What can the 2021 census tell us about commuting to work in Australia’s big CBDs during the COVID19 pandemic?

Sun 2 April, 2023

Note: Since publishing this post, it has come to my attention that Perth’s Fremantle train line was closed on census day in 2021, which may have impacted mode shares in Perth.

The bustling Central Business Districts (CBDs) of Australia’s biggest cities were the powerhouses of the Australian economy, underpinned by public transport networks that delivered hundreds of thousands of commuters each weekday. But the COVID19 pandemic significantly disrupted CBD commuting. Working remotely from home became not just acceptable, but temporarily mandatory, and public transport patronage crashed during lockdowns.

So what might be the new normal in a post-pandemic work for commuting to our CBDs? Will people shift from public to private transport, driving up traffic congestion? How many – and what sorts of people – might work from home?

This post will try to shed some light on those questions by examining what the 2021 Australian census can tell us about how travel to our CBDs altered during the COVID19 pandemic, particularly the differences between locked-down and COVID-free cities. I’ll look at patterns and trends by age, occupation, and commuting distance. I’ll finish with a look at recent transport indications in Melbourne.

As a transport planner, I’m particularly interested in CBDs as there is a significant contest for market share between public and private transport. Before the pandemic, public transport dominated commuter mode share in the biggest CBDs, and CBDs make up a significant share of all public transport commuter trips.

Reminder: what was happening on Census day 2021

Melbourne and Sydney were in “lockdown” with workers required to work from home if possible. Brisbane was just out of lockdown, and the other cities were pretty much COVID-free, although Adelaide had experienced a short lockdown in July 2021. Here’s a summary of some key metrics (CBD office occupancy data sourced from the Property Council):

*The Property Council reported a figure of 60% for August 2021, but this would have been illegal on 10 August as there was a 50% capacity limit just after lockdown. We don’t know the exact dates when the office occupancy survey was conducted, I can only assume later in that month when restrictions were eased. 47% of CBD employees reported working remotely on census day.

What is a Central Business District?

I think of Central Business Districts as the civic, commercial, and business centre of a city, generally characterised by an area dense employment. Unfortunately the ABS’s SA2 boundaries don’t really align with these areas – especially Perth (pre 2021) and Adelaide where the SA2s covering the CBD also included areas of single-storey semi-detached housing.

So for this analysis I’ve created my own CBD boundaries for Australia’s five largest cities. I’ve selected a set of destination zones that were relatively dense in 2021. I’ve tried for reasonably smooth boundaries, and have tried to avoid under-developed areas that might have cheaper car parking. I’ve then created equivalent sets of 2011 and 2016 destination zones – as similar as possible to the 2021 boundary – with the one exception of the Melbourne CBD from which I have excluded south-western parts of Docklands in 2011 due to low employment densities in that year (much of the land was yet to be developed and instead occupied by surface car parking).

Here are maps of these CBD areas. I’ve transparently shaded the CBD for each census year in a different colour which mostly overlap to show dark green. Purple areas are where boundaries are not identical for all years.

Here are the mode splits for those CBD areas, including those who worked at home:

As you would expect, working at home dominated in locked-down Sydney and Melbourne in 2021, but was also quite common in Brisbane and Adelaide. In COVID-free Perth, working at home only accounted for 15.5% of CBD employees with the other 84.5% attending their workplaces on census day.

Public transport mode shares increased between 2011 and 2016 in all CBDs except Brisbane, but then in 2021 there was a significant shift away from all travelling modes to working at home in all cities.

The working at home share may include people who routinely work from their home in a CBD area. To get some idea about these numbers, I’ve split the worked at home share for 2021 into those who lived inside and outside the CBD:

Only a tiny share of CBD workers worked at home and also lived within the CBD. Some of these will have been working remote from their regular workplace and others will have been routinely working at home (I could try to split these apart with deeper analysis but it doesn’t seem worthwhile with such small numbers).

How did working at home vary by age of CBD workers?

A really interesting finding here is that working at home peaked for those in their early 40s in almost all cities – an age with plenty of parents with child caring responsibilities. Teenagers and those in their early 20s were the least likely to work from home, probably because they were more likely to be in jobs not amenable to working at home (eg retail and hospitality). But perhaps also some younger white collar workers may have preferred to build professional networks by being present in the CBD.

In Adelaide and Perth there was a definite trend that younger commuters were more likely to use public transport, and older commuters more likely to use private transport. This was consistent with all cities in earlier censuses (although this was not the case in Brisbane in 2021).

This got me thinking. The COVID19 pandemic and ~18 month border closure surely had some impact on the age distribution of the CBD workforce.

Indeed, here’s a look at the age composition of CBD workers over time:

Between 2011 and 2016 all cities showed a shift in the age composition towards older employees, perhaps as the cohorts of more highly educated Australians got older, people stay in the workforce until later in life, and/or other changing demographics of our cities.

But in most cities (perhaps not Adelaide) there seemed to be a larger shift towards older workers between 2016 and 2021. I suspect this will reflect fewer recent skilled migrants and international students in 2021.

We know from other analysis (see: Why are younger adults more likely to use public transport? (an exploration of mode shares by age – part 1)) that younger adults generally have higher rates of public transport use, so the shift in demographics might be favouring a mode shift away from public transport – all other things being equal (which of course they are not). There was mostly a shift towards public transport for CBD workers between 2011 and 2016, so other factors must have had an overriding impact.

How did working at home vary by CBD worker occupation?

I’ve sorted the occupations by overall worked at home share, which was similar across the cities. This list roughly sorts from blue collar to white collar and I haven’t seen any surprises in this chart. I’ll come back to occupations shortly.

How did working at home vary by distance from work?

The following chart shows working at home rates by approximate distance from home to work, for central area workers.

Technical note: For this analysis I’ve used journey to work data disaggregated by home SA2, work SA2, and whether or not workers worked at home. I’ve defined central city areas as collections of SA2s (so different boundaries to my CBD areas). Distances between home and work SA2s are calculated on SA2 centroids then aggregated to ranges.

In all cities there was a general trend to higher rates of working at home for people living further from the central city, although Sydney rates of remote working were high at all distances (the strictness of lockdown probably overriding the impact of commuting distance). This pattern in other cities likely reflects the increased incentive to work from home when you have a longer commute to avoid.

Did COVID lead to a mode shift from public to private transport?

Some transport planners have been concerned that COVID19 might lead to a permanent mode shift from public transport to private transport, probably for two reasons:

  1. A reduction in total commuter demand might make private transport slightly more competitive (eg if parking costs reduce), resulting in a different mode split equilibrium. We can only really test this aspect in Perth and Adelaide as they were COVID-free but with a small but significant share of workers working remotely.
  2. People have a fear of becoming infected by COVID19 on public transport and therefore switch to private transport (although COVID can also spread in workplaces of course). It’s a bit harder to test this as Sydney and Melbourne were in lockdown (movement restrictions no doubt had much more impact than infection fear). Perth, Canberra, and Adelaide were COVID-free, although there might have been a some fear of undetected COVID circulating – and indeed that was probably happening in Canberra which went into lockdown a few days after the census. Brisbane was just out of lockdown with some restrictions remaining so infection fear may have been higher than in Perth and Adelaide. However the level of infection fear in these “COVID-free” cities in 2021 would certainly be less than that in 2022 and 2023 where COVID is known to be circulating in the community (although there’s since been plenty of opportunity to get vaccinated).

