Update on Australian transport trends (December 2022)

Sat 31 December, 2022

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

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

Driver’s licence ownership

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

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

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

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

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

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

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

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

Ages 25-29 were similar:

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

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

South Australia has less historical data published:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Here’s the same quarterly data for South Australia:

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

For completeness, here are motor cycle licence ownership rates:

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

Car ownership

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

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

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

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

Transport Emissions

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

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

Here are Australian transport emissions since 1975:

And in more detail since 1990:

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

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

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

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

Next up, emissions intensity (per vehicle kilometre):

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

Emissions per passenger-km can also be estimated:

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

Vehicle kilometres travelled

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

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

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

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

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

Vehicle kilometres per capita peaked around 2004-05 and were starting to flatline in some states before the pandemic hit with obvious impacts.

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

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

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

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

Passenger kilometres travelled

Here are passenger kilometres travelled overall (log scale):

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

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

Curiously aviation passenger kilometres per capita peaked in 2014, well before the pandemic. Rail passenger kilometres per capita in 2019 were at the highest level since 1975 before the pandemic hit. Only air travel has rebounded on a financial year basis.

Here’s total car passenger kilometres for capital cities:

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

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

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

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

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

Rail Passenger travel

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

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

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

Bus passenger kilometres have reduced significantly with the pandemic:

Including on a per-capita basis:

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

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

Mode split

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

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

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

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

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

Freight

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

Non-bulk freight volumes are around a quarter of total freight volume, and are arguably more contestable between modes:

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

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

Transport Costs

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

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

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

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

The above chart shows a weighted average of capital cities, which washes out patterns in individual cities. Here’s a breakdown of the change in real cost of private motoring and urban transport fares since 1973 by city (note different Y-axis scales):

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

Urban transport fares have grown the most in Brisbane, Perth, and Canberra – relative to 1973. However all cities have shown a drop in the real cost of urban transport fares in June 2022 – as discussed above.

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

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

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

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

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

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

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


Who worked at home before the pandemic?

Sun 18 September, 2022

Can we learn anything from pre-pandemic working-at-home patterns that will help us predict transport demand “after” the pandemic?

This post investigates work-at-home patterns from the ABS census 2016 for the six largest Australian cities, with some deeper dives for Melbourne and Sydney. I’ll answer questions such as: What occupations and industries were more likely to work-at-home? How did work-at-home rates vary by home and work locations? How many people had their home double as their workplace? Who was ‘remote working’ at home away from their regular workplace?

I’ve found the results quite interesting – and not quite what you might expect from the our current pandemic perspective.

What proportion of workers worked at home in 2016?

The following table shows between 3.3% and 5.2% of major city workers reported that they “worked at home” on census day in 2016 in Australia’s six largest cities:

This highest rate was in Brisbane, and the lowest in Canberra.

What occupations were more likely to work at home in 2016?

Here’s a chart showing 2016 journey to work mode shares across Australia’s six largest cities by main occupation category. Normally I exclude people working at home from mode share charts, but for this analysis I’m including “worked at home” as a “mode”:

Technical note: As usual on my blog, public includes all journeys involving a bus, tram, train and/or ferry trip, Active includes walk-only and cycle-only journeys, with all other journeys counted as Private.

You’ll notice the occupations with the highest rates of working at home were also the occupations with the highest public transport mode shares – professionals, clerical and administrative workers, and managers.

Here’s another view of that data, this time providing the occupation breakdown of commuters for each “mode”:

Again you can clearly see the same three occupation categories that dominated both working at home and public transport commuting.

No surprises there, right? These occupations generally spend a lot of time in offices either at computers or meeting with others – which can more easily be done online so are more likely to be amenable to working from home. The other occupation categories are more likely to necessitate working at a specific workplace.

But there are lots of different types of managers and professionals and they work in many different industries, so let’s dig a little deeper.

How did working at home vary by employment industry?

Here’s a look at the worked-at-home rates in 2016 by industry and occupation (highest level categories). I’ve sorted the occupations and industries such that the highest rates of worked-at-home are towards to the left and top of the table respectively.

The highest rates of working at home were found in Agriculture, Forestry and Fishing, Arts and Recreational Services, and Construction. Not exactly the sorts of jobs you would expect to fill multiple CBD office towers.

It’s also worth looking at the second level of occupation classifications:

Now we start to see working at home rates are very high for farm managers and arts and media professionals. For many of these people their workplace is quite likely to also be their home.

