in vehicle kilometres were in cars, motorcycles, and buses:

All modes showed strong growth in 2022-23.

Here’s the view on a per-capita basis:

Vehicle kilometres per capita peaked around 2004-05 and were starting to flatline in some states before the pandemic hit with obvious impacts. In 2022-23 vehicle kilometres per capita increased in all states and territories except the Northern Territory and Tasmania.

Here is the same data for capital cities:

Cities with COVID lockdowns in 2021-22 (Melbourne, Sydney, Canberra) bounced up in 2022-23, while Brisbane and Perth were relatively flat, Adelaide was slightly up, and Darwin slightly down. All large cities are still well below 2018-19 levels, consistent with an underlying long-term downwards trend.

Canberra has dramatically reduced vehicle kilometres per capita since around 2014 leaving Brisbane as the top city.

Passenger kilometres travelled

Here are passenger kilometres travelled overall (log scale):

The pandemic had the biggest impact on rail, bus, and aviation passenger kilometres. Aviation has bounced back to pre-COVID levels while train and bus are still down (probably due to working from home patterns, reduced total bus vehicle kilometres, amongst other reasons).

Here is the same on a per-capita basis which shows very similar patterns (also a log scale):

Car passenger kilometres per capita have reduced from a peak of 13,113 in 2004 to 10,152 in 2023.

Curiously aviation passenger kilometres per capita peaked in 2014, well before the pandemic. Rail passenger kilometres per capita in 2019 were at the highest level since 1975.

Here’s total car passenger kilometres for cities:

The COVID19 pandemic certainly caused some fluctuations in car passenger volumes in all cities for 2019-20 to 2021-22. In 2022-23, Sydney and Melbourne had not recovered to pre-pandemic levels, while Perth hit a new high.

Here are per capita values for cities:

Car passenger kilometres per capita bounced back in Sydney, Melbourne, and Canberra – however most cities had 2022-23 figures that were in line with a longer-term downward trend – if you disregard the COVID years.

Public transport patronage

BITRE are now reporting estimates of public transport passenger trips (as well as estimated passenger kilometres). From experience, I know that estimating and reporting public transport patronage is a minefield especially for boardings that don’t generate ticketing transactions. While there are not many explanatory notes for this data, it appears BITRE have estimated capital city passenger boardings, which will be less than some ticketing region boardings (Sydney’s Opal ticketing region extends to the Illawarra and Hunter, and South East Queensland’s Go Card network includes Brisbane plus the Sunshine and Gold Coasts). I’ll report them as-is, but bear in mind that they might not be perfectly directly comparable between cities.

Of course bigger cities tend to generate more boardings, so it’s probably worth looking at passenger trips per capita per year:

This chart produces some unexpected outliers. Hobart shows up with very high public transport trips per capita in the 1970s, which might be relate to the Tasman Bridge Disaster which severed the bridge between 1975 and 1977 and resulted in significant ferry traffic for a few years (over 7 millions trips in 1976-77). Canberra also shows up with remarkably high trips per capita in the 1980s for a relatively small, low density, car-friendly city, but has been in steady decline since.

Canberra, Sydney, and Brisbane were seeing rising patronage per capita up to June 2019, just before the pandemic hit.

Most cities (except Darwin and Hobart), showed a strong bounce back in public transport trips per capita in 2022-23, although none reached 2018-19 levels.

There are further reasons why comparing cities is still not straight forward. Smaller cities such as Darwin, Canberra, and Hobart are almost entirely served by buses, and so most public transport journeys will only require a single boarding. Larger cities have multiple modes and often grid networks that necessitate transfers between services for many journeys, so there will be a higher boardings to journeys ratio. If a city fundamentally transforms its network design there could be a sudden change in boardings that doesn’t reflect a change in mode share.

Indeed, here is the relationship between population and boardings over time. I’ve drawn a trend curve to the pre-pandemic data points only (up to 2019).

Larger cities are generally more conducive to high public transport mode share (for various reasons discussed elsewhere on this blog) but also often require transfers to facilitate even radial journeys.

So boardings per capita is not a clean objective measure of transit system performance. I would much prefer to be measuring public transport passenger journeys per capita (as opposed to boardings) which might overcome the limitations of some cities requiring transfers and others not.

The BITRE data is reported as “trips”, but comparing with other sources it appears the figures are boardings rather than journeys. Most agencies unfortunately don’t report public transport journeys at this time, however boardings to journeys ratio could be estimated from household travel survey data for some cities.

Public transport post-pandemic patronage recovery

I’ve been estimating public transport patronage recovery using the best available data for each city (as published by state governments – unfortunately the usefulness and resolution of data provided varies significantly, refer: We need to do better at reporting and analysing public transport patronage). This data provides a more detailed and recent estimate of patronage recovery compared to 2019 levels. Here’s the latest estimates at the time of preparing this post:

Perth seems to be consistently leading Australian and New Zealand cities on patronage recovery, while Melbourne appears to be the laggard in both patronage recovery and timely reporting. For more discussion and details around these trends see How is public transport patronage recovering after the pandemic in Australian and New Zealand cities?.

[refer to my twitter feed for more recent charts]

Passenger travel mode split

It’s possible to calculate “mass transit” mode share using the passenger kilometres estimates from BITRE (note: I use “mass transit” as BITRE do not differentiate between public and private bus travel):

Mass transit mode shares obviously took a dive during the pandemic, but have since risen, although not back to 2019 levels – presumably at least partly because of working from home.

The relative estimates of share of motorised passenger kilometres are quite different to the estimates of passengers trips per capita we saw just above. Canberra is much lower than the other cities, and Brisbane and Melbourne are closer together. The passenger kilometre estimates rely on data around average trip lengths (which is probably not regularly measured in detail in all cities), while the passenger boardings per capita figures are subject to varying transfer rates between cities. Neither are perfect.

So what else is there? I have been looking at household travel survey data to also calculate public transport mode share, but I am getting unexpected results that are quite different to BITRE estimates (especially Melbourne) and with unexpected trends over time (especially Brisbane), so I’m not comfortable to publish such analysis at this point.

What would be excellent is if agencies published counts of passenger journeys (that might involve multiple boardings), so we could compare cities more readily.

Rail Passenger travel

Here’s a chart showing estimates of annual train passenger kilometres and trips.

All cities are bouncing back after the pandemic.

Note there are some variances between the ranking of the cities – particularly Perth and Brisbane (BITRE have average train trip length in Brisbane at around 20.3 km while Perth is 16.3 km).

Here’s rail passenger kilometres per capita, but only up to 2021-22:

Bus passenger travel

Here’s estimates of total bus travel for capital cities:

And per capita bus travel up to 2021-22:

Note that Melbourne has the second highest volume of bus travel (being a large city), but the lowest per-capita usage of buses, primarily because – unlike most other cities – trams perform most of the busy on-street public transport task in the inner city. It probably doesn’t make sense to directly compare cities for bus patronage per capita, and indeed I won’t show such figures for the other public transport modes.

Darwin had elevated bus passenger kilometres from 2014 to 2019 due to bus services to a resources project (BITRE might not have counted these trips as urban public transport).

Ferry passenger travel

Sydney ferry patronage has almost recovered to pre-pandemic levels, while Brisbane’s ferries have not (as at 2022-23).

Light rail / tram passenger travel

Sydney light rail patronage is now growing strongly – after two new lines opened a few months before the pandemic hit.