The hypothesis I want to test for COVID-free cities is that there was a mode shift from public transport to private transport, alongside the overall mode shift to working at home.

Okay, so what can census data tell us?

Unfortunately it’s almost impossible to know the behaviour change of individuals who had the same home and work locations in 2016 and 2021 without another data source. I don’t have access to the census longitudinal dataset and that might not even have a sufficient sample of CBD workers who didn’t change home or work location between the two censuses.

But I can explore this question by looking at the changes in overall volumes and mode shares, and then drilling down into different age and occupation cohorts.

How much mode shift was there between travelling modes?

Let’s first look at the overall change in mode split of people who did commute to CBDs in the last three-four censuses (I have 2006 data for Melbourne and Sydney, but only for those who travelled):

On this split, all cities saw a significant mode shift to private transport travel in 2021. The smallest was 4% in COVID-free Perth, while the largest was 18% in locked-down Sydney.

To explore further, here are the total volumes of commuters to CBDs for each mode, across the last three-four censuses:

In the locked-down cities there was a substantial drop in both public and private transport commuters in 2021, although a larger proportional drop for public transport (in line with mode shifts seen above).

But I’m particularly interested in the then COVID-free cities of Adelaide and Perth, that exhibited COVID-free travel behaviour. Let’s start with a deep dive for Perth.

How did commuting behaviour change for Perth CBD commuters between 2016 and 2021?

The overall CBD workforce increased substantially from 83.0k to 105.7k, and this increase saw 5,164 more private transport trips, and about 85 more public transport trips. But the biggest net increase was for working at home.

If we include remote working, the overall mode share of private transport declined by 1.6% from 36.5% to 34.9%. Any mode shift from public transport to private transport was swamped by the overall shift to working remotely.

But does the overall pattern mask some mode shifts within age or occupation groups?

Did some age groups shift modes more than others? Initially for this analysis I started to look at the change in modal mix by five year age group, but of course the people within these 5 year age bands entirely change between censuses (that are held five years apart), so that wouldn’t be measuring behaviour change of a similar group of people.

Instead I’ve looked at the change in modal mix by approximate birth year cohorts (we only know people’s age in August, so the birth year groups are approximate – for example someone aged 25 at the 2021 census could have been born anytime between 11 August 1995 and 10 August 1996, but I’ve allocated them to the 1996 to 2000 cohort).

Here is the net change in volume of Perth CBD workers by birth year cohort and commuter mode (I’ve included the age of this cohort in 2021 at the bottom of the chart for reference).

As you would expect, people aged in their 20s in 2021 made up a significant share of new CBD employees, and workers aged 60+ in 2021 (55+ in 2016) had a net reduction as many went into retirement.

Public transport had the largest share of net new trips for those aged 20-24 in 2021, although a substantial share also travelled by private transport. There was a more even split of net new trips for those aged 25-29 in 2021.

There was also substantial employee growth for people aged 30+ in 2021 (unlike in 2016), and for those aged 30-54 in 2021 the biggest change was a net increase in working at home.

There were increases in private transport use and decreases in public transport use for those aged 30 to 54 in 2021. This was a net 2270* commuters – about 2.1% of the overall CBD workforce (*summing the absolute values of the smaller of the public or private transport shift). But the overall private transport mode shift was -1.6% so changes in other age groups (particularly young adults) washed out all of this shift of middle-aged workers.

Was this mode shift for middle aged workers something to do with COVID, or was it something that was destined to happen anyway? On this blog I’ve explored the relationship between age and public transport mode share in great detail, and there’s certainly a pattern of decline with age, particularly as people become parents. See: Why are younger adults more likely to use public transport? (an exploration of mode shares by age) – part 1, part 2, and part 3.

What about mode changes for different occupations? Here’s a look at commuter volume changes by mode and occupation for Perth’s CBD:

The Perth CBD put on a lot more professionals and specialist managers between 2016 and 2021, and working at home accounted for most of this net growth. The number of new public and private trips varied considerably by category but private transport growth outnumbered public transport growth for most professions.

In particular, almost all the growth in health professionals, protective service workers, and carers and aides was accounted for by private transport. These are occupations where working remotely from home is often difficult, and the high rates of private transport growth might also reflect significant rates of shift work where off-peak public transport service levels are often less competitive with private transport.

There are not many occupations that saw a net shift from public to private transport – these included office managers, program administrators, and clerical and office support workers. But again these numbers were tiny compared to the size of the Perth CBD workforce – suggesting there was very little net shift from public to private transport.

Overall there was a 1.6% shift away from private transport commuting to the Perth CBD, with most of the other mode shift being from public transport to remote working. The evidence from Perth does not support the hypothesis.

How did commuting behaviour change for Adelaide CBD commuters?

Adelaide saw only a tiny increase in the number of private transport commuters, but a significant decrease in the number of people who travelled on public transport. Overall there was a 5.3% shift away from private transport mode share (when you include remote working).

As per the analysis for Perth, here’s the change in volume of trips by mode and birth year:

For Adelaide most of the net mode shift also appears to be from public transport to working remotely. There was a net increase in private transport commuting for people aged 15 to 34 in 2021, and a small decline in private transport trips for older age groups.

There was only a tiny net shift from public to private transport of 526 people within those aged 30-39 in 2021.

Like Perth, working at home accounted for a smaller share of the employment growth for younger adults.

Here’s a look at occupations for Adelaide:

Again, the biggest mode shift here appears to have been from public transport to working at home, with the notable exception again of carers and aides, and health professionals (although small numbers). In most occupations there was also a mode shift away from private transport. Very few occupations show a net shift from public transport to private transport in Adelaide.

The evidence of Adelaide does not support the hypothesis of mode shift from public to private transport. The biggest change was a mode shift from public transport to remote working (plus some mode shift from private transport to remote working).

How did the mix of CBD car commuters change?

Yet another way of looking at potential mode shifts is whether the people driving to work in the CBD in 2021 were any different to previous censuses. For this analysis I’ve filtered for commuters to CBDs who did not use any public transport, but did travel as a vehicle driver or on motorbike/scooter (you might argue “Truck” should be included as well, but we don’t know whether there people were drivers or passengers and the numbers are tiny so I don’t think it is material).

Firstly here is the occupation split of vehicle drivers to work in the five CBDs over the last three censuses:

In most cities, there was a noticeable change in occupation share between 2016 and 2021 towards technicians and trade, labourers, machinery operators and drivers, and community and personal service workers, and away from professionals and managers. Basically a shift from white collar to blue/fluoro collar jobs, as many white collar workers shifted to working remotely. This shift was largest in the locked down cities of Melbourne and Sydney, but was also visible in Adelaide and Brisbane to a lesser extent.

