Why are young adults more likely to use public transport? (an exploration of mode shares by age – part 4)

I’ve been exploring why younger adults are more likely to use public transport, looking at data sets available for Melbourne. This fourth post in the series looks at the relationship between public transport mode share and income, socio-economic advantage/disadvantage, occupation, hours worked per week, and whether people are studying.

It concludes with a summary of the findings from the four posts in this series. For more detail about the data, see the first post in the series.

(note: I started writing this post quite a while ago – apologies I got distracted by new data releases including the 2021 census data)

Here’s an index as to which posts look at which factors (including many combinations of these factors):

• part 1: age, sex, travelling to city centre (or not), workplace distance from CBD, education qualifications, home distance from CBD.
• part 2: proximity to train stations, population density, job density, motor vehicle ownership, driver’s licence ownership.
• part 3: parenthood, birth year, immigrant arrival year.
• part 4 (this post): income, socio-economic advantage/disadvantage, occupation, hours worked per week, whether people are studying.

Income

Could income explain different levels of PT use by age, if older workers are earning more and therefore more able to afford to drive to work?

Well, do older adults actually earn more than younger adults? Here is the distribution of worker incomes by age group, split between people who work inside and outside the City of Melbourne, for the last pre-pandemic census (2016):

Apart from the few people still working in their 90s (presumably because they are making great money), income was generally highest for people in their 40s in 2016. Older working aged adults generally earnt less! This may well reflect the higher levels of educational attainment of younger adults (as we saw in part 1).

So the idea that older adults are driving to work because they are generally earning more just isn’t supported by the evidence.

The above chart also confirms people working in the City of Melbourne were much more likely to have higher incomes.

But is there a relationship between income and mode choice? The following chart shows public transport mode shares for journeys to work by both income bands and age.

Each line is for an income band, and you can see age-based variations in PT mode share for people within each income band. The biggest age-based variations were for people on lower incomes – with younger workers much more likely to use public transport than older workers.

There was less variation across age groups in public transport mode shares for people on higher incomes, particularly those working in the City of Melbourne.

Most of the higher income bands had high public transport mode shares for journeys to work in the City of Melbourne. The exception was the top band ($3000+ per week), many of whom probably have a car and/or parking space provided by their employer. Also, over 10% of people in the top income band walked or cycled to work which might be because they can afford to live close to work.

For those who worked outside the City of Melbourne, PT mode shares were generally higher for younger workers and those on lower incomes.

Here’s another view of the same data, with income on the X-axis and different colours used for different age ranges:

On this chart you can see income not having a strong relationship with PT mode share within many age groups. For those under 30, PT mode shares generally declined with increasing income. For workers over 40, mode shares slowly went up with income in the City of Melbourne, and declined slowly with increasing income for those working outside the City of Melbourne.

Overall it looks like age probably had a stronger relationship with PT mode shares than incomes, although both factors are relevant.

Here’s a chart that simply shows journey to work mode shares by personal income (regardless of age):

However, personal income is not necessarily the best measure here to measure the impact of income. A person living alone earning $2000 per week has more to spend on their transport than a person earning $2000 per week but also supporting a family. The ABS calculates a metric known as household-equivalised income, which considers total household income in the context of household size and composition. Unfortunately household equivalised income isn’t readily available for journey to work data which includes work location, hence why the above analysis uses personal income. But it is available if I’m only concerned with where people live.

Here’s a chart showing the relationship between household-equivalised income and mode shares for people who live in Greater Melbourne:

This chart is similar to the mode share chart for personal income, but there some noticeable differences at the lower incomes – with high private mode share for those on a household equivalised income between $300 and $1000 per week.

Public transport mode shares were highest at the top and bottom of the income spectrum, and lowest for those earning $400-$499 per week.

Similarly, active transport mode share was highest for the bottom and top income bands (probably out of necessity at the bottom end, and from living in walkable and cycling-friendly suburbs at the top end), while private transport mode share showed the inverse pattern, being highest for incomes between $400 and $1000 per week.

The above data was for journeys to work, but what about other travel purposes?

VISTA data shows some similar patterns for the income/age relationships, although the survey sample size doesn’t allow for a split between travel within/outside the City of Melbourne.

PT mode share was highest for those aged 10-29 for all income bands, although the relationship with income is more mixed.

For those in their 40s and 50s, PT mode share was generally higher for those in higher income bands (with the exception of the bottom income band), which may reflect home and work locations.

Younger children had very low public transport mode shares for all income ranges – which is consistent with other findings on this blog about young families.

Here’s an alternative view of the same data with income on the X-axis and a line per age group:

For those aged 30-59 PT mode share generally increased with income (possibly related to higher incomes more likely to work in the city centre), while for those aged 10-29 it generally declined with increasing income. Again, it would appear that age has a much stronger relationship with PT mode share than household income.

Here are overall travel mode shares by income:

It’s a little hard to see, but the mode share pattern is very similar to journeys to work. PT mode shares were higher for the lowest and second highest income bands and lower at middle income bands – with the exception of the highest income band which had much higher private transport mode share.

Socio-economic advantage/disadvantage

Firstly here is the distribution of Greater Melbourne population by age across the 10 deciles for ABS’s index of socio-economic advantage and disadvantage (part of SEIFA). Those deciles are actually for the state of Victoria, and because Melbourne is relatively advantaged compared to regional Victoria, there is a skew to higher deciles. 10 is for the most advantaged areas, and 1 is the most disadvantaged.

Similar to the analysis of income, people in their 40s were more likely to live in more advantaged areas.

Here is a chart of journey to work mode shares by advantage/disadvantage, split between workers aged 20-39 and 40-69:

Somewhat similar to the pattern with income, public transport mode shares were higher for both the most advantaged and most disadvantaged, bottoming out in the third (lowest) decile. This relationship held over younger and older workers, but there was still variance within age bands. When it comes to public transport use, both age and socio-economic advantage/disadvantage were relevant factors, but again it appears that age has a stronger relationship.

