Melbourne | Charting Transport

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

Sat 13 January, 2024

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.

Leave a Comment » | Melbourne, Migration, Mode share | Tagged: Census Journey to Work, VISTA | Permalink
Posted by chrisloader

Update on Australian transport trends (December 2023)

Mon 1 January, 2024

[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.

2 Comments | Australian Cities, Brisbane, Car ownership, drivers license, Freight, Greenhouse Gas Emissions, Melbourne, Migration, Mode share, Mode shift, Perth, Pricing, Road safety, Road Traffic, Sydney, Vehicle sales | Tagged: BITRE, COVID-19, Pricing | Permalink
Posted by chrisloader

How do commuting distances vary across Melbourne and Victoria?

Mon 18 September, 2023

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).

Leave a Comment » | Melbourne | Tagged: Census Journey to Work | Permalink
Posted by chrisloader

How is public transport patronage recovering after the pandemic in Australian and New Zealand cities?

Tue 8 August, 2023

With the COVID19 pandemic seemingly behind us, what has been happening to public transport patronage? Has it recovered to 2019 levels? In which cities is public transport patronage recovering the strongest?

This post provides my best estimates of how much public transport patronage has recovered in major Australian and New Zealand cities.

In my last post I talked about the problems when transit agencies only publish monthly total patronage (or weekly or quarterly totals). For those cities that don’t publish more useful data, I’ve used what I think is a reasonable methodology to try to adjust those figures to take into account calendar effects.

Unlike most of my posts, I’ll present the findings first then explain how I got them (because I reckon a good portion of even this blog’s readers might be less interested in the methodology).

Estimates of typical school week public transport patronage recovery

Here’s a chart comparing estimated typical school week patronage per month to the same month in 2019 (the year before the COVID19 pandemic) where clean data is available. My confidence levels around estimates for each city is discussed further below.

Technical notes: Sydney+ refers to the Opal ticketing region that includes Greater Sydney, Newcastle/Hunter, Blue Mountains, and the Illawarra. Typical school week patronage is the sum of the median patronage for each day of the week (where available), otherwise an estimate of average school week patronage. More explanation below.

Perth has been at or near the top of patronage recovery for most recent months, perhaps partly boosted by a new rail line opening to the airport and High Wycombe in October 2022.

Wellington – which I suspect is an unsung public transport powerhouse – is in second place at 90%, whilst all other cities are between 75% and 83%.

Looking at the 2023 data, most cities appear to be relatively flat in their patronage recovery (except Perth and Wellington), which might suggest that travel patterns have settled following the pandemic (including a share of office workers working remotely some days per week).

How does patronage recovery compare to population growth?

I’ve calculated the change in population for each city since June 2019. For South East Queensland I’ve used an approximation of the Translink service area, and for “Sydney+” I’ve used an approximation of the Opal fare region covering Sydney and surrounds. At the time of writing, population estimates were only available until June 2022.

There are significant differences between the cities.

So how does public transport patronage recovery compare to population change? The following chart shows June 2022 patronage and population as a proportion of June 2019 levels:

The changes in population are much smaller than the changes in patronage and I have deliberately used a similar scale on each axis to illustrate this. Population growth certainly does not explain most of the variation in patronage recovery, but it is very likely to be a factor.

Perth had the highest patronage recovery in June 2022, but only some of this could be attributed to high population growth. Wellington had little population growth but the second highest patronage recovery to June 2022.

Perth might have the highest patronage recovery rate overall because it spent the least amount of time under lockdown, and so commuters had less time getting used to working at home. Melbourne, Sydney, Canberra, and Auckland spent the longest periods under lockdown, and – with the exception of Canberra – seem to be tracking at the bottom end of the patronage recovery ratings, which might reflect their workers becoming more comfortable with working from home during the pandemic. However I’m just speculating.

How has patronage recovery varied by day type?

Here’s patronage recovery for school weekdays (for cities which publish weekday data):

Note: Canberra estimates are only available for July to December because daily patronage data has unfortunately not been published for January to June 2019.

