Update on Australian transport trends (December 2020)

Sun 3 January, 2021

Each year, just before Christmas, the good folks at the Australian Bureau of Infrastructure, Transport, and Regional Economics (BITRE) publish a mountain of data in their Australian Infrastructure Statistics Yearbook. This post aims to turn those numbers (and some other data sources) into useful knowledge – with a focus on vehicle kilometres travelled, passenger kilometres travelled, mode shares, car ownership, driver’s licence ownership, greenhouse gas emissions, and transport costs.

Of course the world of transport changed significantly in 2020, with suppressed movement from around mid March, as the COVID19 pandemic led to movement restrictions across Australia. Most of the following data is for financial years, so you will see some impacts where data is available for financial year 2019-20.

Vehicle kilometres travelled

Total vehicle kilometres travelled has been increasing most years, until 2019-20, when it fell from 264 to 247 billion kilometres.

Here’s the growth by vehicle type since 1971:

Light commercial vehicle kilometres have grown the fastest, curiously followed by buses (although much of that growth was in the 1980s). In 2019-2020, there were noticeable reductions for most vehicle types, except trucks.

Car kilometre growth has slowed significantly since 2004, and actually peaked in 2016-17 according to BITRE estimates.

On a per capita basis car use peaked in 2004, with a general decline since then. Here’s the Australian trend (in grey) as well as city level estimates until 2015 (from BITRE Information Sheet 74):

Technical note: “Australia” lines in these charts represent data points for the entire country (including areas outside capital cities).

Darwin has the lowest average which might reflect the small size of the city. The blip in 1975 is related to a significant population exodus after Cyclone Tracey caused significant destruction in late 1974 (the vehicle km estimate might be on the high side).

Canberra, the most car dependent capital city, has had the highest average car kilometres per person (but it might also reflect kilometres driven by people from across the NSW border in Queanbeyan).

The Australia-wide average is higher than most cities, with areas outside capital cities probably involving longer average car journeys and certainly a higher car mode share. There was a sharp drop in vehicle kms per capita in 2019-20, almost certainly due to COVID-19.

Passenger kilometres travelled

While BITRE’s passenger km estimates were available up to 2019-20 at the time of writing, city population estimates were only available up until June 2019. So in this section, per capita data stops at 2018-19 (before COVID-19), while total km charts go to 2019-20.

Here are passenger kilometres per capita for various modes for Australia as a whole (note the log-scale on the Y axis). Unfortunately BITRE have not published national estimates beyond 2017-18 in their 2020 Yearbook.

Air travel took off (pardon the pun) in the late 1980s (although with a lull in 1990 due to the pilot’s strike), car travel peaked in 2004, bus travel peaked in 1990 and has been relatively flat since, while rail has been increasing in recent years.

Car passengers

Here’s a chart showing total car passenger kms in each city:

The data shows that Melbourne overtook Sydney in 2016-17 as having the most car passenger travel, but then cities were even again in 2019-20 with COVID19 impacts.

Another interesting observation is that total car passenger travel declined in Adelaide in 2018-19 (pre-COVID) according to (revised) BITRE estimates.

However there are large differences in population growth rates between cities. So here is the data per capita:

While car passenger kilometres per capita peaked in 2004 in all cities, there were some increases from around 2013 to 2018 in some cities, but most cities declined in 2019 and 2020 (the latter being no doubt partly related to COVID-19). Darwin is an outlier with an increase in car passenger kms per capita between 2015 and 2020.

Rail passengers

Here are rail passenger kms per capita to 2018-19:

Sydney had the highest train use of all cities and this has been taking off in recent years, likely due to service level upgrades. Other cities have been flat or were in decline (such as Melbourne).

You can see two big jumps in Perth following the opening of the Joondalup line in 1992 and the Mandurah line in 2007. Melbourne, Brisbane and Perth have shown declines over recent years.

Here is growth in total rail passenger kms since 2010 (NOT per capita):

Sydney trains saw rapid growth in the years up until 2019, again probably reflecting significant service level upgrades to provide more stations with “turn up and go” frequencies at more times of the week.

Adelaide’s rail patronage dipped in 2012, but then rebounded following completion of the first round of electrification in 2014.

All cities saw significant declines in 2019-20 with COVID-19 impacts, although BITRE caveats that the 2019-20 estimates for public transport modes were “rough” at the time of publication.

Bus passengers

Here’s bus passenger kms per capita up to 2018/19:

Bus passenger kms per capita have been declining in most cities in recent years, with the exception of Sydney.

Significant investments in bus services in Melbourne and Brisbane between around 2005 and 2012 led to significant patronage growth.

Melbourne has the lowest bus use of all the cities, but this likely reflects the extensive train and tram networks carrying the bulk of the public transport passenger task. Melbourne is different to every other Australian city in that trams provide most of the on-road public transport access to the CBD (with buses performing most of this function in other cities).