Road deaths

In recent months there has been an uptick in road deaths in NSW and SA. Victorian road deaths dropped during the pandemic but are back to pre-pandemic levels.

It’s hard to compare total deaths between states with very different populations, so here are road deaths per capita, for financial years:

There is naturally more noise in this data for the smaller states and territories as the discrete number of trips in these geographies is small. The sparsely populated Northern Territory has the highest death rate, while the almost entirely urban ACT has the lowest death rate.

Another way of looking at the data is deaths per vehicle kilometre:

This chart is very similar – as vehicle kilometres per capita haven’t shifted dramatically.

Next is road deaths by road user type, including a close up of recent years for motorcycles, pedestrians, and cyclists. I’ve not distinguished between drivers and and passengers for both vehicles and motorcycles.

Vehicle occupant fatalities were trending down until around 2020. Motorcyclist fatalities have been relatively flat for a long time but have risen slightly since 2021.

Pedestrian fatalities were trending down until around 2014 and have been bouncing up and down since (perhaps a dip associated with COVID lock downs).

Cyclist fatalities have been relatively flat since the early 1990s (apart from a small peak in 2014).

It’s possible to distinguish between motorcycles and other vehicles for both deaths and vehicle kilometres travelled, and the following chart shows the ratio of these across time:

The death rate for motorcycle riders and passengers per motorcycle kilometre was 38 times higher than other vehicle types in 2022-23. The good news is that the death rate for other vehicles has dropped from 9.8 in 1989-90 to 2.7 in 2022-23. The death rate for motorcycles was trending down from 1991 to around 2015 but has since risen again in recent years.

Freight volumes and mode split

First up, total volumes:

This data shows a dramatic change in freight volume growth around 2019, with a lack of growth in rail volumes, a decline in coastal shipping, but ongoing growth in road volumes. Much of this volume is bulk commodities, and so the trends will likely be explained by changes in commodity markets, which I won’t try to unpack.

Non-bulk freight volumes are around a quarter of total freight volume, but are arguably more contestable between modes. They have flat-lined since 2021:

Here’s that by mode split:

In recent years road has been gaining mode share strongly at the expense of rail. This is a worrying trend if your policy objective is to reduce transport emissions as rail is inherently more energy efficient.

Air freight tonnages are tiny in the whole scheme of things so you cannot easily see them on the charts (air freight is only used for goods with very high value density).

Driver’s licence ownership

Here is motor vehicle licence ownership for people aged 15+ back to 1971 (I’d use 16+ but age by single-year data is only available at a state level back to 1982). Note this includes any form of driver’s licence including learner’s permits.

Technical note: the ownership rate is calculated as the sum of car, motorbike and truck licenses – including learner and probationary licences, divided by population. Some people have more than one driver’s licence so it’s likely to be an over-estimate of the proportion of the population with any licence.

Unfortunately data for June 2023 is only available for South Australia, Western Australia and Victoria, so we don’t know the latest trends in all states. South Australia and New South Wales regrettably appear to have recently stopped publishing useful licence holder numbers.

2023 saw a decline in licence ownership in the three states that reported. 2022 was a mixed bag with some states going up (NSW, South Australia, Tasmania), many flat, and the Northern Territory in decline.

Licence ownership rates have fluctuated in many states since the COVID19 pandemic hit, most notably in Victoria and NSW which saw a big uptick in 2021.

The data series for the ACT is unusually different in trends and values – with very high but declining rates in the 1970s, seemingly elevated rates from 2010 to around 2018, followed by a sharp drop. BITRE’s Information Sheet 84 (published in 2017) reports that ACT licences might remain active after people leave the territory (e.g. to nearby parts of NSW) because of delays in transferring their licences to another state, resulting in a mismatch between licence holder counts and population. However, New South Wales requires people to transfer their licence within 3 months of moving there, and other states likely do also. But that requirement might be new, changed, and/or differently enforced over time (please comment if you know more).

Here’s the breakdown of reported licence ownership by age band for the ACT:

Many age bands exceed 100 (more licence holders than population) and there are some odd kinks in the data around 2015-2017 for all age bands (especially 70-79). I’m not sure that it is plausible that licencing rates of teenagers might have plummeted quite so fast in recent years. I’m inclined to treat all of this ACT data as suspect, and I will therefore exclude the ACT from further charts with state/territory disaggregation.

Here’s licence ownership by age band for Australia as a whole (to June 2022):

Between 2021 and 2022 ownership rates for 16-24 year-olds fell slightly, while ownership rates continued to rise for older Australians (quite dramatically for those 80 and over, mostly due to NSW, see below).

Let’s look at the various age bands across the states:

Victoria saw a sharp decline in Victoria to June 2020, followed by a bounce back to a higher rate in 2021. The pandemic has also been associated with increased rates in South Australia, Tasmania, and New South Wales (although it dropped again in 2022). Western Australia and the Northern Territory have much lower licence rates, likely due to different eligibility ages for learner’s permits.

For 20-24 year olds the pandemic caused big increases in the rate of licence ownership in most states, however Victoria, South Australia, and Western Australian appear to have peaked. Licence ownership among 20-24 year olds was still surging in Tasmania up to June 2022.

Similar patterns are evident for 25-29 year olds:

One trend I identified a year ago was that the increasing rate of licence ownership seemed to largely reflect a decline in the population in these age bands during the pandemic period when temporary migrants were told to go home, and immigration almost ground to a halt. Most of the population decline was those without a licence, while the number of licence holders remained fairly steady.

New South Wales appears to follow this pattern, although there was strong growth in licence holders in 2021 and 2022 for teenagers.

Victoria saw a decline in licence holders in 2020 (likely teenagers unable to get a learner’s permit due to lockdowns), but the number of teenage licence holders has since grown. While for those in their 20s, the increase in the licence ownership rate is mostly explained by a loss of population without a licence:

Queensland has experienced strong growth in licence holders at the same time as a decline in population aged 20-29 in 2022. This might be the product of departing temporary immigrants partly offset by interstate migration to Queensland.

To illustrate how important migration is to the composition of young adults living in Australia, here’s a look at the age profile of net international immigration over time for Australia:

For almost all years, the age band 20-24 has had the largest net intake of migrants. This age band also saw declining rates of driver’s licence ownership – until the pandemic, when there was a big exodus and at the same time a significant increase in the drivers licence ownership rate. The younger adult age bands have seen a surge in 2022-23, and in the three states with data the licence ownership rates have dropped (as I predicted a year ago).

Curiously as an aside, 2019-20 saw a big increase in older people migrating to Australia (perhaps people who were overseas returning home during the pandemic lock downs). But then big negative numbers were seen in 2020-21, and since then there has continued to be net departures in 65+ age band.

For completeness, here are licence ownership rate charts for other age groups:

There appear to be a few dodgy outlier data points for the Northern Territory (2019) and South Australia (2016).

You might have noticed some upticks for New South Wales in 2022, particularly for those aged over 80. I’m not sure how to explain this. Here’s all the age bands for NSW:

Here’s Victoria, which includes data to 2023:

For completeness, here are motor cycle licence ownership rates:

Motorcycle licence ownership per capita has been declining in most states and territories, except Tasmania. I suspect dodgy data for New South Wales 2016, and Tasmania 2019.

