Update on Australian transport trends (December 2022)

Sat 31 December, 2022

It’s that time of year again when BITRE release their annual yearbook chock full of numbers, and this post aims to turn them into useful information. It’s also a prompter for me to update my feeds of other transport metrics and pull together this post covering the latest trends in licence ownership, motor vehicle ownership, transport emissions, vehicle kilometres, passenger kilometres, freight volumes, and transport pricing.

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 (January 2022, December 2020, December 2019, December 2018).

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.

Overall the trend has been a flattening of licence ownership rates, and indeed Victoria was showing declining licence ownership before the pandemic. The ACT and Northern Territory had much higher rates of licence ownership in the 1970s compared to other states. But then the Northern Territory has maintained lower rates of licence ownership than most other states since the 1990s. The ACT showed very high rates of licence ownership around 2009 to 2017 – not sure if this is real or an artefact of the imperfect data (eg counting people with multiple licences).

Most states saw an uptick in 2021 with the notable exception of Western Australia – a state that was largely COVID-free until early 2022 so any COVID-avoidance incentive to get a driver’s licence might not have been very strong. Licence ownership rates in Queensland and Victoria have somewhat levelled out between 2021 and 2022, perhaps reflecting a return of international arrivals and the end of COVID lockdowns.

Here’s licence ownership by age band for Australia as a whole (to June 2021):

In 2020 and 2021 there was an uptick in ownership for people aged 16 to 29 in particular. Let’s look at the various age bands across the states:

There are some interesting recent trends for people aged 16-19. Victoria saw a big drop in 2020 but then some big increases in 2021 and 2022. South Australia and New South Wales have also seen big increases in recent years.

There were even bigger increases for 20-24 year olds following the start of the pandemic, except Western Australia and the Northern Territory (states that largely avoided COVID in 2021).

Ages 25-29 were similar:

So why have licence ownership rates increased for younger adults? Is it mode shift away from public transport to avoid the risk of COVID infection on public transport? Or is it because non-licence holders left the country?

South Australia and New South Wales publish quarterly licencing data by age band which allows us to see the impact of the pandemic more closely. I’ve combined this with ABS quarterly population data to calculate quarterly licence ownership rates:

South Australia has less historical data published:

The population aged 20-24 declined after March 2019 in both New South Wales and South Australia – a year before the pandemic hit. Then both states saw a more rapid decline after March 2020 – the onset of the pandemic.

However the number of people in this age band with a licence only increased slightly – in line with pre-pandemic trends. That is, the licence ownership rate increased sharply primarily because there was a net loss of non-licence holders.

Here’s a look at Australia’s population by age band:

There are some fairly smooth trends over time in all age bands, but then from 2020 there were some sudden shifts, particularly for age bands 16-19, 20-24, 25-59 and to lesser extent 30-39.

A plausible explanation is that international students and other non-permanent residents left Australia – many could not attend classes and were encouraged to leave Australia by the government of the day. These departures were not replaced by new arrivals as the international borders were essentially closed. Indeed once the borders reopened in early 2022, there was a sharp increase in non-licence holders in New South Wales that sent the motor vehicle licence ownership rate down sharply in March 2022 (June 2022 data has not been published at the time of writing).

Other data shows a sharp fall in the number of international students in Australia between 2019 and 2020, particularly in NSW, Victoria and Queensland (more recent student numbers unfortunately not available at the time of writing):

And there was a dramatic shift to net outbound overseas migration from the June quarter of 2020:

In previous posts (see Why are young adults more likely to use public transport? (an exploration of mode shares by age – part 3) I’ve established that recent immigrants skew to the younger adult ages as Australia generally attracts international students and skilled migrants, which also fits with the hypothesis that there was a great exodus of young adults who didn’t have a driver’s licence.

[Side note: the first quarter of 2022 represented a new record for international migration into Australia as the borders re-opened – almost 98k people.]

It’s entirely plausible that long-time residents also increased their rate of licence ownership during the pandemic, but I think the most likely major explanation is the departure of international students and temporary residents. And so I expect the return of international migration will result in lower licence ownership, car ownership, and increased public transport mode share in 2023.

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

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

To get a better understanding of recent trends, here are quarterly licence ownership rates by age band for New South Wales since mid 2018:

You can see the rise – and more recent fall – in licence ownership rates for the age bands 20-24 and 25-29. There was also a sharp fall for those aged 16-19 in September 2021, possibly due to Sydney entering a long COVID lockdown in the winter of 2021 (perhaps learners permits were not renewed or people didn’t bother applying for them if they could not take lessons). 30-34 year olds showed a small rise in licence ownership from the start of the pandemic and this seems to have been sustained, which might reflect some mode shift to avoid infection risk.

Here’s the same quarterly data for South Australia:

Licence ownership rates rose strongly for those aged 16-34, although there was an initial dip for those aged 16-19 in June-September 2020 around the start of the pandemic. Perhaps it has remained high because international students have not yet returned in great numbers to Adelaide, and/or because of a permanent mode shift towards private transport?

For completeness, here are motor cycle licence ownership rates:

Motorcycle licence ownership has been trending up slightly in New South Wales and Victoria, and slightly down in Queensland, South Australia, Norther Territory and Western Australia.

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. So the following charts will show 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.

Between January 2020 and January 2022 most states show an upwards trend in motor vehicles per population aged 18-84 (an imperfect approximation of the driving age population).

However when you look at the stock of cars per state, there was not a significant uptick in the total number of cars – indeed Victoria saw an almost flattening of total motor vehicles between January 2020 and January 2021:

Again, a highly plausible explanation is that non-driving (and non-licence holding) residents departed Australia while long-term residents largely continued their background trends in motor vehicle ownership. We might therefore see a decline in motor vehicle ownership rates in the January 2023 survey with the return of overseas immigration.

Transport Emissions

Australia’s transport emissions have been reduced by COVID lockdowns over the last couple of years but have more recently bounded back:

The above chart showing rolling 12 months emissions which washed out the lockdown period. The next chart shows seasonally-adjusted quarterly data to get around the rolling 12 month averaging – with the September 2022 quarter close to 2019 levels:

Here are Australian transport emissions since 1975:

And in more detail since 1990:

The next chart shows the more recent growth trends by sector:

Aviation emissions saw the biggest decline from the pandemic but were bouncing back in 2021-22. Car and bus emissions have declined in line with pandemic lockdowns whilst most other modes have continued to see growth in emissions.

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 continued reduction in car emissions per capita since around 2004.

Next up, emissions intensity (per vehicle kilometre):

Curiously the figures suggest a sudden drop in bus emissions per km in 2022, but I am not sure this is plausible as electric buses are still only being rolled out in small numbers. There was also an unexpected dip in emissions per km in 2015 which jumped back up in 2016. The 2015 dip in bus emissions per km is primarily a product of a dip in BITRE’s estimated bus emissions and not bus vehicle kilometres travelled, which is a hard to explain (this bus emissions dip is not seen in AGEIS estimates). I suspect this may be an artefact of BITRE methodological issues.

Emissions per passenger-km 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.

Vehicle kilometres travelled

Vehicle and passenger kilometre figures have been significantly impacted by COVID lockdowns in 2020 and 2021, and so the financial year figures are a mix of restricted and unrestricted travel periods. Accordingly we cannot readily infer new trends from this data, and it should be interpreted with caution.

Total vehicle kms for 2021-22 were lower than 2019-20 and 2020-21:

As per emissions, the biggest declines were in cars, motorcycles, and buses:

Light commercial vehicles and trucks have shown the biggest increase since 1990.

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.

Here is the same data for capital cities (capital city population data comes out only once a year with some delay, so most city data points are only up to financial year 2020-21).

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

Once again BITRE have kindly supplied me data on estimated car vehicle kilometres for capital cities that is not included in the yearbook:

Canberra is still on top for car kilometres per person but this rate has been reducing strongly over recently years.