Many occupations that you might expect to be generally office-based had a working at home rate of around 9% – including HR, marketing, ICT, design, engineering, science and transport professionals.

How did working at home vary by home location?

Here’s a map showing working at home rates for SA2s across Greater Melbourne:

Working at home rates were highest in peri-urban areas, higher than the average in more advantaged suburbs of Melbourne, and the lowest rates of working at home were for employees from more disadvantaged areas.

Here’s the same for Sydney:

The highest rates were also seen in peri-urban areas of Greater Sydney, and the generally wealthy upper north shore.

How did working at home vary by workplace location?

Here’s a chart showing the worked at home share for the Melbourne and Geelong region, by workplace location.

The highest worked at home rate was 47.5% seen on French Island – a sparsely populated island south-east of Melbourne which contains many small farms and some tourist facilities. Other worked-at-home hotspots include the Point Cook East SA2 (which includes an air force base) and Panton Hill – St Andrews (which I understand contains many small farms). In fact, worked-at-home rates were again generally much higher in peri-urban areas and very low in suburban areas.

Some of the lowest rates of working at home were seen for employees in industrial areas and at Melbourne Airport. Many of these jobs are probably hard to do remotely.

2.0% of Melbourne CBD workers worked at home on census day in 2016. And the SA2s surrounding the CBD were all below 3%.

Here’s the same map for Sydney:

Again the highest rates of employees working at home were seen in peri-urban areas, and the Sydney CBD saw 1.9% of employees working at home.

These maps tell us that working at home in 2016 was most common in peri-urban areas, and relatively rare in dense employment areas such as CBDs. The COVID19 pandemic triggered significant levels of working at home during lockdown periods which emptied central city office towers and has remained quite common ever since. So it is likely that the profile of people working at home has changed significantly since 2016 to include a lot more white collar workers.

The fact that working at home rates were high in peri-urban areas when measured as both home location and work location suggests that for many people their home is their workplace. So…

How common was remote working in 2016?

You might have noticed that I’ve been referring to “worked at home” rather than the currently popular term “working from home”. That’s not just because its the wording used by the ABS in reporting the census, but because “working from home” is a little ambiguous as to whether people are working at home and away from their regular workplace, or whether their home is also their regular workplace. Perhaps a better term to describe people working at home and away of their regular workplace is “remote working”.

I have extracted worked-at-home workers’ home and work SA2 locations for people who lived and worked in Greater Melbourne and found that 89% of workers who worked at home, had their usual place of work in the same SA2 as where they lived (SA2s are roughly the size of a suburb).

So while 4.5% of workers who both lived and worked in Greater Melbourne worked at home in 2016, only 0.48% worked at home on census day when their regular workplace was in a different SA2. Remote working was an order of magnitude smaller than working at home.

Now it is also possible that some workers who lived and regularly worked in the same SA2 were actually working at home remote from their workplace on census day. However I expect this to be rare, and some further analysis (detailed in the appendix to the post) found that the almost every worker who worked and lived in the same SA2 had their home SA1 area intersect or overlap with their workplace Destination Zone (both the smallest census land areas available). This doesn’t guarantee that their home was their regular workplace, but it makes it quite probable. These workers would mostly not have had a very long commute, so there would be little incentive to remote work to avoid commuting effort. Also, I’ve found people who travelled to work in the same SA2 as they lived were slightly more likely to work in accommodation and food services, construction and retail trade – industries that are likely to require worksite attendance.

So I think I can fairly safely estimate the 2016 remote working rate in Greater Melbourne to have been 0.5%.

I’ve repeated this calculation for Australia’s six largest cities:

I’ve ordered the cities by working population, and you can see remote working rates decline across the chart for smaller cities. This might reflect there being a larger incentive to avoid longer and/or expensive commutes in larger cities by remote working.

Curiously Brisbane had the highest rate of workers whose home doubled as their workplace (4.9%), while the Australian Capital Territory (i.e. Canberra) had the lowest rates of both working at home and remote working.

I think these quite small estimated rates of remote working are an important finding, as several recent reports from the Productivity Commission, SGS Economics and Planning, Monash University, and iMove may have conflated working from home with working remotely at home, at least in their discussion of the topic. It’s critical that these metrics are not mixed up. And thankfully I’m not aware of any obvious miscalculations in their work.