Darwin saw a massive increase in bus use in 2014 thanks to a new nearby LNG project running staff services.

Australia-wide bus usage is surprisingly high. While public transport bus service levels and patronage would certainly be on average low outside capital cities, buses do play a large role in carrying children to school – particularly over longer distances in rural areas. The peak for bus usage in 1990 may be related to deregulation of domestic aviation, which reduced air fares by around 20%.

Here is growth relative to 2010:

All cities saw a substantial reduction in 2019-20 due to COVID-19, with Hobart having the smallest reduction. Perhaps there is less discretionary and office-commuter travel on Hobart’s buses?

Light rail passengers

Light rail passenger kms per capita is not really meaningful as Melbourne has a large network, while Sydney and Adelaide have very small (although growing) networks. Here is estimated passenger km growth since 2010:

Sydney light rail patronage increased following the Dulwich Hill extension that opened in 2014, and again with the new lines joining the CBD with Randwick and Kingsford opening in 2019-20. The passenger km growth would have been higher if not for COVID-19.

Adelaide patronage increased following an extension to the Adelaide Entertainment Centre in 2010, and then flatlined for several years. In October 2018, new extensions to Festival Plaza and Botanic Gardens opened but passenger kms actually declined in FY 2018-19.

Mass transport

We can sum all of the mass transport modes (I use the term “mass transport” as the numbers include both public and private bus services). Firstly. here is mass transit share of estimated total motorised passenger kilometres in each city (unfortunately there are no estimates of walking and cycling kilometres):

All cities saw a mode shift away from mass transit in 2019-20 due to COVID-19, which likely reflects the shift to working from home for CBD workers (with such commuter trips making up a substantial share of PT patronage). During the recovery after lockdowns, road traffic has returned to almost normal in most cities, whilst public transport patronage is still well down on pre-COVID19 levels. I should mention again that BITRE describe their 2019-20 estimates of non-private passenger kilometres as “rough”.

But looking at trends prior to 2019-20, Sydney was leading the country in mass transport use per capita which was also rising fast to 2019, with a 2% mode shift between 2016 and 2019 (mostly attributable to trains). The Sydney north west Metro line opened in May 2019, so would only have a small impact on these figures.

Melbourne mass transit had been losing mode share between 2012 and 2019, while other cities have been largely flat or trending down (although Brisbane, Adelaide, and Perth has a small increase in 2018-19).

Melbourne made significant gains between 2005 and 2009, and Perth grew strongly 2007 to 2013, but has since shifted away from public transport (which may be related to a decentralisation of employment).

Here is growth in mass transport passenger kms since 2010:

Darwin saw substantial growth associated with staff bus services to a new LNG plant, while Sydney otherwise was leading in mass transit passenger kilometre growth.

Here’s how car and mass transit passenger kilometres have grown since car used peaked in 2004:

Mass transit use has grown much faster than car use in Australia’s three largest cities. In Sydney and Melbourne it has exceeded population growth, while in Brisbane it is more recently tracking with population growth.

Mass transit has also outpaced car growth in Perth, Adelaide, and Hobart:

In Canberra, both car and mass transit use has grown much slower than population, and it is the only city where car growth has exceeded public transport growth.

Motorcycles

Here are motorcycle passenger kms per capita:

Motorcycle travel was declining per capita until 2000, had a resurgence between 2004 and 2009 (perhaps as fuel prices rose?) and has since reduced somewhat in most cities. I’m not quite sure what might have happened in Melbourne in 2006 to suddenly stop the growth in use. I also wonder about the precision of estimates of motorcycle passenger kilometres, given it is such a small mode.

Car ownership

The ABS conduct a Motor Vehicle Census generally once per year (although less often historically), and the following chart includes that data up until January 2020, combined with population estimates released in December 2020.

Car ownership has risen significantly over time, although this growth has slowed considerably more recently in some states.

However the above measure doesn’t take into account people not of driving age. So the following chart looks at passenger cars per persons aged 18-84 (for want of a better definition of driving aged persons):

It’s still a bit hard to see the more recent trends, so here is a chart that looks at 2000-2020, excludes the Northern Territory (zooming on the top-right section):

This data shows that car ownership peaked in Victoria in 2013, Western Australia in 2017, New South Wales in 2017, Queensland in 2018, South Australian in 2018, and Australia overall in 2018. The Australian Capital Territory may have peaked in 2019 but perhaps it is a little too early to call, while Tasmanian now has the highest car ownership in the country and is still growing strongly.

My previous analysis of census data found that Melbourne was the only large city to see a decline in car ownership between 2011 and 2016 (see also an older post on car ownership).

