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:

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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.


Update on Australian transport trends (December 2020)

Sun 3 January, 2021

Each year, just before Christmas, the good folks at the Australian Bureau of Infrastructure, Transport, and Regional Economics (BITRE) publish a mountain of data in their Australian Infrastructure Statistics Yearbook. This post aims to turn those numbers (and some other data sources) into useful knowledge – with a focus on vehicle kilometres travelled, passenger kilometres travelled, mode shares, car ownership, driver’s licence ownership, greenhouse gas emissions, and transport costs.

Of course the world of transport changed significantly in 2020, with suppressed movement from around mid March, as the COVID19 pandemic led to movement restrictions across Australia. Most of the following data is for financial years, so you will see some impacts where data is available for financial year 2019-20.

Vehicle kilometres travelled

Total vehicle kilometres travelled has been increasing most years, until 2019-20, when it fell from 264 to 247 billion kilometres.

Here’s the growth by vehicle type since 1971:

Light commercial vehicle kilometres have grown the fastest, curiously followed by buses (although much of that growth was in the 1980s). In 2019-2020, there were noticeable reductions for most vehicle types, except trucks.

Car kilometre growth has slowed significantly since 2004, and actually peaked in 2016-17 according to BITRE estimates.

On a per capita basis car use peaked in 2004, with a general decline since then. Here’s the Australian trend (in grey) as well as city level estimates until 2015 (from BITRE Information Sheet 74):

Technical note: “Australia” lines in these charts represent data points for the entire country (including areas outside capital cities).

Darwin has the lowest average which might reflect the small size of the city. The blip in 1975 is related to a significant population exodus after Cyclone Tracey caused significant destruction in late 1974 (the vehicle km estimate might be on the high side).

Canberra, the most car dependent capital city, has had the highest average car kilometres per person (but it might also reflect kilometres driven by people from across the NSW border in Queanbeyan).

The Australia-wide average is higher than most cities, with areas outside capital cities probably involving longer average car journeys and certainly a higher car mode share. There was a sharp drop in vehicle kms per capita in 2019-20, almost certainly due to COVID-19.

Passenger kilometres travelled

While BITRE’s passenger km estimates were available up to 2019-20 at the time of writing, city population estimates were only available up until June 2019. So in this section, per capita data stops at 2018-19 (before COVID-19), while total km charts go to 2019-20.

Here are passenger kilometres per capita for various modes for Australia as a whole (note the log-scale on the Y axis). Unfortunately BITRE have not published national estimates beyond 2017-18 in their 2020 Yearbook.

Air travel took off (pardon the pun) in the late 1980s (although with a lull in 1990 due to the pilot’s strike), car travel peaked in 2004, bus travel peaked in 1990 and has been relatively flat since, while rail has been increasing in recent years.

Car passengers

Here’s a chart showing total car passenger kms in each city:

The data shows that Melbourne overtook Sydney in 2016-17 as having the most car passenger travel, but then cities were even again in 2019-20 with COVID19 impacts.

Another interesting observation is that total car passenger travel declined in Adelaide in 2018-19 (pre-COVID) according to (revised) BITRE estimates.

However there are large differences in population growth rates between cities. So here is the data per capita:

While car passenger kilometres per capita peaked in 2004 in all cities, there were some increases from around 2013 to 2018 in some cities, but most cities declined in 2019 and 2020 (the latter being no doubt partly related to COVID-19). Darwin is an outlier with an increase in car passenger kms per capita between 2015 and 2020.

Rail passengers

Here are rail passenger kms per capita to 2018-19:

Sydney had the highest train use of all cities and this has been taking off in recent years, likely due to service level upgrades. Other cities have been flat or were in decline (such as Melbourne).

You can see two big jumps in Perth following the opening of the Joondalup line in 1992 and the Mandurah line in 2007. Melbourne, Brisbane and Perth have shown declines over recent years.

Here is growth in total rail passenger kms since 2010 (NOT per capita):

Sydney trains saw rapid growth in the years up until 2019, again probably reflecting significant service level upgrades to provide more stations with “turn up and go” frequencies at more times of the week.

Adelaide’s rail patronage dipped in 2012, but then rebounded following completion of the first round of electrification in 2014.

All cities saw significant declines in 2019-20 with COVID-19 impacts, although BITRE caveats that the 2019-20 estimates for public transport modes were “rough” at the time of publication.