And here is the same for weekends (again for the same four cities that publish weekend data):

Weekend patronage is a bit more volatile as weekends typically have varying levels of major events and planned service disruptions. Most months also only have 8 weekend days, so a couple of unusual days can skew the month average and create “noise” in the data.

However all cities have been above 90% patronage recovery on weekends. Weekend patronage has returned more strongly than weekday patronage, probably because new remote working patterns only significantly impact weekdays.

How has patronage recovery varied between cities by mode?

I’m only confident about predicting modal patronage in cities that report daily or average day type patronage by mode, as the day type weightings used from another city might not apply equally to all modes.

Here is school weekday train patronage recovery for Sydney, Melbourne, and Auckland:

Auckland is slightly below Sydney and Melbourne, and recovery rates are lower than public transport overall. I suspect this may be due to train networks having a significant role in CBD commuting – a travel market most impacted by remote working.

And here is the data for weekends:

Curiously there is a lot more variation between cities. There’s also a lot more variation between months, which could well be related to the “noise” of occasional planned service disruptions and major events.

Here is average school day bus patronage for four cities where data is available:

Bus patronage recovery is lowest in Sydney, perhaps because buses play a more significant role in Sydney CBD commuter travel which will be impacted by working from home (Melbourne’s bus services are mostly not focussed on the CBD). However buses also play a major role in public transport travel to the CBDs of Auckland and Canberra, although with probably lower public transport mode shares (unfortunately it doesn’t seem possible to get public transport mode share for the Auckland CBD from 2018 NZ Census data).

And for completeness, here is a chart for weekend bus patronage:

Weekend bus patronage recovery is higher than weekdays, and higher than weekend train patronage recovery, in all cities. Reported weekend bus patronage in Canberra, Melbourne, and Auckland has exceeded 2019 level in recent months.

How good are these estimates?

Some agencies publish very useful data such as daily patronage or day type average patronage, while others only publish monthly or quarterly totals which is much less useful for trend analysis. Here’s a summary of how I estimated time-series patronage and therefore patronage recovery in each city (which I will explain below).

City/region	Data used to estimate time-series patronage	Confidence
Melbourne	Reported average patronage by day of the week and day type	High
Sydney+ = Greater Sydney, Newcastle/Hunter, Blue Mountains, Wollongong (Opal catchment)	Reported average school weekday and average weekend day patronage per month (dashboard)	Moderate
South East Queensland (Translink) – including Brisbane, Gold Coast, Sunshine Coast	Reported weekly totals, aggregated to months, and adjusted by day type weightings calculated for Melbourne 2022.	Lower
Adelaide	Reported quarterly totals, adjusted by day type weightings calculated for Auckland 2022.	Lower
Perth	Reported monthly totals, adjusted by day type weightings calculated for Auckland 2022.	Lower
Canberra	Reported daily patronage (from July 2019) and monthly total patronage for May and June 2019 adjusted by day type weightings calculated for Canberra 2022 (weekdays) and 2019 (weekends and public holidays). Data pre-May 2019 has been excluded as there was a step change in boardings when a new network was implemented in late April 2019. May 2019 has been included however I should note it had unusually high boardings.	Moderate
Auckland	Reported daily patronage (up to 23 July 2023 at the time of writing).	High
Wellington	Reported monthly totals, adjusted by day type weightings calculated for Auckland 2022.	Lower

For Melbourne and Auckland excellent data is published that allows calculation of typical school week patronage for February to December, which gives me high confidence in the estimates. Canberra has published daily patronage data but only from July 2019 so I’ve had to estimate school week patronage for May and June 2019 from monthly totals (process described below).

You’ll notice I’ve referred to “typical” patronage rather than average patronage. For cities with daily data, I’ve summed the median patronage of each relevant day of the week, rather than taking a simple average of days of that day type in the month. Taking the median can help remove outlier days, and summing over the days of the week means I’m weighting each day of the week equally, regardless of how many occurrences there are in a month (eg a month with 5 Sundays and 4 Saturdays). For Melbourne I’ve only got the average patronage per day of the week, but I’m still summing one value of each day of the week.