As an aside – because it is interesting – here are some charts showing the interaction between socio-economic advantage/disadvantage and other factors for explaining PT mode share, starting with motor vehicle ownership rates (measured at SA1 geography):

There was a relationship between PT mode share and both socio-economic disadvantage/advantage and motor vehicle ownership (except for areas with very high motor vehicle ownership), but motor vehicle ownership appears to have a much larger impact on PT mode share.

The following chart shows home distance from the CBD had a much stronger relationship with PT mode shares than socio-economic advantage/disadvantage:

The density of central city workers also was a much stronger determinant of average public transport mode share than socio-economic advantage/disadvantage:

Occupation

How do PT mode shares vary by occupation? And could variations in the occupation mix across age groups explain variations in PT mode share across age groups?

Firstly, here is the distribution of workers by occupation (using the most aggregated occupation categories defined by ABS), age, and work location (inside v outside City of Melbourne):

There is some variation in occupation distribution across age groups, with 15-19 and 20-29 the most different with many more sales workers and labourers (noting this data excludes people who did not commute to a workplace on census day). Workers aged 30-49 were more likely to be managers or professionals than most other age groups (consistent with income data).

The next chart shows public transport mode shares for journeys to work by occupation and age, disaggregated by other major factors that I have previously found to be significant: parenting status, work location, and immigrant status:

Clerical and administrative workers and professionals generally had the highest PT mode share for all categories. Labourers, machinery operators and (professional) drivers had the lowest PT mode shares, mostly followed by community and personal service workers (many of whom might do shift work – eg aged care, policing, emergency services, hospitality). Managers had significantly lower PT mode shares than professionals – perhaps due to company subsidised cars and/or parking.

You can see a clear relationship between age and public transport mode share in all “panes” of the chart. That is – even when you control for occupation and the other factors – there were still aged-related variations in public transport mode shares. Either some other factor is at work, of age itself is directly a factor influencing mode shares.

Hours worked

Does the amount of hours people worked in a week vary by age, and does it relate to PT mode shares?

Here is the distribution of hours worked by age group:

Workers aged 30-59 were most likely to be working 35+ hours per week, with those older and younger likely to be working fewer hours. So hours worked does not have a linear relationship with age for working-aged adults, and younger adults tend to work less hours.

So what was the relationship between hours worked, age, and PT mode share? Here’s a heat map table of PT mode share by hours worked and age band:

Technical note: you might be wondering why there is a “None” row. That’s for people who worked on census day, but didn’t work any hours in the previous week, for whatever reason.

This chart shows a very clear relationship between PT mode share and age for all ranges of hours worked.

You can also see public transport mode shares were generally highest for people working “full-time” (35-40 hours) and those who didn’t work in the previous week, and were generally lower for people who worked more then 40 hours (possibly working long shifts or multiple jobs – making public transport less convenient?) or less than 35 hours (juggling part-time paid work with other commitments?).

However this didn’t hold for those aged under 30, with full-time teenage workers less likely to use public transport. We’ve already seen that teenage workers generally had lower qualifications, were less likely to work in central Melbourne, less likely to work near a train station, less likely to work somewhere with high job density, less likely to be a recent immigrant, and more likely to work in occupations with lower public transport mode share.

On the bigger question, while PT mode share was generally higher for “full-time” workers, younger adults were less likely to be working full-time. So hours worked actually works against explaining why younger adults were more likely to use public transport.

Studying

Were younger adults more likely to use PT to get to work because they were more likely to also be students?

Certainly younger adults were more likely to be studying, although this dropped to only 10% for those in their 30s:

Here are average journey to work public transport modes shares by age and student-status:

So while workers who were studying certainly had much higher public transport mode shares than those not studying, there was still a strong relationship between age and PT mode share, regardless of whether workers were also students.

Which got me thinking – we’ve learnt that recent immigrants have been predominantly younger adults, and there have been many international students in Melbourne in recent years (at least up until the pandemic). Do these factors inter-play?

Firstly, census data certainly shows that more-recent immigrants were indeed much more likely to be studying, compared to the rest of the population:

In fact, over half of immigrants living in Melbourne who arrived in Australia between the start of 2016 and the census on 9 August 2016 were studying, and more than a third who arrived in the ten years before the census were studying.

So what if we control for how recently someone immigrated to Australia?

Within most arrival year bands, PT mode shares generally declined with age (except for those under 20). So again, these factors do not explain the total variations in public transport mode share by age.

For interest, here are public transport mode shares by student-status and year of arrival into Australia:

Full-time students who also worked were more likely to use public transport to get to work, although they were overtaken by part-time students for those who arrived before 1996. Also, recent immigrants who were not studying were still much more likely to use public transport.

Summary of geographic and demographic factors influencing public transport mode shares

I’ve covered a lot of material over four long posts. So here’s a summary of what I’ve learnt about demographics and public transport mode share in Melbourne in recent pre-pandemic years:

• Public transport mode share (of all travel) was generally highest for older teenagers, and then fell away with age for those older or younger.
• Public transport mode share of journeys to work was a little different – peaking for those aged in the mid 20s, and was much lower for teenagers and older adults.
• Public transport mode share was generally higher in the following circumstances – all of which are generally more common for younger adults (and many of which are closely interrelated). Most of these relationships are quite strong.
  • Geographic factors:
    • living closer to the city centre (strong)
    • living closer to a train station (strong)
    • living in areas with higher residential densities
    • working closer to the city centre (strong)
    • working closer to a train station (strong)
    • working in areas with higher job density (strong)
    • generally travelling to destinations closer to the city centre (strong)
  • Demographic factors:
    • being highly educated
    • having lower rates of motor vehicle ownership (strong)
    • not owning a driver’s licence (strong)
    • not being a parent (strong), particularly a mother
    • being an immigrant, and having more recently immigrated to Australia (strong)
    • being a student (strong)
• However, these factors don’t seem to fully explain why there are variations in public transport mode share by age (particularly for non-parents). I’ve controlled for several combinations of the stronger factors and still found variations across age bands. There’s likely to be something else about age that influences mode choice.
• There are other factors (all demographic) that have a relationship with public transport mode shares, but these factors did not peak for young adults, unlike public transport mode share. So they actually work against explaining higher public transport use by younger adults. These saw higher public transport mode shares being associated with:
  • both very low and high incomes (but not the highest incomes)
  • both highly socio-economically advantaged areas and highly socio-economically disadvantaged areas
  • working full-time (35-40 hours per week)
  • having a professional or administrative/clerical occupation
  • not being a labourer, machinery operator, or professional driver
• Women were more likely than men to use public transport to get to work for most age ranges (except ages 38-48), and this seems to be at least partly related to their higher levels of education, which in turn probably explains why they are more likely to work in the city centre.

For more about factors associated with higher public transport use, see What explains variations in journey to work mode shares between and within Australian cities?

How are these factors changing over time?

Elsewhere on this blog I’ve uncovered other likely explanations for increased public transport mode share, including things such as increasing population density and employment density – see What might explain journey to work mode shifts in Australia’s largest cities? (2006-2016). However that analysis didn’t look at changes in the geography and demographics of people of different ages.

In this series I’ve confirmed some “demographic” factors that are related to public transport use that have also changed in favour of public transport use over those pre-pandemic years:

• The proportion of the working population who are relatively recent immigrants had increased significantly, particularly for younger adults (see part 3). In 2006 just over 10% of people working in the City of Melbourne had arrived in Australia within the previous 10 years. In 2016 this was up to almost 18%. See also: Why were recent immigrants to Melbourne more likely to use public transport to get to work? Of course immigration all but ground to a halt in 2020 so this has probably contributed to reduce public transport mode share since.
• Motor vehicle licence ownership has declined in younger adults between around 2011 and at least 2019 (see: Update on Australian transport trends (December 2020))
• Motor vehicle ownership was declining for younger adults in Melbourne, Canberra, and on some indicators in Sydney (see: Update on Australian transport trends (December 2023))

But there have been other demographic shifts that probably worked against increasing public transport mode share over the pre-pandemic years:

• The proportion of the working population who were parents rose from 22.6% to 27.1% for those working in the City of Melbourne, and from 25.3% to 27.3% for the rest of Greater Melbourne (2006 to 2016). As an aside: there was the little change in the average age of working parents – for women it went from 38.6 years in 2006 to 39.6 years in 2016 and for men it went from 40.0 to 40.3 years.
• The proportion of people working in the City of Melbourne who were under 40 years of age declined slightly from 58.3% to 57.2% (2006 to 2016).
• Motor vehicle ownership rates have risen significantly for adults over 60. Or put another way, for people born before around 1950, there was almost no change in their rates of motor vehicle ownership between 2011 and 2016, despite them aging 5 years. See: How has motor vehicle ownership changed in Australian cities for different age groups?

In a future post I might look at whether there has been a shift in where younger adults live and work geographically (eg proximity to the CBD, proximity to train stations, residential densities). This would be particularly interesting for the “post-pandemic” world, however it will probably need to wait for 2026 census data.

Update on Australian transport trends (December 2023)

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

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

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

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

• vehicle kilometres travelled
• passenger kilometres travelled
• public transport patronage (new this year)
• public transport mode share
• road deaths (new this year)
• freight volumes and mode split
• driver’s licence ownership
• car ownership
• vehicle fuel types (new this year)
• motor vehicle sales (new this year)
• emissions
• transport consumer costs

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

Vehicle kilometres travelled

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

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

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

All modes showed strong growth in 2022-23.

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

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

Here is the same data for capital cities:

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

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

Passenger kilometres travelled

Here are passenger kilometres travelled overall (log scale):

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

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

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

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

Here’s total car passenger kilometres for cities:

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

Here are per capita values for cities:

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

Public transport patronage

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

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

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

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

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

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

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

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

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

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

Public transport post-pandemic patronage recovery

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

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

[refer to my twitter feed for more recent charts]

Passenger travel mode split

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

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

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

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

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

Rail Passenger travel

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

All cities are bouncing back after the pandemic.

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

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

Bus passenger travel

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

And per capita bus travel up to 2021-22:

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

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

Ferry passenger travel

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

Light rail / tram passenger travel

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

Road deaths

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

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

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

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

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

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

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

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

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

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

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

Freight volumes and mode split

First up, total volumes:

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

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

Here’s that by mode split:

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

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

Driver’s licence ownership

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

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

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

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

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

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

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

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

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

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

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

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

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

Similar patterns are evident for 25-29 year olds:

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

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

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

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

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

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

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

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

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

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

Here’s Victoria, which includes data to 2023:

For completeness, here are motor cycle licence ownership rates:

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

Car ownership

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

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

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

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

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

Vehicle fuel types

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

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

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

Motor vehicle sales

Here are motor vehicle sales by vehicle type:

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

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

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

Transport Emissions

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

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

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

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

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

Here are annual Australian transport emissions since 1975:

And in more detail since 1990:

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

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

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

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

Next up, emissions intensity (per vehicle kilometre):

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

Emissions per passenger kilometre can also be estimated:

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

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

Transport consumer costs

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

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

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

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

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

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

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

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

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

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

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

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

How do commuting distances vary across Australian cities?

Having previously analysed commuting distances in Melbourne and Victoria, this post turns attention to other Australian cities. I’ll answer questions such as: Where are there longer commutes? What might explain differences in commute distances? How long are commutes in outer urban growth areas in different cities?