Bus passengers

Here’s bus passenger kms per capita up to 2018/19:

Bus passenger kms per capita have been declining in most cities in recent years, with the exception of Sydney.

Significant investments in bus services in Melbourne and Brisbane between around 2005 and 2012 led to significant patronage growth.

Melbourne has the lowest bus use of all the cities, but this likely reflects the extensive train and tram networks carrying the bulk of the public transport passenger task. Melbourne is different to every other Australian city in that trams provide most of the on-road public transport access to the CBD (with buses performing most of this function in other cities).

Darwin saw a massive increase in bus use in 2014 thanks to a new nearby LNG project running staff services.

Australia-wide bus usage is surprisingly high. While public transport bus service levels and patronage would certainly be on average low outside capital cities, buses do play a large role in carrying children to school – particularly over longer distances in rural areas. The peak for bus usage in 1990 may be related to deregulation of domestic aviation, which reduced air fares by around 20%.

Here is growth relative to 2010:

All cities saw a substantial reduction in 2019-20 due to COVID-19, with Hobart having the smallest reduction. Perhaps there is less discretionary and office-commuter travel on Hobart’s buses?

Light rail passengers

Light rail passenger kms per capita is not really meaningful as Melbourne has a large network, while Sydney and Adelaide have very small (although growing) networks. Here is estimated passenger km growth since 2010:

Sydney light rail patronage increased following the Dulwich Hill extension that opened in 2014, and again with the new lines joining the CBD with Randwick and Kingsford opening in 2019-20. The passenger km growth would have been higher if not for COVID-19.

Adelaide patronage increased following an extension to the Adelaide Entertainment Centre in 2010, and then flatlined for several years. In October 2018, new extensions to Festival Plaza and Botanic Gardens opened but passenger kms actually declined in FY 2018-19.

Mass transport

We can sum all of the mass transport modes (I use the term “mass transport” as the numbers include both public and private bus services). Firstly. here is mass transit share of estimated total motorised passenger kilometres in each city (unfortunately there are no estimates of walking and cycling kilometres):

All cities saw a mode shift away from mass transit in 2019-20 due to COVID-19, which likely reflects the shift to working from home for CBD workers (with such commuter trips making up a substantial share of PT patronage). During the recovery after lockdowns, road traffic has returned to almost normal in most cities, whilst public transport patronage is still well down on pre-COVID19 levels. I should mention again that BITRE describe their 2019-20 estimates of non-private passenger kilometres as “rough”.

But looking at trends prior to 2019-20, Sydney was leading the country in mass transport use per capita which was also rising fast to 2019, with a 2% mode shift between 2016 and 2019 (mostly attributable to trains). The Sydney north west Metro line opened in May 2019, so would only have a small impact on these figures.

Melbourne mass transit had been losing mode share between 2012 and 2019, while other cities have been largely flat or trending down (although Brisbane, Adelaide, and Perth has a small increase in 2018-19).

Melbourne made significant gains between 2005 and 2009, and Perth grew strongly 2007 to 2013, but has since shifted away from public transport (which may be related to a decentralisation of employment).

Here is growth in mass transport passenger kms since 2010:

Darwin saw substantial growth associated with staff bus services to a new LNG plant, while Sydney otherwise was leading in mass transit passenger kilometre growth.

Here’s how car and mass transit passenger kilometres have grown since car used peaked in 2004:

Mass transit use has grown much faster than car use in Australia’s three largest cities. In Sydney and Melbourne it has exceeded population growth, while in Brisbane it is more recently tracking with population growth.

Mass transit has also outpaced car growth in Perth, Adelaide, and Hobart:

In Canberra, both car and mass transit use has grown much slower than population, and it is the only city where car growth has exceeded public transport growth.

Motorcycles

Here are motorcycle passenger kms per capita:

Motorcycle travel was declining per capita until 2000, had a resurgence between 2004 and 2009 (perhaps as fuel prices rose?) and has since reduced somewhat in most cities. I’m not quite sure what might have happened in Melbourne in 2006 to suddenly stop the growth in use. I also wonder about the precision of estimates of motorcycle passenger kilometres, given it is such a small mode.

Car ownership

The ABS conduct a Motor Vehicle Census generally once per year (although less often historically), and the following chart includes that data up until January 2020, combined with population estimates released in December 2020.

Car ownership has risen significantly over time, although this growth has slowed considerably more recently in some states.