It is also interesting to look at the change in volumes. Note the Y-axis on the following chart has an independent scale for each occupation group, with the biggest occupation groups at the top:

In locked-down Sydney and Melbourne, there was a massive decrease in white collar workers and an increase in machinery operators and drivers. Melbourne also saw an increase in labourers and community and personal service workers. This might reflect a reduction in car parking prices, although I cannot find evidence that prices were actually lower on census day (the City of Melbourne waived parking fees and restrictions from just after the census).

Diving deeper, there was a big increase in protective service workers in the Melbourne CBD, and about 2166 of them drove to work in 2021 (up from 1660 in 2016). This may reflect the opening of the new Victorian Police Centre in Spencer Street in 2020, complete with 600 car parks. Indeed the destination zone that includes this building (and Southern Cross Station) saw an increase of 769 private transport commuters between 2016 and 2021, the biggest increase of any CBD destination zone.

In COVID-free Perth there was an increase in professionals, clerical and administrative workers, managers, community and personal service workers, and machinery operators and drivers who drove to work, and there was only a decline in sales workers.

So what have I learnt from the latest census data?

I’ve covered a bit of ground, so here’s a summary of key findings and some discussion:

  • Locked-down Sydney and Melbourne saw a significant shift to remote working of CBD employees in 2021. COVID-free CBDs saw much less shift to remote working (Adelaide 24% and Perth 15%).
  • Remote working was most common for middle-aged CBD employees (peaking at 40-44 age bracket), and much lower for younger adults and a little less common for older employees.
  • All CBDs saw a step change in the workforce age composition between 2016 and 2021, shifting to an older workforce, probably related to the halt to immigration during the pandemic.
  • In most cities, remote working in 2021 was slightly more common for CBD employees who lived further from their CBD.
  • In all cities, the main mode shift between 2016 and 2021 seems to be from public transport to remote working.
  • No city saw a net mode shift from public transport to private transport (when you include remote working in the modal mix). The main mode shift in COVID-free cities appears to be from public transport to remote working. However it is entirely possible that some public transport commuters switched to private transport, but this was more than offset by other commuters who shifted from private transport to remote working. Few age or occupation cohorts saw a net shift from public to private transport.
  • The only CBD to see a significant increase in private transport commuter trips was Perth (with +5164). However this was still a net mode shift away from private transport mode share due to massive growth in overall CBD employment between 2016 and 2021. I’m curious about how this happened, and I will explore it further in an upcoming post.
  • Occupations likely to include many shift workers saw the biggest net private transport commuter growth in Adelaide and Perth – including health professionals, protective service workers (including police), carers, and aids.

So what can we expect in a “post-pandemic” world?

At the 2021 census all Australian cities were either in lockdown or were perceived to be COVID-free. No Australian cities were “living with COVID”, and in the cities with COVID circulating, few workers faced a choice between workplace attendance and remote working.

At the time of writing (March 2023), COVID is circulating across Australia and there are very few restrictions to restrict spread. There is an ongoing risk of COVID infection when using public transport and attending an indoor workplace (although you can choose to wear a mask of course).

Is this leading to a mode shift from public to private transport in this “post-pandemic” world? Have we even reached a new steady state? The best data to answer this will come from the 2026 census.

In the meantime I have had a quick look at some transport indicators for Melbourne.

Vehicle traffic through CBD intersections in 2022 (excluding Q1) was consistently below 2019 levels in the AM peak in most parts of the CBD. However it’s only a rough indication as much of this traffic will be for purposes other than private transport commuting to the CBD (eg deliveries, through-traffic, buses, etc) (I’ve excluded signals on Wurundjeri Way which is likely to have much through-traffic).

The next chart shows average daily patronage for metropolitan trains, trams, and buses in Melbourne based on published total monthly patronage data but not taking into account the different day type compositions of months between years (I’d much prefer to use average school weekday patronage data to avoid calendar effects, but that data series only ran as far as June 2022 at the time of writing).

This data suggests CBD private transport commuter volumes in 2022 might be a bit below 2019 levels, while there has been a substantial reduction in public transport commuting. This is consistent with what was seen in Adelaide in the 2021 census – mostly a mode shift from public transport to remote working. Furthermore, if there has been a significant increase in Melbourne CBD employment, private transport mode share (when you include remote working) is more likely to have declined below 2019 levels.

Is infection fear still influencing mode choice?

The largest COVID wave in Victoria (so far at the time of writing) occurred in January 2022 peaking at 1229 people in hospital and there was significant public transport patronage suppression (well beyond the usual summer holiday lull) as many people choose to stay home (or were sick and had to stay home). Infection fear was probably having a big impact, as I recall there were few restrictions regarding workplace attendance.

There was also a fairly large COVID wave in winter 2022 peaking at 906 hospitalisations in July, but the above chart shows no significant associated reduction in public transport patronage. This suggests infection fear was probably having a very small impact on transport behaviour in mid-2022.

Certainly in my experience few people are wearing masks on Melbourne’s public transport at the time of writing, but maybe a cautious minority have still not returned to the network.

Emerging indications are that public transport patronage is returning even more strongly in February and March 2023, which might reflect even lower levels of infection fear (hospitalisation numbers have also reached the lowest numbers since September 2021), and/or it might reflect a surge in population growth and CBD employment/student numbers. Things to keep an eye on over time!

Leave a Comment » | Australian Cities, Brisbane, Melbourne, Migration, Mode share, Mode shift, Road Traffic, Sydney, Working at home | Tagged: , | Permalink
Posted by chrisloader

Update on Australian transport trends (December 2022)

Sat 31 December, 2022

It’s that time of year again when BITRE release their annual yearbook chock full of numbers, and this post aims to turn them into useful information. It’s also a prompter for me to update my feeds of other transport metrics and pull together this post covering the latest trends in licence ownership, motor vehicle ownership, transport emissions, vehicle kilometres, passenger kilometres, freight volumes, and transport pricing.

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 (January 2022, December 2020, December 2019, December 2018).

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.

Overall the trend has been a flattening of licence ownership rates, and indeed Victoria was showing declining licence ownership before the pandemic. The ACT and Northern Territory had much higher rates of licence ownership in the 1970s compared to other states. But then the Northern Territory has maintained lower rates of licence ownership than most other states since the 1990s. The ACT showed very high rates of licence ownership around 2009 to 2017 – not sure if this is real or an artefact of the imperfect data (eg counting people with multiple licences).

Most states saw an uptick in 2021 with the notable exception of Western Australia – a state that was largely COVID-free until early 2022 so any COVID-avoidance incentive to get a driver’s licence might not have been very strong. Licence ownership rates in Queensland and Victoria have somewhat levelled out between 2021 and 2022, perhaps reflecting a return of international arrivals and the end of COVID lockdowns.

