Who drives to work in Australia’s CBDs?

Wed 9 April, 2025

Central Business District (CBD) generally have expensive car parking, congested radial roads, and public transport that is usually frequent, direct, and often fast. CBDs in larger cities are naturally strong markets for public transport.

Yet around three hundred thousand Australians drove to work in CBDs of Australian cities in 2016. Who are these people, and why might they have chosen to drive? And could they be enticed away from private transport?

I’ve touched on these topics a little in the past (see: The journey to work and the city centre (Australian cities 2001-2011), What can the 2021 census tell us about commuting to work in Australia’s big CBDs during the COVID19 pandemic?), but some recent social media discourse, an increase in the Melbourne central area parking levy, and the introduction of congestion pricing in New York has inspired me to tackle this question afresh and dig a bit deeper.

This post explores the factors of distance from rapid transit, income, occupation, public v private sector, hours worked, age, gender, parenting status, and distance from work.

Short on time? Just want the answers? Skip to the summary of findings.

About the data

I am focussing on mostly 2016 census data, as the 2021 census was heavily impacted by the COVID19 pandemic, with Sydney and Melbourne in lockdown on census day. Of course, travel behaviour in 2025 will be a bit different to 2016, however I would expect a lot of the mode choice fundamentals to be very similar for people making trips to CBDs (some might just be commuting fewer days per week).

I’ve looked at CBDs in all Australian cities with a population of 100,000+ (except the Sunshine Coast and Gold Coast that lack a clear central CBD). Furthermore, I’ve included some of the larger suburban employment clusters in Sydney that look and feel like CBDs (something quite unique to Sydney – see Suburban employment clusters and the journey to work in Australian cities). Private mode shares are very high for the smaller CBDs, so many of the charts in this post will focus on the larger CBDs where differences in private mode shares can be seen against many variables.

For each CBD I’ve chosen Destination Zones (DZs) that represent the core area of employment density – there is usually a high-contrast in density between a central area and its surroundings that enables a logical grouping of DZs. For the larger city CBDs, I’ve used the same areas as this post.

A lot of smaller cities have a major hospital facility close to the CBD, but just beyond the other areas of higher employment densities, and I’ve erred on the side of not including these destination zones, as we already know that hospitals have peculiar mode shares for employees (likely owing to shift work).

My analysis focuses on mostly on private transport mode share – that is people who used one or more modes of private transport (car, truck, motorbike, taxi), and no modes of public transport (train, tram, bus, ferry). Most – but not all – of these people drove a vehicle, but I’m going to use “driving” as a shorthand for headings in this post.

I don’t have access to unit-record census data, so I cannot easily apply regression-style models to determine factors for individual mode choice. Instead I’ll separately examine the relationship between mode share and various combinations of personal variables (as I often do on this blog).

Did commuters drive because they lived far from rapid transit?

I think of rapid transit as transit services where vehicles move at decent speed on a direct path along their own right of way with no delays from other traffic. This includes train lines, metros, busways, and potentially light rail (however most light railways in Australian cities are not completely separated from regular road traffic).

If people don’t have rapid transit close to their home, you’d expect private transport to be a more attractive option for commuting to CBDs.

The following chart shows the private transport mode share of journeys to major city CBDs by home distance from a train or busway station:

Probably unsurprisingly, people living further from a rapid transit station were generally slightly more likely to use private transport to get to work, as public transport was probably less convenient (they would need to use a feeder bus, bicycle, or car to reach a station, or use slower on-street buses or trams all the way).

Perth private mode shares flatlined (on average) for distances of 2+ km from a station, suggesting Perth still has relatively attractive CBD public transport options for these areas (which includes both high frequency feeder bus services and direct to CBD bus services). However at the same time, Melbourne had lower CBD private transport mode shares than Perth at all distances from train stations.

However the differences in mode share between the cities were often as significant as the differences by station proximity for any one city.

A full 40% of Adelaide CBD commuters who lived within 1 km of a train station used private transport to get to work (probably not helped by the non-central location of Adelaide Train Station).

BUT, if you look at the distribution of commuter home locations by distance from train stations you get a different picture.

In Melbourne and Sydney almost half of CBD commuters lived within 1 km of a train or busway station, and they certainly had a lower private transport mode share. But still, 35% of Melbourne CBD private commuters, and 28% of Sydney CBD private transport commuters lived within 1 km of a train or busway station. That is, despite having good access to high quality public transport they choose private transport.

There must be another reason why these people chose private transport. It might be related to service quality (crowding was a significant issue on Melbourne’s train network in 2016), or it might be something else. I can only easily investigate this in terms of demographic variables so lets get into that.

Did commuters drive because they were on higher incomes?

Here’s the private transport mode share of commuters by personal weekly income for all of the CBDs:

This chart shows a very clear trend – that private transport mode share peaked for people in the highest income bracket in most CBDs (especially large CBDs and suburban Sydney CBDs). No doubt this includes many executives who aren’t fussed by – or don’t themselves pay – parking costs (and possibly also car operating costs). I’ve shown before there’s generally a strong relationship between mode split and paid parking, but these people at the top income band were probably not being influenced much by price signals.

In fact, I recently spoke to an executive who was considering opting for a work car. He said that on an out-of-pocket basis it was cheaper for him to drive to work in his CBD than to use public transport!

Back to the chart.. If you ignore the top income band, for the larger CBDs private transport mode share was low and only rose slowly with income. The Sydney regional centres of North Sydney and Chatswood seem to show the strongest relationship between increasing income and increasing private mode share.

But what if we look at the volumes of commuters by income? The next chart shows the income distribution of private transport commuters, together with all commuters for reference (highest incomes on the left):

This chart shows that private commuter trips heavily skewed towards the highest income bands. Almost 37% of people who used private transport to the Sydney CBD had a weekly income in the top band ($3000+), yet that income band only accounted for 20% of overall commuters.

So it’s pretty clear that income had a strong relationship with private transport mode share, with commuters on high income more likely to drive and being disproportionately represented in general traffic and car parks. Having said that, a majority of commuters in the top income band still did not use private transport in Sydney, Melbourne, and Perth.

CBD parking levies will therefore disproportionately impact high income commuters (or their employers).

So we have disproportionate representation of high income earners and people living near rapid transit station driving to work. How are these dimensions related?

Did commuters drive because they had a high income, even though they lived close to rapid transit stations?

For this analysis I’ve combined the dimensions of income and proximity to rapid transit. Here is private transport mode share across these two dimensions:

The relationship between income and private transport mode share generally held up at all distances from a rapid transit station, and the relationship between distance from a rapid transit station and private mode share held up across most income bands.