However the above measure doesn’t take into account people not of driving age. So the following chart looks at passenger cars per persons aged 18-84 (for want of a better definition of driving aged persons):

It’s still a bit hard to see the more recent trends, so here is a chart that looks at 2000-2020, excludes the Northern Territory (zooming on the top-right section):

This data shows that car ownership peaked in Victoria in 2013, Western Australia in 2017, New South Wales in 2017, Queensland in 2018, South Australian in 2018, and Australia overall in 2018. The Australian Capital Territory may have peaked in 2019 but perhaps it is a little too early to call, while Tasmanian now has the highest car ownership in the country and is still growing strongly.

My previous analysis of census data found that Melbourne was the only large city to see a decline in car ownership between 2011 and 2016 (see also an older post on car ownership).

Motorcycle ownership

Here is a chart showing motorcycles per persons aged 18-84:

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 (I really don’t follow the motorcycle industry very closely so others might better explain this).

Driver’s licence ownership

Thanks to BITRE Information Sheet 84, the BITRE Yearbook 2020, and some useful state government websites (NSW, SA, Qld), here is motor vehicle licence ownership per 100 persons (of any age) from June 1971 to June 2019 or 2020 (depending on data availability):

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, but Victoria has actually seen slow decline since 2011, and the ACT peaked in 2014.

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

Licencing rates have been increasing over time for those aged over 40 (most strongly for those aged over 70), and have been declining for those aged under 40, although there was a notable uptick in licence ownership for 16-19 year-olds in 2018.

The next chart shows licencing rates for teenagers:

Licence ownership rates for teenagers had been trending down in South Australia and Victoria until 2017, while most other states have been trending upwards in recent years. The differences between states partly reflects different minimum ages for licensing.

Here are 20-24 year olds:

The largest states of Victoria and New South Wales had seen downwards trends until 2019, while all other states and territories are trending up. The big upticks in 2020 for Queensland and NSW might be a new trend, might also be impacted by the preliminary nature of June 2020 population estimates from the ABS, and/or might be impacted be an exodus of international students.

25-29 year olds are a mixed bag – Victoria has been trending downwards sharply, New South Wales has (probably) just ended a downwards trend, while most other states have been increasing or relatively steady.

Licencing rates for people in their 70s have been rising in all states, although it may be slowing in Western Australia and NSW more recently (I have excluded 2016 for South Australia as I suspect a data error):

A similar trend is clear for people aged 80+ (Victoria was an anomaly before 2015):

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.

Transport greenhouse gas emissions

According to the latest adjusted quarterly figures, Australia’s domestic non-electric transport emissions peaked in 2018, had been slightly declining (which reflects reduced consumption of petrol and diesel) before COVID impacted the year-ending June 2020 figure.

The seasonally-adjusted estimate for the June quarter of 2020 is 19.2 Mt, which is down 24% on the June quarter of 2019:

Non-electric transport emissions made up 19.1% of Australia’s total emissions as at December 2019 (before the COVID-19 impact).

Here’s a breakdown of transport emissions by financial year:

A more detailed breakdown of road transport emissions is available, but only back to 1990:

Here’s growth in transport sector emissions since 1975:

The 2019-2020 estimates are heavily impacted by COVID-19, most evidently in aviation, but also for road transport.

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

In more recent years non-electric rail emissions have grown strongly. This will include a mix of freight transport and diesel passenger rail services – the most significant of which will be V/Line in Victoria, which have grown strongly in recent years (140% scheduled service kms growth between 2005 and 2019). Adelaide’s metropolitan passenger train network has historically run on diesel, but has more recently been transitioning to electric.

Here is the growth in each sector since 1990 (including a breakdown of road emissions):

Within road transport, COVID-19 has had the biggest impact on cars, buses, motor cycles and light commercials. However, emissions from (larger) articulated trucks continued to grow.

Here are average emissions per capita for various transport modes in Australia, noting that I have used a log-scale on the Y-axis:

Per capita emissions have been decreasing for cars, and – until 2019 – were relatively stable for most other modes. Total road transport emissions per capita peaked in 2004 (along with vehicle kms per capita, as above).

Transport greenhouse gas emissions intensity

It’s possible to combine data sets to estimate average emissions per vehicle kilometre for different vehicle types, but only until 2018 with published data (note I have again used a log-scale on the Y-axis):

Note: I suspect the kinks for buses and trucks in 2015 are issues to do with estimation assumptions made by BITRE, rather than actual changes.