Here’s licence ownership by age band for Australia as a whole (to June 2021):

In 2020 and 2021 there was an uptick in ownership for people aged 16 to 29 in particular. Let’s look at the various age bands across the states:

There are some interesting recent trends for people aged 16-19. Victoria saw a big drop in 2020 but then some big increases in 2021 and 2022. South Australia and New South Wales have also seen big increases in recent years.

There were even bigger increases for 20-24 year olds following the start of the pandemic, except Western Australia and the Northern Territory (states that largely avoided COVID in 2021).

Ages 25-29 were similar:

So why have licence ownership rates increased for younger adults? Is it mode shift away from public transport to avoid the risk of COVID infection on public transport? Or is it because non-licence holders left the country?

South Australia and New South Wales publish quarterly licencing data by age band which allows us to see the impact of the pandemic more closely. I’ve combined this with ABS quarterly population data to calculate quarterly licence ownership rates:

South Australia has less historical data published:

The population aged 20-24 declined after March 2019 in both New South Wales and South Australia – a year before the pandemic hit. Then both states saw a more rapid decline after March 2020 – the onset of the pandemic.

However the number of people in this age band with a licence only increased slightly – in line with pre-pandemic trends. That is, the licence ownership rate increased sharply primarily because there was a net loss of non-licence holders.

Here’s a look at Australia’s population by age band:

There are some fairly smooth trends over time in all age bands, but then from 2020 there were some sudden shifts, particularly for age bands 16-19, 20-24, 25-59 and to lesser extent 30-39.

A plausible explanation is that international students and other non-permanent residents left Australia – many could not attend classes and were encouraged to leave Australia by the government of the day. These departures were not replaced by new arrivals as the international borders were essentially closed. Indeed once the borders reopened in early 2022, there was a sharp increase in non-licence holders in New South Wales that sent the motor vehicle licence ownership rate down sharply in March 2022 (June 2022 data has not been published at the time of writing).

Other data shows a sharp fall in the number of international students in Australia between 2019 and 2020, particularly in NSW, Victoria and Queensland (more recent student numbers unfortunately not available at the time of writing):

And there was a dramatic shift to net outbound overseas migration from the June quarter of 2020:

In previous posts (see Why are young adults more likely to use public transport? (an exploration of mode shares by age – part 3) I’ve established that recent immigrants skew to the younger adult ages as Australia generally attracts international students and skilled migrants, which also fits with the hypothesis that there was a great exodus of young adults who didn’t have a driver’s licence.

[Side note: the first quarter of 2022 represented a new record for international migration into Australia as the borders re-opened – almost 98k people.]

It’s entirely plausible that long-time residents also increased their rate of licence ownership during the pandemic, but I think the most likely major explanation is the departure of international students and temporary residents. And so I expect the return of international migration will result in lower licence ownership, car ownership, and increased public transport mode share in 2023.

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

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

To get a better understanding of recent trends, here are quarterly licence ownership rates by age band for New South Wales since mid 2018:

You can see the rise – and more recent fall – in licence ownership rates for the age bands 20-24 and 25-29. There was also a sharp fall for those aged 16-19 in September 2021, possibly due to Sydney entering a long COVID lockdown in the winter of 2021 (perhaps learners permits were not renewed or people didn’t bother applying for them if they could not take lessons). 30-34 year olds showed a small rise in licence ownership from the start of the pandemic and this seems to have been sustained, which might reflect some mode shift to avoid infection risk.

Here’s the same quarterly data for South Australia:

Licence ownership rates rose strongly for those aged 16-34, although there was an initial dip for those aged 16-19 in June-September 2020 around the start of the pandemic. Perhaps it has remained high because international students have not yet returned in great numbers to Adelaide, and/or because of a permanent mode shift towards private transport?

For completeness, here are motor cycle licence ownership rates:

Motorcycle licence ownership has been trending up slightly in New South Wales and Victoria, and slightly down in Queensland, South Australia, Norther Territory and Western Australia.

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. So the following charts will show 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.

Between January 2020 and January 2022 most states show an upwards trend in motor vehicles per population aged 18-84 (an imperfect approximation of the driving age population).

However when you look at the stock of cars per state, there was not a significant uptick in the total number of cars – indeed Victoria saw an almost flattening of total motor vehicles between January 2020 and January 2021:

Again, a highly plausible explanation is that non-driving (and non-licence holding) residents departed Australia while long-term residents largely continued their background trends in motor vehicle ownership. We might therefore see a decline in motor vehicle ownership rates in the January 2023 survey with the return of overseas immigration.

Transport Emissions

Australia’s transport emissions have been reduced by COVID lockdowns over the last couple of years but have more recently bounded back:

The above chart showing rolling 12 months emissions which washed out the lockdown period. The next chart shows seasonally-adjusted quarterly data to get around the rolling 12 month averaging – with the September 2022 quarter close to 2019 levels:

Here are Australian transport emissions since 1975:

And in more detail since 1990:

The next chart shows the more recent growth trends by sector:

Aviation emissions saw the biggest decline from the pandemic but were bouncing back in 2021-22. Car and bus emissions have declined in line with pandemic lockdowns whilst most other modes have continued to see growth in emissions.

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 continued reduction in car emissions per capita since around 2004.

Next up, emissions intensity (per vehicle kilometre):

Curiously the figures suggest a sudden drop in bus emissions per km in 2022, but I am not sure this is plausible as electric buses are still only being rolled out in small numbers. There was also an unexpected dip in emissions per km in 2015 which jumped back up in 2016. The 2015 dip in bus emissions per km is primarily a product of a dip in BITRE’s estimated bus emissions and not bus vehicle kilometres travelled, which is a hard to explain (this bus emissions dip is not seen in AGEIS estimates). I suspect this may be an artefact of BITRE methodological issues.

Emissions per passenger-km 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.

Vehicle kilometres travelled

Vehicle and passenger kilometre figures have been significantly impacted by COVID lockdowns in 2020 and 2021, and so the financial year figures are a mix of restricted and unrestricted travel periods. Accordingly we cannot readily infer new trends from this data, and it should be interpreted with caution.

Total vehicle kms for 2021-22 were lower than 2019-20 and 2020-21:

As per emissions, the biggest declines were in cars, motorcycles, and buses:

Light commercial vehicles and trucks have shown the biggest increase since 1990.

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.

Here is the same data for capital cities (capital city population data comes out only once a year with some delay, so most city data points are only up to financial year 2020-21).

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

Once again BITRE have kindly supplied me data on estimated car vehicle kilometres for capital cities that is not included in the yearbook:

Canberra is still on top for car kilometres per person but this rate has been reducing strongly over recently years.

Passenger kilometres travelled

Here are passenger kilometres travelled overall (log scale):

The pandemic had the biggest impact on rail, bus, and aviation passenger kilometres.

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

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 before the pandemic hit. Only air travel has rebounded on a financial year basis.

