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 younger adults more likely to use public transport? (an exploration of mode shares by age – part 2)

Sun 27 September, 2020

This is the second post in a series that explores why younger adults are more likely to use public transport (PT) than older adults, with a focus on the types of places where people live and work, including proximity to train stations, population density, job density, motor vehicle ownership and driver’s licence ownership.

In the first post, we found younger adults in Melbourne were more likely to live and work close to the CBD, but this didn’t fully explain why they were more likely to use public transport.

This analysis uses 2016 ABS census data for Melbourne, and data for the years 2012-18 from Melbourne’s household travel survey (VISTA) – all being pre-COVID19. See the first post for more background on the data.

Proximity to train stations

Melbourne’s train network is the core mass rapid transit network of the city offering relatively car-competitive travel times, particularly for radial travel. It’s not Melbourne’s only high quality public transport, but for the want of a better metric, I’m going to use distance from train stations as a proxy for public transport modal competitiveness, as it is simple and easy to calculate.

In 2016 younger adults (and curiously the elderly) were more likely to live near train stations:

Almost 40% of people in their 20s lived within one km of a station. Could this partly explain why they were more likely to use public transport?

Well, maybe partly, but public transport mode shares of journeys to work were quite different between younger and older adults at all distances from train stations:

Public transport mode shares fell away with distance from stations, and age above 20 (the 15-19 age band being an exception).

With VISTA data we can look at general travel mode share by home distance from a train station:

There’s clearly a relationship between PT mode share and proximity to stations, but there’s also a strong relationship between age and PT use, at all home distance bands from train stations.

Younger adults were also more likely to work close to a train station. Indeed 46% of them worked within about 1 km of a station:

And unsurprisingly people who work near train stations are also more likely to live near train stations:

The chart shows around 70% of people who worked within 1 km of a station lived within 2 km of a station. Also, 37% of people who worked more than 5 km from a station, also lived more than 5 km from a station.

But again, journey to work PT mode shares varied by both age and workplace distance from a train station:

For completeness, here is another matrix-of-worms chart looking at journey to work PT mode shares by age for both work and home distances from train stations:

PT mode share declined with age for most distance combinations, but this wasn’t true for the 15-19 age band, particularly where both home and work were within a couple of kms of a station. We know from part one that teenagers are much less likely to work in the city centre, so this might represent teenagers who happen to live near a station, but work locally and can easily walk or cycle to work.

If we take age out for a moment, here is the relationship between PT mode share of journeys to work and both home and work distance from train stations:

The relationship between PT mode share and work distance from a train station is much stronger than for home distance from a station.

So while home and work proximity to train stations influenced mode shares, it doesn’t fully explain the variations across ages. So what if we combine…

Work distance from the CBD, home distance from a train station

Work distance from a station is strongly related to work distance from the CBD, as the CBD and inner city has a higher density of train stations:

I expect workplace proximity to a train station to be a weaker predictor of mode share when compared workplace distance from CBD. That’s pretty evident when looking at journey to work PT mode share by place of work on a map:

And even more evident when you look at PT mode shares for both factors (regardless of age):

So perhaps work distance from the CBD, and home distance from a train station might be two strong factors for mode share? If we control for these factors, is there still a difference in PT mode shares across ages?

Time for another matrix of worms:

The chart shows that even when you control for both home distance from a station, and work distance from the CBD, there is still a relationship with age (generally declining PT mode share with age, with teenagers sometimes an exception). So there must be other factors at play.

Population density

Consistent with proximity to train stations and the CBD, younger adults are more likely to live in denser residential areas:

Higher residential density often comes with proximity to higher quality public transport. Indeed, here is the distribution of population densities for people living at different distances from train stations:

The next chart shows the relationship between residential density and mode shares – split between adults aged 20-39 and those aged 40-69:

The chart shows that both age and residential density are factors for journey to work mode shares. Younger adults had higher public transport mode shares for journeys to work at all residential density bands.

Similarly, VISTA data also shows PT mode shares vary significantly by both age and population density for general travel:

Technical note: data only shown where age band and density combination had at least 400 trips in the survey.

Curiously, people in their 60s living in areas with densities of 50-80 persons/ha were more likely to use public transport to get to work than those in their 40s and 50s living in the same densities (maybe due the presence of children?). For lower densities, PT mode share generally declined with increasing age (from 20s onward).