How did rates of remote working vary across workplace locations in 2016?

The following maps exclude people who lived and worked in the same SA2, to get an estimate of remote working by workplace SA2:

The estimated remote working rate peaked for the Docklands SA2 at 1.8%, with Melbourne’s CBD at 1.7%, Southbank at 1.5%, and Albert Park at 1.4%. These are higher than the worked-at-home rates calculated above for all employees who regularly worked in the city centre, because they remove people who regularly work at their home in the central city.

There were also some seemingly random suburban locations with similar rates of remote working such as Forest Hill and Fawkner at 1.6%.

Here’s the equivalent map for Sydney:

There was a curious hot spot of West Pennant Hills at 5.5%, while the Sydney CBD area was 1.8%, North Sydney – Lavender Bay 2.3%, Macquarie Park – Marsfield 2.0%, and North Ryde – East Ryde 1.8%.

How did rates of remote working vary by home SA2?

Here’s a map estimating remote working rates by home location for Melbourne in 2016:

Generally higher rates were seen in peri-urban areas with Flinders at 2.0%, Mount Eliza at 1.4%, Gisborne at 1.2%, and Panton Hill – St Andrews at 1.6%. This may reflect “sea-changers” and “tree-changers” avoiding a long commute to work. The lowest rates were seen in the more disadvantaged areas of Melbourne, which probably reflects such employees being more likely to work in occupations that require attendance at their workplace.

And for Greater Sydney:

Higher rates of remote working were seen across the upper north shore, with Avalon – Palm Beach at 2.4%, and in many peri-urban areas. But the highest rate was seen at Blackheath – Megalong Valley (in the Blue Mountains) with 3.5%.

In what occupations and industries was remote working more common in 2016?

It’s stretching what you can do with ABS TableBuilder, but I’ve extracted counts of workers by home SA2, work SA2, industry main code, and whether the worker travelled to work or worked at home, for Greater Melbourne for 2016. I’ve then filtered for workers whose regular workplace is not in their home SA2. It’s a little problematic in that about one quarter of the non-zero records in this data were a value of 3, and ABS never reports counts of 1 or 2 as it uses randomisation to protect privacy for very small counts. So the totals are accumulating the impacts of lots of small random adjustments, but it’s not clear that this would introduce a bias to the overall estimate, but we should still treat these with caution and I’m not going to quote more than one decimal place. That said, the estimates do seem very plausible:

The industries with the highest estimated rates of remote working are mostly white collar jobs, whilst those industries with the lowest rates are more blue collar.

I did the same analysis for occupations, and again there are few surprises in the estimated rates of remote work across the categories:

What will the 2021 census tell us?

The 2021 census was conducted during a period of tight lockdowns in Victoria and New South Wales. Most other states had relatively few restrictions, but had experienced lockdowns in 2020, so were arguably in a “post pandemic” scenario – at least temporarily. So it will be very interesting to compare 2016 rates of remote working to those in different cities in 2021. For cities that were not in lockdown we will likely get a good sense of which occupations had high rates of (unforced) remote working, which will be very useful for modelling future rates of remote working and the ongoing impact on transport demand.

I expect the patterns across industries and occupations will be similar between 2016 and 2021, but with much higher rates of remote working in 2021.

The data will be released in October 2022 and I’ll be keen to calculate remote working estimates and share those on the blog.

There have also been several surveys that provide breakdowns of remote working by occupation and/or industry during the pandemic (Productivity Commission, iMove, University of Sydney ITLS).

Appendix: Did anyone live and work in the same SA2 but not have their workplace at their home?

To try to answer this I extracted data for “worked at home” cases in Greater Melbourne at the maximum available resolution – SA1 for home location and Destination Zone for workplaces, and determined whether their home SA1 intersected with their workplace Destination Zone. An intersection between these areas doesn’t guarantee the workplace is at their home, but the absence of an intersection does guarantee that the workplace is not at their home.

Here’s a map extracted from maps.abs.gov.au that shows 2016 destination zone boundaries in blue, and SA1 boundaries in red for part of the northern suburbs of Melbourne:

I dare suggest that if someone lived in an SA1 that intersected with their regular workplace Destination Zone, it’s pretty likely that they ordinarily worked at home.