Passenger kilometres travelled

Here are passenger kilometres travelled overall (log scale):

The pandemic had the biggest impact on rail, bus, and aviation passenger kilometres.

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

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 before the pandemic hit. Only air travel has rebounded on a financial year basis.

Here’s total car passenger kilometres for capital cities:

Melbourne, Sydney, and Canberra were impacted by extensive lockdowns in 2021-22, while the other cities were mostly lockdown free. However the then-unprecedented large wave of COVID cases in the summer of 2021-22 may have led to voluntarily suppressed travel behaviour across many cities.

Here’s car passenger kilometres per capita (again only to 2020-21 for most cities):

It’s hard to estimate any post-COVID trends based on this annual data. However, I have been processing VicRoads traffic signal count data which gives some indication about more recent traffic volumes in Melbourne. The following chart shows the change from 2019 median signalised intersection traffic count volumes per week. I’ve deliberately locked the scale as -20% to +10% as I want to focus on the difference between 2019 and 2022 traffic, and so the 2020 and 2021 lines go off the scale during lockdowns.

It’s very interesting that volumes in late 2022 were about 5% lower than 2019 levels on weekdays (a bit higher on weekends although there’s no such thing as a normal weekend).

And if you look at the time of day profile for Melbourne (below), the biggest reductions have been in the early AM peak, and evenings, while there have been increases during the AM and PM school peaks (which might be a response to COVID infection fear and/or because parents working from home can more easily drive their children to and from school):

Rail Passenger travel

The pandemic has put a large dent in rail passenger kilometres travelled, and these are likely to remain below 2019 for some time as new working-from-home behaviours stick following the pandemic:

Melbourne saw a slight increase in 2021-22, but this was probably more a product of the how long the city was in lockdown during financial years 2020-21 and 2021-22. Sydney saw a reduction in 2021-22 probably because there was little in the way of lockdowns in 2020-21.

Here’s rail passenger kms per capita (again, only up to 2020-21):

Bus passenger kilometres have reduced significantly with the pandemic:

Including on a per-capita basis:

I would expect to see these figure bounce back up as there are unlikely to be any lockdowns during 2022-23.

It would appear that the surge in Darwin bus use due to a major LNG project may have ended.

Mode split

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

Mass transit mode shares have taken a large dive during the pandemic, and I expect this to be strongly associated with COVID lockdowns where many people – especially central city workers – worked from home. It’s still difficult to know to what extent this is people switching travel modes for ongoing trips, to and what extent it is public transport trips being replaced by staying home. I hope to have more to offer on this subject in an upcoming blog post.

Transport for New South Wales conducts a rolling household travel survey, although it was suspended during COVID lockdowns in 2020 and 2021. Estimated total person trips and kilometres by mode are reported, and from this we can get an idea around mode split (including non-motorised modes):

On this data, the public transport mode share of person kilometres travelled is much higher than that derived from the BITRE data, with a peaking of around 20% before the pandemic.

Unlike Victoria, New South Wales unfortunately does not provide any detailed household travel survey data, which means it is not possible to perfectly calculate public transport mode share (ferry and light rail were bundled with “Other” pre 2020), and it’s also not possible to calculate mode share by trip purpose. All this and more is possible with Victorian published data, but unfortunately post-COVID data will not be published until late 2024.

Freight

This data shows a dramatic inflection point in freight volume growth in 2019, with a lack of growth in rail volumes and a decline in coastal shipping. 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, and are arguably more contestable between modes:

2022 saw a sudden flatlining in non-bulk freight volumes, with road increased market share to 80%, seemingly mostly at the expense of coastal shipping:

Air freight tonnages are tiny in the whole scheme of things so you cannot easily see them on the charts.

Transport Costs

The final category for this post is the real cost of transport from a individual perspective. Here are headline real costs (relative to CPI) for Australia, using Q2 ABS Consumer Price Index data up to June 2022:

Technical note: Private motoring is a combination of factors, including motor vehicle retail prices and automotive fuel. Urban transport fares include public transport as well as taxi/ride-share (which possibly move quite independently, which is a little frustrating).

The cost of private motoring mostly declined in real terms from around 2008 to 2020, followed by sharp increases in 2021 and 2022 in line with the rapidly rising cost of automotive fuel. The real cost of motor vehicles has plummeted since 1996, although it bottomed out in 2018.

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.

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 1973 by city (note different Y-axis scales):

Technical note: I suspect there is some issue with the urban transport fares figure for Canberra in June 2019. The index values for March, June, and September 2019 were 116.3, 102.0, and 118.4 respectively.

Urban transport fares have grown the most in Brisbane, Perth, and Canberra – relative to 1973. 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 – eg. 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.

What does all this mean for post-pandemic transport trends?

I also tackled this question a year ago and my thoughts haven’t changed significantly.

One thing that has become clearer is that the increase in motor vehicle licence ownership and car ownership is very likely related to the lack of recent international immigrants during the pandemic. Therefore the reopening of international borders is likely to push these rates down once more across 2022 and 2023, although they may or may not return to pre-pandemic levels. In turn, this will probably increase public transport patronage and mode share, although it is still likely to remain subdued following the wide scale acceptance and adoption of working from home, particularly for central city workers.

A key question for me is the extent to which commuter trips have shifted from public to private transport, as opposed to simply disappearing as many more people work from home. I’ll have more to say on this soon in an upcoming post about 2021 census journey to work data.


Who worked at home before the pandemic?

Sun 18 September, 2022

Can we learn anything from pre-pandemic working-at-home patterns that will help us predict transport demand “after” the pandemic?

This post investigates work-at-home patterns from the ABS census 2016 for the six largest Australian cities, with some deeper dives for Melbourne and Sydney. I’ll answer questions such as: What occupations and industries were more likely to work-at-home? How did work-at-home rates vary by home and work locations? How many people had their home double as their workplace? Who was ‘remote working’ at home away from their regular workplace?

I’ve found the results quite interesting – and not quite what you might expect from the our current pandemic perspective.

What proportion of workers worked at home in 2016?

The following table shows between 3.3% and 5.2% of major city workers reported that they “worked at home” on census day in 2016 in Australia’s six largest cities:

This highest rate was in Brisbane, and the lowest in Canberra.

What occupations were more likely to work at home in 2016?

Here’s a chart showing 2016 journey to work mode shares across Australia’s six largest cities by main occupation category. Normally I exclude people working at home from mode share charts, but for this analysis I’m including “worked at home” as a “mode”:

Technical note: As usual on my blog, public includes all journeys involving a bus, tram, train and/or ferry trip, Active includes walk-only and cycle-only journeys, with all other journeys counted as Private.

You’ll notice the occupations with the highest rates of working at home were also the occupations with the highest public transport mode shares – professionals, clerical and administrative workers, and managers.

Here’s another view of that data, this time providing the occupation breakdown of commuters for each “mode”:

Again you can clearly see the same three occupation categories that dominated both working at home and public transport commuting.

No surprises there, right? These occupations generally spend a lot of time in offices either at computers or meeting with others – which can more easily be done online so are more likely to be amenable to working from home. The other occupation categories are more likely to necessitate working at a specific workplace.

But there are lots of different types of managers and professionals and they work in many different industries, so let’s dig a little deeper.

How did working at home vary by employment industry?

Here’s a look at the worked-at-home rates in 2016 by industry and occupation (highest level categories). I’ve sorted the occupations and industries such that the highest rates of worked-at-home are towards to the left and top of the table respectively.

The highest rates of working at home were found in Agriculture, Forestry and Fishing, Arts and Recreational Services, and Construction. Not exactly the sorts of jobs you would expect to fill multiple CBD office towers.

It’s also worth looking at the second level of occupation classifications:

Now we start to see working at home rates are very high for farm managers and arts and media professionals. For many of these people their workplace is quite likely to also be their home.

Many occupations that you might expect to be generally office-based had a working at home rate of around 9% – including HR, marketing, ICT, design, engineering, science and transport professionals.

How did working at home vary by home location?