Here’s total car passenger kilometres for capital cities:

Melbourne, Sydney, and Canberra were impacted by extensive lockdowns in 2021-22, while the other cities were mostly lockdown free. However the then-unprecedented large wave of COVID cases in the summer of 2021-22 may have led to voluntarily suppressed travel behaviour across many cities.

Here’s car passenger kilometres per capita (again only to 2020-21 for most cities):

It’s hard to estimate any post-COVID trends based on this annual data. However, I have been processing VicRoads traffic signal count data which gives some indication about more recent traffic volumes in Melbourne. The following chart shows the change from 2019 median signalised intersection traffic count volumes per week. I’ve deliberately locked the scale as -20% to +10% as I want to focus on the difference between 2019 and 2022 traffic, and so the 2020 and 2021 lines go off the scale during lockdowns.

It’s very interesting that volumes in late 2022 were about 5% lower than 2019 levels on weekdays (a bit higher on weekends although there’s no such thing as a normal weekend).

And if you look at the time of day profile for Melbourne (below), the biggest reductions have been in the early AM peak, and evenings, while there have been increases during the AM and PM school peaks (which might be a response to COVID infection fear and/or because parents working from home can more easily drive their children to and from school):

Rail Passenger travel

The pandemic has put a large dent in rail passenger kilometres travelled, and these are likely to remain below 2019 for some time as new working-from-home behaviours stick following the pandemic:

Melbourne saw a slight increase in 2021-22, but this was probably more a product of the how long the city was in lockdown during financial years 2020-21 and 2021-22. Sydney saw a reduction in 2021-22 probably because there was little in the way of lockdowns in 2020-21.

Here’s rail passenger kms per capita (again, only up to 2020-21):

Bus passenger kilometres have reduced significantly with the pandemic:

Including on a per-capita basis:

I would expect to see these figure bounce back up as there are unlikely to be any lockdowns during 2022-23.

It would appear that the surge in Darwin bus use due to a major LNG project may have ended.

Mode split

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

Mass transit mode shares have taken a large dive during the pandemic, and I expect this to be strongly associated with COVID lockdowns where many people – especially central city workers – worked from home. It’s still difficult to know to what extent this is people switching travel modes for ongoing trips, to and what extent it is public transport trips being replaced by staying home. I hope to have more to offer on this subject in an upcoming blog post.

Transport for New South Wales conducts a rolling household travel survey, although it was suspended during COVID lockdowns in 2020 and 2021. Estimated total person trips and kilometres by mode are reported, and from this we can get an idea around mode split (including non-motorised modes):

On this data, the public transport mode share of person kilometres travelled is much higher than that derived from the BITRE data, with a peaking of around 20% before the pandemic.

Unlike Victoria, New South Wales unfortunately does not provide any detailed household travel survey data, which means it is not possible to perfectly calculate public transport mode share (ferry and light rail were bundled with “Other” pre 2020), and it’s also not possible to calculate mode share by trip purpose. All this and more is possible with Victorian published data, but unfortunately post-COVID data will not be published until late 2024.

Freight

This data shows a dramatic inflection point in freight volume growth in 2019, with a lack of growth in rail volumes and a decline in coastal shipping. 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, and are arguably more contestable between modes:

2022 saw a sudden flatlining in non-bulk freight volumes, with road increased market share to 80%, seemingly mostly at the expense of coastal shipping:

Air freight tonnages are tiny in the whole scheme of things so you cannot easily see them on the charts.

Transport Costs

The final category for this post is the real cost of transport from a individual perspective. Here are headline real costs (relative to CPI) for Australia, using Q2 ABS Consumer Price Index data up to June 2022:

Technical note: Private motoring is a combination of factors, including motor vehicle retail prices and automotive fuel. Urban transport fares include public transport as well as taxi/ride-share (which possibly move quite independently, which is a little frustrating).

The cost of private motoring mostly declined in real terms from around 2008 to 2020, followed by sharp increases in 2021 and 2022 in line with the rapidly rising cost of automotive fuel. The real cost of motor vehicles has plummeted since 1996, although it bottomed out in 2018.

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.

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 1973 by city (note different Y-axis scales):

Technical note: I suspect there is some issue with the urban transport fares figure for Canberra in June 2019. The index values for March, June, and September 2019 were 116.3, 102.0, and 118.4 respectively.

Urban transport fares have grown the most in Brisbane, Perth, and Canberra – relative to 1973. However 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 – eg. 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.

What does all this mean for post-pandemic transport trends?

I also tackled this question a year ago and my thoughts haven’t changed significantly.

One thing that has become clearer is that the increase in motor vehicle licence ownership and car ownership is very likely related to the lack of recent international immigrants during the pandemic. Therefore the reopening of international borders is likely to push these rates down once more across 2022 and 2023, although they may or may not return to pre-pandemic levels. In turn, this will probably increase public transport patronage and mode share, although it is still likely to remain subdued following the wide scale acceptance and adoption of working from home, particularly for central city workers.

A key question for me is the extent to which commuter trips have shifted from public to private transport, as opposed to simply disappearing as many more people work from home. I’ll have more to say on this soon in an upcoming post about 2021 census journey to work data.

5 Comments | Australian Cities, Brisbane, Car ownership, drivers license, Greenhouse Gas Emissions, Melbourne, Migration, Mode share, Mode shift, Pricing, Road Traffic, Sydney | Tagged: , | Permalink
Posted by chrisloader

How and why does driver’s licence ownership vary across Sydney?

Sat 27 February, 2021

In a recent post I confirmed the link between driver’s licence ownership and public transport use at the individual level in Melbourne:

Unfortunately, spatial data around driver’s licence ownership is quite scarce in Australia, so not a lot is known about the spatial variations of licence ownership, nor what might explain them.

However, Transport for New South Wales do publish quarterly licensing statistics at the postcode level, and so this post takes a closer look at the patterns and possible demographic explanations of driver licence ownership across Sydney. I’ll also touch on the relationship between licence ownership and journey to work mode shares.

I have measured rates of licence ownership at the postcode level, and then compared these with other demographic factors that have shown to be significant in explaining variations in public transport mode shares in Melbourne (see my series on “Why are young adults more likely to use public transport”, parts 1, 2, and 3). These factors include socio-economic advantage and disadvantage, workplace location, age, recency of immigration, educational attainment, parenting status, motor vehicle ownership, population weighted density, proximity to high quality public transport, English proficiency, and student status.

I’m sorry it’s not a short post, but I have put some less profound analysis in appendices.

About the data

To calculate licence ownership rates you need counts of licences and population for geographic areas for the same point in time (or very close). Estimates of postcode population are only available from census data, so for most of the following analysis, I’ve combined 2016 “quarter 2” driver’s licence numbers (which includes learner permits) with (August) 2016 ABS census population counts. This is of course pre-COVID19, and patterns may (or may not) have changed since then.

I’ve mostly used population counts for persons aged 16-84. Obviously there are people over the age of 84 with licences, but I am attempting to discount people who may lose their eligibility to hold a licence due to aging.