So we know that people on high incomes were over-represented in private transport trips, and they also had relatively higher private transport mode shares even if they were close to rapid transit stations.

So were CBDs filled with the cars of high income commuters who lived close to rapid transit but choose not to use it? The following chart shows these commuters as a proportion of all commuters, and as a proportion of private commuters:

These commuters were certainly over-represented in the private transport volumes, but still didn’t make up a large proportion of the private transport volume.

Did commuters drive because of their occupation?

A common refrain when it comes to mode choice is that some types of workers cannot use public transport because they carry a lot of gear around with them.

Another explanation for high private transport mode share might be people more likely to be working shift work, and therefore needing to travel at times when public transport is less frequent, or perhaps not available at all.

To explore this question, the following chart shows average private transport mode share by occupation (ANZSCO level 1) and income, across Australia’s five largest city CBDs. The line thickness represents the number of commuters with occupation and income range (I’ve excluded low volume data points).

Observations:

  • Machinery operators and drivers had very high private transport mode share – but there weren’t many of them and those that make the chart were on high incomes. I am guessing many commuted using their work vehicle, or were train drivers who might have registered a CBD station as their place of employment but might not have driven there (in other exploration I’ve found unusually long commute distances for employees in destination zones around Melbourne’s two main train stations).
  • Community and personal service workers tended to have higher private transport mode shares, particularly those on higher incomes – and I suspect many of these might have worked shifts, and therefore commuted when public transport is less available/attractive.
  • Labourers, community and personal service workers, technicians and trades, and sales workers had a strong relationship between income and private mode share.
  • Managers and professionals did not show strong increasing private transport mode share with increasing income, except for the top income band. I suspect these are largely office workers commuting in traditional peak periods where public transport is an attractive and competitive option.
  • For managers, professionals, and clerical and administrative workers, private transport mode shares were higher for those earning less than $800 per week, than those earning closer to $1000 per week. I suspect many of these people might have been working part-time and/or shift work, where public transport might be less convenient. More on that shortly.
  • Clerical and administrative workers showed a slightly stronger relationship between income and private transport trends towards higher incomes. I suspect this occupation actually includes a lot of finance professionals but I’m not quite sure why they would have a higher private transport mode share than other professionals (maybe they worked longer hours?).

But how much did each of these occupation categories contribute to total private transport trips to CBDs?

If you study this chart, you’ll see that managers were the most over-represented occupation making up private transport trips, with the starkest difference in North Sydney. Professionals were actually under-represented in private transport trips in all cities, particularly the largest CBDs.

And those machinery operators and drivers who had such high private transport share – made up only a tiny portion of CBD workforces.

So were some types of managers more likely to use private transport than others? Here’s a heatmap table looking at private transport mode share by occupation AND industry of employment:

Some more distinct patterns emerge here. Managers in the construction, mining, rental, hiring and real estate services, manufacturing, and accommodation and food service industries had significantly higher private transport mode shares than managers in other industries. I suspect these could be explained by high salaries (eg mining), shift work (accommodation and food services), and need for a vehicle during the work day (rental, hiring, and real estate).

You can also see some other high private transport mode shares that seem pretty explainable:

  • 47% for public administration and safety / community and personal service workers – which probably includes a lot of police who might have done shift work,
  • 42% for professionals in health care and social assistance – probably including many shift working hospital staff,
  • 57% for sales workers in rental, hiring, and real estate services, who possibly need their car for work and/or are allowed to take company cars home.

So far I’ve been speculating about mode shares for more specific occupation types, but it is possible to drill down the ANZSCO codes to understand things more deeply. First up, here are the average private transport mode shares and commuter volumes for level 2 occupation categories:

The highest private transport mode share was for mobile plant operators, but also there were only 421 such commuters across the five cities. The first large occupation category with a high private transport mode share was Chief Executives, General Managers and Legislators.

So while private transport mode shares do vary by occupation and industry, it seems to boil down to higher private transport mode share for people working shift work, people whose day job involves operating a motor vehicle, and tradies who clearly need to bring specialist equipment, amongst others. However these workers are a fairly small proportion of all CBD workers.

I’ve dug even deeper down to ANZSCO level 4 occupations. The top ten occupations with the highest private transport mode share (minimum 100 commuters) were:

  • 86% automobile drivers (no surprise here, this presumably this includes taxi drivers)
  • 81% bus and coach drivers (but only 165 workers across the five cities)
  • 81% earthmoving plant operators
  • 80% primary school teachers (that was unexpected!)
  • 79% surgeons (likely some shift work)
  • 75% fire and emergency workers (no doubt including many shift workers)
  • 75% truck drivers
  • 74% wall and floor tilers
  • 73% air conditioning and refrigeration mechanics
  • 72% aged and disability carers (many shift workers?)

Working down the list, other occupations of interest (to me) include:

  • legislators at 67% – sometimes parliament sits until very late at night
  • registered nurses at 61% – many shift workers no doubt
  • chief executives and managing directors at 55% – not quite as high as I expected – 46% in Sydney, 52% in Melbourne, and 73+% in the other cities (for interest: public transport mode shares were 45% in Sydney and 37% in Melbourne)
  • train and tram drivers at 48% – but much higher in Perth, Brisbane, and Adelaide (only 42% in Melbourne and 23% in Sydney)
  • police at 47% – likely many shift workers
  • software and applications programmers at 8% – at the very bottom of the list

And here’s the top ten occupations (at level 4) that used private transport to get to work, by volume :

  • 5.1% Barristers & Solicitors*
  • 4.5% Accountants and accounting clerks*
  • 3.1% Advertising, public relations and sales managers
  • 2.3% Contract, program and project administrators
  • 2.2% Management and organisation analysts
  • 2.0% ICT managers
  • 1.9% Chief executives and managing directors
  • 1.7% General Clerks
  • 1.7% Real Estate Sales Agents
  • 1.7% General Managers

These ten occupations accounted for 26.7% of all private transport commuter trips to work in CBDs in the five cities in 2016. I would expect many people with these occupations to also be on high incomes.

*I’ve (arbitrary) bundled two similar occupations here.

Were private sector commuters more likely to drive?

While private sector workers had the highest private transport mode share in most of the big cities (except Perth), there are big differences between occupations, so I think it’s worth looking at private mode shares by both occupation and sector of employment:

Local government professionals had the lowest private transport mode share in Sydney, Melbourne, and Brisbane.

The starkest difference between public/private sectors was in the “community and personal service workers” occupation category. I would expect many of the state government workers in this category to be hospital staff and police, many of whom would have been working shift work.