Car ownership

Thankfully BITRE has picked up after the ABS terminated it’s Motor Vehicle Census, and are now producing a new annual report Motor Vehicle Australia. They’ve tried to replicate the ABS methodology, but inevitably have come up with slightly different numbers in different states for different vehicle types for 2021 (particularly Tasmania). So the following chart shows two values for January 2021 – both the ABS and BITRE figures so you can see the reset more clearly. I suggest focus on the gradient of the lines between surveys and try to ignore the step change in 2021.

Let’s zoom in on the top-right of that chart:

All except South Australia, Tasmania, and ACT showed a decline in motor vehicle ownership between January 2022 and January 2023. This might reflect the recent return of “recent immigrants” (as I predicted a year ago).

Tasmania had a large difference in 2021 estimates between ABS and BITRE that seems to be closing so who knows what might be going on there.

Several states appear to have had peaks – Tasmania in 2017, Western Australia in 2016, and ACT in 2017.

Vehicle fuel types

Petrol vehicles still dominate registered vehicles, but are slowly losing share to diesel:

Can you see that growing slither of blue at the top, being electric vehicles? Nor can I, so here’s the share of registered vehicles that are fully electric (battery or fuel cell, but not hybrids):

The almost entirely urban Australian Capital Territory is leading the country in electric vehicle adoption, while the Northern Territory is the laggard.

Motor vehicle sales

Here are motor vehicle sales by vehicle type:

The trend to larger and heavier vehicles (SUVs) might make it harder to bring down transport emissions (and perhaps reduce road deaths).

Electric vehicle sales are small but currently growing fast in volume and share:

[Updated 7 January 2024:] I’ve included calendar year 2023 sales from FCAI (their 2022 figures were very close to BITRE’s) and calculated the percentage of sales that were battery electric based on FCAI/ABS totals.

Transport Emissions

Transport now makes up 19% of Australia’s greenhouse gas emissions (excluding land use), up from 15% in 2001:

You can see that Australia’s total emissions excluding land use have actually increased since 2001. Emissions reductions in the electricity sector have been offset by increases in other sectors, including transport.

Australia’s transport rolling 12 month emissions dropped significantly with COVID lockdowns, but are bouncing back strongly:

Here are seasonally-adjusted quarterly estimates, showing September 2023 emissions back to 2018 levels:

Transport emissions are around 34% higher in September 2023 than in September 2001, the second highest growth of all sectors since that time:

Here are annual Australian transport emissions since 1975:

And in more detail since 1990:

The next chart shows the growth trends by sector since 1990:

Aviation emissions saw the biggest dip during the pandemic but are now back above 2018 levels.

Here are per capita emissions by transport sector (note: log scale used on Y-axis):

Truck and light commercial vehicle emissions per capita have continued to grow while many other modes have been declining, including a trend reduction in car emissions per capita since around 2004.

Next up, emissions intensity (per vehicle kilometre):

I suspect a blip in calculation assumptions in 2015 for bus and trucks.

Emissions per passenger kilometre can also be estimated:

Car emissions have continued a slow decline, but bus and aviation emissions per passenger km increased in 2021, presumably as the pandemic reduced average occupancy of these modes.

Aviation was reducing emissions per passenger kilometre strongly until around 2004, but has been relatively flat since, and the 2022-23 value is above 2004 levels. This seems a little odd as newer aircraft are generally more energy efficient.

Transport consumer costs

The final category for this post is the real cost of transport from a consumer perspective. Here are headline real costs (relative to CPI) for Australia, using quarterly ABS Consumer Price Index data up to September 2023:

Technical note: Private motoring is a combination of factors, including motor vehicle retail prices and automotive fuel.

The cost of motor vehicles was in decline from around 1995 to 2018 and has been stable or slightly rising since then. Automotive fuel has been volatile, which has contributed to variations in the cost of private motoring.

Urban transport fares (a category which unfortunately blends public transport and taxis/rideshare) have increased faster than CPI since the late 1970s, although they were flat in real terms between 2015 and 2020, then dropped in 2021 and 2022 in real terms – possibly as they had not yet been adjusted to reflect the recent surge in inflation. They picked up slightly in 2023.

The above chart shows a weighted average of capital cities, which washes out patterns in individual cities. Here’s a breakdown of the change in real cost of private motoring and urban transport fares since 1972 by city (note different Y-axis scales):

Technical note: The occasional dips in urban transport fares value are likely related to periods of free travel – eg May 2019 in Canberra.

The cost of private motoring moves much same across the cities.

Urban transport fares have grown the most in Brisbane, Perth, and Canberra – relative to 1972. However all cities have shown a drop in the real cost of urban transport fares in June 2022 – as discussed above.

If you choose a different base year you get a different chart:

What’s most relevant is the relative change between years – e.g. you can see Brisbane’s experiment with high urban transport fare growth between 2009 and 2017 in both charts.

Melbourne recorded a sharp drop in urban transport fares in 2015, which coincided with the capping of zone 1+2 fares at zone 1 prices.

And that’s a wrap on Australian transport trends. Hopefully you’ve found this useful and/or interesting.

2 Comments | Australian Cities, Brisbane, Car ownership, drivers license, Freight, Greenhouse Gas Emissions, Melbourne, Migration, Mode share, Mode shift, Perth, Pricing, Road safety, Road Traffic, Sydney, Vehicle sales | Tagged: , , | Permalink
Posted by chrisloader

How is public transport patronage recovering after the pandemic in Australian and New Zealand cities?

Tue 8 August, 2023

With the COVID19 pandemic seemingly behind us, what has been happening to public transport patronage? Has it recovered to 2019 levels? In which cities is public transport patronage recovering the strongest?

This post provides my best estimates of how much public transport patronage has recovered in major Australian and New Zealand cities.

In my last post I talked about the problems when transit agencies only publish monthly total patronage (or weekly or quarterly totals). For those cities that don’t publish more useful data, I’ve used what I think is a reasonable methodology to try to adjust those figures to take into account calendar effects.

Unlike most of my posts, I’ll present the findings first then explain how I got them (because I reckon a good portion of even this blog’s readers might be less interested in the methodology).

Estimates of typical school week public transport patronage recovery

Here’s a chart comparing estimated typical school week patronage per month to the same month in 2019 (the year before the COVID19 pandemic) where clean data is available. My confidence levels around estimates for each city is discussed further below.

Technical notes: Sydney+ refers to the Opal ticketing region that includes Greater Sydney, Newcastle/Hunter, Blue Mountains, and the Illawarra. Typical school week patronage is the sum of the median patronage for each day of the week (where available), otherwise an estimate of average school week patronage. More explanation below.

Perth has been at or near the top of patronage recovery for most recent months, perhaps partly boosted by a new rail line opening to the airport and High Wycombe in October 2022.

Wellington – which I suspect is an unsung public transport powerhouse – is in second place at 90%, whilst all other cities are between 75% and 83%.

Looking at the 2023 data, most cities appear to be relatively flat in their patronage recovery (except Perth and Wellington), which might suggest that travel patterns have settled following the pandemic (including a share of office workers working remotely some days per week).

How does patronage recovery compare to population growth?

I’ve calculated the change in population for each city since June 2019. For South East Queensland I’ve used an approximation of the Translink service area, and for “Sydney+” I’ve used an approximation of the Opal fare region covering Sydney and surrounds. At the time of writing, population estimates were only available until June 2022.

There are significant differences between the cities.