Most modes have shown slight declines in emissions per vehicle km, except trucks. On these estimates, car have dropped from 281 g/km in 1990 to 243 g/km in 2018.

However, the above figures don’t take into account the average passenger occupancy of vehicles. To get around that we can calculate average emissions per passenger kilometre for the passenger-orientated modes (data only available until 2018 unfortunately):

Domestic aviation estimates go back to 1975, and you can see a dramatic decline between then and around 2004 – followed little change (even a rise in recent years). However I should mention that some of the domestic aviation emissions will be freight related, so the per passenger estimates might be a little high.

Car emissions per passenger km in 2017-18 were 154.5g/pkm, while bus was 79.0g/pkm and aviation 127.2g/pkm.

Of course the emissions per passenger kilometres of a bus or plane will depend on occupancy – a full aeroplane or bus will have likely have significantly lower emissions per passenger km. Indeed, the BITRE figures imply an average bus occupancy of around 9 people (typical bus capacity is around 70) – so a well loaded bus should have much lower emissions per passenger km. The operating environment (city v country) might also impact car and bus emissions. On the aviation side, BITRE report a domestic aviation average load factor of 79.3% in 2019-20.

Cost of transport

The final topic for this post is the real cost of transport. Here are headline real costs (relative to CPI) for Australia:

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.

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 that trend may have stopped in 2018. Urban transport fares have been increasing faster than CPI since the late 1970s, although they have grown slower than CPI (on aggregate) since 2013.

Here’s a breakdown of the real cost of private motoring and urban transport fares by city (note different Y-axis scales):

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.

I hope you have found this interesting.


Why were recent immigrants to Melbourne more likely to use public transport to get to work?

Mon 7 December, 2020

I’ve recently been analysing how public transport mode share varies with age and associated demographic factors. In part 3 of that series, I found that immigrants – and particularly recent immigrants – were much more likely to use public transport (PT) in their journey to work. This post explores why that might be, using data for Melbourne from the ABS Census (mostly 2016).

About immigrant data

The census covers both temporary and permanent residents. I’ve counted all people who were born overseas and came to Australia intending to stay for at least one year as “immigrants”, regardless of whether they were temporary or permanent residents.

It’s worth looking at the number of immigrants living in Greater Melbourne by age and arrival year, as at 2016:

Except for the first and last columns, each column represents 10 arrival years. You can see a significantly larger population of immigrants who arrived between 2006 and 2015, and they skewed significantly to ages 20-39. We know from previous analysis that younger adults are more likely to use public transport, so age is likely to play a role.

But how many immigrants are temporary residents? The census doesn’t include a question about permanent residency, but it is possible to track arrival year range cohorts over time.

The following chart tracks the number of immigrants for arrival year ranges between the 2006, 2011 and 2016 censuses (using Significant Urban Area geography).

If there were a significant number of temporary residents (although still intending to stay at least one year), then you’d see a large drop in the population of people who arrived 1996 to 2005 over time between 2006 and 2011/2016. There certainly was a drop off, but it was a small proportion.

This suggests most migrants end up being long-term residents (including many who enter on temporary visas but then gain permanent residency).

Numbers in all arrival year ranges dropped slowly over time through people leaving Melbourne (and possibly Australia) and deaths (particularly for immigrants from earlier years many of whom would be in their senior years).

Immigrants and public transport mode share of journeys work

To recap my previous analysis, the relationship between immigration year and PT mode share has held for the last three censuses (2006, 2011, and 2016), regardless of parenting status, birth year, or whether the someone worked inside or outside the City of Melbourne (local government area):

So why might recent immigrants be more likely to use public transport? From looking at the data, I think there are several plausible explanations.

To start with, they were more likely to work in the City of Melbourne, and we know journeys to work in the City of Melbourne have much higher public transport mode shares:

They were also more likely to live in areas with lower levels of motor vehicle ownership. Each column in the following chart represents the population of immigrants for a range of arrival years, and that population is coloured based on the motor vehicle ownership rate of all residents in the (SA1) areas in which they live (including non-immigrants). Note: immigrants themselves may have had different rates of motor vehicle ownership to the average of people in the areas in which they lived.

As I’ve mentioned previously, I do not have access to data to calculate the ratio of household motor vehicles to driving-aged adults within immigrant households, but I can calculate the ratio of household vehicles to all household residents (not all of whom may be of driving age).