Here’s a map showing working at home rates for SA2s across Greater Melbourne:

Working at home rates were highest in peri-urban areas, higher than the average in more advantaged suburbs of Melbourne, and the lowest rates of working at home were for employees from more disadvantaged areas.

Here’s the same for Sydney:

The highest rates were also seen in peri-urban areas of Greater Sydney, and the generally wealthy upper north shore.

How did working at home vary by workplace location?

Here’s a chart showing the worked at home share for the Melbourne and Geelong region, by workplace location.

The highest worked at home rate was 47.5% seen on French Island – a sparsely populated island south-east of Melbourne which contains many small farms and some tourist facilities. Other worked-at-home hotspots include the Point Cook East SA2 (which includes an air force base) and Panton Hill – St Andrews (which I understand contains many small farms). In fact, worked-at-home rates were again generally much higher in peri-urban areas and very low in suburban areas.

Some of the lowest rates of working at home were seen for employees in industrial areas and at Melbourne Airport. Many of these jobs are probably hard to do remotely.

2.0% of Melbourne CBD workers worked at home on census day in 2016. And the SA2s surrounding the CBD were all below 3%.

Here’s the same map for Sydney:

Again the highest rates of employees working at home were seen in peri-urban areas, and the Sydney CBD saw 1.9% of employees working at home.

These maps tell us that working at home in 2016 was most common in peri-urban areas, and relatively rare in dense employment areas such as CBDs. The COVID19 pandemic triggered significant levels of working at home during lockdown periods which emptied central city office towers and has remained quite common ever since. So it is likely that the profile of people working at home has changed significantly since 2016 to include a lot more white collar workers.

The fact that working at home rates were high in peri-urban areas when measured as both home location and work location suggests that for many people their home is their workplace. So…

How common was remote working in 2016?

You might have noticed that I’ve been referring to “worked at home” rather than the currently popular term “working from home”. That’s not just because its the wording used by the ABS in reporting the census, but because “working from home” is a little ambiguous as to whether people are working at home and away from their regular workplace, or whether their home is also their regular workplace. Perhaps a better term to describe people working at home and away of their regular workplace is “remote working”.

I have extracted worked-at-home workers’ home and work SA2 locations for people who lived and worked in Greater Melbourne and found that 89% of workers who worked at home, had their usual place of work in the same SA2 as where they lived (SA2s are roughly the size of a suburb).

So while 4.5% of workers who both lived and worked in Greater Melbourne worked at home in 2016, only 0.48% worked at home on census day when their regular workplace was in a different SA2. Remote working was an order of magnitude smaller than working at home.

Now it is also possible that some workers who lived and regularly worked in the same SA2 were actually working at home remote from their workplace on census day. However I expect this to be rare, and some further analysis (detailed in the appendix to the post) found that the almost every worker who worked and lived in the same SA2 had their home SA1 area intersect or overlap with their workplace Destination Zone (both the smallest census land areas available). This doesn’t guarantee that their home was their regular workplace, but it makes it quite probable. These workers would mostly not have had a very long commute, so there would be little incentive to remote work to avoid commuting effort. Also, I’ve found people who travelled to work in the same SA2 as they lived were slightly more likely to work in accommodation and food services, construction and retail trade – industries that are likely to require worksite attendance.

So I think I can fairly safely estimate the 2016 remote working rate in Greater Melbourne to have been 0.5%.

I’ve repeated this calculation for Australia’s six largest cities:

I’ve ordered the cities by working population, and you can see remote working rates decline across the chart for smaller cities. This might reflect there being a larger incentive to avoid longer and/or expensive commutes in larger cities by remote working.

Curiously Brisbane had the highest rate of workers whose home doubled as their workplace (4.9%), while the Australian Capital Territory (i.e. Canberra) had the lowest rates of both working at home and remote working.

I think these quite small estimated rates of remote working are an important finding, as several recent reports from the Productivity Commission, SGS Economics and Planning, Monash University, and iMove may have conflated working from home with working remotely at home, at least in their discussion of the topic. It’s critical that these metrics are not mixed up. And thankfully I’m not aware of any obvious miscalculations in their work.

How did rates of remote working vary across workplace locations in 2016?

The following maps exclude people who lived and worked in the same SA2, to get an estimate of remote working by workplace SA2:

The estimated remote working rate peaked for the Docklands SA2 at 1.8%, with Melbourne’s CBD at 1.7%, Southbank at 1.5%, and Albert Park at 1.4%. These are higher than the worked-at-home rates calculated above for all employees who regularly worked in the city centre, because they remove people who regularly work at their home in the central city.

There were also some seemingly random suburban locations with similar rates of remote working such as Forest Hill and Fawkner at 1.6%.

Here’s the equivalent map for Sydney:

There was a curious hot spot of West Pennant Hills at 5.5%, while the Sydney CBD area was 1.8%, North Sydney – Lavender Bay 2.3%, Macquarie Park – Marsfield 2.0%, and North Ryde – East Ryde 1.8%.

How did rates of remote working vary by home SA2?

Here’s a map estimating remote working rates by home location for Melbourne in 2016:

Generally higher rates were seen in peri-urban areas with Flinders at 2.0%, Mount Eliza at 1.4%, Gisborne at 1.2%, and Panton Hill – St Andrews at 1.6%. This may reflect “sea-changers” and “tree-changers” avoiding a long commute to work. The lowest rates were seen in the more disadvantaged areas of Melbourne, which probably reflects such employees being more likely to work in occupations that require attendance at their workplace.

And for Greater Sydney:

Higher rates of remote working were seen across the upper north shore, with Avalon – Palm Beach at 2.4%, and in many peri-urban areas. But the highest rate was seen at Blackheath – Megalong Valley (in the Blue Mountains) with 3.5%.

In what occupations and industries was remote working more common in 2016?

It’s stretching what you can do with ABS TableBuilder, but I’ve extracted counts of workers by home SA2, work SA2, industry main code, and whether the worker travelled to work or worked at home, for Greater Melbourne for 2016. I’ve then filtered for workers whose regular workplace is not in their home SA2. It’s a little problematic in that about one quarter of the non-zero records in this data were a value of 3, and ABS never reports counts of 1 or 2 as it uses randomisation to protect privacy for very small counts. So the totals are accumulating the impacts of lots of small random adjustments, but it’s not clear that this would introduce a bias to the overall estimate, but we should still treat these with caution and I’m not going to quote more than one decimal place. That said, the estimates do seem very plausible:

The industries with the highest estimated rates of remote working are mostly white collar jobs, whilst those industries with the lowest rates are more blue collar.

I did the same analysis for occupations, and again there are few surprises in the estimated rates of remote work across the categories:

What will the 2021 census tell us?

The 2021 census was conducted during a period of tight lockdowns in Victoria and New South Wales. Most other states had relatively few restrictions, but had experienced lockdowns in 2020, so were arguably in a “post pandemic” scenario – at least temporarily. So it will be very interesting to compare 2016 rates of remote working to those in different cities in 2021. For cities that were not in lockdown we will likely get a good sense of which occupations had high rates of (unforced) remote working, which will be very useful for modelling future rates of remote working and the ongoing impact on transport demand.

I expect the patterns across industries and occupations will be similar between 2016 and 2021, but with much higher rates of remote working in 2021.

The data will be released in October 2022 and I’ll be keen to calculate remote working estimates and share those on the blog.

There have also been several surveys that provide breakdowns of remote working by occupation and/or industry during the pandemic (Productivity Commission, iMove, University of Sydney ITLS).

Appendix: Did anyone live and work in the same SA2 but not have their workplace at their home?

To try to answer this I extracted data for “worked at home” cases in Greater Melbourne at the maximum available resolution – SA1 for home location and Destination Zone for workplaces, and determined whether their home SA1 intersected with their workplace Destination Zone. An intersection between these areas doesn’t guarantee the workplace is at their home, but the absence of an intersection does guarantee that the workplace is not at their home.