Population density is also generally related to distance from the CBD:

And here is a chart showing how PT mode share of journeys to work varied across both:

The chart shows home distance from the CBD had a larger impact on mode shares than population density. Indeed population density only seemed to have a secondary impact for densities above 40 persons/ha. However, as we saw in the first post, people living closer to the CBD were more likely to work in the city centre, and therefore more likely to use public transport in their journey to work.

Job density

Young adults were more likely to work in higher density employment areas in 2016, where public transport is generally more competitive (with more expensive car parking):

But yet again, there is a difference in mode shares between age groups regardless of work location job density:

So job density doesn’t fully explain the difference in PT mode shares across age groups.

I should add that job density is also strongly related to workplace distance from the CBD:

and workplace distance from train stations:

And putting aside age, PT mode shares for journeys to work are related to both workplace distance from the CBD and job density:

PT mode shares are also related to both job density and workplace distance from stations:

You might be wondering about the dot of higher job density (200-300 workers/ha) that is between 3 and 4 km from a train station. It’s one destination zone that covers Doncaster Westfield shopping centre – a large shopping centre on a relatively small piece of land (almost all of the car parking is multistory – see Google Maps)

Motor vehicle ownership

Are younger adults more likely to use public transport because they are less likely to own motor vehicles?

With census data, it is possible to measure motor vehicle ownership on an SA1 area basis by adding up household motor vehicles and persons aged 18-84 (as an approximation of driving aged people) and calculating the ratio. Of course individual households within these areas will have different levels of motor vehicle ownership.

Using this metric, young adults were indeed more likely to live in areas which have lower levels of motor vehicle ownership (in 2016):

But yet again, the PT journey to work mode shares varied between younger and older adults regardless of the levels of motor vehicle ownership of the area (SA1) in which they live:

Using VISTA data, we can calculate motor vehicle ownership at a household level. I’ve classified households by the ratio of motor vehicles to adults.

VISTA data shows PT mode shares strongly related to both age and motor vehicle ownership (I’ve shown the most common ratios):

You might be wondering why I didn’t calculate motor vehicle ownership at the household level for census data. Unfortunately it’s not possible for me to calculate the ratio of household motor vehicles to number of adults because ABS TableBuilder doesn’t let me combine the relevant data fields (for some reason).

The best I can do is the ratio of household motor vehicles to the usual number of residents (of any age). The usual residents may or may not include children under driving age – we just don’t know.

Nevertheless the data is still interesting. Here is how public transport mode shares of journeys to work varied across different vehicle : occupant combinations for households in Greater Melbourne:

Yes that’s a lot of squiggly lines – but for most combinations (excluding those with zero motor vehicles) there was a peak of PT mode share in the early 20s, and then a decline with increasing age.

The lines with green and yellow shades – where the ratio is around 1:2 or 1:3 – show a sharp drop around the mid 20s. I expect these lines are actually a mix of working parents with younger children, and working adult children living with their (older) parents. The high mode shares for those in their early 20s could represent many adult children living with their parents (but without their own car), while those in their 30s and 40s are more likely to be parents of children under the driving age. So the sharp drop is probably more to do with a change in household age composition.

If we want to escape the issue of children, the highest pink line is for households with one motor vehicle and one person (so no issues about the age of children because there are none present) – and that line has a peak in PT mode share in the mid 30s and then declines with age, suggesting other age-related factors must be in play.

But motor vehicle ownership levels aren’t only related to age. They are strongly related to population density,

..home distance from the CBD,

..and home distance from train stations:

And public transport mode shares are related to both motor vehicle ownership rates and population density (with motor vehicle ownership probably being the stronger factor):

Technical note: for these charts I’ve excluded data points with fewer than 5 qualifying SA1s to remove anomalous exceptions.

Public transport mode shares are also related to both motor vehicle ownership and home distance from the CBD:

And shares are also related to both motor vehicle ownership and home distance from a train station:

In all three cases, PT mode shares fell with increasing levels of motor vehicle ownership, but this effect mostly stopped once there were more motor vehicles than persons aged 18-84.

Drivers licence ownership

I’ve previously shown on this blog that people without a full car driver’s licence are much more likely to use public transport, which will surprise no one. So are younger adults less likely to have a driver’s licence?