Any approach to congestion pricing in CBD areas might want to give consideration to these essential shift workers, who generally made up around 5% of CBD commuters (on a Tuesday at least, and it will vary by CBD).

Did commuters drive because they worked more hours per week?

The census asks people how many hours they worked in the previous week – which hopefully also generally reflects how many hours they might have worked in the week of the census (there will be some minor exceptions, such as people returning from leave). The following chart shows the relationship between hours worked and private transport mode share across all the CBDs:

For the smaller CBDs there was very high mode private share, no matter what the working hours, so I will focus in the on larger CBDs.

In the larger CBDs:

  • Private transport mode share was lowest for those reporting 35-39 hours. About a quarter of these were public sector workers – the highest public sector share for any grouping of worked hours.
  • Private transport mode share had a mini-peak for 25-34 hours, which probably represents many people working part time 3-4 days per week. Perhaps these people were juggling other responsibilities and activities (eg parenting, studying) and decided a car better supported their complex multi-stop travel journeys? We will come back to parenting shortly.
  • Private transport mode share increased as hours worked increased from 35+ hours, peaking at those working 49+ hours in the week. Perhaps people working very long hours could not afford the extra time to use any other mode, perhaps they commuted outside traditional peak periods (perhaps as shift workers, or just because of very long days in the office), and/or perhaps they were very high income earners (more on this shortly).

One word of caution on this is that some people on higher incomes might not have worked many hours in the week before the census for random reasons (eg they were on leave). When I look at the approximate average income per hour worked of people who worked 1-15 hours, it was much higher than all other hours-worked bands.

Here’s a view of the distribution of commuters (private transport commuters and all commuters) by hours worked in the last week for larger CBDs:

Commuters using private transport absolutely skewed toward people working longer hours (compared to all commuters). In particular a large share of private transport commuters worked 49 hours or more, which I’d suggest is pretty extreme overtime (these people are probably also time-poor so might be prepared to pay a premium for a faster mode of transport).

People working long hours naturally tended to have higher incomes. I’ve roughly estimated the average hourly income for each range of worked hours, and this shows a big step up in hourly income at 40 hours per week, and then another step up for those working 49+ hours per week. People working 40+ hours were also much more likely to be working in the private sector.

However not everyone working long hours was on a high income, so I’m wondering if these variables are strongly co-correlated.

Here’s a chart showing private transport mode share by hours worked and weekly income for the five big city CBDs combined:

The chart suggests that both hours worked and weekly income were relatively independent drivers of mode share. Private transport mode share was higher for people on higher incomes regardless of hours worked. Private transport mode share was lowest for people working 35-39 hours across all income bands, except people on the lowest incomes working limited hours.

There were just under a thousand people working 49+ hours on a relatively low weekly income of $500-$649 who had a 37% private transport mode share across the five big cities. perhaps these people were working multiple jobs and/or shift work. These commuters were probably doing it tough with high transport costs and lower incomes – but thankfully there weren’t many of them.

Were older and/or parenting commuters more likely to drive?

In answering this question I’m going to also throw in the dimension of gender, as these three dimensions have shown up to be quite significant when it comes to mode shares generally in my previous analysis (see: How and why do travel patterns vary by gender and parenting status?).

Technical note: I am talking about gender, but the data reported by ABS is actually based on (binary) sex.

Here’s how private transport CBD commuter mode shares vary across age, gender, and parenting status:

There’s a bit going on here:

  • For males, there’s generally a strong relationship with private transport mode share increasing with increasing age.
  • For females, private transport mode share generally increased with age, but sometimes flatlined or declined for females over 50.
  • Parents generally had higher private transport mode shares, with dads having higher shares than mums in the largest centres, but then curiously mums had higher private shares in the smaller CBDs.
  • For non-parenting workers, males had higher private transport mode shares in the bigger CBDs, but again this curiously flipped for the smaller CBDs.

Here’s the distribution of CBD commuters by age for private commuters, and all commuters:

Private transport commuters are significantly skewed towards older age bands in most CBDs. Commuters over the age of 40 made up 41% of all Sydney CBD commuters, but accounted for 57% of private transport journeys. Melbourne was similar.

Here’s the distribution of parenting status for private commuters and all commuters:

Parents were slightly over-represented in private transport commuters in most CBDs, but the majority of private commuters were still non-parents.

Here’s the gender distribution (reminder: ABS census data only includes binary sex):

Males were over-represented in private transport commuters, but not always by a large margin.

So are older commuters more likely to drive to work simply because they are more likely to be earning high incomes?

It looks like there was a strong relationship between age and mode choice, regardless of income band.

I suspect the peak in lower income commuters in their 40s might reflect part time parenting commuters. Younger people on higher incomes were much less likely to choose private transport.

This is all similar to previous analysis on public transport mode share in general against these variables (see: Why are young adults more likely to use public transport? (an exploration of mode shares by age – part 4). Older people generally used public transport less often, regardless of income and parenting status.

Did commuters drive because they were a long way from their CBD?

For people who drove to work in CBD, how far did they live from their CBD? And so, will CBD parking levies disproportionately hit people in the inner or outer suburbs?

The next chart shows the distribution of CBD commuter home locations by distance from CBDs for all commuters, and private transport commuters (ABS calculates or estimates the on-road distance between each employee’s home and work location).

For very short journeys driving probably isn’t competitive with walking, so in all cities few private transport commutes were less than 2.5 km.

But if you look at the further distance bands, there’s not a lot of difference between private commuters compared to all commuters. In Melbourne private commuters skew slightly further out, while in Sydney they skew slightly further in. It varies a little by city.

It is also worth noting that CBD commuters don’t tend to live a long way from the CBD, because not many people make life choices that require long distance commutes. Around two-thirds of Sydney and Melbourne CBD commuters lived within 20km of the city.

The following chart show the mode split of people who travelled to work, based on their distance from home to work:

Observations:

  • Active transport dominates very short trips, especially in the biggest CBDs. Walking is way cheaper than public or private transport, and possibly often faster. Public transport mode share for very short journeys was tiny, except for the Melbourne CBD which is probably explained by the Free Tram Zone in the CBD area making very short tram trips to work free (although a similar effect doesn’t show up for Perth CBD’s Free Transit Zone).
  • Active transport mode share fades away by 5 km from the CBD, and then it’s a contest between private and public transport. Public transport won the largest share in the biggest CBDs, while private transport dominated for the smaller CBDs.
  • For distances of more than 5 km, the split between public and private transport shows different patterns in different CBDs.
    • For Perth, Wollongong, Geelong, and the Sydney secondary CBDs of North Sydney, Parramatta, Chatswood, St Leonards, and Kogarah, public transport mode share increased with increasing distance. The secondary CBDs in Sydney are all well connected by the train network, and perhaps this is competing well with road tolls for private transport that generally increase with distance travelled. Perth, Wollongong, and Geelong are well connected by rail for many long distance commuters which might influence this pattern (rail being relatively cost and time competitive for longer distance trips).
    • For many CBDs the mode split remains fairly flat across distances, except for a spike in private mode share for trips 50-100km in Brisbane, Adelaide, and Parramatta.
    • Kogarah keeps showing up with wacky patterns in these charts. Public transport mode share is only strong for trips of 20-50 km, which probably represents many trips where metropolitan train travel is highly time and cost competitive.