I’ve also mapped postcodes to the Greater Sydney Greater Capital City Statistical Area boundary, and filtered for postcodes with a significant region within the Greater Sydney boundary (note that the boundaries do not perfectly align).

How does driver’s licence ownership vary across Sydney?

Here’s a map showing 2016 licence ownership rates for Sydney postcodes, with red signifying very high ownership, and green very low.

Technical note: For this map I have filtered to only show postcodes averaging at least 3 persons per hectare to focus on urban Sydney, but some excluded postcodes will be a mix of urban and non-urban land use so this is imperfect. Postcodes are not a great spatial geography for analysis as they vary significantly in size, but unfortunately that’s how the data is published (much easier for TNSW to extract I am sure).

The lowest licence ownership rates can be seen in and around the Sydney CBD, around major university campuses (especially UNSW/Randwick, Macquarie Park, University of Sydney/Camperdown), and at Silverwater (which includes a large Correctional Complex – inmates probably don’t renew their licence and would have a hard time gaining one!). There are also relatively low rates in some inner southern suburbs, in and near Parramatta, and near Sydney Airport.

Most outer urban postcodes have very high levels of licence ownership. One exception is postcode 2559 in the outer south-west, which contains a large public housing estate in the suburb of Claymore. More on that shortly.

Is there a relationship between licence ownership and journey to work transport mode share?

It will probably surprise no one that there was a relationship between driver’s licence ownership and private transport mode share of journeys to work. The following chart shows the average postcode mode share for the commuter population within specified bands of driver’s licence ownership.

I should point out that this a relationship, but not necessarily direct causality (either way). People might be more likely to get a driver’s licence because that is the only practical way to get work from where they live, and other people who do not want to – or cannot – get a driver’s licence may be able to choose to live and work in places that don’t require private transport to get to work.

And then there are some postcodes with pretty much saturated driver’s licence ownership but less than 60% private transport journey to work mode shares (top right). I’ll have more to say on these postcodes shortly.

The rest of this post will consider potential explanations for the spatial patterns of licence ownership, using demographic data for postcodes.

Socio-economic advantage and disadvantage

The following chart compares licence ownership with ABS’s Index of Socio-economic relative advantage and disadvantage (ISRAD, part of SEIFA), at the postcode level:

Near-saturated licence ownership was more common in the more advantaged postcodes, but lower rates of licence ownership were seen in postcodes in deciles 1, 7, and 8. Decile 1 stands to reason as areas of disadvantage (probably including many people unable to get a driver’s licence, eg due to disability), and the postcodes with very low licence ownership rates in deciles 7 and 8 contain or are adjacent to major university campuses.

However there are postcodes with licence ownership rates below 80 in all deciles – the relationship here is not super-strong and there are many exceptions to the pattern.

For people less familiar with the demographics of Sydney, here is a map showing 2016 ISRAD deciles for Sydney postcodes. Note that these deciles are calculated relative to the entire New South Wales population, and Sydney overall is more advantaged than the rest of the state, hence more green areas than red.

Workplace location

Workplace location is a known major driver of commuter mode share, with people working in the CBD much more likely to commute by public or active transport (see Why are younger adults more likely to use public transport? (an exploration of mode shares by age – part 2, plus analysis below). So how does it compare with licence ownership?

Here’s a scatter plot that shows that relationship. I’ve added socio-economic advantage and disadvantage colouring for further context, and labelled selected outlier and cloud-edge postcodes (unfortunately there is a slight bias against labelling postcodes containing many suburbs).

There is perhaps a weak relationship between work in Sydney CBD percentage and licence ownership, with postcodes containing larger shares of commuters going to the CBD (30%+) having lower licence ownership.

The chart also shows that disadvantaged postcodes generally had both fewer CBD commuters (as a proportion) and lower rates of licence ownership.

Commuter mode shares were much more strongly related to workplace location than licence ownership, as the following chart shows. Note that for this chart colour indicates licence ownership rate.

Within the main cloud, postcodes with lower rates of licence ownership (shades of orange) had slightly lower private transport mode shares and/or slightly lower percentage of commuters heading to the CBD. The upper outliers from the cloud include many wealthy postcodes that were not well connected to the CBD by the train network, while postcodes in the bottom-left of the cloud are on the train network.

To explore that further, here’s a similar chart, but with the data marks coloured by a relatively blunt measure: whether or not the postcode contained a train or busway station (based on point locations for stations, which is not perfect as some postcodes are very large and only part of the area might be within reach of a station, while other postcodes might have a station just outside the area):

Generally the postcodes with a train or busway station are towards the bottom-left of the cloud, and those without towards the top-right. I’ve labelled a few exceptions, which include university suburbs such as Macquarie Park, Kensington, Camperdown, and some larger postcodes where a station only serves a minority of the postcode area (eg 2027 and 2069).

The next chart plots commuter mode shares, licence ownership, and socio-economic advantage/disadvantage:

You can see a significant – but not tight – relationship between licence ownership and commuter mode share. Within the main cloud, disadvantaged postcodes are to the top-left, and the more advantaged postcodes to the bottom-right. That is, many disadvantaged postcodes had high private transport mode share despite lower licence ownership, and many more advantaged areas had lower private mode share despite higher licence ownership.

This suggests licence ownership was not the strongest driver of commuter mode choice, at least at the postcode level. Workplace location seems far more influential.

Many advantaged areas are closer to CBD(s) and often have higher quality public transport, walking, and cycling options. People in more advantaged areas are also more likely to work in well-paying jobs in the central city, where public transport is a more convenient and affordable mode. These people also probably face fewer barriers in obtaining a driver’s licence for when they do want to drive (eg access to a car).

While disadvantaged postcodes generally had lower rates of licence ownership, fewer people in these postcodes worked in the Sydney CBD, and they also tended to have high private transport commuter mode shares. I suspect this may be related to many lower income workplace locations being generally less accessible by public transport (particularly jobs in industrial areas). Any cost advantage of public transport is less likely to offset the relatively high convenience of private transport (not to suggest the design quality of public transport services is not important, and not to go into the issues of capital v operating cost of private transport).

However, I suspect public transport could be more competitive for travel from these disadvantaged low-licence-ownership areas to local schools and activity centres. I am aware of some disadvantaged areas of Melbourne that have highly productive bus routes, but not necessarily high public transport mode shares of journeys to work (particularly parts of Brimbank). These areas may be worth targeting for all-day public transport service upgrades, to contribute to both patronage growth and social inclusion objectives.

Just to round this out, here’s a very similar chart, but with Sydney CBD commuter percentage used for colour:

For most rates of licence ownership, there was a wide range of private transport mode shares and a wide range of Sydney CBD commuter percentages. There is a relationship between licence ownership and mode share, but it is not nearly as tight as the relationship between Sydney CBD commuter percentage and mode share.