So how does public transport patronage recovery compare to population change? The following chart shows June 2022 patronage and population as a proportion of June 2019 levels:

The changes in population are much smaller than the changes in patronage and I have deliberately used a similar scale on each axis to illustrate this. Population growth certainly does not explain most of the variation in patronage recovery, but it is very likely to be a factor.

Perth had the highest patronage recovery in June 2022, but only some of this could be attributed to high population growth. Wellington had little population growth but the second highest patronage recovery to June 2022.

Perth might have the highest patronage recovery rate overall because it spent the least amount of time under lockdown, and so commuters had less time getting used to working at home. Melbourne, Sydney, Canberra, and Auckland spent the longest periods under lockdown, and – with the exception of Canberra – seem to be tracking at the bottom end of the patronage recovery ratings, which might reflect their workers becoming more comfortable with working from home during the pandemic. However I’m just speculating.

How has patronage recovery varied by day type?

Here’s patronage recovery for school weekdays (for cities which publish weekday data):

Note: Canberra estimates are only available for July to December because daily patronage data has unfortunately not been published for January to June 2019.

And here is the same for weekends (again for the same four cities that publish weekend data):

Weekend patronage is a bit more volatile as weekends typically have varying levels of major events and planned service disruptions. Most months also only have 8 weekend days, so a couple of unusual days can skew the month average and create “noise” in the data.

However all cities have been above 90% patronage recovery on weekends. Weekend patronage has returned more strongly than weekday patronage, probably because new remote working patterns only significantly impact weekdays.

How has patronage recovery varied between cities by mode?

I’m only confident about predicting modal patronage in cities that report daily or average day type patronage by mode, as the day type weightings used from another city might not apply equally to all modes.

Here is school weekday train patronage recovery for Sydney, Melbourne, and Auckland:

Auckland is slightly below Sydney and Melbourne, and recovery rates are lower than public transport overall. I suspect this may be due to train networks having a significant role in CBD commuting – a travel market most impacted by remote working.

And here is the data for weekends:

Curiously there is a lot more variation between cities. There’s also a lot more variation between months, which could well be related to the “noise” of occasional planned service disruptions and major events.

Here is average school day bus patronage for four cities where data is available:

Bus patronage recovery is lowest in Sydney, perhaps because buses play a more significant role in Sydney CBD commuter travel which will be impacted by working from home (Melbourne’s bus services are mostly not focussed on the CBD). However buses also play a major role in public transport travel to the CBDs of Auckland and Canberra, although with probably lower public transport mode shares (unfortunately it doesn’t seem possible to get public transport mode share for the Auckland CBD from 2018 NZ Census data).

And for completeness, here is a chart for weekend bus patronage:

Weekend bus patronage recovery is higher than weekdays, and higher than weekend train patronage recovery, in all cities. Reported weekend bus patronage in Canberra, Melbourne, and Auckland has exceeded 2019 level in recent months.

How good are these estimates?

Some agencies publish very useful data such as daily patronage or day type average patronage, while others only publish monthly or quarterly totals which is much less useful for trend analysis. Here’s a summary of how I estimated time-series patronage and therefore patronage recovery in each city (which I will explain below).

City/regionData used to estimate time-series patronageConfidence
MelbourneReported average patronage by day of the week and day typeHigh
Sydney+ = Greater Sydney, Newcastle/Hunter, Blue Mountains, Wollongong (Opal catchment)Reported average school weekday and average weekend day patronage per month (dashboard)Moderate
South East Queensland (Translink) – including Brisbane, Gold Coast, Sunshine CoastReported weekly totals, aggregated to months, and adjusted by day type weightings calculated for Melbourne 2022.Lower
AdelaideReported quarterly totals, adjusted by day type weightings calculated for Auckland 2022.Lower
PerthReported monthly totals, adjusted by day type weightings calculated for Auckland 2022.Lower
CanberraReported daily patronage (from July 2019) and monthly total patronage for May and June 2019 adjusted by day type weightings calculated for Canberra 2022 (weekdays) and 2019 (weekends and public holidays). Data pre-May 2019 has been excluded as there was a step change in boardings when a new network was implemented in late April 2019. May 2019 has been included however I should note it had unusually high boardings.Moderate
AucklandReported daily patronage (up to 23 July 2023 at the time of writing).High
WellingtonReported monthly totals, adjusted by day type weightings calculated for Auckland 2022.Lower

For Melbourne and Auckland excellent data is published that allows calculation of typical school week patronage for February to December, which gives me high confidence in the estimates. Canberra has published daily patronage data but only from July 2019 so I’ve had to estimate school week patronage for May and June 2019 from monthly totals (process described below).

You’ll notice I’ve referred to “typical” patronage rather than average patronage. For cities with daily data, I’ve summed the median patronage of each relevant day of the week, rather than taking a simple average of days of that day type in the month. Taking the median can help remove outlier days, and summing over the days of the week means I’m weighting each day of the week equally, regardless of how many occurrences there are in a month (eg a month with 5 Sundays and 4 Saturdays). For Melbourne I’ve only got the average patronage per day of the week, but I’m still summing one value of each day of the week.

Transport for NSW have an interactive dashboard from which you can manually transcribe (but not copy or download) the average school weekday patronage and average weekend daily patronage for each mode and each month. I’ve compiled a typical school week estimate using 5 times the average school weekday plus 2 times the average weekend day. This is likely pretty close to what true average school week patronage is (more discussion below).

But what about the other cities?

How can you estimate patronage trends in cities where only monthly, quarterly, or weekly total patronage data is available?

Rather than simply calculating percentage patronage recovery on monthly totals (which has all the issues I explained in my previous post), I’ve made an attempt to compensate for the day type composition of each month in each city.

Basically this method involves calculating a weighting for each month, based on the day type composition of each month. If you divide total monthly patronage by the sum of weightings for all days of each month you can get a school weekday equivalent figure on which you can do time series analysis.

This requires a calendar of day types, and assumptions around the relative patronage weightings of each day type.

I’ve compiled calendars for each city using various public sources (including this handy machine readable public holiday data by data.gov.au).

Technical note: In New Zealand it seems schools generally are able to vary their start and end of year by up to 5 regular weekdays. I’ve excluded these 10 weekdays from many calculations because they do not represent clean school or school holiday weekdays. For December 2019 I have also excluded two weeks for Auckland due to unusually low reported patronage due to bus driver industrial action.

The assumed day type weightings need to come from another city, on the hope that they will be similar to the true value. But which city, and measured in what year?

I’ve calculated the relative patronage weights of each day type for Melbourne, Canberra, Auckland, plus one school week sample from February 2020 for Sydney+ (Opal region). These are indexed to a school Monday being 1.

Note: no data is available for public holidays in Melbourne, and the Sydney data does not include school holiday weekdays or public holidays.

Melbourne, Canberra, and Auckland weightings are pretty similar across days of the week for school days, but Melbourne’s school holiday weekdays and weekends were relatively busier than both Auckland and Canberra. The Canberra school holiday figures are highly variable between weekdays and are only available for the second half of 2019 (so are impacted more significantly by the timing of Christmas).

The data suggests the big cities of Sydney and Melbourne attract much more weekend patronage compared to the smaller cities. They also have higher public transport mode shares – refer Update on Australian transport trends (December 2022) for comparisons between Australia cities. In terms of public transport share of journeys to work, Auckland was at around 14% in 2018, while Melbourne was 18.2% in 2016). This suggest Melbourne day type weightings might be suitable for larger cities while Auckland day type weightings might be suitable for smaller cities.