This analysis is stretching the data, because when you extract small counts from ABS they apply random small adjustments to protect privacy and also you never see a count of 1 or 2 people. Problematic as it is, the sum of people living and working in the same SA2, but living in an SA1 that does not intersect the destination zone in which they work was just 95 for all of Greater Melbourne, out of around 70,000 people who lived and worked in the same SA2. This is a lower bound on the true number, but I expect the true number to still be very small. Hence I’m comfortable with an estimate of 0.5% remote working in Melbourne in 2016 (to one decimal place).

Another potential issue is that SA2s are not consistently sized across cities, and are generally smaller in Brisbane and Canberra. This means remote working from a nearby workplace would be more likely to be detected those cities. However I suspect these instances will still be tiny, and the estimated remote working rates in Brisbane and Canberra certainly don’t appear to be outliers.


What impact has the 2020 COVID-19 pandemic had on pedestrian volumes in central Melbourne?

Mon 25 May, 2020

[last updated 8 July 2020]

Consistent with reductions in vehicle and cycling traffic, central city pedestrian volumes measured by the the City of Melbourne also dropped considerably during the pandemic.

How much have volumes reduced? How has this varied by day types, locations, and times of day? Join me as I dive into the data.

The City of Melbourne have installed 64 pedestrian counters in and around the CBD. Here’s a map of the sites and some (arbitrary) groups (which I’ll use later):

The sensors are not evenly distributed over the city, with a bias towards the central retail core, so they are unlikely to be perfectly representative of central city pedestrian activity, but the data is available and is interesting.

Of course sensors fail from time to time, so we don’t have a complete time series for all sites for all days. There have also been many more sensors added over time. Here is a chart showing the sensors reporting for each day since counting began in 2009:

To get a reasonable comparison, the following chart aggregates data from 44 measuring sites that have complete or near-complete data for 2019 and 2020 (so far):

The gaps in the lines are due to public holidays, which have been excluded (I have not coded Easter Saturday as a public holiday).

You can see volumes drop significantly from around week 12 onward in 2020 (starts Sunday 15 March), as restrictions were introduced.

You can also see significant week to week variations in volumes in 2019, so when measuring the decline I’m going to compare volumes with those in the first two weeks of March (when universities had commenced on-campus teaching).

Here are daily volumes relative to the average of the first two weeks in March:

You can see volumes down over 80% by early April, followed by some small growth until late June when things went back into decline as the number of COVID-19 cases reports in Victoria rose.

The reductions were fairly consistent across all day types, until May when Saturday volumes grew faster. The variation between consecutive days in February reduced dramatically, suggesting perhaps there was a lot less discretionary pedestrian activity during the lock down.

During the first recovery phase there have been a few outliers:

  • Thursday 9 April was the day before Good Friday when most retail trading is restricted.
  • Wednesday 29 April was a very wet day (23.6 mm of rain)
  • Saturday 16 May was the first Saturday after restrictions were eased (also a fine sunny day of maximum 18 degrees).
  • Monday 1 June was cold and wet.

While the Sunday decline appears to be the largest, Sunday 8 March was during the Moomba festival on a long weekend, so there were many more people in the city than normal that night, inflating the baseline.

Likewise the first two Saturdays in March had quite different volumes, which may be related to special events as well. So I would suggest not getting carried away with the exact decline percentages.

How have volumes changed in different parts of the city?

Of course the pedestrian volume reductions have not been uniform by place or day of the week. Here are the reductions on weekdays for week 14 (29 March – 4 April), when overall volumes bottomed:

Volumes were down the most around Melbourne University, and reductions of around 85% were typical in the CBD grid. There were smaller reductions in Docklands (which might reflect many pedestrians being residents), and around Queen Victoria Market (one site only down 44%).

Here’s the same again for Saturday 4 April:

The largest reductions around the arts precinct in Southbank, the retail core of the CBD, and around Melbourne University. Lesser declines are again in Docklands and around Queen Victoria Market.

And here is Sunday 5 April:

Patterns are similar again.

What are the trends in different parts of the city?

The next chart looks at the volumes trends for my sensor groups over time for weekdays:

The relative decline was initially fairly consistent across the groups over the weeks, with the university sites showing the biggest declines, and the residential and retail sites showing the least decline. The retail precincts of Lygon Street, CBD central, and Melbourne Central (around the station) have shown the most growth since May, as cafe restrictions were listed.