Here’s a map extracted from maps.abs.gov.au that shows 2016 destination zone boundaries in blue, and SA1 boundaries in red for part of the northern suburbs of Melbourne:

I dare suggest that if someone lived in an SA1 that intersected with their regular workplace Destination Zone, it’s pretty likely that they ordinarily worked at home.

This analysis is stretching the data, because when you extract small counts from ABS they apply random small adjustments to protect privacy and also you never see a count of 1 or 2 people. Problematic as it is, the sum of people living and working in the same SA2, but living in an SA1 that does not intersect the destination zone in which they work was just 95 for all of Greater Melbourne, out of around 70,000 people who lived and worked in the same SA2. This is a lower bound on the true number, but I expect the true number to still be very small. Hence I’m comfortable with an estimate of 0.5% remote working in Melbourne in 2016 (to one decimal place).

Another potential issue is that SA2s are not consistently sized across cities, and are generally smaller in Brisbane and Canberra. This means remote working from a nearby workplace would be more likely to be detected those cities. However I suspect these instances will still be tiny, and the estimated remote working rates in Brisbane and Canberra certainly don’t appear to be outliers.


Update on Australian transport trends – January 2022

Sun 23 January, 2022

Once again, the good folks at the Bureau of Infrastructure, Transport and Regional Economics (BITRE) have published their annual yearbook chock full of data just before Christmas. This annual post aims to turn the numbers into insights about transport trends in Australia.

I’ll cover vehicle kilometres, passenger kilometres, mode shares, car ownership, driver’s licence ownership, greenhouse gas emissions, and transport costs. This year there’s also a new section of freight volumes and mode shares.

While most data series are available up until 2020-21, at the time of writing there were only June 2021 estimates of population for states and territories, not cities. So most charts for cities will end at 2019-20, the financial year in which the COVID19 pandemic had significant impacts for only the last third (i.e. from March 2020).

I will finish the post with some thoughts about what the data suggests for post-pandemic transport trends. Settle in, there are quite a few charts!

Vehicle kilometres travelled

Total vehicle kms travelled in Australia increased slightly in 2020-21, after a small but significant fall in 2019-20 due to the pandemic.

Here’s the percentage growth by vehicle type since 1971:

Light commercial vehicles have seen the largest growth overall since 1971, followed by buses (mostly in the 1980s), with motorcyles having the least growth.

In percentage terms, buses saw the largest decline in vkms with the pandemic (I’m guessing largely related to charter and tour operations), but there were also substantial declines for cars and motorcycles as people endured lockdowns and borders were closed. There was no clear impact on trucks and only a small impact on light commercial vehicles. All vehicle types except buses rose in total vehicle kms in 2020-21.

Vehicle kilometres travelled per capita

Here’s a view at the state and national level:

Vehicle kms per capita peaked in all states in 2004 or 2005 and have declined since then, with some variation between states.

Vehicle kms per capita were highest in Queensland and Western Australia, and lowest in the Northern Territory, followed by New South Wales, South Australia and the ACT – at least until the COVID19 pandemic.

All states saw a big reduction in 2019-20 with the pandemic (although less so in the NT which I understand didn’t lock down), and things bounced up in 2020-21 in all states except Victoria – no doubt due to a long lockdown in the second half of calendar 2020 due to a second wave of COVID19.

Similar patterns were seen in cities (data for most cities is only until 2019-20). Before the pandemic, Melbourne and Sydney showed the biggest declines in vehicle kms per capita.

BITRE have been kind enough to supply me with estimates of car vehicle kilometres for cities (not yet part of the yearbook data), which show similar patterns:

Passenger kilometres travelled

Firstly here are passenger kilometres travelled at the national level – and note I have used a log-scale on the Y-axis.

The COVID19 pandemic brought massive reductions in rail, bus, and air passenger kilometres travelled, and a smaller reduction in car passenger kilometres. This will likely reflect a significant proportion of the workforce shifting to working at home, an aversion to shared transport, and the closure of interstate borders during the pandemic.

Prior to the pandemic, there was a massive increase in air travel between the mid-1980s and the early 2010s, and rail saw strong growth from 2005.

Here’s passenger kms per capita:

Car passenger travel per capita peaked in 2004, and domestic air travel per capita peaked around 2014. Bus travel per capita peaked in 1990, the same year aviation was significantly disrupted by a pilot’s strike. Rail passenger travel was growing strongly until the pandemic hit.

The next sections will look at passenger kms (total and per capita) for capital cities, by mode.

Car passenger travel

After a long run of strong growth, the pandemic brought declines in car travel in all cities in 2019-20. There was a bounce back in 2020-21, except Melbourne which saw a further decline to 17% below 2019-20 levels (roughly equal to 2003 levels), no doubt due to COVID19 lockdowns. 2020-21 car passenger kms in Perth, Adelaide, and Brisbane were above 2019-20 volumes, suggesting a snap back to the growth trend.

All cities saw a significant decline in car passenger kms per capita in 2019-20, due to the pandemic.

The longer-term trend shows peaking of car use in 2004 or 2005 in all cities.

Rail passenger travel

There were massive reductions in (heavy) rail passenger kms in both 2019-20 and 2020-21 with the COVID19 pandemic, as many central city workers shifted to working from home and cities went into lockdown.

Just before the pandemic, Sydney’s rail passenger kms were rocketing up. Sydney’s rail network carries significantly larger volumes than Melbourne despite having almost the same population.

Before the pandemic, rail passenger kms per capita were increasing in Sydney, declining in Melbourne, and increased slightly in other rail cities in 2018-19. Things obviously changed with the pandemic in 2019-20.

Here is growth in rail passenger kms since 2010:

Pre-pandemic, Adelaide and Sydney has the strongest growth relative to 2010, while Brisbane had the least. However the chart would look quite different with a different base year (eg Perth would look worst on a base year of 2013). Adelaide train patronage was significantly impacted in the period 2011-2014 by electrification and other upgrade works that involved extended line closures.

Bus passenger travel

Sydney has the highest bus use of all Australian cities. It’s worth noting that Melbourne is unique in that trams dominate inner city radial street-based public transport, resulting in a lower rate of bus use compared to other cities.

All cities saw big bus patronage reductions with the pandemic, with Melbourne bus usage falling below than of Brisbane in 2020-21.

In per capita terms, Darwin has seen a massive increase in bus use due to a large staff bus network being created for a major LNG project just outside of Darwin.

Sydney overtook Brisbane for bus use per capita in 2017-18, perhaps due to some service investment, network reform, and/or reduced transfer penalties from fare reform. Brisbane saw massive increases in bus usage between 2004 and 2012, likely related to the expansion of the busway network and some service upgrades (including “BUZ” routes), which might then have been eroded by significant fare hikes.

Growth in bus passenger kms since 2010 shows these patterns in another way:

Pre-pandemic, Sydney and Canberra were showing particularly strong growth. Perth peaked in 2014 – which might be partly explained by a decentralisation of employment (see: What might explain journey to work mode shifts in Australia’s largest cities? (2006-2016)).

Again, these types of charts would look quite different if a different reference year was applied.

Light rail passenger travel

Melbourne has by far the largest light rail network, so little surprise it has the highest passenger kms. None of these light rail networks are designed to serve the entire city, so we need to be cautious comparing cities, and I won’t provide a per capita chart.

Despite the COVID19 pandemic, Sydney saw an increase in light rail use in 2019-2020, which would reflect the opening of the new south-eastern lines to Randwick and Kingsford in December 2019.

Motorcycle passenger travel

Motorcycle travel had a dip in the 1990s on these figures, then picked up strongly in the early 2000s. The patterns in 2019-2021 are similar to car passenger travel.

On this data, Melbourne bucked the trend of other cities in 2006 and started a decline in motorcycle travel. However all these figures are estimates only, and I would not be surprised if there were some “broad” assumptions behind the estimates, as motorcycle travel doesn’t usually get a lot of measurement attention, and most of the cities are estimated to have remarkably similar trends.