VISTA data shows us that younger adults are indeed less likely to have a car driver’s licence, with licence ownership peaking around 97% for those in their late 40s and early 50s, and only dropping to 91% by age 75 (there is a little noise in the data):

So the lack of a driver’s licence by many young adults will no doubt partly explain why they are more likely to use public transport.

Consistent with VISTA, data from the BITRE yearbooks also shows that younger adults have become less likely to own a licence over time:

At the same time, those aged 60-79 have been more likely to own a licence over time.

But do public transport mode shares vary by age, even for those with a solo driver’s licence? (by solo, I mean full or probationary licence). The following chart shows public transport mode shares for age bands and licence ownership levels (data points only shown where 400+ trips exist in the survey data).

PT mode shares peaked for age band 23-29 for most licence ownership levels, including no licence ownership (there isn’t enough survey data for people older than 22 with red probationary licences – the licence you have for your first year of solo driving).

As an aside, there is a curious increase in public transport mode share for those aged over 60 without a drivers licence – this may be related to these people being eligible for concession fares and occasional free travel with a Seniors Card (if they work less than 35 hours per week).

So even younger adults who own a driver’s licence are more likely to use public transport.

But is this because they don’t necessarily have a car available to them? Let’s put the two together…

Motor vehicle and driver’s licence ownership

For the following chart I’ve classified households as:

  • “Limited MVs” if there were more licensed drivers than motor vehicles attached to the household,
  • “Saturated MVs” if there was at least as many motor vehicles as licensed drivers, and
  • “No MVs” if there were no motor vehicles associated with the household.

If there were any household motor vehicles I’ve further disaggregated by individuals with a solo licence and those without a solo licence (the latter may have a learner’s permit). I’ve only shown data points with at least 400 trip records in the category to avoid small sample noise (I am reliant on VISTA survey data).

Except for households with no motor vehicles, public transport mode share peaked for age band 18-22 or 23-29 and then declined with increasing age. So again there must be other age-related factors. However the impact of age is smaller than that of motor vehicle ownership and licence ownership.

Unfortunately driver’s licence ownership data is not collected by the census, so it is not possible to combine it with other demographic variables from the census.

Summary

So, what have we learnt in part two:

  • Younger adults are more likely to work and live near train stations, but that only partly explains why younger adults are more likely to use public transport.
  • Workplace distance from the CBD has a much bigger impact on public transport mode shares for journeys to work than home distance from a train station.
  • Younger adults are more likely to live in areas with higher residential density, but this only partly explains why they are more likely to use public transport.
  • Younger adults are more likely to work in areas with higher job density but this is highly correlated with workplace distance from the CBD, which is a stronger factor influencing mode shares.
  • Younger adults are more likely to live in areas with lower motor vehicle ownership (these areas are generally also have higher residential density and are closer to the city centre and to train stations), but this again only partly explains why they are more likely to use public transport. Motor vehicle ownership appears to be a stronger factor influencing mode shares than population density, distance from stations, or distance from the city.
  • Younger adults are less likely to have a driver’s licence, but again this only partly explains why they are more likely to use public transport.

While this analysis confirms younger adults tend to align with known factors correlating with higher public transport use, we are yet to uncover a factor or combination of factors that mostly explain the differences in public transport use between younger and older adults. That is, when we control for these factors we still see differences in public transport use between ages.

The next post in this series will explore the impacts on public transport use of parenting responsibilities, generational factors (birth years), and year of immigration to Australia.


Update on Australian transport trends (December 2019)

Mon 30 December, 2019

Each year, just in time for Christmas, the good folks at the Australian Bureau of Infrastructure, Transport, and Regional Economics (BITRE) publish a mountain of data in their 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.

There are some interesting new patterns emerging – read on.

Vehicle kilometres travelled

According to the latest data, road transport volumes actually fell in 2018-19:

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).

Car kilometre growth has slowed significantly since 2004, and actually went down in 2018-19 according to BITRE estimates (enough to result in a reduction in total vehicle kilometres travelled).

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 to 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.

Passenger kilometres travelled

Overall, here are passenger kms per capital for various modes for Australia as a whole (note the log-scale on the Y axis):

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

It’s possible to look at car passenger kilometres per capita, which takes into account car occupancy – and also includes more recent estimates up until 2018/19.

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

The data shows that Melbourne has now overtaken Sydney as having the most car travel in total.