I’m using ABS calculated on-road distances between homes and regular workplaces from the 2021 census, regardless of whether people travelled to work on census day. For more on the data and calculations see the last post.

How do median distances to work vary by city overall?

I’ve measured the median distance to work for both the usual residents and the workers of each greater capital city statistical area (GCCSA) for 2021. These are often a bit different because some people live and work in different GCCSAs, and I’ll come back to that.

The chart shows that the capital city areas all have longer median distances to work than other parts of each state, which is unsurprising. Here’s some comments on the cities in order:

• Perth tops the chart with the longest median distances to work. Perth has a large and long low density footprint sprawled along the coastline, so long commuter distances are not hugely surprising.
• Melbourne comes in second place. It is the largest city by area, but is more dense than Perth.
• Brisbane comes in third place. Brisbane is slightly larger than Perth in area, but not stretched out quite so far, and with a larger population than Perth, but lower density than Melbourne.
• Canberra is next. It’s a relatively small city so you might expect shorter commute distances, but overall it is quite a low density city with a fragmented urban structure (divided by green areas). It also has an extensive high-speed and rarely-congested highway network that makes driving longer distances relatively easy.
• Sydney is next, the largest city by population and population density, and a city with multiple significant employment clusters, which probably contributes to a smaller median distance than most other big cities.
• Darwin is a tiny city, but like Canberra it has a fragmented urban structure, and Darwin’s CBD is at the end of a peninsula (with a median distance for employees of 12 km), which probably contributes to relatively long median commutes.
• Adelaide is the smallest of the five larger cities, with a mostly contiguous urban structure, which probably explains it’s lower median distances.
• Hobart is another very small city, which probably explains shorter commutes, although it is split over a wide river mouth which would lengthen many commute distances.

On the chart you can also observe small differences between median distances for usual residents and workers in some cities that I think are worth mentioning:

• Canberra has a longer median distance for workers, which probably reflects many workers living across the border in NSW.
• Perth has a longer median distance for usual residents than workers, which might reflect fly-in-fly-out commuters who live in Perth.
• Sydney and Melbourne have a longer median distance for workers, which might reflect commuters from outside the metropolitan area (particularly Melbourne’s many commuter towns which I explored in the last post).
• Workers in the “rest of WA” and “rest of NT” have relatively long median distances, which I suspect reflects fly-in-fly-out employment in the resources sector.

How do distances to work vary across cities

I’ve already examined Melbourne in my last post. What follows are maps and some discussion for other cities, followed by some observations across the cities.

Sydney

(you might want to click/tap to expand some of these maps to see the detail more clearly)

Shorter median distances were found around in areas around the Sydney CBD, which is no surprise. Generally longer distances were seen in the growth areas to the south-west (including Oran Park, Leppington, Gledswood Hills, Gregory Hills, Edmondson), north-west (including Schofields, Marsden Park, Box Hill) and eastern Blue Mountains (including Springwood and Hazelbrook, but not Katoomba).

Other relative outliers include:

• Bundeena in the far south-east (median distances up to 50 km), which is connected to the rest of Sydney by a very long and windy road journey through the Royal National Park, plus a short ferry to Cronulla (not considered by ABS when calculating commute distances).
• Pockets of Bonnet Bay in the south (median distance of 26 km) which have a rather indirect access road to the rest of Sydney.
• Palm Beach (median distance of 37 km) at the tip of the northern beaches region.

Does Sydney have commuter towns? Yes, but perhaps not as many as Melbourne. The map above shows long median distances as far as Hazelbrook in the west, and the map below shows several towns to the south that show longer median distances (many commuters from these towns might also work in Wollongong).

Here’s how Sydney looks for the ratio of jobs to workers in SA3s:

The outer south-west has a low ratio and is quite remote from any SA3 with a surplus of jobs, hence relatively long median distances to work. Some pockets of the north-west had low ratios, but were adjacent to higher ratio areas nearby.

Here are median distances to work by workplace destination zones (DZs):

Unlike Melbourne there were not large industrial areas with median distances over 20 km.

There were a few isolated pockets with long distances including Badgerys Creek (Western Sydney International Airport construction site), the Holsworthy Army Barracks, and Waterfall (maybe related to a rail depot).

Here’s the proportion of workers who were employed in central Sydney (including Sydney CBD, Haymarket, Millers Point, The Rocks):

Like most cities, the influence of the central city declines with distance from the CBD. Some relative anomalies for their distance include:

• Outer north-western suburbs (including Baulkham Hills and Blacktown – North SA3s) have relatively high dependence on the Sydney CBD for jobs, and associated longer median commuter distances.
• Bankstown is relatively close to the Sydney CBD but with with many SA2s below 10% for central city workers, perhaps reflecting relative socio-economic disadvantage.

South East Queensland

First up, Brisbane medians distances by home SA1:

The longest median distances can be found in some low density suburban areas around Jimboomba, Yarrabilba, New Beith, Lowood, and the Lockyer Valley. Some relatively long median distances were also seen around Ormeau and Pimpama (suburbs between Brisbane and the Gold Coast), Springfield Lakes, parts of Caboolture, and Bribie Island. Looking at the urban fabric, these appear to be mostly relatively modern low density residential estates (rather than old towns). I’m not seeing many commuter towns around Brisbane.

Curiously there are relatively short median distances around the outer suburban area of Ipswich in the west (I’ll come back to this).

Here’s the Gold Coast:

Median distances are mostly relatively short except for the northern fringe and around Tambourine Mountain in the hinterland. Jobs are much more distributed across the Gold Coast (see map below) compared to other cities dominated by one CBD, which might explain relatively short commute distances.

Here’s the Sunshine Coast:

Distances are relatively short except for the Glass House Mountains and Beerwah to the south (probably containing commuters to Brisbane and the Sunshine Coast).

Here’s how South East Queensland looks for jobs to workers ratio:

You can see surpluses of jobs in the central parts of Brisbane, the Gold Coast and the Sunshine Coast.