This means that there isn’t a clear over-representation of private transport users for shorter or longer distances across the cities (beyond ~5km from CBD). Private transport commuters tend to travel similar distances to public transport commuters.

As something of an aside – another way of looking at this data is the estimated approximate average distance to work (I have to estimate these because journey lengths are reported in bands rather than precise figures). The following chart shows the approximate average distance to work, and the width of the bars is scaled to the relative mode share of each mode. So a skinny line shows the average distance of a minority mode.

Technical note: To calculate the approximate average distance from home, I take a weighted average of the mid-distances of each reported distance band, with the weighting based on the number of commuters reported in each distance band (I hope that makes sense!).

For the Sydney CBD, public transport trips had a slightly longer average commuting distance than private transport, but for the Melbourne CBD it was the other way around. There’s no clear pattern for the larger CBDs.

However for the smaller CBDs public transport tended to have much longer average distances (with Toowoomba the longest). This suggests public transport was more competitive for longer travel distances to smaller cities.

In summary – who drove to work in Australian CBDs in 2016?

My analysis has found that people with the following characteristics were significantly over-represented in private transport CBD commuting in Australia’s largest cities:

  • commuters on the highest incomes
  • commuters working very long hours
  • commuters living further from rapid transit stations
  • commuters who were parenting
  • older commuters (particularly for males)
  • males
  • commuters likely to be doing shift work – particularly police and medical workers

CBD commuters living within around 5 km of large CBDs and within around 2.5 km of smaller CBDs were more likely to use active transport for obvious reasons. Beyond these near-CBD areas, there were not clear relationships between mode split and distance from CBD.

There were also some types of commuters who had high private transport mode shares, but made up only a small volume of CBD commuters so were not significantly over-represented in private transport commuting. These included:

  • Part-time workers on high incomes
  • Workers who need a motor vehicle as part of their day job

What can public transport agencies do to try to shift these private transport CBD commuters towards public transport?

Many of these private transport commuters were on high incomes, and didn’t choose public transport, even though it was relatively close to home. These commuters probably won’t respond to fare reductions, but they (or their employers) may respond to private transport disincentives (eg parking levies, reduced parking availability, (de)congestion charging, changes to tax incentives).

For commuters working part time, shift work, and/or long hours, public transport agencies might want to ensure there is a reasonably frequent services from early morning until well into the evening on rapid transit lines. In 2017 Sydney boosted off peak train frequencies to 15 minutes or better over a very long span of hours, and patronage grew strongly (until the pandemic hit).

For parenting commuters, perhaps locating childcare centres, kindergartens, and schools closer to rapid transit stations might make it easier to mix work and parenting responsibilities while travelling on public transport. But of course relocating such facilities is hardly trivial, and high land values around rapid transit stations would also be a challenge. It may however assist with getting higher public transport mode shares for school travel, which is a significant travel demand in peak periods.

I haven’t been able to explore the issue of public transport service quality and mode choice, but ensuring public transport has sufficient capacity and reliability would obviously be desirable, and I suspect might be particularly important to people on higher incomes and/or working longer hours who consider themselves time-poor.

Of course the absence of rapid transit is associated with higher private transport mode share in most cities. Public transport agencies might want to consider which parts of their cities are generating higher concentrations of private transport CBD commuters, and whether that might be related to public transport service quality. All cities will have areas remote from rapid transit stations, but only some of these areas will have higher concentrations of CBD employees.

My next post on this topic will look at the spatial distribution CBD commuter mode shares and private commuter volumes in the larger cities.

3 Comments | Australian Cities, Brisbane, Gender, Melbourne, Mode share, Mode shift, Parenting, Perth, Road Traffic, Sydney | Tagged: , | Permalink
Posted by chrisloader

How and why do travel patterns vary by gender and parenting status?

Mon 1 July, 2024

A lot of published transport analysis – including on this blog – has been gender-blind. Yet there are quite significant differences in travel patterns between men and women, and also between parents and non-parents. Advances in equality of opportunity have not eliminated these differences.

This post goes all-in with disaggregating a wide range of available data on transport behaviour on gender and parenting status in Melbourne, and explores some factors likely influencing these behaviours.

I will look at trip rates, trip chaining, time spent travelling, destination distance from home, distance travelled, travel to the central city, time of day, mode splits, use of different modes, trip purposes, and radial-ness of travel. I’ll also look at explanatory variables including main activity, occupation, employment industry, access to independent private mobility, and geographic distribution of home and work locations. Yeah that’s a lot, but don’t worry, there is a summary towards the end.

There’s also an interesting aside about dwelling bedroom counts around train stations.

This post is mostly focussed on working aged people (approximated by the age range 20-64), as children and seniors are likely to have different travel patterns again. And for the purposes of this analysis, I’m classifying people as “parents” or “parenting” if they live with their children – i.e. they are likely caring for their children (although some might have relatively independent adult children living with them). Parents whose adult children have all left home will be classified as other males/females.

About the data

I have access to very detailed household travel survey data for my home city of Melbourne for the pre-pandemic years 2012-2018, so that’s my primary source (officially VISTA – the Victorian Integrated Survey of Travel and Activity, get data here). It covers all types of non-commercial travel by residents, on all days of the year. Of course that data is pre-COVID and things will have changed somewhat since then but rich post-COVID data is not yet available.

I’m aggregating outputs to differentiate school weekdays, non-school weekdays, and weekends (I have excluded data for public holidays).

The VISTA data reports on binary gender, so unfortunately I can only cover males and females. That said, even if it did include more diverse gender categories, it would likely be very difficult to get statistically significant sample sizes for non-binary gender groupings.

There’s no special treatment required for same-sex parenting couples – they each count as mums or dads based on their reported gender.

Here’s how prevalent the different gender + parenting classifications are by age band in the weighted VISTA data for 2012-18:

The survey weightings don’t quite lead to a perfectly balance between genders across all age bands.

Parenting was most common amongst those aged 40-49 (almost three-quarters), and lower prevalence in younger and older age groups (under 8% for those aged 20-29).