Mass transit mode share of passenger kilometres

It is possible to calculate the ratio of “mass” transit passenger kms (rail, light rail, ferry, and bus) against total passenger kms in cities, which essentially provides a mode share. Note however that this will include estimates of private bus travel, so it’s not exactly public transport mode share, but probably not far off.

The pandemic has led to significant falls in mass transit mode share in all cities, with perhaps the largest reduction in Melbourne (again, likely related to the second wave lockdown in 2020-21).

As I’ve shown on this blog several times, a significant portion of public transport travel is around journeys to work and education in city centres, a trip type that became a lot less frequent during the pandemic as people work and learn from home. The removal of these trips from total travel has undoubtedly shifted the overall mode share calculation.

What’s not yet clear to me is the extent to which trips not suppressed by the pandemic might have shifted from public to private transport, and whether these trips might shift back to public transport “after” the pandemic (assuming there comes a time when there is no longer heightened infection fear).

Car ownership

The following charts use vehicle count data from the ABS Motor Vehicle Census, with January 2021 unfortunately the last census taken (although hopefully Austroads take over in 2022). I’ve calculated per capita car ownership using interpolation from the most recent ABS population estimates at the time of writing.

Not everyone is of driving age, so I usually also look at motor vehicles per 100 residents aged 18-84, as an approximate representation of people of driving age:

Here’s a closer look at the last few decades:

Motor vehicle ownership has risen considerably since the survey began. However from around 2017 until the pandemic it actually decreased in most Australian states and territories (Tasmania an exception).

There has been a small but significant uptick in motor vehicle ownership in January 2021 in all states. As I mentioned in my recent blog post on motor vehicle ownership by age, I see two likely main reasons for this:

  • A lack of recent international immigrants during the pandemic – who generally have very low rates of motor vehicle ownership in the first years in Australia, and are skewed towards young adult age bands which themselves also have lower rates of motor vehicle ownership in general.
  • A mode shift from public transport, as people want to avoid the risk of catching COVID19 on public transport (whether this risk is large or small). However with working/learning from home, it’s hard to know how much of this is mode shift of continued trips, versus trips of certain modes not being made as often.

Motorcycle ownership

This chart shows a slightly different pattern to that of motorcycle passenger kilometres per capita in cities (above). Ownership and usage bottomed out around the 1990s or 2000s (depending on the state/city). However ownership has risen in most states since then, but usage apparently peaked around 2009 in most cities. This perhaps suggests motorcycles are now more a recreational – rather than everyday – vehicle choice. But I really don’t follow motorcycle trends closely so cannot be too sure.

Driver’s licence ownership

Thanks to BITRE Information Sheet 84, the BITRE Yearbook 2021, and some useful state government websites (NSWSAQld), here is motor vehicle licence ownership per 100 persons (of any age) from June 1971 to June 2020 or June 2021 (only some state agencies have published 2021 data at the time of writing):

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.

There’s been slowing growth over time, although Victoria has actually seen slow decline since 2011, and the ACT peaked in 2016.

However in both states with 2021 data (South Australia and New South Wales) there was a significant uptick in 2021 of more than 1 licence per 100 people. This is likely related to the pandemic – either more people opting for a driver’s licence to shift away from shared modes, and/or a lack of recent immigrants (many were young adults) who usually take some time to get their licence. I would not be surprised to see similar trends in other states when data is made available.

Here’s a breakdown by age bands for Australia as a whole:

Licencing rates had been increasing over time for those aged over 40 (most strongly for those aged over 70) up until 2019, but that changed for the 60-69 and 70-79 bands in 2020.

Licencing rates had been declining for those aged under 40 until 2019, although there was a notable uptick in licence ownership for 16-19 year-olds in 2018, and increases in 2020 for those aged 20-29.

However the above charts show national trends that can wash out variations at the state level. So let’s break it down for states per age band:

Licence ownership rates for teenagers has been declining significantly in Victoria, with a large fall in 2020. There were also declines in 2020 in Tasmania, South Australia and Western Australia. NSW had a significant increase in 2020, and even more so in 2021.

Note: the differences between states for this age band at least partly reflect different minimum ages for licencing.

The largest states of Victoria and New South Wales were trending downwards until 2019, but have since shot back up, quite spectacularly in NSW. This might partly reflect the absence of new immigrants who generally have low levels of driver’s licence ownership. There may also be issues with ABS’s population estimates in the unprecedented pandemic.

All states showed an increase in 2020 except the Northern Territory.

Victoria and New South Wales did have a downwards trend in this age band, but that turned around in 2020. Tasmania and the ACT have shot up since 2017.

Licence ownership for those in their 30s had been declining in NSW, SA, WA and Victoria up to 2020, with NSW again showing an uptick in 2021. Tasmania has seen strong growth in recent years.

Licence ownership for those in their 40s was declining slightly in SA, Victoria, and WA until 2020, but was still very high. NSW had a smaller uptick in this age band in 2021, compared to younger age bands.

Licence ownership for those in their 50s was declining slightly until 2020 in most states (except Queensland and Tasmania). NSW had a relatively small uptick in 2021 compared to younger age bands.

Licence ownership for those in their 60s was slowly growing in most states until 2019 but then fell in 2020 with the pandemic. The 2021 uptick in NSW did not fully recover from the drop in 2020.

Licencing rates for those in their 70s have been growing strongly in all states (except recently in WA). NSW saw a dip in 2020, but bounced back in 2021. I suspect a data error for NT in 2019.

Licencing rates for those over 80 were increasing in most states to 2020, and NSW only had a small dip in 2020.

New South Wales is the first state to give us insights into the impact of the pandemic, so here is a look at the licencing trends per age band in that state:

You can see more clearly the big growth for those aged under 30 (people who generally used public transport more often before the pandemic), whilst older age groups (60+) saw a temporary decline in licence ownership in 2020 with a bounce-back in 2021.

See also an older post on driver’s licence ownership for more detailed analysis.

For completeness, here is a chart showing motorcycle full licence ownership rates:

Queensland has two types of motorcycle licence and I suspect many people hold both, which might explain a licence ownership rate being so much higher than other states.

Freight

There has been a massive increase in domestic freight volumes since the 1970s, and according to this data, rail has accounted for most of this growth in recent decades. However keep in mind that a majority of these freight-kilometres are bulk commodities (such as iron ore, coal, and grain) which are ideally suited to energy-efficient rail and coastal shipping. Indeed in 2020-21, road transport only moved 11% of bulk goods, and 93% of rail freight movements were bulk goods.

Here are the volumes for non-bulk freight movements, which are arguably more contestable:

And non-bulk freight mode shares:

Road transport dominates non-bulk freight movements in Australia, while air freight is trivial in terms of volume (but probably non-trivial in terms of value). Coastal shipping’s mode share fell significantly in the late 1970s and early 1980s but has remained mostly around 4-6% since then.

Rail transport’s mode share of freight movements declined in the 1970s and 1980s, had a small peak of 22% in 2006, but has fallen back to 16% in 2021. That’s despite the estimated rail freight volume in 2020-21 being the highest of any year reported – it’s just that road volumes have grown even more.

Transport greenhouse gas emissions

Total emissions

According to the latest quarterly figures, Australia’s domestic non-electric transport emissions peaked in around 2018, and had been slightly declining ahead of the COVID19 pandemic.

The above chart showing rolling 12-month figures, which hides the big and sudden changes in recent quarters. So here’s a look at seasonally-adjusted transport emissions by quarter:

Data available at the time of writing was to June 2021, a quarter with fewer impacts from the pandemic (there were some lockdowns in Melbourne). As pandemic conditions eased (before the COVID19 delta wave in the second half of 2021), transport emissions shot back up to near-2019 levels. I expect we will see a decline in the September 2021 data as Victoria and NSW experienced COVID lockdowns. Reductions in Australia’s transport emissions so far appear to be only temporary.