The following chart shows that more recent immigrants were likely to have much lower levels of motor vehicle ownership that those who have been living in Australia longer.

Aside: Immigrants who arrived in Australia 1900-1945 had much higher rates of motor vehicle ownership than people born in Australia, but they were also all aged over 70 in 2016.

BUT if you look at PT mode shares for each vehicle : person ratio, there is still a relationship with year of arrival (see next chart), so car ownership doesn’t fully explain why recent immigrants were more likely to use public transport.

Looking at other factors, recent immigrants were slightly more likely to live closer to the city centre:

And they were more likely to live near a train station:

However not all recent immigrants to Melbourne lived near the city or a train station. Here’s a map showing the density of persons who arrived in Australia between 2006 and 2016 as at the August 2016 census.

There were significant concentrations in outer growth areas such Point Cook, Tarneit, and Craigieburn. These suburbs also happen to have very well patronised rail feeder bus routes, and unusually higher concentrations of central city commuters for their distance from the CBD.

Recent immigrants were more likely to live in areas of higher residential density:

And they were more likely to work near the city centre:

More-recent immigrants were also more likely to have a higher level of educational attainment than less-recent immigrants, and generally much higher than those born in Australia:

This probably reflects skilled immigration programs favouring people with higher educational qualifications. Indeed 60% of workers who arrived between January 2016 and the August 2016 census had a Bachelor or higher qualification. And we know from a previous post that highly qualified workers were more likely to work in central Melbourne, and were more likely to have used public transport in their journey to work.

Not only were more recent immigrants generally highly educated, many came to Melbourne to study to raise their educational attainment. Here is a chart showing the proportion of immigrants who were full-time or part-time students, by arrival year groups:

I will explore the relationship between student status and journey to work mode shares in an upcoming post.

How did immigrants shift around Melbourne over time?

Could internal migration explain why immigrants shifted away from public transport over time? Using census data across 2006, 2011, and 2016, it is possible to roughly track the population distribution of particular immigrant cohorts (although it’s not perfect because these immigrants may have moved in/out of Melbourne or left Australia between censuses, including temporary residents).

The following map shows the density of immigrants who arrived in Australia between 1996 and 2005 across census years 2006, 2011, and 2016:

In 2006 there were concentrations around the central city and many rail stations. But these concentrations reduced over time, with many of these people moving into other suburbs by 2011 or 2016 (or leaving Melbourne). In particular, many moved to outer suburbs such as Tarneit, Truganina, Point Cook, Derrimut, Craigieburn, Roxburgh Park, and Narre Warren South.

To help summarise these shifts, the following chart shows the distribution of this cohort across census years by distance from train stations, distance from the Melbourne CBD, and the motor vehicle ownership rate of the areas in which they lived:

You can see that they generally moved further away from train stations, further away from the CBD, and into areas that had higher levels of motor vehicle ownership. All these shifts are associated with reduced public transport mode share, and I suspect this pattern would not be unique to those who arrived 1996-2005.

Is there a relationship between PT mode shares and where people were born?

Firstly, here’s a chart showing the birth regions of Melbourne workers who were born outside Australia, by year of immigration (mostly 5 year bands). I’ve used ABS’s country of birth groups, except that I’ve separated North America from the other Americas.

The early half of the 20th century saw significant immigration from Europe, whereas in more recent times this has shifted to Asia, with southern and central Asia now the biggest source of immigrants. (Southern and central Asia includes India, Sri Lanka, Bangladesh, many former Soviet republics south of Russia and all “-stan” countries.)

So do journey to work public transport mode shares vary by immigrants’ region of birth?

There certainly is some variance between birth regions, but not quite what I was expecting. Immigrants from seemingly car-dominated north America had much higher PT mode shares than those born in European countries with reputations for higher quality public transport.

Of course people born in different parts of the world may be more or less likely to work in the City of Melbourne, and might be more or less likely to be parents. These factors strongly influence PT mode shares. So the next chart disaggregates the data by parenting status and work location (note a different X-axis scale used for each work location division).

This birth regions in this chart have the same ordering as the previous chart, but in most quadrants the mode shares are no longer in order (the top-right quadrant being the exception: non-parenting, working outside the City of Melbourne). Southern and central Asia tops PT mode shares for the other three quadrants, and by quite a large margin for City of Melbourne workers.