The outer suburban Ipswich area comes in surprisingly high at 0.8, which almost certainly explains the relatively shorter distances to work found in the area. I’m not very familiar with Brisbane’s urban history, but the presence of so many jobs in the Ipswich area is probably saving a fair amount commuting distance and taking some pressure of the transport network.

Jimboomba and The Hills District had a ratio as low as 0.3. Jimboomba’s low density, fragmented urban structure, lack of local jobs, and remoteness from the main Brisbane urban area likely explains the very long median distances to work, and likely high levels of car dependency.

Here are median distances to work by workplace DZs for the Brisbane area:

Long distances were seen around Brisbane Airport and the Port of Brisbane (24-25 km, both relatively remote from residential areas), the Yatala industrial areas on Brisbane’s outer south (25-26 km), Wacol (21 km, which is dominated by correctional facilities), Swanbank (22 km, dominated by power stations), and the RAAF Amberley air base in Rosewood (22 km).

Here is map showing the proportion of workers who worked in “central Brisbane” (defined as the Brisbane CBD plus Spring Hill SA2):

There aren’t huge anomalies by distance. But I might perhaps call out New Beith in the south, Elimbah in the north, and North Stradbroke Island in the east as relative outliers with not-so-low (5-10%) percentages working in central Brisbane. You can also see the Ipswich area had a low dependence on central Brisbane for employment, consistent with the relatively high rate of job self-sufficiency.

Perth

Perth has the longest median distance to work of all capital cities, and you can see many suburbs with relatively long distances, most acutely in the far-north around Two Rocks and Yanchep (several SA1s having a median above 40 km) and Yunderup (between Mandurah and Pinjarra in the south). Long median distances are seen north of Joondalup, throughout the satellite Ellenbrook region in the north-east, in Mount Helena and other hills towns to the east, around Byford in the south-east, around Wellard and Baldivis in the south, and in coastal areas between Rockingham and Mandurah.

I should point out that the map only includes Greater Perth SA1s. The SA2 of Chittering to the north east of Perth (including Muchea and Bindoon) has a median distance to work of 46 km, and 54% of its workers worked in Greater Perth (to which is it connected by a freeway). It contains quite a few very low density rural-living residential areas.

Here’s the jobs to worker ratio map:

There were very low ratios in the outer northern, eastern, and south-eastern suburbs, which explains the long median distances to work from these areas.

Here are median distances to work by workplace destination zones:

The longest medians were seen for Perth Airport and around the Kwinana industrial areas. Other destination zones with long distances are rural areas outside of Perth (not unexpected), plus Wadjemup (Rottnest Island) where distances are obviously not on-road but imputed to be 1.3 times the straight line distance. Many workers are likely to commute by ferry from Perth.

Here’s the proportion of workers who work in central Perth (defined as including the CBD, Northbridge and East Perth):

The dependence on central Perth extends a fair way into the jobs-poor northern suburbs. Both the northern suburbs train line and the Mitchell Freeway have been extended several times as the urban area has expanded, perhaps a case of transport-driven sprawl.

The CBD’s influence also extends a fair distance south including Wellard and Baldivis that have relatively long median distances to work (and are closer to the Kwinana Freeway than the Mandurah rail line).

Adelaide

Median distances to work were relatively short for most of the main contiguous urban area of Adelaide. Higher medians were seen in the detached urban areas of Gawler in the north, Aldinga Beach in the south, and many Adelaide Hills towns (particularly outer parts of Mount Barker).

Here is the jobs to workers ratio map:

The outer suburbs on all sides had low ratios and hence longer median distances to work.

Here are median distances to workplaces by destination zone:

Median distances are relatively short for most workplace areas with the relatively urban exceptions of North Haven / Outer Harbour (at the tip of a peninsula), and the RAAF Edinburgh air base in the north.

Canberra

Most areas of Canberra had median distances under 20km, except around Banks in the far south, and Googong to the south-east (over the border in New South Wales, where 73% of workers work in the ACT).

I’ve previously described towns with a very long median distance as commuter towns – and for Canberra this would include Murrumbateman, Gundaroo, Bungendore, and Collector.

Here is the jobs to worker ratio map for SA3s:

Canberra East had a huge ratio – only 532 workers lived in that SA3 dominated by employment land uses. Low ratios were seen in Tuggeranong in the south, Gungahlin in the north, and Queanbeyan to the east (which had a ratio 0.5 and 71% of workers in the Queanbeyan SA2 worked in the ACT).

An extremely low ratio of 0.1 was seen around the Molonglo Valley, but this area is right next door to jobs rich areas of central Canberra.

The Young – Yass SA3 to the north west of Canberra came in at 0.7, unusually low for a regional area suggesting some dependence on Canberra for jobs. In fact 52% of workers in Yass Surrounds and 34% of the Yass township worked in the ACT. The town of Yass had median distances to work mostly under 5 km, however the 75th percentile distances to work in many parts of Yass was over 40 km.

Here are median distances to work for workplace destination zones:

The only urban area with relatively long workplace median distances was Canberra airport.

I’m not going to do as detailed analysis for the smaller cities that follow.

Hobart

The main urban areas of Hobart had relatively short distances, with outlying commuters towns such as New Norfolk, Brighton, Sorell, Dodges Ferry, Snug, and particularly South Arm showing much longer medians.

Newcastle / Central Coast / Hunter region

Longer median distances are seen at several small urban areas between Wyong and Newcastle, around Kurri Kurri – Abermain. Branxton, Clarence Town, Lemon Tree Passage, and Tanilba Bay. Singleton, Cessnock, and Nelson Bay have relatively short median distances and are likely less reliant on Newcastle for employment.

Wollongong

Note data is not shown for urban areas around Robertson and Mittagong.