Age

There’s obviously a relationship between age and licence ownership and NSW thankfully publishes detailed data on licence ownership by individual age. The following chart shows licence ownership by age, animated over time from 2005 to 2020.

Licence ownership peaks for ages around 35-70, and is lower for younger adults and tails off for the elderly as people become less capable of driving.

But there is a very curious dip in licence ownership around age 23-24, which became more pronounced after around 2008. Why might this be?

One hypothesis: People getting learner’s permits around age 18 but not progressing to a full licence and having their learner’s permit expire after 5 years – i.e. around age 22 or 23. I wonder whether people are getting a learner’s permit largely for proof of age purposes. NSW does have a specific Photo Card you can get for that, but the fee is $55 (or $5 at the time you get your driver’s licence), whereas a learner’s permit costs just $25 (and an Australia Post Keypass proof of age card costs $40). As of September 2020, there were 185,329 people aged 18-25 with a Photo Card, and 211,004 people aged 16-25 with a learner’s permit (unfortunately data isn’t available for perfectly aligning age ranges). Did something change about proof of age in 2008? I don’t live in Sydney but maybe locals could comment further on this?

However, I think I have uncovered a more likely explanation which I’ll discuss in the next section.

It would stand to reason that postcodes with more people in age ranges with lower licence ownership might have lower rates of licence ownership overall. I’ve calculated the ratio of the population aged 35-69 (roughly the peak licence-owning age range for 2016) to the population aged 15-84 (roughly the age range of most licence holders) for all postcodes to create the following chart:

You can see a very strong relationship between age make-up and licence ownership rates for postcodes (a linear regression gives an R-squared of 0.75). That is, the more the population skews to people aged 35-69, generally the higher the licence ownership rate.

Recent immigrants

My previous analysis found a strong relationship between public transport use and recency of immigration to Australia (see: Why were recent immigrants to Melbourne more likely to use public transport to get to work?). So does a similar relationship apply for licence ownership?

While I cannot directly match licence ownership and immigrant status at the individual level, I can compare these measures at the postcode level.

For the following chart I have classified postcodes by the percentage of residents who arrived between 2006 and 2016 – as at the 2016 census (my arbitrary definition of “recent immigrants” based on available data for this analysis), and compared that with licence ownership levels.

This chart shows a fairly strong relationship, and suggests more recent immigrants were less likely to have a driver’s licence – although the relationships is weaker for more disadvantaged postcodes (red/orange postcodes).

So why might recent immigrants be less likely to have a licence?

  • As we’ve already seen, some of these postcodes with low licence ownership are adjacent to universities, and no doubt included many international students who did not have a need for licence to get to study or work.
  • Many other skilled immigrants would work in the CBD(s), for which high quality public transport connections are generally available. In Melbourne, I found many recent immigrants live closer to the city where public transport is more plentiful, and many also live near train stations. Sydney is likely to be similar (more on that in a moment).
  • For some it might be because they cannot (yet) afford private transport (particularly immigrants on humanitarian visas) and/or that they don’t have sufficient English to get a learner’s permit (more on that later).
  • For some it might be that they are happy and attuned to using public transport, walking and/or cycling to get around, like they did in their country of origin. However when I analysed Melbourne commuter PT mode shares by immigrant country of origin, I didn’t find relationships I expected.
  • The age profile of immigrants skew towards younger adults, who for various reasons are less likely to own a driver’s licence.
  • I had wondered if some immigrants were driving using international licences instead, but NSW rules state that you can only drive on an international licence for up to three months, so that’s unlikely to explain the pattern.

Here’s a chart showing that immigrants skew towards young adults. The chart shows the New South Wales 2011 population for each calculated approximate age of immigrants when they arrived in Australia (= age + arrival year – 2011) (the best data I have available at present):

The most common ages at arrival were around 23-25 years. Sound familiar? It is also the age where driver’s licence ownership rates dip in New South Wales. I reckon there’s a good chance the influx of immigrants of this age may explain the dip in licence ownership rates for people in their early 20s.

My recent Melbourne research found recent immigrants were also less likely to own a motor vehicle. This evidence suggests low rates of driver’s licence ownership is also strongly related to the relatively high use of public transport by recent immigrants.

For reference, here’s a map showing the percentage of residents in 2016 who had moved to Australia between 2006 and 2016. If you know a little about the urban geography of Sydney, you’ll see higher concentrations around the CBDs, university campuses, and along some major train lines.

Parenting status

We know parents are less likely to use public transport (at least in Melbourne, but probably in all Australian cities), so are they also more likely to own a driver’s licence? The following data compares licencing and parenting rates (defined as proportion of adults doing unpaid caring work for their own children aged under 15) for postcodes:

There is a significant relationship, with postcodes with higher rates of parenting generally have higher rates of driver’s licence ownership. This may well be related to licence ownership rates also peaking for people of the most common parenting ages, and also the fact many young families live in the outer suburbs (where private transport is often more competitive than public transport). The postcodes with the lowest licence ownership rates also have very low proportions of parents (and probably contain many young adults who are studying).

For reference here is a map of parenting percentages for Sydney postcodes:

Motor vehicle ownership

It stands to reason that areas with higher driver’s licence ownership rates might also have higher motor vehicle ownership rates. I’ve calculated the ratio of persons aged 18-84 to household motor vehicles for each postcode, to create the following chart:

You can see the relationship is very strong, with more advantaged (and often near-CBD) postcodes towards the top of the cloud, and more disadvantaged postcodes mostly at the bottom and middle of the cloud.

Silverwater is an outlier – but I should point out that my calculation of motor vehicle ownership only counts people living in private dwellings while licence ownership is for all residents (including the many who resided in Silverwater’s correctional facilities).

There are also a small curious bunch of outliers with around 100 motor vehicles per 100 persons aged 18-84 but only 70-90 licences per 100 persons aged 16-84. These include urban fringe suburbs such as Marsden Park, Riverstone, Oakville, Rossmore, Gregory Hills, Leppington, Voyager Point, Kemps Creek, and Horsley Park. Perhaps these areas may contain farm vehicles that might skew the motor vehicle ownership rates.

While spatial data about licence ownership is unfortunately not readily available for most states of Australia, this chart suggested that motor vehicle ownership (something thankfully still captured by the census, despite ABS trying to drop the question) is a reasonably strong proxy for licence ownership.

Population weighted density

Given postcodes can be quite large (one has a population of over 100,000!), I prefer to use population-weighted density as a metric of urban density (as opposed to raw density). Here’s how that related to licence ownership (note a log scale on the X-axis):

That’s a pretty strong relationship, and of course not unexpected. Areas with higher population density generally have great public transport services, and more services and jobs would likely be accessible by walking, reducing the need for a car or driver’s licence.

Proximity to high quality public transport

I’ve previously confirmed a relationship between public transport mode share and proximity to high quality public transport, so does the presence of high quality public transport also relate to driver’s licence ownership?