We know year of arrival into Australia is a significant factor in PT mode shares, and relative composition of immigrants has certainly changed over time. Also, age itself is likely to be a factor. The next chart adds these two dimensions. However, I have had to remove people working in the City of Melbourne, those under 20 and those over 60 – because the population for these categories became too small, introducing meaningless noise.

You can see there was a relationship between year of arrival and PT mode share within each age band, for both parenting and non-parenting workers. Central and Southern America generated the highest average PT mode shares while North Africa and the Middle East often had the lowest PT mode shares.

Here’s another look at that data, but comparing mode shares primarily by age rather than year of arrival. For this chart I’ve (also) removed parenting workers, and those who arrived before 1982, because they are mostly spread across just two 10 year age bands which isn’t really enough to show an age-based trend:

This chart shows that there was certainly a relationship between age and PT mode share for most birth regions (as well as year of arrival), at least for non-parents working outside the City of Melbourne.

I cannot be certain that this pattern also existed for all birth-regions for parenting workers and people who worked within the City of Melbourne, but I have previously shown a relationship between age and PT mode share for these categories (when ignoring birth region), so a relationship is likely.

So even with a changing mix of immigrant sources over time, age (or some other age-related factor) remains a significant factor when it comes to explaining public transport mode shares.

I hope you’ve found this at least half as interesting as I did.


Why are young adults more likely to use public transport? (an exploration of mode shares by age – part 3)

Wed 18 November, 2020

I’ve been exploring data to explain why younger adults are more likely to use public transport (PT) than older adults in Melbourne. This third post in a series looks at the relationship between public transport mode share and parenthood, the year in which people were born, whether people were born in Australia or overseas, and how recently immigrants arrived in Australia.

I’m using VISTA household travel survey data (all travel) and ABS Census data (journey to work only). For more detail about the data, see the first post in the series.

Parenthood

Are younger adults more likely to use public transport because they don’t (yet) have dependent children?

Consistent with previous analysis on this blog, there is a relationship between PT mode share and whether people are parents within family households. Here’s the data for general travel from VISTA 2012-18:

Parents were much less likely to use public transport than non-parents of the same age, with mums between 35 and 55 having a lower mode share (on average) than dads in the same age range.

The census tells us whether a worker did unpaid child care for a child of their own in the two weeks prior to the census, which I am using to distinguish parents and non-parents.

The following chart shows the proportion of working men and women who were parents, animated over censuses 2006 to 2016.

Parenting peaked around age 40 for male and female workers, and the proportion of workers who were parenting went up between 2006 and 2016.

So how do public transport mode shares vary by age if we separate out parents and non-parents? The following chart answers this, and also separates workers by whether or not they worked in the City of Melbourne local government area (a known major factor influencing mode shares), animating results over 2006, 2011, and 2016:

Note there is a different Y-axis scale for City of Melbourne and elsewhere.

There are a few really interesting take-aways here:

  • Parenting workers mostly had lower public transport mode shares than non-parenting workers of the same age, except for:
    • dads over 30 who worked in the City of Melbourne,
    • mums in their early 30s who worked in the City of Melbourne in 2016, and
    • mums and dads in their 50s who worked outside the City of Melbourne (who had low PT mode shares around 4-5%, similar to non-parenting workers of the same age)
  • Public transport mode shares increased over time for almost all age bands, work locations, and for parenting and non-parenting workers.
  • Public transport mode shares for journeys to work in the City of Melbourne mostly declined with increasing age between 20 and 50, regardless of parenting responsibilities. Other age-related factors must be at play.
  • For people who worked outside the City of Melbourne, the mode share profile across age changed significantly over time for young adults. In 2006 there was a steady decline with age, but in 2011 PT mode shares were generally flat for those in their 20s, and in 2016 PT mode shares peaked for women in their late 20s (and also had a quite new pattern for dads in their 20s).
  • For parenting workers who work outside the City of Melbourne there was actually a slightly higher PT mode share for those over the age of 50. Parents over 50 might have older children who are more independent and therefore less reliant on their parents for transport. This might make it easier for the parents to use public transport. However this trend did not hold for dads in 2016.
  • PT mode shares for non-parenting women increased slightly beyond age 55 for all work locations. This will include women who were never parents, as well mums with non-dependent children so might again reflect a small return to public transport once children become independent. It may also be influenced by discounted PT “Seniors” fares available to people over 60 who are not working 35+ hours per week.