Median distances were mostly relatively short, with exceptions in the north (Helensburgh) and south (Albion Park, Kiama, and Gerringong) of what is also a skinny coast-hugging urban settlement pattern.

How do the urban growth areas of big cities compare?

For this analysis I’ve filtered for new (in 2021) outer urban growth SA1s, and calculated the population-weighted-average median distance to work of these SA1s aggregated to SA3 level (not a perfect calculation, but hopefully close enough).

Note: The Tullamarine – Broadmeadows SA3 in Melbourne is perhaps poorly named – it actually includes Craigieburn and stretches north to Mickleham.

The outer urban growth SA3s with the longest median distances to work (perhaps call them commuter suburbs) were Sunbury in Melbourne’s north-west, followed by Melton – Bacchus Marsh in Melbourne’s west, Jimboomba south of Brisbane, Rockingham and Kwinana south of Perth, and Bringelly – Green Valley in Sydney’s west.

The outer urban growth SA3s with the shortest median distances to work included those around the smaller city of Canberra, the Ipswich region of western Brisbane, and the Baulkham Hills region of north-western Sydney. New residents in these areas will be generating fewer commuter kilometres to their city’s transport task (relative to other outer growth areas).

You might be wondering why Adelaide is missing from the above chart. It is a city with quite slow population growth and did not have enough growth in each SA3s to qualify with my filters.

Here’s the same data aggregated up to city level, which shows Adelaide actually with the longest commute distances from outer growth areas, followed by Perth.

What can we take away from this city analysis?

Longer commute distances seem to be strongly associated with imbalances in the distribution of jobs and workers within cities, particularly where these imbalances stretch out over long distances (Perth being the classic example). That’s probably no great surprise to many readers.

So if a city wanted to reduce commuting distances (and therefore demand on its transport system) it could consider:

• slowing urban sprawl – particularly in corridors which already have worker to jobs imbalances and long commute distances,
• increasing residential densities around existing major employment clusters, and/or
• attempting to distribute more employment to outside the CBD – probably easier said than done, but Sydney has done it successfully with relatively high public transport mode share, while Canberra has done it with low public transport mode share (~12%) in town centres.

How do commuting distances vary across Melbourne and Victoria?

Where do workers have the longest travel distance to work? What workplace locations have workers that live far away? How far are commuters in new urban greenfield areas from their workplaces? How do distances to work vary by gender? Where is a lack of local jobs leading to longer commute distances? Where are Victoria’s commuter towns?

This post explores ABS census data on the on-road distances between homes and workplaces around Melbourne and Victoria (a future post may cover other parts of Australia).

See the appendix at the end of this post for more details on the data and calculations.

Melbourne and surrounds

Here are median on-road distances to work around Melbourne for 2021:

Technical note: I’ve filtered for SA1s with 2+ persons aged 15+ per hectare to focus on relatively urban areas.

The shortest median distances in 2021 were around the central city. Longer distance were seen in the outer suburbs with the longest distances on the urban fringe – particularly Manor Lakes, Werribee West, and Pakenham, the “satellite” urban areas of Melton, Sunbury, and Eynesbury, and in some hills towns between Belgrave and Gembrook in the east. This makes sense as outer suburban area are generally further away from jobs.

Urban fringe growth areas

The following map shows the typical distances to work from greenfields areas on the western and northern urban fringe of Melbourne.

You might want to click/tap on this one to make the labels easier to read.

And here are the south-east urban growth areas:

Technical notes: I’ve filtered for brand new SA1s (in 2021) on the urban fringe where the containing SA2 has had population growth of 1000+ people between 2016 and 2021 (consistent with previous analysis of urban fringe areas on this blog). I’ve then aggregated to a median distance to work for each SA2 (being the median of the new SA1 medians). Labels are mostly SA2 names but I’ve renamed some for clarity.

Different growth fronts have very different median distances to work. For example, median distances to work from Manor Lakes were almost double those of Truganina, Wollert, Roxburgh Park, and Cranbourne.

How did distance to work relate to distance to Melbourne?

Here’s a scatter plot comparing home distances from the Melbourne CBD and median distances to work at SA1 geography (using same urban filter as above):

There’s a bit going on here. In areas very close to the Melbourne CBD, median distances to work increase pretty much linearly with distance from the CBD, suggesting these areas are probably fairly dependent on central Melbourne for employment. Then things start to spread out a bit as you get further from the city, with some median distances to work being largely proportional to distance from the CBD, while many other areas have median distances to work of 10-15km. The linear trend fades away as you get further from Melbourne.

A series of orange dots form a “V” shape either side of 65km from the CBD – these are in the Geelong SA4 area, and central Geelong is around 65 km from Melbourne (straight-line distance). This suggests median distances to work in the Geelong region are largely proportional to distance from central Geelong.

The chart is a bit messy with lots of overlapping dots so let’s simplify things by aggregating to SA2s. For each SA2 I’ve calculated the median straight-line distance to the CBD (of centroids of the SA1s in the SA2), and the weighted average of the median on-road distances to work of the SA1s in the SA2 (weighted by number of workers in each SA1):

You can see more clearly that in Melbourne’s west and north west the median distance to work is roughly proportional to the distance from the CBD, while in Melbourne’s outer east and south east, the median distance doesn’t rise as much with increasing distance from the CBD – suggesting these areas are less dependent on central city jobs with more people working locally.

Melbourne’s commuter towns

The top-right of the above chart shows towns remote from the main Melbourne urbanised area including Bacchus March, Kilmore, Riddells Creek, Gisborne, Kinglake, Eynesbury, Wallan, Melton, Lancefield, Ballan, Kilmore, Romsey, and Woodend. These all have a long median distance to work, suggesting they are fairly dependent on Melbourne for employment.

So let’s go back to the map and focus on towns to the north-west of Melbourne:

Firstly, the regional cities of Ballarat, and Bendigo have quite low median distances to work – suggesting the “median worker” is working locally.