The next question is: which year’s weightings should be used? The chart above showed day type weightings from pre-pandemic times, but it turns out they have changed a bit since the pandemic. Here are 2022 day type weightings:

In all cities in 2022 there is a lot more variation across Monday to Friday school days (Mondays and Fridays being popular remote working days) and school holiday weekdays are much more similar between Melbourne and Auckland, while weekends remain quite different.

In fact here’s how the cities with available data compare for ratios between weekends and school weekdays in 2019 and 2022:

The ratios increased in all cities between 2019 and 2022 except Canberra. The 2019 ratios are remarkably close between Melbourne and Sydney, but the 2022 data shows a higher weighting for weekends in Melbourne than Sydney. The Auckland and Canberra ratios are substantially lower in both years. The ratio went down in Canberra in 2022 possibly due to issues obtaining enough drivers to run weekend timetables in that city.

So what day type weightings should we use for each city?

Should we use Melbourne, Auckland, or Canberra weightings, and from what year should we derive these weightings? And how worried should we be about getting these weightings right?

Well, Auckland provides us with daily patronage data for a “medium sized” city, which allows us to compare calculated typical school week patronage, and also allows calculations as if only more summary data was available (as per other cities). However we need to exclude both January and December, as there were no normal school weekdays in those months in 2019.

The red line (total monthly patronage with no calendar effect adjustments) has the most fluctuations month to month and I’m pretty confident this is misleading for all the reasons mentioned in my last post.

Most of the other methodologies produce a figure fairly close to the best estimate (teal line), except in 2021 and 2023.

The green line (compiled 5 x average school day + 2 x average weekend day) is mostly within 2% of the (arguably) best estimate, but there are variations that will be explained by the green line not taking into account the day of the week composition of the month, nor excluding outlier busy/quiet days (unlike medians). So if you only have average school weekday and average weekend day data you’re not going to be too far off the best estimate. That gives me “moderate” confidence to use Sydney’s average school weekday and average weekend day patronage data to estimate patronage recovery.

But what if you only have total monthly patronage and have to use day type weightings? It’s a bit hard to see the differences in the above chart, so here’s a zoom in for 2022 and 2023:

There’s not a lot of difference between the 2019 and 2022 day type weightings, and notably both methods underestimate patronage recovery for most months of 2023, which is not ideal. Note: February 2023 had several days of significant disruptions due to major flooding events which impacted most measures (except the “typical school week” measure that uses medians to reduce the impact of outliers).

Sydney also provides data that allows us to compare day type weighting estimates to the probably quite good compiled school week estimate (based on 5 average school weekdays and 2 average weekend days). The next chart includes estimates of Sydney patronage recovery using day type weightings from Melbourne and Auckland for different years:

Technical note: I have assumed Melbourne public holidays have the same day type weighting as Sundays, for want of more published data.

The estimates are mostly pretty close, but let’s zoom into recent months to see the differences between the methodologies more clearly:

The closest estimate to the compiled average school week data is using Melbourne 2022 day type weightings to adjust monthly totals (the difference is up to 0.9% in April 2023). This suggests Melbourne is probably the best city from which to source day type weightings to apply to Sydney (both large cities), and 2022 (a post-pandemic year) might be a better source year for these weightings. That’s consistent with Sydney having similar ratios of weekday to weekend patronage as Melbourne.

You can see the red line (a simple total monthly patronage comparison) is again often the biggest outlier, which is what happens when you don’t control for calendar effects. I mentioned at the start of my last post that the raw monthly totals suggested a misleadingly large 6.4% drop in patronage recovery from 79.5% in March 2023 to 73.1% in April 2023. On the average school week estimates, patronage recovery dropped only 1.8% from 77.2% to 75.6%.

So which city’s day type weightings are most appropriate for the smaller cities of Perth, Adelaide, Wellington, and Brisbane that don’t currently publish day type patronage? Does it even make a lot of difference?

Well here are patronage recovery estimates for Adelaide, Brisbane, Wellington, and Perth using both Melbourne and Auckland day type weightings from 2022.

Most of the estimates are within 1%, although there are some larger variances for Wellington and Perth.

The Wellington recovery line is smoother for Melbourne weightings in 2021, but smoother with Auckland weightings in 2022 and 2023 (so far). The Wellington estimates can differ by up to 2% and a smoother trend line may or may not mean that one source city for day type weightings is better than the other.

The fact that Melbourne day weightings worked better than Auckland day weightings when it came to Sydney suggests that larger city weightings might be appropriate for other large cities, and perhaps smaller city weightings might be appropriate for other smaller cities.

I have adopted Melbourne day type weightings for South East Queensland, and Auckland day type weightings for Adelaide, Perth, and Wellington, on the principle that larger cities are likely to have relatively higher public transport patronage on weekends (compared to weekdays). Of course I would really rather prefer to not have make assumptions.

That was pretty complicated and involved – is there a lazy option?

Okay, so if you don’t have – or want to compile – calendar data and/or you don’t want to use day type weightings from another city, you can still compile rolling 12 month patronage totals and compare those year-on-year to estimate patronage growth.

The worst times of year at which to measure year-on-year patronage growth are probably at the end of March, June, September, and December (because of when school holidays fall). Of course being quarter ends, these are also probably the most common times it is measured!

It’s slightly better to measure year on year growth for 12 month periods ending with February, May, August, and/or November, as years ending in these months will contain four complete sets of school holidays, and exactly one Easter (at least for countries with similar school terms to Australia and New Zealand). However there will still be errors because of variations in day type composition of those 12 month periods.

In my last post I introduced the mythical city of Predictaville, where public transport patronage is perfectly constant by day type and they follow Victorian school and public holidays. Here is what Predictaville patronage growth would look like measured year on year at end of November each year:

Calculated growth ranges between +0.8% and -0.9%, which is about half as bad as +1.6% to -1.6% when measured at other month ends, but still not ideal (the true value is zero). The errors in the real world will depend on the relative mix of patronage between day types (Predictaville patronage per day type was modelled on Melbourne’s buses).

That’s a not-too-terrible option for patronage growth, but if you are interested in patronage recovery versus 2019 on a monthly basis, I’m not sure there is any reasonable lazy option.

Let’s hope the usefulness of published patronage data improves soon so complicated assumptions-based calendar adjustments and problematic lazy calculation options can be avoided!

5 Comments | Australian Cities, Brisbane, Melbourne, New Zealand cities, Perth, Sydney, Working at home | Tagged: , | Permalink
Posted by chrisloader

We need to do better at reporting and analysing public transport patronage

Wed 12 July, 2023

Sydney’s public transport total patronage in March 2023 was at 79.5% of March 2019 patronage, but then April 2023 total patronage was 73.1% of April 2019 patronage. Does that mean there was a 6.4% decline in the rate of public transport use in April 2023? Actually, no, not at all.

The most common, simple, and obvious way to report public transport patronage is monthly totals. Plenty of agencies do this, but I’m here to argue that invites bad analysis and false conclusions. We can and need to do better.

Let me explain…

Not all days are the same

This is stating the obvious, but patronage generally varies by day of the week, and also between school and non-school weekdays. Here’s how it looks for Melbourne 2019 (thanks to newly published data):

The variations across Monday to Friday have increased even more post the pandemic, but that’s another story.

