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.


Are Australian cities sprawling with low-density car-dependent suburbs?

Wed 30 January, 2019

Many people talk about urban growth in Australian cities being car-dependent low-density suburban sprawl. But how true is that in more recent times? Are new greenfield density targets making a difference? Are cities growing around their rapid public transport networks? And how do growth areas compare to established areas at a similar distance out from city centres?

This post takes a look at what census data can tell us about outer urban growth areas in terms of population density, motor vehicle ownership, distance from train/busway stations, and journey to work mode shares.

How much of city population growth is in outer areas?

Firstly a recap, here is the percentage of annual population growth in each city that has occurred in “outer” areas (defined by groupings of SA3s around the edges of cities – refer my previous post for maps showing outer areas) for Greater Capital City Statistical Areas.

Sydney has had less than a third of its population growth in outer areas since around 2003, while Perth has mostly had the highest outer growth percentage (since 1996), and more recently pretty much all population growth in Perth has been on the fringe. You can see how the other cities sit in between.

However, not all of this “outer” population growth was in urban growth on the fringe. For that we need to distinguish between urban growth and infill development, even in “outer” areas. So we really need a better definition of outer growth areas.

How to define outer urban growth areas

I have built groupings of SA1s (Statistical Area Level 1) that try to represent outer urban greenfield residential development. SA1s are the smallest census geographic areas (average population 400) for which all census data variables are available.

I’ve selected 2016 SA1s that meet all of the following criteria:

  • Brand new SA1 or significant population growth: The 2016 SA1 is new and cannot be matched to a 2011 SA1 (by location/size and/or ABS correspondences), or if it can be matched, the population at least doubled between 2011 and 2016. Brand new SA1s are very common in urban growth areas as new SA1s are created to avoid oversized SA1s on last census boundaries (except this doesn’t always happen – more on that shortly).
  • In an SA2 with significant population growth: The SA2 (Statistical Area Level 2 – roughly suburb sized with typically 3,000 to 25,000 residents) that contains the SA1 had population growth of at least 1000 people between 2011 and 2016 (based on 2016 boundaries). That is, the general area is seeing population growth, not just one or two SA1s.
  • Are on – or close to – the urban fringe. I’ve filtered out particular SA2s that I’ve judged to be contain all or mostly in-fill development rather than greenfield development, or that are largely surrounded by existing urban areas and are not close to the urban fringe. I’ll be the first to admit that some of the inclusions/exclusions are a little arbitrary.

The criteria aren’t perfect, but it seems to work pretty well when I inspect the data. I’m calling these “Growth SA1s” or outer urban growth in this post.

For urban centres, I’m using Significant Urban Area 2016 boundaries (rather than Greater Capital City boundaries), and I’ve bundled Yanchep with Perth, Melton with Melbourne, and the Sunshine Coast and Gold Coast with Brisbane to form South East Queensland (SEQ).

Where are these outer urban growth areas?

What follows are maps for each city with the density of these growth SA1s shown by colour.

Melbourne’s northern and western growth areas:

Technical note: The maps do not show non-growth SA1s with fewer than 5 people per hectare, or “growth SA1s” with fewer than 1/hectare, although these SA1s are including in later analysis.

And the south of Melbourne:

Note: not shown on these Melbourne maps are isolated tiny growth SA1s in Rosebud and Mooroolbark.

Here are Sydney’s growth SA1s – all in the western suburbs:

Next up South East Queensland, starting in the north with the Sunshine Coast:

Northern Brisbane:

Outer urban growth is scattered in southern Brisbane and northern Gold Coast:

Gold Coast – Tweed Heads:

Perth’s northern and eastern growth areas:

Perth’s southern growth areas:

Note: Canning Vale East is an inclusion you could debate – the previous land use of the growth SA1s appear to have been rural based on satellite imagery.

Northern Adelaide:

Southern Adelaide:

And finally Canberra:

So how much of each cities’ population growth has been in outer growth areas?

Here’s a breakdown of the population growth for my six urban areas:

Over the five-year period, outer urban growth areas accounted for 19% of Sydney’s population growth, 43% of Melbourne’s, 37% of SEQ’s, 60% of Perth’s, 27% of Adelaide’s and 69% of Canberra’s.