Curiously there was a slight uptick in parents living with their children for ages 80+, which might be elderly parents living with – and being cared by – their adult children.

Across the approximate working aged population (20-64), parents accounted for 45% of the population.

In some sections I’ve also used ABS Census data from 2016 and 2021. This data is segmented slightly differently, with parenting being indicated by whether the person does unpaid work to care for their own children (so might exclude parents with relatively independent adult children living with them). Unless noted otherwise, it includes people aged 15+, and journey to work data only includes those who travelled to work and reported their travel modes.

Let’s get into it..

Trips per day

For this analysis a trip is travel between two places where a purposeful activity takes place, and may involve multiple trip legs (eg walk-bus-walk-train-walk).

Mums easily made the most number of trips on school weekdays, but dads made more trips on weekends than mums. Trip rates were higher on weekends for all person classifications except mums.

Trip chaining

I’ve heard much about women doing a lot more trip chaining – where a person leaves home and travels to one activity, then one or more other activities, before returning home. For example: home to school drop-off to work to school pickup to home.

As a simple measure of trip chaining, I’m counting the number of trips that don’t have an origin or destination at a place of accommodation (places of accommodation almost always being the survey home). I am aware of other definitions of trip chaining that only count where there is a short activity between trips but that would be require much more complex analysis.

As expected, mums were doing a lot of trip chaining on school weekdays, but curiously dads weren’t that far behind. And in the school holidays and on weekends dads were doing more train chaining than mums (perhaps to give mums a break?).

Trip chaining was much less common on weekends for all groups.

For mums the most common trip type not involving travel to or from home was between work and pick-up or drop-off of someone (most likely between a school and a workplace). A long way behind was travel between work and shopping, pick-up/drop-off someone and shopping, and between two pick-up/drop-off someone activities.

For dads the most common trip type not involving travel to/from home was between two work-related activities, closely followed by between work and pick-up / drop-off someone, and then between work and social activities.

So mums’ trip chaining was dominated by pick-ups and drop-offs of people, while dads’ was not.

Time spent travelling

There’s not a huge variation in median travel time per day between person groups, but dads had the highest on weekdays and mums generally had the lowest. Note that reported travel times were very often rounded to multiples of 5 minutes hence most of these medians are also multiples of 5.

Technical note: I have created a chart with average travel times and the numbers were higher but the shape of the chart was almost identical so I’m not including that here.

Travel distance from home

So were dads travelling further from home? I’ve calculated the straight distance between the home location and all travel destinations, and this chart shows the medians:

Dads sure did travel further from home on weekdays (particularly on school holidays when they might not be doing school drop-offs / pick-ups), with mums generally staying much closer to home.

Curiously, other males also travelled further from home than other females, so this pattern appears to be related to gender to some extent.

There was a lot less variation on weekends, with people generally travelling closer to home, as you might expect.

Daily distance travelled

Let’s broaden that out to median total distance travelled per day:

Dads generally travelled further on all day types, and mums the least. Everyone generally travelled less on weekends, and to some extent during school holidays (compared to school weekdays).

Travel distance to work

We can use ABS Census data to understand the on-road distance between home and workplaces, including for 2021. This data is for the working population aged 15+, and differentiates people based on whether they are caring for their own children (which is slightly different from living with their children).

The median distances to work were highest for dads at around 15.4 km for dads, followed by 11.9 km for mums, 11.7 km for other males, and 10.2 km for other females.

Travel to/from Central Melbourne

Public transport has its highest mode shares for travel to/from central Melbourne, so how did that vary by sex and parenting status? (for this analysis I’ve defined central Melbourne as the SA2s of Melbourne, Docklands, Southbank, and East Melbourne – on 2016 boundaries).

Before you get too excited about the differences, it’s worth pointing out all the proportions are small. The vast majority of people in Greater Melbourne don’t travel to central Melbourne on any given day. And of course people who lived in central Melbourne had many of their trips counted in this chart.

Sure enough, on weekdays dads were much more likely to travel to central Melbourne, and mums were least likely (although it was higher in the school holidays). On weekends, non-parents were much more likely to travel to the central city than parents (a fair bit of socialising by younger independent adults, no doubt).

Time of day of travel

The following chart shows the share of trip start times across the day for the different person types, and different day types:

Technical note: due to smaller sample sizes, weekend travel has been aggregated into 2-hour intervals. Weekdays have been aggregated into 1-hour intervals.

You can clearly see that on school weekdays, mums are doing a lot of travel between 8 and 9am, and between 3 and 4pm, which obviously relate to school start and finish times. In the school holidays, mums are doing a lot more travel through the interpeak period, probably reflecting parenting activities for kids not at school.

On school days, trips by dads started earlier and finished later than mums. But during school holidays dads made a smaller proportion of their trips between 8am and 9am, suggesting they also had a significant role in school drop offs in the morning.

During the weekday inter-peak period dads were less likely to travel than mums (except around lunchtime). Other females had a small peak in travel around 5-6pm, which is probably related to them being more likely to work full time.

On weekends it seems dads were slightly more likely to travel in the morning compared to mums who were slightly more likely to travel in the afternoon.

Did mum or dad take the kids to/from school?

We’re seeing some pretty strong themes related to the school peaks. It is possible to filter for trips to pick up or drop off someone from a place of education on school weekdays and then disaggregate between mums and dads. I’ve split this analysis into an AM peak, a PM school peak (2-4pm), and a PM commuter peak (4-6pm) – as there were significant numbers of pick ups later in the afternoon – presumably following after-school care.

Mums did the bulk of school drop offs and pick ups at all times of day, particularly in the PM school peak. In the PM commuter peak, dads share of pick ups rose to 35% – no doubt related to the ability to do these pick ups after a full-time day at work.

What types of adults are using modes at different times of day?

For this question I have limited analysis to school weekdays, aggregated all of public transport to one group, and aggregated vehicle drivers, passengers, and motorcyclists into “vehicle” to overcome issues with small sample sizes. I’ve included the proportion of the working aged population sample on the right-hand side for ready reference.

In general, parents were over-represented in vehicles in peak periods, mums were over-represented in the interpeak in vehicles, and parents were under-represented in public and active transport at most times of day.

The peak periods saw more public transport trips by dads than mums, while the roads (and footpaths) saw a lot more trips by mums than dads.

Early morning travel was predominately by males (76%), while females were slightly more prevalent in vehicles during the interpeak (60%). Reported walking trips skewed female at all times of day.

However if we look at travel time, rather trip counts, we get a slightly different picture:

Dads spent more time travelling than mums in peak periods on both public and private transport, but much less time than mums in the inter-peak.