Transport for NSW have an interactive dashboard from which you can manually transcribe (but not copy or download) the average school weekday patronage and average weekend daily patronage for each mode and each month. I’ve compiled a typical school week estimate using 5 times the average school weekday plus 2 times the average weekend day. This is likely pretty close to what true average school week patronage is (more discussion below).

But what about the other cities?

How can you estimate patronage trends in cities where only monthly, quarterly, or weekly total patronage data is available?

Rather than simply calculating percentage patronage recovery on monthly totals (which has all the issues I explained in my previous post), I’ve made an attempt to compensate for the day type composition of each month in each city.

Basically this method involves calculating a weighting for each month, based on the day type composition of each month. If you divide total monthly patronage by the sum of weightings for all days of each month you can get a school weekday equivalent figure on which you can do time series analysis.

This requires a calendar of day types, and assumptions around the relative patronage weightings of each day type.

I’ve compiled calendars for each city using various public sources (including this handy machine readable public holiday data by data.gov.au).

Technical note: In New Zealand it seems schools generally are able to vary their start and end of year by up to 5 regular weekdays. I’ve excluded these 10 weekdays from many calculations because they do not represent clean school or school holiday weekdays. For December 2019 I have also excluded two weeks for Auckland due to unusually low reported patronage due to bus driver industrial action.

The assumed day type weightings need to come from another city, on the hope that they will be similar to the true value. But which city, and measured in what year?

I’ve calculated the relative patronage weights of each day type for Melbourne, Canberra, Auckland, plus one school week sample from February 2020 for Sydney+ (Opal region). These are indexed to a school Monday being 1.

Note: no data is available for public holidays in Melbourne, and the Sydney data does not include school holiday weekdays or public holidays.

Melbourne, Canberra, and Auckland weightings are pretty similar across days of the week for school days, but Melbourne’s school holiday weekdays and weekends were relatively busier than both Auckland and Canberra. The Canberra school holiday figures are highly variable between weekdays and are only available for the second half of 2019 (so are impacted more significantly by the timing of Christmas).

The data suggests the big cities of Sydney and Melbourne attract much more weekend patronage compared to the smaller cities. They also have higher public transport mode shares – refer Update on Australian transport trends (December 2022) for comparisons between Australia cities. In terms of public transport share of journeys to work, Auckland was at around 14% in 2018, while Melbourne was 18.2% in 2016). This suggest Melbourne day type weightings might be suitable for larger cities while Auckland day type weightings might be suitable for smaller cities.

The next question is: which year’s weightings should be used? The chart above showed day type weightings from pre-pandemic times, but it turns out they have changed a bit since the pandemic. Here are 2022 day type weightings:

In all cities in 2022 there is a lot more variation across Monday to Friday school days (Mondays and Fridays being popular remote working days) and school holiday weekdays are much more similar between Melbourne and Auckland, while weekends remain quite different.

In fact here’s how the cities with available data compare for ratios between weekends and school weekdays in 2019 and 2022:

The ratios increased in all cities between 2019 and 2022 except Canberra. The 2019 ratios are remarkably close between Melbourne and Sydney, but the 2022 data shows a higher weighting for weekends in Melbourne than Sydney. The Auckland and Canberra ratios are substantially lower in both years. The ratio went down in Canberra in 2022 possibly due to issues obtaining enough drivers to run weekend timetables in that city.

So what day type weightings should we use for each city?

Should we use Melbourne, Auckland, or Canberra weightings, and from what year should we derive these weightings? And how worried should we be about getting these weightings right?

Well, Auckland provides us with daily patronage data for a “medium sized” city, which allows us to compare calculated typical school week patronage, and also allows calculations as if only more summary data was available (as per other cities). However we need to exclude both January and December, as there were no normal school weekdays in those months in 2019.