Closer to Melbourne are what you might call commuter towns that I listed above. Basically, at least half of the workers in these towns worked way out of town, the median distance to work not dissimilar to the town’s distance from central Melbourne. Most of these towns have a relatively fast and frequent train service to the Melbourne CBD, which no doubt helps facilitates some such long commutes.

These commuter towns only spread so far out, likely reflecting a limit to how far (or how long) people are prepared to commute. While in most parts of Woodend the “median” worker was a long distance commuter, the median worker in Kyneton (the next town down the line) appears to have worked locally. Broadford was more a mix. The limit appears to be around 70 km straight-line distance from Melbourne’s CBD.

Similarly south east of Melbourne, the small towns of Garfield, Bunyip, Longwarry, Koo Wee Rup and Lang Lang had long median distances to work, but then then Korumburra, Drouin, and Warragul mostly had short median distances, as shown in the following map:

Okay so the median worker is doing a long commute in these towns, but do those distances drop away at lower percentiles? Below is a map showing the 25th percentile distance to work. The commuter towns still have very long distances (although Woodend is now a mix and Broadford comes in around 20 km):

In the mostly red towns, over three-quarters of workers had workplaces a long distance out of town (although of course many may work some or all of their hours from home / remote from their workplace, particularly in the post-pandemic world).

But were these towns actually dependent on central Melbourne jobs?

How dependent are different areas on Melbourne CBD employment?

The next map shows the percentage of workers in each SA2 with a workplace in central Melbourne (defined by a set of SA2s, refer chart).

Technical note: I’ve capped the top end of the colour scale at 40% but the central city itself was higher.

The proportion of workers working in central Melbourne generally declined with distance from the CBD, with relative anomalies in Melbourne’s south west, along the Bendigo rail corridor to the north-west, and in coastal areas south of Melbourne.

The commuter town with the highest share of central Melbourne workers was Woodend at just 14%. This suggests these commuter towns are not so much dependent on central Melbourne, but broader Melbourne for employment, which means a lot of long car journeys to work.

In fact, here is a similar map showing the proportion of workers who worked in Greater Melbourne statistical area:

All home SA2s that are within Greater Melbourne show us as a shade of green (over 60%) – as the many local workers in these SA2s will be classed as working in Greater Melbourne.

The Woodend SA2 comes in with 48% of workers working within Greater Melbourne, which means 34% of Woodend workers had a workplace in Greater Melbourne but outside the central city. In fact around 235 of them worked in nearby Gisborne, Romsey, and Macedon which are included within Greater Melbourne.

Greater Melbourne accounted for 14% of Geelong workers, 6% of Ballarat workers, and just 2% of Bendigo workers. The Lorne-Anglesea SA2 is a relatively anomaly, with 24% of workers working in Greater Melbourne (I wonder if it contained some people working remotely from holiday homes who considered their holiday home to be their “usual residence” at the time of the census, which was a time of COVID lockdown in Melbourne).

You might be wondering why many distances to work were almost directly proportional to the distances to Melbourne for commuter towns, but that only a small proportion worked in central Melbourne. This can be explained in that the distances to work are measured on-road, while I’ve calculated straight-line distances to central Melbourne. The ABS says that on-road distances are typically 30% longer than straight line distances. When I look at origin-destination data I see that many of these workers worked on their home side of the Melbourne CBD.

What about the rest of Victoria?

If we expand the SA2 scatter plot out to include the whole state it looks like this (you might need to click/tap to enlarge to read the labels):

The diagonal pattern at the left of the chart burns out with Kinglake and Bacchus Marsh surrounds (around 70 km from the Melbourne CBD). Most further out towns are along the bottom of the chart – i.e. the median distance to work is very short, probably to a workplace in that town.

However there are some SA2s remote from Melbourne that have relatively long median commuter distances. I’ve looked at the home SA2 to work SA2 volume data and confirmed several are towns (or SA2s) that are within the catchment of a much larger nearby town (or set of towns), as per the table below (which is not exhaustive). They are in effect commuter towns for nearby larger towns.

Small town / SA2	Nearby larger town/SA2(s)
Beaufort	Ballarat
Shepparton Surrounds (including Tatura, Murchison, Merrigum, Tallygaroopna), Numurkah	Shepparton
Trafalgar	Warragul, Moe, and Morwell
Rosedale	Sale, Traralgon
Maffra	Sale
Paynesville	Bairnsdale
Yackandanda, Chiltern, Towong	Wodonga / Albury
Red Cliffs	Mildura
Moyne West / Port Fairy	Warrnambool
Loddon (including Inglewood and Wedderburn)	Bendigo
Winchelsea	Geelong

Does distance to work differ by gender?

Inspired by the Gender Equality Toolkit In Transport (with the wonderful acronym GET-IT), I’m going to make more effort to disaggregate transport data by gender (where possible) on this blog. Unfortunately the ABS only provides 2021 census data for binary sex categories, so this will restrict the analysis that can be undertaken.

I’ve calculated the median distance to work by sex for every SA1, but unfortunately it is more susceptible to issues around small counts being randomly adjusted. ABS’s TableBuilder never reports counts of 1 or 2 and this might impact the median distance calculation in SA1s with a smaller number of workers of a sex (particularly women). So there may be some noise in the calculations.

Here’s a side by side comparison of median distance to work around Melbourne (you will probably want to click/tap this to expand):

Both male and female workers show a trend to longer distances in the outer suburbs of Melbourne, but a bit less so for female workers. Indeed the outer suburban areas of Melton, Bacchus March, Sunbury, Wyndham, and Pakenham show a more speckled pattern for female workers, with some SA1s having short median distances and other long median distances.

This variation (or noise) is more evident when I plot the ratio of male to female median distances to work:

In many outer suburban areas (both recent growth and more established) there are SA1s where the male median distance to work is two or three times longer than the female median.