Mode split

Here’s how it looks for travel in general:

Mums were the least likely to use public transport (especially on the weekend), closely followed by dads.

Non-parents had the lowest private transport mode share (although still a majority mode share), and were most likely to use active transport.

Here’s overall mode shares of journeys to work (Greater Melbourne 2016), which I’ve disaggregated for workplaces inside and outside the City of Melbourne area (as workplace location has a massive impact on mode shares):

Parents were much more likely to use private transport across the geographies and sexes. Of those working outside the City of Melbourne, parents also had about half the public transport mode share of non-parents.

Men were much more likely to cycle to work than women, and dads were more likely to cycle than other men.

Here is a look at private transport mode shares by distance between home and work, gender and parenting status:

The difference in private mode share between parents and non-parents was largest for journeys up to 10 km. Mums had the highest private mode share for journeys 1 to 20 kms. For journeys over 25 km, sex became more influential than parenting status with men more likely to use private transport.

Another curiosity here is the very short journeys (less than 0.5 km) where men were much more likely to use private transport than women (regardless of parenting status) – for what is probably a walkable distance for most people. Are men more lazy when it comes to short walks to work? And/or are men more likely to need their car at work?

I have previously also analysed public transport mode share by age and family position. I’ve reproduced that analysis here:

For ages 35 to 59, mums generally had lower public transport mode share than dads. Younger non-parenting women had higher public transport mode shares than younger non-parenting men.

Here’s how it looks for 2016 journeys to work (I’m not using 2021 data because of COVID lockdowns):

Female public transport mode share was signficantly higher than males for most ages – except for those typical parenting years between their late 30s to early 50s. Younger adults were much more likely to work in the inner city, and even more so for females. For more discussion on this, see Why are younger adults more likely to use public transport? (an exploration of mode shares by age – part 1)

I’ve also split this by sex and parenting status and analysed the changes between 2006 and 2016 (analysis lifted from: Why are young adults more likely to use public transport? (an exploration of mode shares by age – part 3))

Note there is a very different Y-axis scale for City of Melbourne and elsewhere.

There were a few really interesting take-aways:

  • Public transport (PT) mode shares increased over time for almost all age bands, work locations, and for parenting and non-parenting workers.
  • Parenting workers mostly had lower public transport mode shares than non-parenting workers of the same age, except for:
    • dads over 30 who worked in the City of Melbourne,
    • mums in their early 30s who worked in the City of Melbourne in 2016, and
    • mums and dads in their 50s who worked outside the City of Melbourne (who had low PT mode shares around 4-5%, similar to non-parenting workers of the same age)
  • Public transport mode shares for journeys to work in the City of Melbourne mostly declined with increasing age between 20 and 50, regardless of parenting responsibilities.
  • For people who worked outside the City of Melbourne, the mode share profile across age changed significantly over time for young adults. In 2006 there was a steady decline with age, but in 2011 PT mode shares were generally flat for those in their 20s, and in 2016 PT mode shares peaked for women in their late 20s (and also had a quite new pattern for dads in their 20s).
  • For parenting workers who worked outside the City of Melbourne there was actually a slightly higher PT mode share for those over the age of 50. Parents over 50 might have older children who are more independent and therefore less reliant on their parents for transport. This might make it easier for the parents to use public transport. However this trend did not hold for dads in 2016.
  • PT mode shares for non-parenting women increased slightly beyond age 55 for all work locations. This will include women who were never parents, as well mums with non-dependent children so might again reflect a small return to public transport once children become independent. It may also be influenced by discounted PT “Seniors” fares available to people over 60 who are not working 35+ hours per week.

Mode split of public transport use

Which modes of public transport were the different person classifications using in Melbourne? Sufficient survey sample is only available for school weekdays, and it’s important to keep in mind that trams dominate inner city radial on-street public transport in Melbourne (unlike most comparable cities where buses dominate this function). This chart adds up all trip legs so there is no data loss with multi-modal public trips:

Unfortunately this data doesn’t line up with reported public transport patronage for the same time period (below), suggesting that tram travel may be under-reported in VISTA (although the above chart is filtered for persons aged 20-64):

Biased as the VISTA data might be towards certain modes, it still suggests dads were more likely to be using trains and least likely to be using buses.

I’ve also looked at use of public transport in journeys to work for 2016. Workers can report up to three modes of travel, and I’ve extracted counts of workers who used each of the three main modes of public transport in Greater Melbourne (note: people who used multiple public transport modes will be counted in multiple columns).

Parents (who travelled to work) were much less likely use bus or tram to get to work than non parents. But the story is bit different for trains: Dads were slightly more likely to commute by train than other males, while mums were less likely to commute by train than other females. This might be related to where mums work – more on that soon.

Mode use by sex and parenting

We can flip the mode-split charts around to look at the composition of adult users of different travel modes:

Technical Note: there’s insufficient sample of tram, bus, and bicycle travel on non-school weekdays and weekends so those are not on the chart.

Trams, buses, private vehicles, and walking generally skewed female, while trains and particularly bicycles skewed male (except weekend trains).

Mums were under-represented on all modes except private vehicles where they were significantly over-represented. Mums were least represented on bicycles.

Dads were under-represented on trams and buses, and over-represented in vehicles, and on bicycles.

Non-parents were over-represented on trains and trams, and walking on weekends.

There were many more dads than mums on trains on weekdays, and many more mums than dads travelling in (private) vehicles on school weekdays (but not so much on weekends and school holidays).

Trip purposes

We want to know the purposes of people’s travel, but actually purpose can only really be attributed to the activity before and after a trip. For this analysis I’ve used the trip destination purpose as the trip purpose, and I’ve excluded trips where the destination was home (as that would be close to half of trips and not very interesting). Also keep in mind that trips can also vary considerably in length and duration.

On weekdays, significantly more trips by males were work-related. Mums had a standout different pattern on school weekdays with many more trips being about someone else’s travel (particularly school children) and much less often being work-related (or should we say “paid work”-related).

During school holidays, about 1 in 5 trips by mums were about other people’s travel. But on weekends dads were doing slightly more trips that are about other people’s travels (perhaps to make up for them doing less of such trips on weekdays?).

On weekends social and shopping trips were much more common than work trips, as you’d expect.

Radial-ness of travel

A while ago I looked at the radial-ness of travel – that is the difference in bearing (angle) between a trip aligned directly to/from the Melbourne CBD and the actual alignment of the trip. Trips generally skew towards being radial, reflecting the importance of the central city, and just generally the shape of the city. Previously I’ve disaggregated by age, sex, and many other variables.

So how does radial-ness vary across sex and parenting status?