Technical note: These “outer urban growth” figures are different to the chart at the top of this post which had a coarser definition of “outer” and used Greater Capital City boundaries. Some of my “outer urban growth” areas actually don’t quality as “outer” in the coarser definition, and I’ve also excluded several “outer” SA2s from “outer urban growth” where I’ve deemed the growth to be mostly infill. Hence the differences.

In case you are wondering, it’s not easy to create a longer-term time-series analysis about the proportion of population growth in “outer urban growth” areas because the classification of SA2s would have to change on a year-by-year basis which would be messy and somewhat arbitrary.

A challenge for density analysis: some SA1s are over-sized

You might have noticed some SA1s in the maps above are very large and show a low average density of 1-5 persons per hectare (I’ve coloured them in a light cyan). Many of these SA1s had thousands of residents in 2016, which is way more than the ABS guideline of 200 to 800 residents. Unfortunately what seems to have happened for 2011 and 2016 in some cities is that the ABS did not create enough SA1s to account for new urban areas. Some Melbourne SA1s had a population over 4000 in 2016. Many of these SA1s contain a combination or urban and rural land use, so their calculated density is rather misleading.

I’m designating any SA1s with more than 1000 residents and larger than 100 hectares as “oversized”, and I’ve exclude these from some density analysis below. Here’s a chart showing the proportion of outer growth area populations that are in oversized SA1s:

You can see it is a substantial problem in Sydney, Melbourne, Perth and South East Queensland, but miraculously not a problem at all in Adelaide or Canberra (I’m sure someone in ABS could explain why this is so!).

If you are interested, in 2011 it was a bigger problem in Melbourne, and only Canberra was fully clean.

So how dense are outer urban growth areas?

Firstly, I am excluding over-sized SA1s from this analysis for the reasons just mentioned.

Secondly, all cities will also have growth areas that were partially developed at the time of the census (ie some lots with occupied houses and other lots empty) so the densities measured here may be understated of the likely fully built-out density of these SA1s. That said, those areas perhaps are more likely to be in over-sized SA1s, but it’s hard to be sure. So keep this in mind when looking at growth area densities.

You can see dramatic differences, with Sydney, Canberra, and Melbourne showing higher densities, and South East Queensland with much lower densities. As we saw on the maps above, South East Queensland’s outer growth areas are very dispersed, so perhaps more of them are growing slowly and more of them are partially built-out? It’s hard to be sure.

But perhaps what is most remarkable is that Canberra had the highest densities in outer urban growth areas of any city – nothing like what you might consider suburban sprawl. Here’s what was 144.5 people per hectare in 2016 in Wright on Canberra’s new western growth front looks like:

(pic from Google Streetview, dated December 2016)

The densest SA1 in Sydney’s growth areas was 101 persons/ha. Nothing like this was seen in other cities.

Canberra’s outer growth areas are actually, on average, denser than the rest of Canberra (on a population weighted density measure):

The same was also true by a slim margin in both Perth and Adelaide, but they have relatively “suburban” densities for both growth and established areas. The growth areas of Sydney and Melbourne are more dense than Perth and Adelaide, but not compared to the rest of these cities as a whole. That’s probably got to do a lot with the large cities having dense inner suburbs.

So perhaps it is better to compare the urban growth areas with established areas a similar distance from city centres, which the following chart does (I’ve filtered out 5 km distance intervals without growth areas of at least 2000 population, and apologies for rather squashed Canberra label):

Technical note: for South East Queensland I’ve measured distances from the Brisbane CBD.

Outer growth areas were much more dense than the rest of each city at most distances from the city centre, except in Sydney.

One issue with the above chart is that different distance intervals have different populations – for example only 2,815 people were in growth SA1s at a distance of 45-50 km from the Perth CBD (just above my threshold of 2000), so the low population density of that interval is not hugely significant.

To get around that issue, I’ve calculated the overall population weighted density of non-growth SA1s that are within these 5 km distance intervals from the CBD (including all of SEQ beyond 15 km from the CBD). The following chart compares those calculations with the population weighted density of the growth areas overall:

This shows that urban growth areas are on average more dense than other parts of the city at similar distance from the CBD, except in South East Queensland. And remember, many of the growth SA1s will be partially built out, so their expected density is understated.

Are outer urban growth areas near rapid public transport?

The next chart shows the proportion of growth SA1 population by distance from the nearest train or busway station:

Technical notes: Distances are measured from the centroid of each SA1 to a point location defined for each station (sourced from August 2016 GTFS feeds). For oversized SA1s these distances might be a little longer than reality for the average resident. I haven’t excluded oversized SA1s because I want to see the population alignment of growth areas overall. Canberra excluded due to lack of separated rapid transit.