Another interesting observation is that total car travel declined in Perth, Adelaide, and Sydney in 2018-19. The Sydney result may reflect a mode shift to public transport (more on that shortly), while Perth might be impacted by economic downturn.

While car passenger kilometres per capita peaked in 2004, there were some increases until 2018 in some cities, but most cities declined in 2019. Darwin is looking like an outlier with an increase between 2015 and 2018.

BITRE also produce estimates of passenger kilometres for other modes (data available up to 2017-18 at the time of writing).

Back to cities, here is growth in rail passenger kms since 2010:

Sydney trains have seen rapid growth in the last few years, 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.

Here’s a longer-term series looking at per-capita train use:

Sydney has the highest train use of all cities. 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 recent growth in (public and private) bus use:

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

In more recent years Sydney, Canberra, and Hobart are showing rapid growth in bus patronage.

Here’s bus passenger kms per capita:

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

Bus passenger kms per capita have been declining in most cities in recent years.

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%.

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).

For completeness, here’s growth in light rail patronage:

Sydney light rail patronage increased following the Dulwich Hill extension that opened in 2014, while Adelaide patronage increased following an extension to the Adelaide Entertainment Centre in 2010.

We can sum all of the mass transit modes (I use the term “mass transit” to account for both public and private bus services):

Sydney is leading the country in mass transport use per capita and is growing strongly, while Melbourne, Brisbane, Perth have declined in recent years.

Mass transit mode share

We can also calculate mass transit mode share of motorised passenger kilometres (walking and cycling kilometres are unfortunately not estimated by BITRE):

Sydney has maintained the highest mass transit mode share, and in recent years has grown rapidly with a 3% mode shift in the three years 2016 to 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 made significant gains between 2005 and 2009, and Perth also grew strongly 2007 to 2013.

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 use 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.

Car ownership

The ABS regularly conduct a Motor Vehicle Census, and the following chart includes data up until January 2019.

Technical note: Motor Vehicle Census data (currently conducted in January each year, but previously conducted in March or October) has been interpolated to produce June estimates for each year, with the latest estimate being for June 2018.

In 2017-18 car ownership declined slightly in New South Wales, Victoria, and Western Australia, but there was a significant increase in the Northern Territory. Tasmania has just overtaken South Australia as the state with the highest car ownership at 63.1 cars per 100 residents.

Victorian car ownership has been in decline since 2011, which is consistent with a finding of declining motor vehicle ownership in Melbourne from census data (see also an older post on car ownership).

Driver’s licence ownership

Thanks to BITRE Information Sheet 84, the BITRE Yearbook 2019, 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 2018 or 2019 (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 seen slow decline since 2011, and the ACT peaked in 2014.

Here’s a breakdown by age bands for Australia as a whole (note each chart has a different Y-axis scale):

There was a notable uptick in licence ownership for 16-19 year-olds in 2018. Otherwise licencing rates have increased for those over 40, and declined for those aged 20-39.

Licencing rates for teenagers (refer next chart) had been trending down in South Australia and Victoria until 2017, but all states saw an increase in 2018 (particularly Western Australia). The most recent 2019 data from NSW and Queensland shows a decline. The differences between states partly reflects different minimum ages for licensing.

The trends are mixed for 20-24 year-olds: the largest states of Victoria and New South Wales have seen continuing declines in licence ownership, but all other states and territories are up (except Queensland in 2019).

New South Wales, Victoria, and – more recently – Queensland are seeing downward trends in the 25-29 age bracket:

Licencing rates for people in their 70s are rising in all states (I suspect a data error for South Australia in 2016):

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.

Transport greenhouse gas emissions

[this emissions section updated on 8 January 2020 with BITRE estimates for 1975-2019]

According to the latest adjusted quarterly figures, Australia’s domestic non-electric transport emissions peaked in 2018 and have been slightly declining in 2019, which reflects reduced consumption of petrol and diesel. However it is too early to know whether this is another temporary peak or long-term peak.

Non-electric transport emissions made up 18.8% of Australia’s total emissions as at September 2019.

Here’s a breakdown of transport emissions:

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

Here’s growth in transport sectors since 1975:

Road emissions have grown steadily, while aviation emissions took off around 1991. 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 run on diesel, but more recently has been transitioning to electric.

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

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 are increasing for most modes, except cars. Total road transport emissions per capita peaked in 2004 (along with vehicle kms per capita, as above).