To reduce the noise a bit, I’ve aggregated median distances at SA2 geography (using a weighted average of SA1 median distances), and plotted this against distance from central Melbourne:

The weighted average ratio (grey line) was just above 1 in the central city, and then increased to around 1.2 to 1.3 in the middle suburbs, then grew to almost 1.4 in the outer suburbs and commuter towns. But as you can see there is significant variation between SA2s, and I’ve labelled as many SA2s as possible on the chart. I notice many relatively wealthy areas at the top of the chart, while the bottom of the chart contains many more disadvantaged areas.

Where was there a job / worker imbalance?

We can calculate the ratio of workers to jobs in a region to understand if there is a surplus of workers or jobs. However it is important to keep in mind that around 5% of workers do not have a fixed workplace and will be excluded from the count of jobs, so the average ratio will be around 0.95.

I have done this analysis at SA3 geography as I think SA2s are too small (some include employment areas and many do not) and SA4s are a bit too big.

This chart shows the ratio of workers to jobs for SA3s around Melbourne:

Technical note: this analysis counts only employed persons. You could repeat this analysis including looking for work to understand access (or lack thereof) to opportunities, but that’s another issue.

As you’d expect there was a big surplus of jobs relative to workers in the central city, with many people commuting into the City of Melbourne. There was also a surplus of jobs in SA3s that contain major employment areas, including Monash, Dandenong, Keilor, and Tullamarine – Broadmeadows (which includes Melbourne Airport).

The grey areas were pretty well balanced including Kingston, Stonnington, and Geelong. Box Hill and Maribyrnong were just below 1.

The orange areas had a large surplus of workers compared to jobs. This generally leads to longer commutes, although a neighbouring region with a surplus of jobs might mean these commutes are not very long. The biggest worker surpluses around Melbourne were in the SA3s of Casey – South, and Manningham – East, Sunbury, and Nillumbik – Kinglake. These areas generally had the longest median commutes as we saw above.

Wyndham and Melton – Bacchus Marsh SA3s in Melbourne’s outer west had slightly higher ratios but they were also a long way from SA3s with surpluses – you needed to travel to Keilor, central Melbourne or Port Melbourne to find an SA3 with a surplus, so this explains the long median distances to work. By comparison, in the outer south-east of Melbourne the Casey – South SA3 had a low ratio but is adjacent to Dandenong which had a surplus of jobs.

What about the worker : job balance in regional Victoria?

There was an even balance of workers and jobs in the major regional cities of Ballarat, Bendigo, Shepparton, and the Latrobe Valley. In rural areas further away from Melbourne the ratios were 0.9 or 1.

Commute distances by work location

We can also do distance to work analysis for workplace locations. Here are median commute distances by workplace locations around Melbourne:

The longest median commutes were to jobs in:

• West Melbourne and the Port of Melbourne
• Fishermans Bend
• Melbourne Airport
• a pocket of Werribee South including the Werribee Open Range Zoo
• some industrial areas in the west
• the Police Academy in Glen Waverley
• a pocket of Lalor – West that includes the Melbourne Wholesale Fruit and Vegetable Market which was relocated from West Melbourne in 2015.

Many of these areas contain blue collar jobs where employees perhaps cannot readily afford to live in nearby housing, and/or there was no immediately adjacent housing areas because of land use segregation.

Then in a lot of residential areas the median distances were relatively short – most jobs being filled by relatively local residents.

Here’s a closeup of central Melbourne:

Most of the CBD had median distances of between 11 and 17 km, while Docklands was mostly a bit longer – between 15 and 22 km (I’m not sure I have a good explanation for that difference).

Curiously the zones around North Melbourne Station, Flinders Street Station, and Southern Cross Station had very long median distances – perhaps including train drivers with a notional workplace address of a central station or train yard who might actually start their day at a stabling yard in the suburbs?

There’s also a block on the corner of La Trobe Street and Spring Street with a 23 km median distance. In 2021 the dominant industries of employment for this block were construction and telecommunications services (with a total of only 376 employees).

I’ve examined data for peri-urban and regional employment areas. Most had median commute distances below 15 km with the exceptions of:

• Pakenham South West 23km (which is on the edge of the Melbourne metropolitan area)
• Broadford 17 km (which includes the Mitchell Shire Council offices and a major Nestle factory)
• Parts of Corio 17 km (which is on the northern edge of Geelong)
• Tatura 16 km (which might be attracting workers from Shepparton, Mooroopna, and Kyabram)

And for anyone interested, regional areas with relatively long 75th percentile commuter distances were:

• Warracknabeal 39 km
• Castlemaine 38 km
• Broadford 39 km
• Daylesford 37k
• Seymour 37 km
• Kyneton 35 km
• Beechworth 31 km
• Warragul South 32 km
• Wonthaggi 31 km

I hope you’ve found this interesting. In future posts I hope to compare Melbourne to other Australian cities, and look at how distances vary by industry of employment.

Appendix: estimating percentile distances to work

Distance to work is estimated by the ABS looking at the mesh block location of the persons usual residence and workplace address and calculating the shortest on-road distance between these locations. Where a worker does not have a fixed workplace address there is no calculation (about 5% of workers).

The ABS don’t publish the actual distance to work for every worker (that would be too much data and could breach privacy) but workers are banded into distance intervals that are 0.5 km wide up to 3 km, then 1 km wide up to 30 km, then 2km wide up to 80 km, then 5 km wide up to 100 km, and so on.

I’ve extracted a count of employees in each of these intervals, and then looked up the intervals either side of the 25th, 50th, and 75th percentile worker. I’ve then used a straight line interpolation between the middle distance of the interval below the percentile and the middle distance of the interval above the percentile to estimate the median distance to work. It’s not perfect but I reckon it would be pretty close to the true value, and the maps show a fairly smooth pattern across the city (except sometimes when disaggregated by sex).