What sticks out clearly is that just over half the of the population in Perth’s outer growth areas was more than 5 km from a station in 2016. That is to say Perth has had the least alignment of outer urban growth areas and rapid public transport networks of all five cities. I’m not sure many urban planners would recommend such a strategy.

However, Perth’s MetroNet program appears to be trying to rectify this with new lines and stations proposed near urban growth areas such as Yanchep, Canning Vale East, Ellenbrook, Byford, and Karnup (Golden Bay). It will however take some time to get to them all built and open.

South East Queensland was second to Perth in terms of urban growth remote from stations, with a lot of the growth scattered rather than concentrated around rail corridors. I haven’t included the Gold Coast light rail in my proximity calculation – it runs at an average speed of 27 km/h (which is slower than most train networks) and doesn’t serve outer urban growth areas.

Sydney and Adelaide had the highest proximity of growth areas to stations.

Around half of Melbourne’s growth SA1s that were more than 5km from a train station were in Mernda and Doreen, a corridor in which a rail extension opened in 2018. Many of the rest are not in the current designated growth corridors, or are where future train stations are planned. Melbourne’s current designated urban growth corridors are fairly well aligned to its train network. From a transport perspective this is arguably a better kind of sprawl than what Perth has been experiencing.

Adelaide’s outer growth areas more than 5 km from a station were in Mount Barker (satellite town to the east) and Aldinga (on the far south coast of Adelaide).

Are the outer urban growth areas better aligned to rapid public transport stations than non-growth areas at the same distance from city centres? Here’s the chart as above but with an extra column for non-growth areas within the same distance intervals from the CBD (as before).

The populations of urban growth areas are less likely to be within a couple of kilometres of a station (most of that land probably has long-established urban development), but curiously in Adelaide and South East Queensland the urban growth areas are more likely to be within 5 kilometres of a station than the non-growth areas, suggesting better rapid public transport alignment than older urban growth areas. Older urban areas in other cities are more closely aligned to stations, particularly in Perth.

As an interesting aside, here’s a breakdown over the last three censuses of population by distance from train/busway stations (operational in 2016 – so it overstates 2006 and 2011 slightly):

You can really see how Perth has had much population growth remote from its rapid public transport network, which probably goes some way to explaining the overall 1.2% journey to work mode shift towards private transport between 2011 and 2016.

So how did people in these outer growth areas get to work?

Technical note: The figures here for “private transport” are for journeys involving only private transport modes – i.e. they exclude journeys involving both private and public transport (eg car+train).

While private transport (mostly car driver only journeys) dominated journeys to work from almost all growth areas, Melbourne and Sydney were the only cities to see significant numbers of residents in outer growth areas with private transport mode shares below 80%.

South East Queensland’s outer urban growth areas were the most reliant on private transport to get to work, with an overall private transport mode share of 93%, followed by Adelaide on 92%, Canberra on 91%, Perth on 90%, Melbourne on 86%, and Sydney on 81%.

Here’s how the growth area mode shares compare to other areas a similar distance from city centres (note: the Y-axis is not zero-based):

Significantly, the growth areas of Sydney and Melbourne had lower private transport mode shares of journeys to work than other parts of the city a similar distance out – even though they are generally further away from train or busway stations (as we saw above)! That’s not to say they didn’t drive themselves to a train station to get to work.

Similar to population density, here is a summary of growth areas compared to other areas in the same distance interval from the CBD:

There’s really not a huge amount of difference within cities. Sydney’s growth areas had a mode share 1.5% lower than non-growth areas, while Canberra’s growth areas had a mode share 2.5% higher.

What are motor vehicle ownership rates like in the outer growth areas?

My preferred measure is household motor vehicles per persons aged 18-84 (roughly people of driving age).

Motor vehicle ownership rates are generally very high across the growth areas – with the notable exceptions of Melbourne and Canberra where around a quarter of the growth area population had a motor vehicle ownership rate of less than 80 (although that is still pretty high!). (I explored this in more detail in an earlier post on Melbourne)

South East Queensland, Perth, and Adelaide outer urban growth areas had the highest motor vehicle ownership rates. Perth’s urban growth areas overall averaged 96.7 motor vehicles per persons aged 18-84 – pretty close to saturation.