Not all months are the same

Obviously months are not all the same length. A month with 31 days is generally likely to have higher patronage than a month with 28 or 30 days.

Also, most months have a number of days that is not a multiple of 7 – which means that any month is going have a different mix of days of the week in any year (although three-quarters of Februarys are an exception). And we know patronage varies by individual day of the week.

Furthermore, school holidays and public holidays don’t always fall in the same months each year. In particular, Easter is sometimes in March and sometimes in April, and many jurisdictions shift school holidays to line up with Easter in each year. The end result is that the composition of each month can vary considerably between years, both in terms of days of the week and day types.

Here’s the day type make up of each month for Victoria for 2000 to 2025:

Some months are pretty consistent – May generally has 21 to 23 school weekdays. But other months vary wildly. March can have anywhere between 8 and 17 school weekdays and anywhere between 1 and 5 public holidays (counting all days of the Easter long weekend as public holidays). There are also big fluctuations in June and July, with school holidays mostly falling in July but sometimes partly or fully in June. And any given month might have 4 or 5 Saturdays and 4 or 5 Sundays (or maybe even 3 if one of them is a public holiday).

In March 2006, Victorian autumn school holidays were in March (when Melbourne hosted the Commonwealth Games) instead of the normal April, and the winter school holidays were entirely in June (normally mostly in July). This will happen again in 2026 when Victoria again hosts the Commonwealth Games.

Not all quarters are the same

If months are quite variable in composition, does aggregating to quarters reduce the issues?

In Victoria (and most Australian states), school holidays generally straddle or fall very close to the start/end of quarters. This means there is a fair amount of variability in the day type makeup of most quarters:

In Victoria, quarter 1 can have anywhere between 35 and 44 school days, and between 3 and 7 public holidays. You might also notice that a new Q3 public holiday was introduced in 2016 (Grand Final Eve in late September), and then there was a one-off extra public holiday for the Queen’s death in 2022. The number of public holidays also increases when Christmas Day falls on a Saturday or Sunday.

If you want to understand underling patronage trends, you don’t want to be led astray by these sorts of changes. Quarters are no better than months for analysing total public transport patronage.

Not all financial years are the same either

Another very common way to report patronage data – especially in annual reports – is by financial years (July – June) but they aren’t all that consistent over time either, given that school holidays can slide between June and July, like has happened in Victoria:

Hopefully you’ve got the idea that it isn’t a great idea to analyse total monthly, quarterly, or even financial year patronage if you want to assess trends over time. Yet that’s exactly the most common data you are likely to find.

Calendar years are slightly better for day type composition consistency, but in Victoria calendar years can vary between 50 and 54 school holiday weekdays. And of course every fourth year is one day longer than the others.

A better way: average daily patronage by day of the week and day type

Victoria is now publishing average daily patronage by day of the week and day type (school or non-school weekday) for each month (excluding public holidays).

This means it is possible to compile average school week patronage (being the sum of average daily Sunday, school Monday, school Tuesday, school Wednesday, school Thursday, school Friday, and Saturday patronage). An average school week patronage figure is readily calculable for all months except January (because they have few school days and variable start dates between schools make it a bit messy).

An average school week figure can be calculated regardless of shifting dates of school holidays, public holidays and the general day of the week composition of any month across years. And analysts don’t need to worry about having their own calendar of school and public holidays.

How much cleaner is average school week patronage? Victoria also publishes monthly totals, which makes it possible to compare to average school week figures, as per the following chart:

Note: This time period of course includes the COVID pandemic including lockdowns which is interesting in itself, but for the point of this post I suggest you focus on pre-pandemic years 2018 and 2019.

Monthly total patronage jumps up and down a fair bit in 2018 and 2019 (with higher totals for most 31-day months, who would have guessed?), but average school weekday patronage was relatively smooth across the year as you might expect. The average school week patronage data also shows clearly that March is the busiest month of the year. But if you looked at the monthly totals you might draw the false conclusion that days in May are generally as busy as days in March (at least for 2018).

To further illustrate the differences, the next chart compares monthly average school week patronage to total monthly patronage, for all months February 2018 to March 2023 (excluding Januarys). Each dot is a mode and a month/year, and if total monthly patronage was as good a representation of patronage as school week patronage then this would need to be a fairly thin cloud as data points.

You can see the cloud is not very thin. March and April are often outliers, as they are subject to shifting Easter and school holidays.

March 2020 is actually one of the biggest outliers – as it was also when Melbourne first went into a COVID lockdown.

Another common form of analysis in recent (pandemic) times has been to compare monthly patronage to the same month in (pre-pandemic) 2019, to get an indication of patronage recovery. The following chart shows patronage relative to 2019 using both total monthly patronage and average school week patronage for Melbourne’s public transport:

You can see the orange line (total monthly patronage) is prone to bouncing around month to month, while the blue line (average school week) generally shows a smoother trend. Someone not knowing any better might have been slightly alarmed or confused about the steep decline in total monthly patronage recovery in October 2022, whereas on the average school week measure it was only a slight drop on September 2022 (perhaps because of planned disruptions to the network?).

Total March 2023 Melbourne public transport patronage was 78.8% of the total in March 2019, but 75.3% of the March 2019 average school week patronage. The total monthly patronage approach arguably overestimates the likely underlying patronage recovery by 3.5%.

How much noise gets introduced when analysing monthly totals?

Let me introduce you to the mythical city of Predictaville. Nothing ever changes in Predictaville. There is no population growth, no behaviour change, no pandemics, no seasons, no illness. It’s very boring and entirely predictable.

Every school weekday there are exactly 440,000 public transport boardings, every school holiday weekday it is 340,000, every Saturday 195,000, Sunday 135,000, and public holiday 110,000. Remarkable round numbers, I know. It’s been like this since 2001 and all indications are that it will be like this until at least 2025.

These numbers just happen to be pretty similar to average Melbourne bus patronage in 2019, and Predictaville just happens to follow the Victorian school and public holiday calendar.

So public transport patronage growth in Predictaville is going to be exactly zero all the time, right?

That’s what you will get when measuring growth by average school weeks.

But what if you were measuring growth year on year using total monthly patronage?

According to this measure, patronage in Predictaville sometimes grows at +1.6% per annum and sometimes declines at -1.6% per annum. You might think 2012 and 2020 were growth years, while 2005, 2008 and 2021 saw declines. All misleading.

This is actually a chart indicating the sort of avoidable error introduced when you do analysis on total monthly patronage. We don’t need and shouldn’t have this sort of error in our analysis; particularly if it is going to influence policy decisions.

How is patronage data being reported in Australia and New Zealand now?

At the time of writing…

Transperth and Wellington’s Metlink only publish monthly totals, which is problematic, as explained above.

Transport for NSW publishes monthly total patronage which is likewise problematic.

They also have an interactive dashboard that effectively provides average weekday patronage, average school weekday patronage, and average weekend/public holiday daily patronage (but not average school holiday weekday patronage). Unfortunately you cannot download this data, and it doesn’t take into account that different months contain different numbers of Sundays, Mondays, Tuesdays, Wednesday, Thursdays, Fridays, and Saturdays. We know patronage varies by day of the week. Worse still, there is likely a significant difference between typical Saturday and Sunday patronage and different months might have 4 or 5 Saturdays and 4 or 5 Sundays. This means the average weekend daily patronage figures have avoidable misleading variations between months and years.