On weekdays mums were the clear outlier, with substantially fewer radial trips and more non-radial trips, likely including many trips to/from schools and other caring destinations.

Weekend travel was a fair bit less radial in general, and again mums had the least radial travel of all person groups.

Okay so that’s a lot of ways we can compare travel patterns by sex and parenting (let me know if you think I’ve missed any other useful breakdowns). Now…

What can explain these differences?

A lot of the above data is probably unsurprising, because males and females, and particularly mums and dads, generally have different levels of workforce participation and caring responsibility, amongst other differences. What follows is an examination of potential explanatory variables for the different travel behaviour observed.

Main activity

First up, main activity as captured by VISTA:

Dads were most likely to be working full-time, and mums least likely to be working full-time. Mums were much more likely to be working part-time or “keeping house”.

As an aside: I actually find “keeping house” to be a bit devaluing of parents (usually mums) who dedicate much of their time doing the critically important work of raising children. And I know from personal experience it’s pretty hard to actually “keep house” when you have young children who need active engagement across most of their waking hours. No doubt others falling in the “keeping house” category might be caring for other adults or the elderly. Is it time for a caring-related category?

Curiously non-parenting females were much less likely to be working full time than non-parenting males. Perhaps non-parenting females were more likely to be doing some caring for others not living with them? Perhaps some mums decide to stay working part-time after their children move out? Or it might be something else?

We can break the analysis down further by age:

Technical note: Data isn’t presented for mums and dads aged 20-29 due to insufficient survey sample.

Curiously, dads were less likely to be working full-time with increasing age, while mums became slightly more likely to be working full-time at older ages (as children get older and require less supervision?).

Occupation (employment)

We call drill down further by looking at employment occupations:

Mums were much less likely to be in the workforce than dads, but curiously had almost the same proportion of professionals (perhaps reflecting women’s slightly higher levels of education, on average).

Men were more likely to work in occupations where public transport is probably less competitive, including technicians, trades workers, labourers, and machinery operators and drivers (with likely exceptions for central city work sites).

Employment Industry

There are also notable differences in employment industries by sex and parenting:

There are probably no great surprises in the above chart, with men much more likely to work in construction, information media and telecommunications, manufacturing, transport, postal, and warehousing, and women much more likely to work in education, training, health care, and social assistance.

Access to independent private mobility

Does the ability of people to drive themselves around in private vehicles differ by gender and parenting status? And could this explain their different travel patterns?

For this analysis, I’ve re-used the following household classifications from a previous post:

  • No MVs – no motor vehicles,
  • Limited MVs – fewer motor vehicles than licenced drivers, or
  • Saturated MVs – at least as many motor vehicles as licenced drivers.

I’ve also classified individuals as to whether or not they have a “solo” driving licence (i.e. probationary or full licence, but not learner’s permit).

I’ve then combined these two dimensions (except for people in households with no motor vehicles as driver’s licence ownership is largely immaterial for this analysis).

There were small differences between mums and dads, with mums slightly less likely to have a solo driver’s licence than dads (95% v 98%), mums slightly less likely to have independent private mobility (75.5% v 78.6%), and mums slightly more likely to live in a household without any motor vehicles (1.7% v 1.0%). These slight differences might suggest mums would have lower private transport mode shares than dads, but we’ve actually seen above that the opposite is true. Therefore access to independent private mobility is unlikely to explain much of the differences in travel between mums and dads.

There weren’t substantial differences between non-parenting men and women, other than non-parenting men having slightly high solo licence ownership (91% v 88%).

Parents were more likely to have a solo driver’s licence than non-parents, and over three-quarters lived in a household with saturated motor vehicle ownership. Access to independent private mobility aligns strongly with parents’ much higher private transport mode shares, and is probably considered essential for parents in most parts of Melbourne.

Indeed, we can also break this down by geography – using a simple inner/middle/outer disaggregation of Melbourne:

For all person categories there’s a strong relationship with distance from the city centre, with significantly lower levels of motor vehicle ownership in the inner areas. However solo licence ownership was very high for parents even in the inner suburbs (94% of mums and 98% of dads).

86% of dads and 87% of mums in outer Melbourne lived in households with saturation motor vehicle ownership. However, 5% of mums in the outer suburbs didn’t have a solo licence, which could make getting around quite challenging, and highlights the importance of quality public transport services in these areas.

Around 14% of non-parents in the inner suburbs lived in households without motor vehicles.

Where do parents tend to live?

It probably won’t surprise many readers to hear that parents made up a much larger share of the residential population in the outer suburbs, particularly urban growth areas:

But if you look closely, you’ll also see quite low proportions of parents along train lines, tram lines, and the public transport rich inner suburbs.

In fact, it’s possible to examine the type of households per dwelling by distance from train stations (I’m excluding areas within 3 km of the CBD).

Technical notes: I’ve calculated straight distance between SA1s centroids and their nearest train station points as per GTFS data in May 2024. The only significant change in train stations between August 2021 and May 2024 was the merger of Surrey Hills and Mont Albert into Union Station in 2023. So it’s not perfect analysis but I’m also not interested in precision below 1% resolution. I’ve also excluded unoccupied and non-private dwellings.

Dwellings close to train stations are significantly less likely to contain parents.

Is this because parents cannot afford family-friendly dwellings near train stations? Is it because dwellings near train stations are less family-friendly? Or is it because many parents like to build their own home on the urban fringe? Or some combination of these?

Well, the census tells us how many bedrooms there are in most occupied private dwellings, and the following chart shows the relationship between number of bedrooms and distance from train stations (again, excluding areas within 3 km of the CBD):

Sure enough, dwellings near train stations generally had fewer bedrooms.

And we can also use census data to show the relationship between number of bedrooms in a dwelling, and whether the household includes parents + children:

Over 90% of parenting households had three or more bedrooms, and half had four or more bedrooms. But almost half of all dwellings within 1 km of a train station had two or fewer bedrooms rendering them not very family-friendly.

Just to take it slightly further, I’ve put all three dimensions on one chart and this shows that dwellings close to stations with three or more bedrooms were slightly less likely to house parenting families:

I think the lower availability of family-friendly housing near rapid public transport is quite likely to be contributing to lower public transport mode shares for parents, particularly as there is a clear relationship between public transport use and proximity to rapid transit stations (see: Are Australian cities growing around their rapid transit networks?)

That said, there may also be an issue around whether many families can afford three-bedroom homes close to train stations as they often have less than two full-time incomes supporting three or more people. Might young professional couples with no kids and/or share houses of young professionals be better placed to compete for this housing?

Where do men and women work in Melbourne?