How does motor vehicle ownership compare to established areas a similar distance from the city centre? The following chart compares motor vehicle ownership between urban growth and other areas at the same distance from the CBD (note: the Y-axis is not zero-based):

Motor vehicle ownership tends to increase with distance from the CBD, and in Sydney and South East Queensland the growth areas have higher ownership compared to non-growth areas. But the opposite is true in Melbourne, Perth and Canberra.

The population at each distance interval varies considerably, so here is a summary of the data across all distance intervals that have growth SA1s for each city:

The growth areas of Melbourne, Perth and Canberra had slightly lower motor vehicle ownership than other areas a similar distance from the city, while the opposite was true in other cities. That said, motor vehicle ownership rates are very high across all cities.

 

Finally, I’ll look at the relationships between these measures for growth areas (see another post for analysis for whole cities).

How does motor vehicle ownership relate to distance from stations?

Technical note: for scatter plots I’ve filtered out SA1s with less than 50 population as they are more likely to have outlier results (one person can change a measure by 2% or more).

Lower rates of motor vehicle ownership are generally only found close to train/busway stations (and are dominated by Melbourne examples), but close proximity to a station does not guarantee lower rates of motor vehicle ownership. Quite a few Adelaide SA1s are found the top middle part of the chart – these are all in Mount Barker which has frequent peak period express buses to the Adelaide CBD operating along the South East Freeway – which is similar to rapid transit although without a dedicated right of way.

How do journey to work mode shares relate to distance from stations?

Here’s a scatter plot of private transport mode shares of journeys to work and distance from train/busway station:

This shows that lower private transport mode shares are only generally seen within proximity of train or busway stations, and areas remote from stations are very likely to have high private transport mode shares. But also that proximity to a station does not guarantee lower private transport mode shares of journeys to work (particularly in SEQ).

Technical aside: You might have noticed that almost no SA1s report 99% private mode share. How can that be? The ABS make random adjustments to small figures to avoid identification of individuals which means you never see counts of 1 and 2 in their data. To get a mode share of 99% you’d need at least 300 journeys to work with “3” being non-private (or a similar but larger ratio). Very few SA1s have 300+ journeys to work, and even for over-sized SA1s, they are very unlikely to have only 3 or 4 non-private journeys to work. A mode share of 100% is much easier because you can get that no matter the total number of journeys.

How does population density relate to distances from train/busway stations?

Densities above 45 persons/ha were mostly only found within 5 km of stations, and almost entirely in Sydney and Melbourne. The highest densities were very close to train stations in Sydney. In the middle area of the chart you can see quite a few Perth SA1s that are around 30-40 persons/ha but remote from stations. These are all in the Ellenbrook area of Perth’s north-east, generating a lot of car traffic.

How does motor vehicle ownership relate to private transport mode shares of journeys work to work?

For interest, here is the relationship as a scatter plot:

There is certainly a relationship, but it’s not strong (r-squared = 0.22). Other factors are at play.

Conclusions

  • Perth and Canberra are seeing most of their population growth on the fringe, with Sydney, Adelaide, Melbourne, and South East Queensland seeing most of their population growth in established areas.
  • Growth areas in Sydney, Melbourne, and Canberra have higher than traditional urban densities, indeed Sydney and Canberra have a few very high density greenfield developments. Perth, Adelaide, and particularly South East Queensland have urban growth at relatively low densities. In fact, SEQ is the only major urban centre where growth areas are measured as less dense than non-growth areas at similar distances from the CBD.
  • Perth’s urban growth areas are largely remote from rapid transit stations, and this is likely contributing directly to very high and increasing rates of motor vehicle ownership and private transport mode shares. Melbourne’s current urban growth corridors are closely aligned to train stations (thanks to the opening of the Mernda line), and this is also largely true of Sydney and Adelaide.
  • Almost all outer urban growth areas had high rates of motor vehicle ownership. Overall, Melbourne, Perth, and Canberra’s outer urban growth areas had slightly lower rates of motor vehicle ownership compared to other areas at the same distance from the CBD. Only Sydney, Melbourne and Canberra have some growth areas with lower motor vehicle ownership and/or lower private transport mode shares of journeys to work – and these were all close to train or busway stations.

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

For a similar and more detailed analysis around these topics, see this excellent 2013 BITRE research report on changes between 2001 and 2006.