NSW have also published very detailed Opal data for selected weeks in 2016 and 2020 (only two were pre-pandemic school weeks), but no such data on an ongoing basis.

Transport Canberra reports average weekday and weekend daily boardings by quarter, but doesn’t distinguish school and non-school weekdays and bundles public holidays with weekends, which makes it very difficult to cleanly analyse growth trends. However they also publish daily data which is useful, but you need a calendar of school and public holidays.

The South Australian government reports quarterly Adelaide boardings (buried in a dataset about complaints) which is problematic as I’ve explained above. You can also get daily Metrocard validations by route (not quite the same as boardings), although exact numbers are not reported – just bands in multiples of 10, which makes it pretty much impossible to sum to estimate total daily patronage.

Translink (south east Queensland) publishes weekly passenger trip counts (to 2 decimal places!), which is slightly better than monthly, but analysts still need to have their own calendar of school holidays or public holidays to make sense of variations week to week. And at the time of writing data unfortunately hadn’t been updated since October 2022.

Auckland publishes monthly and daily patronage figures which is very handy. The daily data allows you to construct average school week patronage, but you need a calendar of school and public holidays.

So my plea to all agencies is to consider publishing average daily patronage by day type and day of the week, as Victoria now does. This will enable external analysts to do cleaner patronage analysis with much less effort. Perhaps an organisation like BITRE could even compile a national database of such data.

Even better would be for agencies to also publish daily patronage estimates, along with a day type calendar including school and public holidays, which would enable analysts to do even more with the data.

Can we do even better for patronage reporting and analysis?

Average patronage by day of the week and day type avoids much of the misleading variations in total monthly patronage data, helping us to better understand underlying trends.

But there will still be other smaller sources of misleading variations and you could definitely take this further, for example:

  • Filter out Mondays next to a Tuesday public holiday and Fridays next to a Thursday public holiday, as these are popular days for workers to take leave to create a long weekend.
  • Likewise, filter out weeks with more two or more public holidays falling within Monday to Friday – which are also popular times to generate longer holidays with fewer annual leave days.
  • Filter out the first / last weeks of the school year if some schools follow a slightly different calendar (particularly in New Zealand). I discarded January above partly for this reason. All Decembers (in Australia at least) will also be impacted by senior students finishing school earlier, but the impact might vary year to year.
  • Filter only for weeks where both school and most universities are teaching (although that leaves you with only about six months of the year, plus not all universities follow the same academic calendar).
  • Filter out any days with free travel or large-scale disruptions (planned or unplanned). For example, free travel is usually offered on New Year’s Eve and Christmas Day in Victoria. And Sydney had several free travel days following major unplanned disruptions in 2022.
  • Filter out Saturdays and Sundays on long weekends and during school holidays, which are probably more likely to be impacted by larger scale planned disruptions.

I mentioned at the start of this post that variations in Sydney monthly total patronage recovery figures were misleading, and hopefully you now understand why. In an upcoming post I’ll estimate underlying patronage recovery across big cities in Australia and New Zealand, and explain why I think actual underlying patronage recovery in Sydney didn’t change so much between March and April 2023.

1 Comment | Australian Cities, Melbourne, Sydney | Tagged: , | Permalink
Posted by chrisloader

How is population density changing in Australian cities? (2023 update)

Sat 10 June, 2023

With the release of more detailed 2021 census data and June 2022 population estimates, it’s now possible to look more closely at how Australia’s larger capital cities have changed, particularly following the onset of the COVID19 pandemic in 2020.

This post examines ABS population grid data for 2006 to 2023 for Greater Capital City Statistical Areas, including:

  • Trends in overall population-weighted density for cities;
  • Changes in the distribution of population living at different densities;
  • Changes in the distribution of population living at different distances from each city’s CBD;
  • Changes in population density by distance from each city’s CBD;
  • Changes in the distribution of population living at different distances from train and busway stations;
  • Changes in population density in areas close to train and busway stations;
  • The population density of “new” urban residential areas in each city (are cities sprawling at low density?); and
  • Changes in the size of the urban residential footprint of cities.

I’ve also got some animated maps showing the density of each city over those years, and I’ve had a bit of a look at how the ABS corrected population estimates for 2007 to 2021 following the release of 2021 census data.

For some other detailed analysis – and a longer history of city population density – see How is density changing in Australian cities? (2nd edition).

I’ve not included the smaller cities of Hobart and Darwin as they have a small footprint, and too many grid cells are on the edge of an urban area.

Population weighted density

My preferred measure of city density is population-weighted density, which takes a weighted average of the density all statistical areas in a city, with each area weighted by its population (this stops lightly populated rural areas pulling down average density – for more discussion see How is density changing in Australian cities? (2nd edition)).

I also prefer to calculate this measure on a consistent statistical area geography and the only consistent statistical area geography available for Australia is the square kilometre population grid published by the ABS.

With the recent release of 2021 census data, ABS issued revised population grid estimates for all years from 2017 onwards, which saw significant corrections in some cities (see appendix for more details). There has also been a slight change in the methodology for the 2021 grid that ABS say may result in a more ‘targeted representation’, but it’s unclear what that means.

Here’s the revised trend in population weighted density calculated on square km grid geography for Greater Capital City Statistical Areas in June of each year:

Sydney has almost double the population density of most other Australian cities (on this measure), with the exception being Melbourne which sits halfway in between.

Population weighted density was rising in all cities until 2019, although the growth was notably slowing in Sydney from about 2016.

The pandemic hit in March 2020 and led to a flatlining of density in Melbourne and a decline in Sydney by June 2020, while other cities continued to densify. Then Sydney and Melbourne’s population weighted density dropped considerably in the year to June 2021 – probably a combination an exodus of temporary international migrants and internal migration away from the big cities (particularly Melbourne that had experienced long lockdowns). Most other cities flatlined between June 2020 and June 2021.

Then by June 2022 density had increased again in all cities, after international borders reopened in early 2022.

I expect some fairly substantial changes between June 2022 and June 2023 in some cities as migration has surged further and rental vacancy rates have plummeted in several cities.

Population living at different densities

The following chart shows the proportion of the population in each city living at different density ranges over time:

All cities show a sustained pre-pandemic trend towards more people living at higher densities. However the pandemic saw significant drops in people living at the higher density categories in 2021 in Melbourne, Sydney, and Canberra.

So where was this loss of density? The next chart shows the change in population for grid squares across Melbourne between June 2020 and June 2021. Larger dots are more change, blue is an increase and orange is a decline:

You can see significant declines in population (and hence population density) in the inner city areas – so much so that the dots overlap. This is likely largely explained by the exodus of many international students and other temporary migrants.

You can also see population decline around Monash University’s Clayton campus in the south-eastern suburbs.

At the same time there were large increases in population in the outer growth areas, as is normally the case. Other pockets of population growth include Footscray, Moonee Ponds, Box Hill, Port Melbourne, Clayton (M-City), and Doncaster, likely related to the completion of new residential towers.

Here’s the same for Sydney:

There was significant population decline in the inner city and around Kensington (which has a major university campus), and the largest growth was seen in urban fringe growth areas to the north-west and south-west. Pockets of population growth were also seen at Wentworth Point, Eastgardens, Mascot, North Ryde, and Mays Hill, amongst others.