The next chart shows a long term trend of rapid rising annual transport emissions (according to BITRE data):

A more detailed breakdown of road transport emissions is available from 1990 onwards:

To better see the trends per mode, here is net growth since 1975:

Domestic aviation emissions have seen the biggest reduction from the COVID19 pandemic, followed by road emissions. Rail and marine emissions have also shown a decline in the last two years, however I cannot be certain to what extent this is due to the pandemic.

Road emissions grew steadily until 2019, while aviation emissions took off around 1991 (pardon the pun). You can see that 1990 saw a lull in aviation emissions, probably due to the pilots strike around that time.

In the years before the pandemic, non-electric rail emissions grew strongly, mostly driven by increases in bulk freight volumes (as discussed above). I suspect the small decline in rail emissions in recent years is unlikely to be related to diesel passenger trains (most of which have continued to run to normal timetables during the pandemic).

The next chart shows growth by sector since 1990 (including a more detailed breakdown of road transport):

This data suggests the pandemic has had no impact on truck emissions, but has reduced car, bus, and light commercial vehicle emissions.

Per capita emissions

While per capita emissions aren’t directly relevant to climate change impacts, it is interested to look at whether emissions growth has decoupled from population growth for different modes. Note I’ve used a log scale on the Y-axis.

Per capita car emissions for all modes except trucks have been in decline in recent years – and more so with the pandemic. Aviation and bus emissions per capita have fallen the most with the pandemic.

Emissions intensity

We can also calculate emissions per vehicle kms travelled. I’ve labelled the value estimates for 2021 (note again a log scale on the Y axis).

There has been a slow decline in emissions per km for cars, motorcycles and buses, while light commercial vehicles remain flat, and emissions per truck km have increased (although average truck loads have also increased over time).

I’d like to be able to calculate freight emissions intensity per tonne-kilometre by mode, but it’s hard to do that sensibly with the available data (eg rail emissions are not split by freight and passenger, and many flights carry both passengers and freight).

Transport costs

The final category for this post is the real cost of transport from a individual perspective. Here are headline real costs (relative to CPI) for Australia, using ABS data:

Technical note: Private motoring is a combination of factors, including motor vehicle retail prices and automotive fuel. Urban transport fares include public transport as well as taxi/ride-share (which possibly move quite independently, which is a little frustrating).

The cost of private motoring has tracked relatively close to CPI, although it seems to be trending down since 2008, probably largely related to reductions in the price of automotive fuel (which peaked in 2008). The real cost of motor vehicles has plummeted since 1996, although it may have bottomed out in 2018.

Urban transport fares have increased faster than CPI since the late 1970s, although they have grown slower than CPI (on aggregate) since 2013.

However 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 1973 by city (note different Y-axis scales):

Technical note: I suspect there is some issue with the urban transport fares figure for Canberra in June 2019. The index values for March, June, and September 2019 were 116.3, 102.0, and 118.4 respectively.

Urban transport fares have grown the most in Brisbane, Perth and Canberra – relative to 1973.

However if you choose a different base year you get a different chart:

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

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

What do these trends suggest for post-pandemic transport?

There are some emerging trends in the data above that suggest a shift towards private transport:

  • An uptick in driver’s licence ownership in 2021 evidenced in NSW and South Australia, and likely replicated in other states (data not yet available). The increases were sharpest for young adults, normally a natural market for public transport. Motor vehicle licence ownership has a strong relationship with mode choice, and even if/when the fear of infection on public transport is gone, there may be some people who stick to habits formed during the pandemic. See also: Why are younger adults more likely to use public transport? (an exploration of mode shares by age – part 2)
  • Likewise, an uptick in motor vehicle ownership in all states in 2021 could also see some people sticking to new driving habits formed during the pandemic. Again, see Why are younger adults more likely to use public transport? (an exploration of mode shares by age – part 2)
  • The biggest reductions in transport volumes were seen in public transport, no doubt strongly associated with office workers switching to working from home during the pandemic (a large portion of whom work in CBDs). They will likely not return to working in the office as frequently as they did before the pandemic, and this may see future public transport patronage and mode share lower than pre-pandemic projections. In other analysis (not yet published here sorry) I’ve found high rates of pre-pandemic public transport use amongst occupations that are most likely amenable to working from home.

However a shift to private transport will hit headwinds if traffic congestion rises (a highly effective form of demand management) and/or car parking prices increase.

Also, the resumption of international migration will probably see an influx of people who are less likely to own and use private vehicles, at least in their early years of living in Australia (see: Why were recent immigrants to Melbourne more likely to use public transport to get to work?) – although this may depend on their perspectives of infection risk.

I think a key issue will be whether a heightened fear of infection can ultimately be removed on public transport, which would enable people to switch back to using public transport, or resume making trips where public transport is/was the “default” mode for many (eg commuting to CBDs).

A sustained mode shift to private transport following the pandemic could have significant consequences for increasing traffic congestion and transport emissions (not to mention many other issues).


How has motor vehicle ownership changed in Australian cities for different age groups?

Sun 18 July, 2021

Motor vehicle ownership has a strong relationship with private transport mode share, and has recently seen declines in some Australian cities (e.g. Melbourne). In addition, we know that younger adults more recently have been deferring the acquisition of a driver’s licence (see: Update on Australian transport trends (December 2020)), so have they also been deferring motor vehicle ownership? For which age ranges has motor vehicle ownership increased and decreased? How might this have influenced journey to work mode shares? And how do changes in motor vehicle ownership relate to changes in driver’s licence ownership?

This post aims to answer those questions for Australia’s six largest cities, primarily using 2011 and 2016 census data, but also using household travel survey data for Melbourne.

But first…

A quick update on motor vehicle ownership trends in Australia

As I was writing this post, ABS released data for their Census of Motor Vehicle use – January 2021 (sadly the last motor vehicle census run by the ABS). I’ve matched this up with the latest available population data, and found a small but significant uptick in motor vehicle ownership rates in all Australian states in 2021 following the onset of the COVID-19 pandemic:

Image

I suspect this uptick will be at least partly due to a massive reduction in immigrants into Australia – who I’ve recently found to have much lower rates of motor vehicle ownership for the first few years they live in Australia (see Why were recent immigrants to Melbourne more likely to use public transport to get to work?) and also probably low motor vehicle ownership – see How and why does driver’s licence ownership vary across Sydney?).

It could also reflect a mode shift from public to private transport, as people seek to avoid the perceived risk of COVID-19 infection on public transport.

But there’s another likely explanation of this uptick and it relates to ages, so keep reading.

What does household travel survey data tell us about motor vehicle ownership by age in Melbourne?

My preferred measure is the ratio of household motor vehicles to adults of driving age (notionally 18 to 84).

Using Melbourne household travel survey data (VISTA), I can calculate the average ratio by age group pretty easily, and the following chart also breaks this down for parents, children, and other people (living in households without parent-children relationships):

With 2-year age bands there is a limited span of age ranges for some categories due to the small survey sample sizes (I’m only showing data points with 400+ people). So here is a similar chart using 4-year age bands, which washes out some detail but provides values for wider age ranges:

You can see some pretty clear patterns. Motor vehicle ownership was high for households with children (peaking for ages 12-13), parents – particularly in their late 40s, and those aged in their 50s and early 60s in households without children. Average motor vehicle ownership was lowest for young adults living away from their parents, and for those in older age groups.

Unfortunately the VISTA dataset isn’t really big enough to enable significant analysis of changes over time – the sample sizes for age bands get too thin when you split the data over years or even groups of years. I’d like to understand changes over time, so…

What can census data tell us about motor vehicle ownership by age?

Unfortunately it’s not possible to calculate the ratio of household motor vehicles to adults using Census (of Housing and Population) data (at least when using ABS Census TableBuilder).

The numerator is pretty easy for the 2011 and 2016 censuses which classify private dwellings as having zero, 1, 2, 3, 4, …, 28, 29, or “30 or more” motor vehicles. Only a very small number of households report 30+ motor vehicles. Unfortunately the 2006 census’s top reporting category is “4 or more” motor vehicles which means you cannot calculate the motor vehicle ratio for many households.