Lower public transport mode shares for parents is not surprising – they may be more time-poor and need more transport flexibility to link trips – eg dropping kids at childcare on the way to work can be more difficult with public transport (although it doesn’t seem to impact men travelling to the city centre nearly as much as women – I suspect because women are more likely to be working part time and doing childcare drop-offs and pick-ups). Parents may decide that time saving and convenience moving children is more important to them than lower transport costs from using PT.

However, addition of parenting responsibilities does not fully explain why public transport mode share generally declined with increasing age, particularly for non-parenting workers.

But I’m curious about the changing profile of mode share by age over time. Could it be influenced by…

Birth year / generations

Does public transport mode share vary by age and/or does it vary by when people were born, with different mode choices by different generations? To answer this I’m going to look at mode shares by both age and birth year.

For this analysis, I want to compare mode shares of birth year cohorts over time. The exact composition of these cohorts will change over time as there are deaths, new immigrants, and people who move overseas. I can only easily control for immigrants using census data – so for this section of my analysis I’m going to remove people not born in Australia (I will return to look at mode shares of immigrants shortly). Of course some people born in Australia who worked in Melbourne in 2016 may have spent a significant time living outside Australia before a census so this isn’t perfect.

Firstly, here’s a chart of journey to work PT mode shares by age, work location, and parenting status across 2006, 2011, and 2016 for people born in Australia:

Technical note: I’ve excluded data points where there was a small number of commuters, or a small number of public transport journeys where calculations are impacted by ABS randomisation to protect privacy.

You can see the general shape of the census year curves are similar within each quadrant (with a little noise at the extremes of age probably due to smaller volumes), suggesting a similar relationship between PT mode share and age holds over time.

We can clearly see how mode shares have increased over time for age bands, including:

  • those who work in the City of Melbourne (top row),
  • non-parenting younger adults who worked outside the City of Melbourne (bottom-left), and
  • to a smaller extent parents in their 30s and 40s who worked outside the City of Melbourne (bottom-right).

There were PT mode share spikes at age band 16-17 (at least for people working outside the City of Melbourne), which is just before people can gain independent licences. Those aged 18-19 working outside the City of Melbourne had a much lower PT mode share than those aged 16-17, and PT mode shares were lower again for those aged 15 who are perhaps more likely to work locally or be driven to work by parents.

So is this a general mode shift over time, or is it something intrinsic to birth years or “generations”? The next chart is similar to the previous, but the X-axis is notional birth year (approximated by census year – age at census time, which will be within a year of actual birth years).

Technical notes: For this chart I’ve calculated the 2 year birth year bands so that the youngest birth band is for ages 15-16 in all census years.

Let’s walk through the quadrants:

  • Non-parenting, City of Melbourne (top-left): PT mode shares have increased over time for those born between the 1940s and mid-1980s, despite these commuters getting older (assuming the same people still work within the City of Melbourne). This suggests general mode shift to PT over time has been stronger than any mode shift away from PT due to aging.
  • Parenting, City of Melbourne (top-right): PT mode shares have increased over time for almost all birth years, despite getting older (although the membership of this cohort will change as people acquire and lose parenting responsibilities). This is the same as non-parenting workers in the City of Melbourne.
  • Non-parenting, outside City of Melbourne (bottom-left): Ignoring those aged 15-22, mode shifts are generally smaller and not all in the same direction. If there was a negative relationships between age and PT mode share, you’d expect to see a shift away from PT between 2006 and 2016 for all cohorts. But for those born between around 1975 and 1985 (younger generation X and older millennials) and those born between 1940 and 1958 (mostly Baby Boomers) there was a small shift towards PT over time, despite them getting older. However these mode shifts were in the order of only 1-2%. Those born between 1960 and 1974 (mostly Gen X) shifted away from PT over time.
  • Parenting, outside City of Melbourne (bottom-right): For people born between around 1955 and 1980 (baby boomers and gen X) there was a shift towards PT between census years 2011 and 2016, despite people ageing. However in this quadrant mode shares were pretty flat and low over most ages (except at the young and old extremes of the cohort).

Of course many people will move from the left column to the right column as they start families, and then perhaps back to left column when they have adult children who no longer need care, so this analysis isn’t perfect.