Here is the same for Brisbane:

Inner-city Brisbane was much more a mixed bag, which explains the less overall change in the density composition of the city. Some areas showed declines (including St Lucia, New Farm, Kelvin Grove, Coorparoo) while others saw increases (including Fortitude Valley, West End, South Brisbane, Buranda, CBD south).

Proportion of population living at different distances from the city centre

The next chart shows the proportion of people living at approximate distance bands from each city’s CBD over time:

All cities have seen a general trend towards more of their population living further from the CBD, with the notable exception of Canberra which has seen the outer urban fringe expanding by little more than a couple of kilometres at the most, and substantial in-fill housing at major town centres and the inner city (see also animated density map below). I should note that the Greater Capital City Statistical Area boundary for Canberra is simply the ACT boundary, and does not include the neighbouring NSW urban area of Queanbeyan, which is arguably functionally part of “greater Canberra”.

In 2021, Sydney and Melbourne saw a step change towards living further out, in line with the sudden reduction in central city population.

Population density by distance from a city’s CBD

Here’s an animated chart showing how population weighted density has varied by distance from each city’s CBD over time:

In most cities there has been a trend to significantly increasing density closer to the CBD, with central Melbourne overtaking central Sydney in 2017.

Sydney has maintained significantly higher density than all other cities at most distances from CBDs, with Melbourne a fair step behind, then most other cities flatten out to around 20-26 persons/ha from around 6+km out from their CBDs in 2022.

Canberra appears to flatten out to around 20 persons/ha at 3-4 kms from its CBD (Civic) however it is important to note that Canberra has a lot of non-residential land relatively close to Civic which reduces density for many grid cells that are on an urban fringe (refer maps toward the end of this post).

Population living near rapid transit stations

I’ve been maintaining a spatial data set of rapid transit stations (train and busway stations) including years of opening and closing, and from this it’s possible to assess what proportion of each city lives close to stations:

Sydney has the largest proportion of it’s population living quite close to rapid transit stations, with Perth having the lowest.

There are step changes on this chart where new train lines have opened. Sydney, Brisbane, and Adelaide have been successful at increasing population close to stations. The opening of the Mandurah rail line made a big difference in Perth in 2009 but the city has been growing remote from stations since then (MetroNet projects will probably turn this around significantly in the next few years). Melbourne was roughly keeping the same proportion of the population close to stations although that changed in 2021 with the exodus of inner city residents (I anticipate a substantial correction in 2023).

Population density around rapid transit stations

The following animated chart shows the aggregate population-weighted density for areas around rapid transit stations in the five biggest cities over time:

Sydney has lead Australia with higher densities around train stations, followed by Melbourne. Perth has only slightly higher densities around stations (in aggregate) compared to other parts of the city. Population density is generally lower around Adelaide train and busway stations compared to the rest of the city – the antithesis of transit orientated development.

How dense are new urban areas?

I’ve previously looked at the density of outer urban growth areas on my blog, and here is another way of looking at that using square kilometre grid data.

I’ve attempted to identify new urban residential grid squares by filtering for squares that averaged less than 5 persons per hectare in 2006 and more than 5 persons per hectare in 2022 (using 5 persons/ha as an arbitrary threshold for urban residential areas, and I think that’s a pretty low threshold).

The vast bulk of these grid cells (and associated population) are on the urban fringe, but a handful in each city are brownfield sites that were previously non-residential (for Melbourne 99% of the population of these grid cells are in urban fringe areas).

It’s also not perfect because square kilometre grid cells will often contain a mix of residential and non-residential land uses, but it is analysis that can be done easily and quickly, and in aggregate I expect it will be broadly indicate of overall patterns.

The following chart shows the population of new urban residential grid cells (since 2006), and the proportion of this population by 2022 population density:

You can see Melbourne has almost double the population in these new urban residential grid squares compared to Perth, Brisbane, and Sydney. This indicates Melbourne has been sprawling more than any other city since 2006. Slow-growing Adelaide only put on about 56k people in new urban grid squares, slightly less than Canberra.

The bottom half of the chart shows that new urban grid squares in Sydney, Melbourne, and Canberra are generally much more dense than those in other cities. This likely reflects planning policies for higher residential densities in new urban areas in those cities. In fact, all of these grid cells with density 40+ in 2022 are on the urban fringes, except one brownfield cell in Mascot (Sydney).

But of course planning policies can change over time, so here is the equivalent chart looking at new urban residential squares since 2012:

It’s not a lot different. The density of these more recent new urban residential grid cells is generally highest in Sydney, following by Melbourne and Canberra. New urban residential grid cells in Adelaide mostly had fewer than 20 persons/ha, but then also there are not that many such grid cells and they didn’t have much population in 2022.

Perth has managed one new grid cell with over 40 persons/ha in 2022 – it is located in Piara Waters (which has many single storey houses with tiny backyards).

How much has the urban footprint of cities been expanding?

The population grid data only measures residential population so it cannot be used to estimate the size of the total urban footprint of cities over time, but we can use it to estimate the urban residential footprint. I’ve again used 5 persons/ha as a threshold, and here’s how the cities have growth since 2006:

Melbourne and Sydney had much the same footprint in 2006 but Melbourne has since grown significantly larger in size than Sydney, although Sydney still has a larger Capital City Statistical Area population.

The bottom half of the chart shows that Perth has had the largest percentage growth in urban residential area, followed by Brisbane then Melbourne. Sydney and Adelaide have had the least growth in footprint, and are also seeing the least population growth in percentage terms.

Animated density maps of Australian cities

Here are some animated density maps for Australia’s six largest cities from 2006 to 2022 for you to ponder.

Some things to watch for:

  • Limited urban sprawl and significant densification of pockets of established areas in Canberra
  • Much larger areas of higher density in Sydney and Melbourne
  • Relatively high densities in some urban growth areas in Melbourne, Brisbane, and Sydney from the late 2010s
  • Low density sprawl in Perth, but also densification of some inner suburban areas (along the Scarborough Beach Road and Wanneroo Road corridors, and inner suburbs like Subiaco and North Perth)
  • Limited urban sprawl in Adelaide, along with densification of inner suburbs

Appendix: Corrections to ABS population estimates following Census 2021

The 2021 census resulted in quite large revisions to estimated population in many cities as shown in the following chart.

Melbourne’s estimated 2021 population was revised down 2.4%, Sydney down 1.9%, while Canberra and Hobart were revised up more than 5%. To be fair to the ABS, the pandemic and border closures were unprecedented and their impacts on regional population were not easy to predict.

These corrections sum to a linear trend between 2016 and 2021 at the city level, although there was a redistribution of the estimated population within each city.

The following chart shows some detail of estimated population revisions at SA2 level for Melbourne in 2021:

The biggest reduction was in Carlton (-25% right next to University of Melbourne), and there were also reductions near other university campuses, including Kingsbury (-19%), Burwood (-14%) and Clayton (-13%). The biggest upwards revision was Fishermans Bend (+84%), and there were plenty of upwards revisions in outer urban growth areas.

And here is Sydney:

There were big reductions in Kensington (-28%, centred on UNSW), Redfern-Chippendale (-17%), many other areas near university campuses, and around the Sydney CBD.

Like Melbourne, urban growth areas on the fringe were revised upwards, including +35% in Riverstone-Marden Park.

2 Comments | Australian Cities, Brisbane, Melbourne, Perth, Sydney, Urban density, Urban Planning | Tagged: , , | Permalink
Posted by chrisloader

« Previous Entries