The red line (total monthly patronage with no calendar effect adjustments) has the most fluctuations month to month and I’m pretty confident this is misleading for all the reasons mentioned in my last post.

Most of the other methodologies produce a figure fairly close to the best estimate (teal line), except in 2021 and 2023.

The green line (compiled 5 x average school day + 2 x average weekend day) is mostly within 2% of the (arguably) best estimate, but there are variations that will be explained by the green line not taking into account the day of the week composition of the month, nor excluding outlier busy/quiet days (unlike medians). So if you only have average school weekday and average weekend day data you’re not going to be too far off the best estimate. That gives me “moderate” confidence to use Sydney’s average school weekday and average weekend day patronage data to estimate patronage recovery.

But what if you only have total monthly patronage and have to use day type weightings? It’s a bit hard to see the differences in the above chart, so here’s a zoom in for 2022 and 2023:

There’s not a lot of difference between the 2019 and 2022 day type weightings, and notably both methods underestimate patronage recovery for most months of 2023, which is not ideal. Note: February 2023 had several days of significant disruptions due to major flooding events which impacted most measures (except the “typical school week” measure that uses medians to reduce the impact of outliers).

Sydney also provides data that allows us to compare day type weighting estimates to the probably quite good compiled school week estimate (based on 5 average school weekdays and 2 average weekend days). The next chart includes estimates of Sydney patronage recovery using day type weightings from Melbourne and Auckland for different years:

Technical note: I have assumed Melbourne public holidays have the same day type weighting as Sundays, for want of more published data.

The estimates are mostly pretty close, but let’s zoom into recent months to see the differences between the methodologies more clearly:

The closest estimate to the compiled average school week data is using Melbourne 2022 day type weightings to adjust monthly totals (the difference is up to 0.9% in April 2023). This suggests Melbourne is probably the best city from which to source day type weightings to apply to Sydney (both large cities), and 2022 (a post-pandemic year) might be a better source year for these weightings. That’s consistent with Sydney having similar ratios of weekday to weekend patronage as Melbourne.

You can see the red line (a simple total monthly patronage comparison) is again often the biggest outlier, which is what happens when you don’t control for calendar effects. I mentioned at the start of my last post that the raw monthly totals suggested a misleadingly large 6.4% drop in patronage recovery from 79.5% in March 2023 to 73.1% in April 2023. On the average school week estimates, patronage recovery dropped only 1.8% from 77.2% to 75.6%.

So which city’s day type weightings are most appropriate for the smaller cities of Perth, Adelaide, Wellington, and Brisbane that don’t currently publish day type patronage? Does it even make a lot of difference?

Well here are patronage recovery estimates for Adelaide, Brisbane, Wellington, and Perth using both Melbourne and Auckland day type weightings from 2022.

Most of the estimates are within 1%, although there are some larger variances for Wellington and Perth.

The Wellington recovery line is smoother for Melbourne weightings in 2021, but smoother with Auckland weightings in 2022 and 2023 (so far). The Wellington estimates can differ by up to 2% and a smoother trend line may or may not mean that one source city for day type weightings is better than the other.

The fact that Melbourne day weightings worked better than Auckland day weightings when it came to Sydney suggests that larger city weightings might be appropriate for other large cities, and perhaps smaller city weightings might be appropriate for other smaller cities.

I have adopted Melbourne day type weightings for South East Queensland, and Auckland day type weightings for Adelaide, Perth, and Wellington, on the principle that larger cities are likely to have relatively higher public transport patronage on weekends (compared to weekdays). Of course I would really rather prefer to not have make assumptions.

That was pretty complicated and involved – is there a lazy option?

Okay, so if you don’t have – or want to compile – calendar data and/or you don’t want to use day type weightings from another city, you can still compile rolling 12 month patronage totals and compare those year-on-year to estimate patronage growth.