As mentioned above, I’ve classified postcodes as to whether or not there was a train or busway station contained within the postcode boundary in 2016. It’s a blunt measure because stations may only serve a small part of large postcodes, or there may be a station just outside a postcode’s boundary that still provides good rail access to that postcode. Some postcodes were also served by light rail and/or very high frequency bus services, just not a train or busway station. I’d love to be able to look at licence ownership by distance from stations, but licensing data is unfortunately only available for postcodes, which does not provide enough resolution.

You can see postcodes with a station generally have lower rates of licence ownership than those without, but there is still plenty of variance across postcodes.

The green postcodes in the top of the left column include Camperdown (University of Sydney, close to the CBD with very high frequency on-road buses), Ultimo (just next to Central Station and the CBD), Kensington (includes UNSW campus, with strong bus (and now light rail) connections), Chippendale / Darlington (wedged between Central and Redfern Stations), and Waterloo / Zetland (very close to Green Square Station and also served by high frequency on-road buses).

Many of the postcodes with stations but high licence ownership (bottom of right hand column) are in the outer suburbs, where train frequencies may be lower, and public transport services in non-radial directions may have lower quality.

So the exceptions to the relationship are quite explainable, and I’d suggest there is a strong relationship. Again, it may be people without a licence choosing to live near public transport, and/or people not near high quality public transport deciding they must have a licence to get around.

Educational qualifications

I have also found a relationship between educational qualifications and commuter mode shares in Melbourne, so are licencing rates related to levels of educational attainment in Sydney?

There’s not much of a relationship happening here between licence ownership and education, other than some inner city postcodes with a high proportion of educated residents and lower rates of licence ownership. There is of course an (expected) relationship between advantage and education.

But just on that, one curious outlier postcode on the chart is Lakemba / Wiley Park (2195), with 29% of the population having a Bachelor’s degree or higher, but it being in the most disadvantaged decile. This postcode has a large proportion of people not born in Australia, with significant numbers born in Lebanon and Bangladesh. Perhaps this reasonably well-educated but highly disadvantaged population is a product of lack of recognition of overseas qualifications, and/or maybe issues with discrimination.

Distance from Sydney CBD

In Melbourne, distance from the CBD has a strong relationship with mode choice, and I would not be surprised if there was similarly a relationship with licence ownership. However Melbourne only has one large dense employment cluster (the central city), while Sydney has multiple large dense employment clusters which is likely to lead to different patterns (see Suburban employment clusters and the journey to work in Australian cities).

From the first map in this post you cannot see a strong relationship between licence ownership and distance from the Sydney CBD – it is clear that many other factors are influencing licence ownership rates across Sydney (such as proximity to university campuses and employment clusters). Having said that, it seems clear that most “outer” suburban postcodes have high levels of licence ownership, but distance from the CBD is probably not a good proxy for “outer”.

Also some postcodes are quite large, and are a little problematic to assign to a distance value or range from the CBD, and the presence of two large harbours means crow-flies distance to the Sydney CBD is not necessarily reflective of ease/speed of travel to the Sydney CBD.

For these reasons I’ve not crunched data on home distance from the Sydney CBD. With a lot more effort, perhaps a metric could be created that considers travel time to Sydney’s major centres (although these centres vary in size).

Which factors have the strongest relationship with licence ownership?

The factors shown above had the strongest relationships with licence ownership (I tested three other factors which had weaker relationships, covered in the appendices below).

I put all the factors for Greater Sydney postcodes into a simple linear multiple regression model, and without labouring the details, I found that the following factors were significant at explaining postcode licence ownership rates (each with p-values less than 0.05 and overall an R-squared of 0.83), listed with the most significant first:

  • Ratio of population aged 35-69 : population aged 15-84. For every 1% this ratio is higher, licence ownership per 100 persons aged 16-84 is generally 1.0 higher (all other things being equal)
  • Rate of motor vehicle ownership: every extra motor vehicle per 100 persons aged 18-84, there are generally 0.35 more licences per 100 persons aged 16-84 (all other things being equal)
  • People who have a bachelors degree or higher: For every 1% this is higher, licence ownership per 100 persons aged 16-84 is generally 0.18 higher (all other things being equal)
  • Postcodes containing or adjacent to a major university campus or correctional centre. These postcodes generally had 14 fewer licences per 100 persons aged 18-64 (all other things being equal)

Factors that fell out of the regression as not significant were Sydney CBD commuter percentage, presence of a train or busway station, socio-economic advantage/disadvantage, population weighted density, parenting percentage, student status, and percent of population speaking English very well. Of course many of these metrics would correlate with the four significant factors above.

I was a little surprised to see educational qualifications show up as significant, given the weak direct relationship seen in the scatter plot, however the impact was small (0.18) and it may be acting as a proxy for other factors such as proportion of commuters working in the Sydney CBD (which was the “strongest” factor that fell out – having a p-value of 0.11).

This analysis was done using postcode level which has issues in terms of blending populations. It is possible to look at individuals using household travel survey data, and I’ve had a quick look using VISTA data from Melbourne. Without going into full detail in this post, I’ve found stronger relationships with age, sex, household income, parenting status, main activity, distance from train stations, and a weaker relationship with distance from CBD. Maybe that could be the focus of a future post.

I hope you’ve found this interesting.

Appendix 1: English proficiency

Probably related to recent immigrant figures, postcodes with a larger proportion of residents speaking English very well generally had slightly higher levels of licence ownership, although the relationship is not tight:

Curiously though, the relationship seems to be stronger for more advantaged postcodes. Disadvantaged postcodes with lower levels of English proficiency still had licence ownership rates of around 80 per 100 persons aged 16-84 (top-left of the cloud).

As an aside: is English proficiency lower in postcodes with many recent immigrants?

The answer is yes, but lower levels of English proficiency are not always explained by recent immigration. Of course some of the recent immigrants will speak English very well (many settling in places like Manly, Darlinghurst, Waterloo, Pyrmont), while others will not, depending on their country of origin. The large red dot to the bottom-left is postcode 2166, which includes the migrant area of Cabramatta (sorry about the label that overlaps other data points). It would appear that this postcode has many longer term residents who don’t speak English very well (although they might rank themselves as speaking English “well” rather than “very well”, which is below my arbitrary threshold of “very well” plus native English speakers).

Appendix 2: Student status

I have recently found a relationship between student-status and and journey to work mode shares in Melbourne (although yet to be published at the time of writing). So does the proportion of residents (over 15) who are studying have a relationship with driver licence ownership rates?

Here’s a scatter plot, with socio-economic advantage and disadvantage overlaid:

Apart from some exceptional postcodes with larger proportions of students, there appears to be little to no relationship between studying and licence ownership.

1 Comment | Car ownership, drivers license, Migration, Mode share, Sydney, Urban density | Tagged: , , | Permalink
Posted by chrisloader

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

Mon 7 December, 2020

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

About immigrant data

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

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

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

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

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

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

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

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

Immigrants and public transport mode share of journeys work

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

How did immigrants shift around Melbourne over time?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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