My preferred denominator – the number of adults of driving age – is not available in ABS’s Census TableBuilder. The closest I can get is the “number of persons usually resident” for dwellings – and private dwelling are classified as having 1, 2, 3, 4, 4, 5, 6, 7, or “8 or more” usual residents in the 2006, 2011 and 20216 censuses. Obviously I cannot calculate the ratio of motor vehicles to usual residents if there were “8 or more” usual residents.

(For the census data nerds out there: I tried to get a good guess of adults by using family composition, but it can only distinguish parents (who may or may not be of driving age), children under 15, and dependent students aged 15-24. And worse still, that doesn’t work for multi-family households, and you cannot filter for single family households as well as distinguish family types.)

So I’m stuck with household motor vehicles per person usually resident. And an obvious drawback is that motor vehicle ownership will be lower for adults living in households with children, compared to those without children.

Here’s the distribution of motor vehicle : household size ratios for Greater Melbourne for 2011 and 2016 (I’ve left out 2006 because too many households cannot be calculated). There are a lot of different ratio values, but only about a dozen common ratios, several of which I have labelled on the chart.

Sure enough, there were much lower ownership ratios for children’s households, and adult ages where children were more likely to be resident (generally mid-20s to around 60). Higher ratios peaked for people in their early 60s and then steadily declined into older ages, with most people in their 90s living in dwellings with no motor vehicles (if they are not living in non-private dwellings). For adults in their 60s, one car per person was the most common ratio.

I can also calculate the average motor vehicle ownership ratio for each age as an aggregate statistic (excluding 3-4% of households where I don’t know the precise number of residents and motor vehicles). Here’s how that looks for 2011 and 2016:

As mentioned, I cannot calculate this ratio for households where I don’t know the precise number of both motor vehicles and usual residents (or where I don’t know the number of usual residents, but do know there were zero motor vehicles). Across Australia’s five biggest cities that’s 4.1% of population in the 2016 census, 3.4% in 2011, and 10.4% in 2006 (but much higher proportions of younger adults). They sound like small numbers, but aren’t that small when you consider the shifts in ownership between censuses.

But there is another way to classify households with fewer unknowns – whether they have:

  • no motor vehicles;
  • fewer usual residents than motor vehicles; or
  • at least one motor vehicle per usual resident.

The benefit of this approach is that you can classify almost half of the households where you cannot calculate an exact ratio:

  • If a household had 30+ motor vehicles (very rare) but fewer than 8 usual residents, then it had at least one vehicle per person.
  • If a household had 4+ motor vehicles (quite common in 2006 census) and 4 or fewer usual residents, then it had at least one vehicle per person.
  • If a household had 8+ usual residents (about 1.3% of population in 2016), but 7 or fewer motor vehicles (93.5% of the 1.3%), then it had less than one vehicle per person.

Across Australia’s biggest five cities I can now classify all but 2.5% of the 2016 population, 2.3% of the 2011 population and 6.1% of the 2006 population.

The next chart shows the distribution of this categorisation for Melbourne (using Melbourne Statistic Division for 2006, and “Greater Melbourne” for 2011 and 2016). I’ve put the remaining people living in uncategorisable households (“unknown”) in between 0 and <1 motor vehicles per person, as it is likely households who did not answer the question about household motor vehicles probably had few or no motor vehicles (refer to the appendix at the end of this post for more discussion).

I have also removed people who did not provide an answer to the usual residents question (hoping they are not overly biased – they are probably households who didn’t respond to the census), and non-private dwellings (where motor vehicle ownership is not recorded).

The patterns are similar to the previous chart, with a double hump pattern of 1+ motor vehicles per person. There are some changes over time, which I’ll discuss shortly.

Unfortunately the unknown band is still pretty wide in 2006 – in fact I still cannot categorise around 15% of 20 year olds in 2006 (many must have lived in households with 4+ motor vehicles), so it doesn’t really support good time series evaluation between 2006 and 2011.

So how has motor vehicle ownership by age changed over time in Melbourne?

Many of the previous charts were animated over 2-3 censuses but there’s a lot of take in with different lines moving in different directions for different age groups. To help to get better sense of those changes, what follows are a set a static charts, and then some discussion summarising the patterns.

Firstly, the change in average motor vehicles per usual resident for each age year (but only for households where the exact number of motor vehicles and usual residents is known):

Secondly, here’s a static chart that shows the proportion of population living in households known to have 1+ motor vehicles per person for both 2011 and 2016 for Melbourne, and the difference between 2011 and 2016 (I’ve excluded 2006 as there were too more unknowns). I haven’t removed uncategorisable households from the calculations, on the assumption they bias towards lower motor vehicle ownership (as discussed above).

This chart shows very little change for children under 18, but also very few such households had 1+ motor vehicle per occupant in 2011 or 2016 so it’s not a very useful metric. Lower ownership ratios are much more common for households with children, so here’s a chart showing the proportion of the population living in dwellings with at least 0.5 motor vehicles per person, and the change between 2011 and 2016: (I used equivalent rules to classify households with 8+ usual residents or 30+ motor vehicles, where possible)

And finally, here’s a chart showing the proportion of the population living in dwellings reported to have no motor vehicles (probably an underestimate as I think many “not stated” responses are likely to be zero motor vehicles).

Each of these charts paints a similar picture. Here’s a summary by age ranges:

Age rangeMotor vehicle ownership trend
0-17Slight increase
18-26Certainly a decline, including around 1-2% more people living in dwellings with no motor vehicles.
27-45Small decline of around 2-3% living in households with 1+ or 0.5+ motor vehicles per person. But there was no significant increase in households with no motor vehicles, and average motor vehicles per person was relatively stable.
46-64Very small decline (around 1%) of people living in households with 1+ and 0.5+ motor vehicles per person, but little change in households without motor vehicles.
65+Significant increase in metrics of motor vehicle ownership, and a significant decline in dwellings without any motor vehicles.

So while overall motor vehicle ownership in Melbourne declined between 2011 and 2016, it was mostly in working aged adults, partly offset by family households and older adults increasing their rates of motor vehicle ownership.

And going back to the uptick in motor vehicle ownership in January 2021… recent immigrants to Australia have skewed towards young adults (particularly through skilled migrant visas). The massive reductions in immigrants in 2021 will mean the population contains proportionately fewer young adults – who generally have low car ownership, particularly recent immigrants. This slightly but significantly smaller number of young adults will no longer be fully offsetting those over 70 who are increasingly retaining motor vehicles longer into their life.

What about other Australian cities?

As above, I’ll present a series of charts showing the various metrics then summarise the trends.

Firstly, a chart showing the average ratio of motor vehicles per resident by age for all cities between 2011 and 2016 – for private dwellings where the exact number of vehicles and usual occupants is known:

To help see those changes, here is a static chart showing the change in average motor vehicles per person by age (I’ve used three-year age bands as the data otherwise gets a bit too noisy):

Here’s an animated chart showing the percentage of people living in private dwellings with 1+ motor vehicle per person:

There’s a lot going on in that animation (and the data gets a bit noisy for Canberra due to the relatively small population), so next is a chart showing the difference in population living with 1+ motor vehicles per usual resident:

As before, the threshold of 1 motor vehicle per person is not useful for examining the households of children, so here’s a similar change chart for the 0.5 motor vehicles per person threshold:

These difference charts mostly form duck-shaped curves with a slight increases for children, a mixture of increases and decreases for working aged adults, and a large increase for older adults (particularly for those in their 70s).

For young adults (18-30), motor vehicle ownership mostly declined in Melbourne and Canberra, but for Perth and Adelaide there was a large increase in ownership for those aged 21-39.

There was less change in ownership for those aged 40-54. On the metrics of proportion of population with 1+ and 0.5+ motor vehicles per resident there was a small decline in all cities, but for average motor vehicles per person, some cities declined and some increased. So perhaps the amount of variation in motor vehicle ownership narrowed in this age range.