The story is quite different on Saturdays:

There is historically a lot more week to week variation, and the numbers for Docklands have bounced around a fair bit – with 16 May close to normal levels of pedestrian activity (a dry day with maximum 18 degrees, lower days had rain). Saturday 23 May was a fairly wet day, so might have discouraged travel.

Queen Victoria Market has also shown considerable growth since early April – with volumes within the bounds of regular volumes.

The spike for CBD east on 7 June was due to the Black Lives Matter protest.

Saturday pedestrian volumes have declined from late June as concern rose about a second wave.

Sundays are similar:

Docklands, Queen Victoria Market and Melbourne Central all increased on Sundays during May (all with little or no rain).

The fact that the Southbank / River group has shown the largest decline is probably related to it having a high base – with the Moomba festival causing a spike in pedestrian volumes on 8 March.

How have volumes changed by time of day?

Here’s the profile of hourly volumes for sites with complete data for 2020 on weekdays:

You can see the normal AM peak, lunchtime peak, and PM peak, which have been largely flattened since the pandemic hit. Weekdays are now busiest in the afternoons – which is a profile much more like a normal weekend day (more on that in a moment).

If you follow the colours carefully you can see the rapid decline in late March, followed by slow growth, peaking in the week of 14 June, then declining again.

Here are hourly volumes relative to the first two weeks of March:

The biggest reductions have been in the AM peak and evenings, which reflects a reduction in commuters and hospitality activity. The reductions are slightly smaller mid-morning and mid-afternoon (between the regular peaks) reflecting a flattening of the profile.

The smallest percentage reductions have been at 4-5am in the morning, off a small base.

Here is Saturdays:

Reductions have again been largest in the evenings, just after midnight (Friday night), and least around dawn. You can see Saturday afternoons showing growth until mid June, but little growth in the evenings as restaurants, bars, and theatres remained closed.

Same again for Sundays:

Sunday 8 March is an outlier in the day and evening – with the Moomba festival on, and the following Monday being a public holiday.

Another way to visualise hourly data

Here’s a chart that shows pedestrian volumes for every hour of 2020 up to and including 7 July 2020. The rows are days, and the columns are hours of the day:

You can see how pedestrian activity very quickly became quiet in March. Before the shutdown you can also see the weekly patterns, with weekend activity starting later and finishing later.

The top row is New Years Day, and you can see high volumes in the first few hours from new year celebrations.

May 16th was the first Saturday after restrictions were eased and that shows up as the first spike in the recovery phase.

This can be filtered for locations. For example, here is the data for Queen Victoria Market sensors:

You can see clear stripes for days the market was open (including night markets). The first busy day after the shut down was the Thursday before Good Friday – perhaps people cramming shopping ahead of Good Friday (Easter Saturday was also busy). The market continued to trade throughout this time. Things started to quieten down in late June / early July.

I will try to periodically update this post during the recovery.

An aside: visualising activity over a long weekend

Nothing to do with the pandemic, but a bit of fun to finish. Here is an animation of pedestrian volumes over the Labour Day long weekend 6-9 March 2020 (Friday to Monday):

If you watch carefully you’ll spot some sudden surges from a Saturday evening event at Docklands Stadium.


What impact has the 2020 COVID-19 pandemic had on cycling patterns in Melbourne?

Sun 17 May, 2020

[fully revised 12 July 2020 with data up to 5 July 2020]

There has been talk about about a boom in cycling during the COVID-19 pandemic of 2020 (e.g. refer The Age), but has that happened across all parts the city, across lanes and paths, and on all days of the week?

About the data

In Melbourne there are bicycle counters on various popular bike paths and lanes around the city (mostly inner and middle suburbs), and so I thought it would be worth taking a look at the data. But I should stress that the patterns at these sites may or may not cycling patterns across Melbourne.

Here’s a map of the sites, including my classification of bike path sites into ‘recreational’ and ‘other’ (based on time of day demand profiles):

There’s also one bicycle counting site on Phillip Island, which I’m excluding this from this analysis as it is outside Melbourne.

But before plotting the data, it’s important to understand data quality. Since 2015 there have been 35 bicycle counting sites in Melbourne (most of them pairs counting travel in each direction). But for whatever reasons, data is not available at all sites for all days. Here is the daily number of sites reporting from January 2015 to 5 July 2020 (at least with data available as of 9 July 2020).

There are notable gaps in the data, including most of the latter part of November 2017, and around mid-2018.