Could differences in journey to work mode splits be explained by differences in workplace location?

Here’s a map of gender balance by workplace location across Melbourne for 2021 at destination zone geography (DZs) (sorry not all outer suburbs included on the map as I didn’t want to lose the inner area detail). Blue areas skew male, orange areas skew female.

Anyone with knowledge of Melbourne’s urban geography will instantly see large industrial areas shaded blue, and plenty of orange in most other places.

These skews follow industries with male and female dominant workforces. In fact, I’ve manually done some rough grouping of destination zones where there is a clear dominant land uses (not exhaustive but results should be fairly indicative), and here is the sex breakdown by land use type:

Industrial areas and Melbourne Airport skewed heavily male, while hospitals and large shopping centres skewed female. Universities skewed female, and the CBD and surrounding areas slightly skewed male.

What about parenting? Something to keep in mind is that 43% of the working population were living with their children.

Parenting workers were seen more in the middle and outer suburbs, which is also where parents skewed as a home location, so there’s undoubtedly a relationship there.

Here’s the parenting breakdown by dominant land use classification:

Parents were under-represented in major shopping centres (I’m guessing a skew to younger employees), but also to a small extent universities and the central city. Parents were slightly over-represented in hospitals, Melbourne Airport, industrial areas, and the rest of Melbourne.

Another way to represent this data is looking at the distribution of workplace locations by distance from the Melbourne CBD:

Probably the biggest stand-out is that mums skewed towards suburban employment locations, while non-parenting females were more likely to be working closer to the city centre.

The distribution of workplace distance from the CBD for males only differed slightly between those parenting and non-parenting. Dads were less likely to be work between 2-10 km from the Melbourne CBD than non-parenting males.

Employment density

I’ve previously shown that private transport mode shares are generally much lower in areas with higher job density (likely due to higher car parking costs and increased public transport accessibility). So do mums/dads/others typically work in areas of lower or higher job density, and could this explain differences in their mode splits?

To answer this I’ve calculated an aggregate weighted job density of the areas in which each category of person tends to work. How does that work? Well to start with I’ve calculated the job density of every destination zone in Greater Melbourne. I’ve then calculated a weighted average of these densities, where the density of each destination zone is weighted by the number of dads/mums/other males/other females working in that zone.

For females, those non-parenting generally worked in more jobs dense areas, compared to mums. This probably partly explains the lower public transport mode shares of mums.

For males it was the reverse – dads generally worked in more jobs-dense locations.

Overall was only a tiny difference between men and women in aggregated weighted job density:

That was a lot of charts, can you summarise that?

The following table attempts to highlight key variations from the overall average for different types of adults:

Type of adultTravel patternsDestination patternsMode split Explanatory factors
ParentsMore trips per person on weekdays.
More trip chaining.		Higher private mode share.Live further from public transport.
Lack of family-friendly dwellings near public transport.
Live in outer suburbs.
Higher car ownership.
MumsMore travel during weekday interpeak.
Highest trip chaining.		Travel closer to home.
Work closer to home.
Less radial travel.
Least likely to work in CBD.		Very high private transport mode share.Do most school drop offs / pick ups.
Least likely to work full time.
Less likely to work in job-dense areas.
DadsTravel longer distances.
Travel further from home.
More time spent travelling.		Travel further from home.
Work further from home.
More likely to work in CBD.		More likely to use trains.
More likely to use bicycles.
Most likely to work full time.
More likely to work in job-dense areas.
Non-parenting womenTravel closer to home.
Work closer to home.		Higher public transport use.More likely to work in job-dense areas.
Most likely to work in central city.

The explanatory factors in the right hand column will not be independent. For example, many parents probably find it infeasible to live near public transport, so they live further away and are more car-dependent.

What does all this mean for transport planning interventions?

I won’t say a lot on this topic (I tend to avoid policy prescriptions on this blog) but I will say I think some caution is required here.

One perspective might be that the proportion of males and females travelling on a mode at a particular time of the week will not change, and therefore interventions might predominantly benefit the existing user base (eg higher inter-peak public transport service frequencies might benefit women more than men).

However another perspective might be that interventions remove the barriers for one gender to utilise a mode of transport and might have significant benefits for the minority gender in the current user base. For example, significantly safer cycling infrastructure might encourage more women to cycle and lead to a more even balance between genders – indeed I’ve uncovered evidence about that on this blog.

So many mums driving kids to school!

One thing that really stands out to me is that mums do the vast majority of school drop offs and pick ups, and most of this travel is (now) happening by private vehicle. This is potentially impacting women’s workforce participation, and the traffic volumes are certainly contributing to road congestion. It might also be impacting women’s mode choices as school trips are generally more difficult on public transport, and mums do a lot of trip chaining. They might be using private transport for some trips mostly because those trips are chained with school drop-off/pick-ups.

What could you do to reduce private transport trips for school drop off / pick ups, and potentially also increase women’s workforce participation and public transport mode share?

  • Make interventions that increase the share of school students who travel to/from school independently by active or public transport
  • For school trips that are accompanied by a parent, encourage a mode shift towards active transport (realistically, public transport is less likely to be an attractive mode for many accompanied trips to school, unless it is on the way to another destination)
  • Provide at-school before-school and after-school care to enable both parents the opportunity to work full time (indeed government subsidies are provided in Victoria at least)

How might things have have changed post-COVID?

Unfortunately at the time of writing rich data is only really available for pre-COVID times.

A major change post-COVID is that many white collar professionals are now working from home some days per week, which has reduced travel to major office precincts.

I would not be surprised to see dads taking a slightly higher share of the school drop-off pick-up task as this can be easier to do on a work-from-home day. Might this have enabled women to work longer hours? There have also been higher child-care subsidies implemented recently that might also lift women’s workforce participation.

Indeed here’s a chart summarising female labour force status since 2012 (not seasonally-adjusted):

Technical note: I would have preferred to use seasonally adjusted or trend series numbers to remove the noise, but these data sets do not include counts for “not in labour force”

Following the major COVID disruption period around 2020-2021, women have been more likely to be working full time and more likely to be in the labour force. This might be partly related to new working-from-home patterns.

Hopefully more post-COVID travel data will be released before too long and I can investigate if there are any substantial shifts in the patterns between men and women, parents and non-parents.

Do let me know if you think there is more that should explored regarding the differences in travel patterns and explanatory variables for men and women, parents and non-parents.

1 Comment | Car ownership, Cycling, drivers license, Employment density, Gender, Melbourne, Mode share, Parenting, Road Traffic | Tagged: , , | 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:

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: , , | Permalink
Posted by chrisloader

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

Sun 2 April, 2023

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!

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