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

Note: I suspect the kinks for buses and trucks in 2015, and motor cycles in 2011 are issues to do with assumptions made by BITRE, rather than actual changes.

The only mode showing significant change is cars – which have reduced from 281 g/km in 1990 to 243 g/km in 2019.

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:

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 2018-19 were 154.5g/pkm, while bus was 79.4g/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 60) – 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 78% in 2016-17.

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 trended down between 2008 and 2016. The real cost of motor vehicles has plummeted since 1996. 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.

Hopefully this post has provided some useful insights into transport trends in Australia.


Are Australian cities growing around their rapid transit networks?

Sun 31 March, 2019

My last post showed half of Perth’s outer urban population growth between 2011 and 2016 happened in places more than 5 km from a train station (see: Are Australian cities sprawling with low-density car-dependent suburbs?). It’s very car-dependent sprawl, with high levels of motor vehicle ownership (96 per 100 persons aged 18-84) and high private transport mode share of journeys to work (88%).

But what about population growth overall in cities? Is most growth happening close to rapid transit stations? How are cities orientated to rapid transit overall? And how does rapid transit orientation relate to mode shares?

Let’s dive into the data to find out.

Why is proximity to rapid transit important?

Public transport journey to work mode shares are generally much higher close to stations:

And motor vehicle ownership rates are generally lower closer to stations:

However it is worth noting that the proximity impact wears off mostly after only a couple of kms. Being 2-5 kms from a station is only useful if you can readily access that station – for example by bus, bicycle, or if you are early enough to get a car park.

Also, proximity to a station does not guarantee lower car dependence – the rapid transit service has to be a competitive option for popular travel destinations. I’ve discussed the differences between cities in more detail
(see: What explains variations in journey to work mode shares between and within Australian cities?), and I’ll a little have more to say on this below.

But in general, if you want to reduce a city’s car dependence, you’ll probably want more people living closer useful rapid public transport.

Is population growth happening near train/busway stations?

The following chart (and most subsequent charts) are built using ABS square kilometre grid population data for the period 2006 to 2018 (see appendix for more details).

Melbourne has seen the most population growth overall, followed by Sydney and Brisbane. Population growth in Perth has slowed dramatically since 2014, and has been remained slow in Adelaide.

Population growth in areas remote from stations most cities has been relatively steady. By contrast, the amount of population growth nearer to stations fluctuates more between years – there was a noticeable dip in growth near stations around 2010 and 2011 in all cities.

You can also see that in recent years the majority of Perth’s population growth has been more than 5 km from a station.

To show that more clearly, here’s the same data, but as a proportion of total year population growth:

You can see that most of Perth’s population growth has been remote from stations. In the year to June 2016, 85% of Perth’s population growth was more than 5 km from a train station (the chart actually goes outside the 0-100% range in 2016 because there was a net decline in population for areas between 2 and 4 km from stations). That was an extreme year, but in 2018 the proportion of population growth beyond 5 km from a station had only come down to 57.5%. That is not a recipe for reducing car dependence.

At the other end of the spectrum, almost half of Sydney’s population growth has been within 1 km of a train or busway station. No wonder patronage on Sydney’s train network is growing fast.

Melbourne has had the smallest share of population growth being more than 5 km from a station over most years since 2006. The impact of the South Morang to Mernda train line extension, which opened in August 2018, won’t be evident until the year to June 2019 data is released (probably in March 2020). Melbourne’s planned outer growth corridors are now largely aligned with the rail network, so I would expect to see less purple in upcoming years.

Here’s the same data for the next largest cities that have rapid transit:

Gold Coast – Tweed Heads has seen the most population growth, followed by the Sunshine Coast and more recently Geelong population growth has accelerated.

The Sunshine Coast stands out as having the most population growth remote from rapid transit (a Maroochydore line has been proposed), while Wollongong had the highest share of population growth near stations.

What about total city population?

The above analysis showed distances from stations for population growth, here’s how it looks for the total population of the larger cities:

Sydney has the most rapid transit orientated population, with 67% of residents within 2 km of a rapid transit station. Sydney is followed by Melbourne, Brisbane, Adelaide, and then Perth.

The most spectacular step change was in Perth in 2008, following the opening of the Mandurah rail line in the southern suburbs. This brought rail access significantly closer for around 18% of the city’s population. However, Perth has since been sprawling significantly in areas remote from rail while infill growth has all but dried up in recent years. 22% of the June 2018 population was more than 5 km from a train station, up from 19% in 2008. But it’s still much lower than 37% in 2007. Perth remains the least rapid transit orientated large city in Australia.