Melbourne was mostly at the bottom of the pack, with Brisbane, Adelaide or Perth mostly on top.

To continue this analysis, I want to know whether these changes in motor vehicle ownership might be impacted mode share, but first we need to look at…

How did journey to work mode shares change by age?

Here are public transport mode shares of journeys to work by age for Australia’s six biggest cities, 2006 to 2016:

Public transport mode shares were much higher for younger adults in all cities in all censuses. Most cities rose between 2006 and 2011, but then different cities went in different directions between 2011 and 2016.

Here’s the mode shift between 2006 and 2011:

Most cities and ages had a mode shift towards public transport, particularly for those aged around 30, but less so for young adults.

Here’s the mode shift between 2011 and 2016:

Between 2011 and 2016 there was a mode shift to public transport in most cities for people in their 30s and 40s, but for younger adults there was a decline in public transport mode share in most cities, with only Sydney, Melbourne, and Canberra seeing growth.

However we are talking about motor vehicle ownership, and declining motor vehicle ownership may be because of mode shifts to walking, cycling, and/or public transport. So it is worth also looking at private transport mode shares (journeys involving private motorised modes but not public transport modes).

To help see the differences, here is the mode shift for private transport 2006 to 2011:

There’s a similar curve for all cities, but different cities are higher or lower on the chart. There was a shift towards private transport for young workers, a shift away in most cities for those in their 20s and 30s, and smaller shifts for those in their 40s and 50s

And from 2011 to 2016:

Again similar curves across the cities, with younger adults again more likely to shift towards private transport in most cities, a big shift away from private transport for those in their 30s and early 40s in Sydney and Melbourne, and smaller shifts for those in their 50s and 60s.

What’s really interesting here is that the mode share and mode shift curves are similar shapes across most cities (except the much smaller city of Canberra). There are some age-related patterns of travel behaviour change consistent across Australia’s five biggest cities.

How did changes in motor vehicle ownership compare to changes in private transport mode share?

If motor vehicle ownership increases you might expect an increase in private transport mode shares, and likewise you might expect a decrease in ownership to relate to a decline in private transport mode shares.

Indeed when you look at cities as a whole, there is generally a strong relationship between these measures, although different cities moved in different directions between 2011 and 2016.

In this post I’m interested in shifts for people in different age groups. The following chart shows the changes in motor vehicle ownership and private transport mode shares for each city and age group: (note different axis scales are used in each row of charts)

However I’m particularly interested in the change in these factors, rather than where they landed in each of 2011 and 2016. So the following chart plots the change in motor vehicles per 100 persons and the change in private transport mode share of journeys to work between 2011 and 2016 for five-year age bands (noting that of course every living person got five years older between the censuses).

That’s a busy chart but let me take you though it.

There’s one mostly empty quadrant on this chart (top-left): for no city / age band combinations did motor vehicle ownership decline but private transport mode share increase, which isn’t really surprising.

But in city / age band combinations where motor vehicle ownership did increase there there wasn’t always an increase in private transport mode shares – quite often there was actually a decline. So increasing motor vehicle ownership doesn’t necessarily translate into higher private transport mode shares – for journeys to work at least. Perhaps increasing affordability of motor vehicles means more people own them, but don’t necessarily switch to using them to get to work.

The largest declines in private transport mode share occurred in city/age band combinations that actually saw a slight increase in motor vehicle ownership.

The cloud is quite spread out – which to me suggests that motor vehicle ownership is probably not a major explanation for changes in mode share between 2011 and 2016 – there must be many other factors at play to explain changes in mode shares across cities. Indeed, see my post What might explain journey to work mode shifts in Australia’s largest cities? (2006-2016) for more discussion on these likely factors.

What is the relationship between motor vehicle ownership and driver’s licence ownership?

As I’ve previously covered on this blog (eg see: Update on Australian transport trends (December 2020)), data is available on the number of licenced drivers by different age groups, but only at the state level.

I’d prefer not to be using state level data as city and country areas might wash each other out, but I’d don’t have a lot of choice because of data availability. (Licencing data is available at postcode resolution in New South Wales (see How and why does driver’s licence ownership vary across Sydney?), but unfortunately you cannot disaggregate by both geography and age.)

Here’s another (busy) chart showing the relationship between licence and motor vehicle ownership by age band and city, across 2011 and 2016:

The main thing to take away here is that most of the points are within a diagonal cloud from bottom-left to top-right – as you might expect: there is less value having a driver’s licence if you don’t own a car, and little point owning a car if you don’t have a licence to drive it. The exceptions to the diagonal cloud are mostly age bands 30-39 and 40-49, where the average motor vehicle ownership rates are lower because many of these people often have children in their households, and I cannot remove children from the calculation using census data.

But I can control for the issue of children by going back to city geography by using household travel survey data for Melbourne (VISTA, 2012-2018). The following chart shows the relationship between average motor vehicle and driver’s licence ownership for adults by different age brackets.

The data points again generally form a diagonal cloud as you’d expect. Higher motor vehicle ownership generally correlates with higher licence ownership.

The change in ownership rates by age are interesting. Children under 10, on average, lived in households where adults have very high levels of motor vehicle and licence ownership. Licence ownership was slightly lower for adults in households with children aged 10-17 (although this could just be “noise” from the survey sample). Young adults (18-22) then on average lived in households with relatively low motor vehicle and licence ownership. As you move into older age brackets licence ownership increased, followed by increases in motor vehicle ownership, with both peaking again around ages 40-69 (although not as high as households with children). Those aged 70-79 and 80+ then had significantly lower rates of licence and vehicle ownership, as you might expect as people age and become less able to drive. These patterns are fairly consistent with the census data scatter plot, except for the key parenting age bands of 30-39 and 40-49 where the census data analysis cannot calculate ownership per adult (just per person).

How has licence and motor vehicle ownership been changing for different age groups?

Across Australia, licence ownership has been increasing in recent years for older adults (particularly those over 70), and declining in those aged under 30 in states such as Victoria, New South Wales and Tasmania (for more detail see Update on Australian transport trends (December 2020)).

The following chart shows state-level changes in motor vehicle ownership and licence ownership between 2011 and 2016 by age bands: (note different scales on each axis)

This chart also shows something of a direct relationship between changes in motor vehicle and licence ownership, with people aged 70+ having the largest increases in both measures (except for Victorians aged 80+ who saw a decline in licence ownership). Younger age bands often had a decline in licence ownership, even if motor vehicle ownership in their households increased slightly (on average). For those aged in their 40s, there was generally an increase in licence ownership but only small changes in motor vehicle ownership – including slight declines in most states.

Teenagers in the ACT were an outlier, where there was a significant decline in licence ownership between 2011 and 2016 that someone with local knowledge might be able to explain.

Overall the relationship between changes licence ownership and changes in motor vehicle ownership is not super strong. Increasing licence ownership does not automatically translate into increasing motor vehicle ownership. There must be more factors at play.

I hope you’ve found this post interesting.

Appendix: What about households where census data is missing?

The non-response rate to the question about household motor vehicles was around 8.4% in 2016 (up from 6.5% in 2011) and most of these were for people who did not respond to the census at all. Non-response was fairly consistent across age groups as the next chart shows. Quite a few people had a response to the question about number of usual occupants, but did not respond to the question about motor vehicles. Poking around census data, these people often:

  • didn’t answer other questions;
  • were less likely to be in the labour force;
  • were generally on lower incomes;
  • were more likely to be renting;
  • were less likely to have a mortgage; and
  • were more likely to live in a flat, apartment or unit, and less likely to live in a standalone/separate house.

So my guess is that they were less likely to have high motor vehicle ownership.

The number of “not applicable” responses increased significantly into older age groups, and I expect most of these will be people in non-private dwellings (e.g. aged care). I have removed people with “not applicable” responses for usual occupants and household motor vehicles as they are likely to be non-private dwellings.

The chart gets a bit noisy for ages above 100 as very few such people live in private dwellings.