So any year-on-year comparison needs to includes sites that were active in both years. For my first chart I’m going to filter for sites with complete data for 2019 (all) and 2020 (to 5 July). I’ve also filtered out a few sites with unusual data (very low counts for a period of time – possibly due to roadworks).

How do 2020 volumes compare to 2019?

Here is a chart showing average daily counts as a four week rolling average, dis-aggregated by whether the site was a bike lane (5 sites), recreational path (12 sites), or other path (9 sites) and whether the day was a regular weekday, or on a weekend/public holiday.

Weekday bike lane travel has reduced substantially, although in June the percentage reduction was less. This makes sense as most of these sites are on roads leading to the CBD, and most workers who normally work in the CBD have been working from home.

Volumes in bike lanes on weekends in 2020 have been very similar to 2019. This might reflect bike lanes not attracting additional recreational cyclists, or perhaps an increase in recreational cycling is offset by a decline in commuter cycling.

Volumes on recreational paths have been much higher than in 2019, most significantly on weekends. Again this makes sense, as people will be looking to exercise on weekends in place of other options no longer available (eg organised sports, gyms). In late June and early July, volumes did however reduce, perhaps as gyms reopened (22 June) and some sports resumed. This may change again during the second Melbourne lock down that commenced 9 July 2020.

Weekday traffic on paths not classified as recreational was down significantly in 2020. I’ll explore this more shortly.

How have volumes changed at different sites?

Here’s a look at the percentage change at each site on weekdays. I’m comparing weeks 14-19 of years 2020 and 2019 (33 sites have complete data for both periods). Weeks 14-19 mark the first four full weeks of the first full lock down.

You can see significant reductions near the CBD, and on major commuter routes (lanes and paths). The biggest reduction was 71% on Albert Street in East Melbourne.

The blue squares are mostly recreational paths where there has been massive growth, the highest being the Anniversary trail in Kew at +235%! However I should point out that these growth figures are often off very low 2019 counts. It may be that people working from home (or who have lost their jobs) are now going for recreational rides on weekdays.

You might notice one square with two numbers attached – the +27% is for the Main Yarra Trail (more recreational), and the -32% is for the Gardiners Creek rail (probably more commuter orientated at that point). The two counters are very close together so the symbols overlap.

Here is the same again, but with the changes in average daily counts:

Many of the high growth percentages were not huge increases in actual volumes. The bay-side trail experienced some of the bigger volume increases.

On weekends and public holidays, there were smaller percentage reductions near the city centre, and large increases in the suburbs:

The percentage increases on weekends are not as high because there was a higher base in 2019. The reductions in the central city are smaller, but still significant – this may reflect fewer CBD weekend workers with a downturn in retail activity.

Again, here is a map of the changes in volume on weekends:

How have volumes changed by distance from the CBD?

Here’s another way to view the data – sites by distance from the CBD:

Bike lane volumes are down significantly at most sites, particularly on weekdays. Bike path volumes are down on weekdays at most sites within 6 km of the CBD, but up at sites further out, and up at most sites on weekends.

How have volumes changed by time of day?

I’m curious about the volume changes on paths on weekdays, so I’ve drilled down to hourly figures. Here are the relative volumes per hour for the months April – June:

Bicycle volumes are down in weekday peak periods on all site types, which you might expect as a lot of peak period cycling trips are to/from the central city.

Daytime bicycle volumes are significantly higher on recreational paths, and to a lesser extent on other paths. For the warmer months of April and May there was a recreational peak around 4pm, while in June the profile was more of a single hump across the day.

So the changes in volumes on paths are certainly a mix of reduced peak traffic, and increased off-peak traffic. The middle of the day increase is perhaps people breaking up the day when working from home, or people who are no longer working.

On weekends bike lane volumes are slightly up in the middle of the day, but down in the morning and evening. There’s been a substantial increase in bike path volumes on weekends – suggesting people seeking recreational riding opportunities on the weekend are choosing the much more pleasant bike path environments.

What will happen post COVID-19?

Of course this data only tells us about what’s been happening during the first lock down and the gradual recovery thereafter (until just before the second lock down).

It will be interesting to see if there is an uptick in recreational cycling during the second lock down period, although it is in mid-winter.

Eventually there may well be a boom in cycling (particularly on bike lanes) when more people start returning to work – particularly in the central city – and look for alternatives to (what might be) crowded public transport.

I’ll try to keep an eye on the data over time and update this post periodically.