Brisbane has also seen some big step changes with new rail lines to Springfield (opening December 2013) and Redcliffe Peninsula (opening October 2016).

Several new station openings around Melbourne have kept the overall distance split fairly stable – that is to say the new stations have been just keeping up with population growth. The biggest noticeable step change was the opening of Tarneit and Wyndham Vale stations in 2015.

Adelaide’s noticeable step change followed the Seaford rail extension which opened in February 2014.

Sydney’s step change in 2007 was the opening of the North West T-Way (busway). The opening the Leppington rail extension in 2015 is also responsible for a tiny step (much of the area around Leppington is yet to be developed).

Here are the medium sized cities:

Woolongong is the most rapid transit orientated medium sized city, followed by Geelong and Newcastle.

In the charts you can see the impact of the Gold Coast train line extension to Varsity Lakes in 2009, the truncation of the Newcastle train line in 2014 and subsequent opening of “Newcastle Interchange” in 2017, and the opening of Waurn Ponds station in Geelong in 2015.

Average resident distance from a rapid transit station

Here’s a single metric that can be calculated for each city and year:

average distance to station

Many cities have barely changed on this metric (including Melbourne which has had a reduction of just 26 metres between 2006 and 2018). Brisbane, Perth and the Gold Coast are the only cities to have achieved significant reductions over the period.

It will be interesting to see how this changes with new rail extensions in future (eg MetroNet in Perth), and I’ll try to update this post each year.

How strong is the relationship with public transport mode shares at a city level?

Here is a comparison between average population distance from a train/busway station, and public transport mode share of journeys to work, using 2016 census data:

While there appears to be something of an inverse relationship (as you might expect), there are plenty of other factors at play (see: What explains variations in journey to work mode shares between and within Australian cities?).

In particular, Newcastle, Geelong, and Wollongong have relatively low public transport mode shares even though they have high average proximity to rapid transit stations.

Most journeys to work involving train from these smaller cities are not to local workplaces but to the nearby capital city, and those long distance commutes make up a relatively small proportion of journeys to work.

Here are some headline figures showing trains have minimal mode share for local journeys to work in the smaller cities:

CityTrain mode share
for intra-city
journeys to work
Train mode share
for all journeys
to work
Gold Coast0.6%2.2%
Sunshine Coast0.1%0.8%
Newcastle0.5%1.0%
Wollongong1.2%4.9%
Central Coast1.2%9.3%
Geelong0.5%4.5%

Appendix: About the data

I’ve used ABS’s relatively new kilometre grid annual population estimates available for each year from June 2006 onwards (to 2018 at the time of writing), which provides the highest resolution annual population data, without the measurement problems caused by sometimes irregularly shaped and inconsistently sized SA2s.

I’ve used train and busway station location data from various sources (mostly GTFS feeds – thanks for the open data) and used Wikipedia to source the opening dates of stations (that were not yet open in June 2006). I’ve mostly ignored the few station closures as they are often replaced by new stations nearby (eg Keswick replaced by Adelaide Showgrounds), with the exception of the stations in central Newcastle.

As with previous analysis, I’ve only included busways that are almost entirely segregated from other traffic.

I haven’t included Gold Coast light rail on account of its average speed being only 27 km/h (most Australian suburban railways average at least 32 km/h). I have to draw the line somewhere!

I also haven’t included Canberra as it lacks an internal rapid transit system (light rail is coming soon, although it will have an average speed of 30 km/h – is that “rapid transit”?).

Distances from stations are measured from the centroid of the grid squares to the station points (as supplied) – which I have segmented into 1 kilometre intervals. Obviously this isn’t perfect but I’m assuming the rounding issues don’t introduce overall bias.

Here’s what the Melbourne grid data looks like over time. If you watch carefully you can see how the colours change as new train stations open over time in the outer suburbs:

Melbourne grid distance from station 2

On this map, I’ve filtered for grid squares that have an estimated population of at least 100 (note: sometimes the imperfections of the ABS estimates mean grid squares get depopulated some years).

Finally, I’ve used Significant Urban Areas on 2016 boundaries to define my cities, except that I’ve bundled Yanchep into Perth, and Melton into Melbourne.