The worst times of year at which to measure year-on-year patronage growth are probably at the end of March, June, September, and December (because of when school holidays fall). Of course being quarter ends, these are also probably the most common times it is measured!

It’s slightly better to measure year on year growth for 12 month periods ending with February, May, August, and/or November, as years ending in these months will contain four complete sets of school holidays, and exactly one Easter (at least for countries with similar school terms to Australia and New Zealand). However there will still be errors because of variations in day type composition of those 12 month periods.

In my last post I introduced the mythical city of Predictaville, where public transport patronage is perfectly constant by day type and they follow Victorian school and public holidays. Here is what Predictaville patronage growth would look like measured year on year at end of November each year:

Calculated growth ranges between +0.8% and -0.9%, which is about half as bad as +1.6% to -1.6% when measured at other month ends, but still not ideal (the true value is zero). The errors in the real world will depend on the relative mix of patronage between day types (Predictaville patronage per day type was modelled on Melbourne’s buses).

That’s a not-too-terrible option for patronage growth, but if you are interested in patronage recovery versus 2019 on a monthly basis, I’m not sure there is any reasonable lazy option.

Let’s hope the usefulness of published patronage data improves soon so complicated assumptions-based calendar adjustments and problematic lazy calculation options can be avoided!

5 Comments | Australian Cities, Brisbane, Melbourne, New Zealand cities, Perth, Sydney, Working at home | Tagged: COVID-19, patronage | Permalink
Posted by chrisloader

Charting Transport

How do commuting distances vary across Melbourne and Victoria?

Melbourne and surrounds

Urban fringe growth areas

How did distance to work relate to distance to Melbourne?

Melbourne’s commuter towns

How dependent are different areas on Melbourne CBD employment?

What about the rest of Victoria?

Does distance to work differ by gender?

Where was there a job / worker imbalance?

Commute distances by work location

Appendix: estimating percentile distances to work

How is public transport patronage recovering after the pandemic in Australian and New Zealand cities?

Estimates of typical school week public transport patronage recovery

How does patronage recovery compare to population growth?

How has patronage recovery varied by day type?

How has patronage recovery varied between cities by mode?

How good are these estimates?

How can you estimate patronage trends in cities where only monthly, quarterly, or weekly total patronage data is available?

So what day type weightings should we use for each city?

That was pretty complicated and involved – is there a lazy option?

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Charting Transport

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

Income

Socio-economic advantage/disadvantage

Occupation

Hours worked

Studying

Summary of geographic and demographic factors influencing public transport mode shares

How are these factors changing over time?

Update on Australian transport trends (December 2023)

Vehicle kilometres travelled

Passenger kilometres travelled

Public transport patronage

Public transport post-pandemic patronage recovery

Passenger travel mode split

Rail Passenger travel

Bus passenger travel

Ferry passenger travel

Light rail / tram passenger travel

Road deaths

Freight volumes and mode split

Driver’s licence ownership

Car ownership

Vehicle fuel types

Motor vehicle sales

Transport Emissions

Transport consumer costs

How do commuting distances vary across Melbourne and Victoria?

Melbourne and surrounds

Urban fringe growth areas

How did distance to work relate to distance to Melbourne?

Melbourne’s commuter towns

How dependent are different areas on Melbourne CBD employment?

What about the rest of Victoria?

Does distance to work differ by gender?

Where was there a job / worker imbalance?

Commute distances by work location

Appendix: estimating percentile distances to work

How is public transport patronage recovering after the pandemic in Australian and New Zealand cities?

Estimates of typical school week public transport patronage recovery

How does patronage recovery compare to population growth?

How has patronage recovery varied by day type?

How has patronage recovery varied between cities by mode?

How good are these estimates?

How can you estimate patronage trends in cities where only monthly, quarterly, or weekly total patronage data is available?

So what day type weightings should we use for each city?

That was pretty complicated and involved – is there a lazy option?

Subscribe to Blog via Email

Twitter Updates

RSS feeds for this blog

Recent Posts

Blog Stats

Category cloud

Tags