So are there generational effects on PT mode share? Between 2006 and 2016 there was a significant shift towards PT in Melbourne for most birth years, parenting statuses and work locations, with only non-parenting workers born between 1960 and 1974 and working outside the City of Melbourne shifting away from PT. So the answer is no – any impact from birth year appears to be very small, and was generally swamped by an overall mode shift towards public transport.

That analysis was for for people born in Australia, but what about immigrants?

Immigrants to Australia

Are people who immigrated into Australia more recently, more likely to use PT to get to work? The next chart provides a clear “yes” answer to that question. I’ve included parenting status and work location as known significant factors, and animated the chart over censuses 2006, 2011 and 2016.

While the lines appear to shift left, they are really shifting up or down (people’s birth year doesn’t change), and are growing on the left with new younger workers entering the labour market, and falling away on the right as people leave the workforce.

Time of immigration had a big impact on PT mode shares – with people who arrived in Australia in the five years before a census most likely to use PT to get to work. The biggest difference in PT mode shares was for recent non-parenting immigrants working outside the City of Melbourne (bottom-left quadrant). Perhaps if public transport quality was boosted in areas with many recent immigrants there might be less loss of mode share over time. Or the drop in mode share might reflect people moving to areas with lower quality public transport.

For those working outside the City of Melbourne, PT mode share quickly fell after arrival into Australia, and after around 20 years living in Australia immigrant’s mode shares are similar to those who have been in Australia longer (or were born in Australia).

The chart also shows that there are age-related factors at play (beyond parenting and work location), regardless of whether people were born in Australia or immigrated – although much less so if they are parenting.

So could it be that recent immigrants make up a greater share of young adults, and might this explain the overall average mode shares across age groups?

The next chart shows the distribution of working population in five-year age bands by year of immigration / those born in Australia. I’ve animated this over 2006 to 2016. While the chart appears to animate with vertical movements, people actually shift one column to the right between census years.

If you watch the chart you’ll see that the immigrant share of the working population under 45 years increased between 2006 and 2016, with strong surges in immigration after 2006. This will undoubtedly impact the overall mode share for younger adults, but it doesn’t explain all of the mode share variance by age. The previous chart showed age-related factors influencing PT mode shares, regardless of when people moved to Australia.

I will explore potential reasons why recent immigrants were more likely to use public transport to get to work in an upcoming post.

Can we now explain why young adults are more likely to use public transport?

So far we’ve established that the following factors appear to have a strong impact on public transport mode shares:

  • Workplace distance from the CBD
  • Recency of immigration to Australia
  • Parenting status
  • Age

If the first three of these factors are the most important, and age or other age-related factors were not important, then we would expect flat mode shares across ages when you control for the other three factors.

The following matrix-of-worms chart combines all four factors. But please note that the Y axis within each row has a different scale and doesn’t necessarily start at zero. I am really looking at the slope of the line within each matrix cell, so I’m not too concerned about the actual values. I’ve only shown data points that included at least 400 commuters, and I’ve removed some columns and rows where data was too sparse to show meaningful trends.

How to make sense of this chart? Well, if the factors of parenting, arrival year and work distance from the CBD explained all the differences between age groups, then you would expect these lines to be flat within each matrix cell.

On the parenting (right) side of the chart, many of the worm lines are indeed quite flat, with the exception of those who arrived between 2006 and 2015 and those who worked within 2 km of the Melbourne CBD. Even though many of these matrix cells only have two or three data points, most data points include thousands of commuters so I don’t expect much false “noise” in the chart.

Within non-parenting commuters:

  • There is generally either a relationship with age, or a very low and flat PT mode share across ages, suggesting age itself, or some other age-related factors were at play.
  • The relationship with age appears to be strongest for more recent immigrants. The Born in Australia column also shows a strong relationship with age but this also has the widest age range. The columns for those who arrived between 1976 and 1995 only contain 3 to 5 age bands (all 30+), which will partly explain why there is less of an evident slope in the line.
  • Age band 60-69 is often an outlier to the trend, particularly for those born in Australia or who arrived between 1976 and 1985, again perhaps related to discounted “Seniors” public transport fares.

So I haven’t been able to fully explain variations in PT use by age. Age itself may be a factor, there may be other age-relevant factors that are important, or more likely there are lots of complex interactions between factors that are hard to unpick.

The next post in this series will look at the impact of income, socio-economic advantage/disadvantage, and occupation on PT mode shares across ages.