Motorcycle ownership

Here is a chart showing motorcycles per persons aged 18-84:

This chart shows a slightly different pattern to that of motorcycle passenger kilometres per capita in cities (above). Ownership and usage bottomed out around the 1990s or 2000s (depending on the state/city). However ownership has risen in most states since then, but usage apparently peaked around 2009 in most cities. This perhaps suggests motorcycles are now more a recreational – rather than everyday – vehicle (I really don’t follow the motorcycle industry very closely so others might better explain this).

Driver’s licence ownership

Thanks to BITRE Information Sheet 84, the BITRE Yearbook 2020, and some useful state government websites (NSW, SA, Qld), here is motor vehicle licence ownership per 100 persons (of any age) from June 1971 to June 2019 or 2020 (depending on data availability):

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.

There’s been slowing growth over time, but Victoria has actually seen slow decline since 2011, and the ACT peaked in 2014.

Here’s a breakdown by age bands for Australia as a whole:

Licencing rates have been increasing over time for those aged over 40 (most strongly for those aged over 70), and have been declining for those aged under 40, although there was a notable uptick in licence ownership for 16-19 year-olds in 2018.

The next chart shows licencing rates for teenagers:

Licence ownership rates for teenagers had been trending down in South Australia and Victoria until 2017, while most other states have been trending upwards in recent years. The differences between states partly reflects different minimum ages for licensing.

Here are 20-24 year olds:

The largest states of Victoria and New South Wales had seen downwards trends until 2019, while all other states and territories are trending up. The big upticks in 2020 for Queensland and NSW might be a new trend, might also be impacted by the preliminary nature of June 2020 population estimates from the ABS, and/or might be impacted be an exodus of international students.

25-29 year olds are a mixed bag – Victoria has been trending downwards sharply, New South Wales has (probably) just ended a downwards trend, while most other states have been increasing or relatively steady.

Licencing rates for people in their 70s have been rising in all states, although it may be slowing in Western Australia and NSW more recently (I have excluded 2016 for South Australia as I suspect a data error):

A similar trend is clear for people aged 80+ (Victoria was an anomaly before 2015):

See also an older post on driver’s licence ownership for more detailed analysis.

For completeness, here is a chart showing motorcycle full licence ownership rates:

Queensland has two types of motorcycle licence and I suspect many people hold both, which might explain a licence ownership rate being so much higher than other states.

Transport greenhouse gas emissions

According to the latest adjusted quarterly figures, Australia’s domestic non-electric transport emissions peaked in 2018, had been slightly declining (which reflects reduced consumption of petrol and diesel) before COVID impacted the year-ending June 2020 figure.

The seasonally-adjusted estimate for the June quarter of 2020 is 19.2 Mt, which is down 24% on the June quarter of 2019:

Non-electric transport emissions made up 19.1% of Australia’s total emissions as at December 2019 (before the COVID-19 impact).

Here’s a breakdown of transport emissions by financial year:

A more detailed breakdown of road transport emissions is available, but only back to 1990:

Here’s growth in transport sector emissions since 1975:

The 2019-2020 estimates are heavily impacted by COVID-19, most evidently in aviation, but also for road transport.

Road emissions had grown steadily to 2019, while aviation emissions took off around 1991 (pardon the pun). You can see that 1990 was a lull in aviation emissions, probably due to the pilots strike around that time.

In more recent years non-electric rail emissions have grown strongly. This will include a mix of freight transport and diesel passenger rail services – the most significant of which will be V/Line in Victoria, which have grown strongly in recent years (140% scheduled service kms growth between 2005 and 2019). Adelaide’s metropolitan passenger train network has historically run on diesel, but has more recently been transitioning to electric.

Here is the growth in each sector since 1990 (including a breakdown of road emissions):

Within road transport, COVID-19 has had the biggest impact on cars, buses, motor cycles and light commercials. However, emissions from (larger) articulated trucks continued to grow.

Here are average emissions per capita for various transport modes in Australia, noting that I have used a log-scale on the Y-axis:

Per capita emissions have been decreasing for cars, and – until 2019 – were relatively stable for most other modes. Total road transport emissions per capita peaked in 2004 (along with vehicle kms per capita, as above).

Transport greenhouse gas emissions intensity

It’s possible to combine data sets to estimate average emissions per vehicle kilometre for different vehicle types, but only until 2018 with published data (note I have again used a log-scale on the Y-axis):

Note: I suspect the kinks for buses and trucks in 2015 are issues to do with estimation assumptions made by BITRE, rather than actual changes.

Most modes have shown slight declines in emissions per vehicle km, except trucks. On these estimates, car have dropped from 281 g/km in 1990 to 243 g/km in 2018.

However, the above figures don’t take into account the average passenger occupancy of vehicles. To get around that we can calculate average emissions per passenger kilometre for the passenger-orientated modes (data only available until 2018 unfortunately):

Domestic aviation estimates go back to 1975, and you can see a dramatic decline between then and around 2004 – followed little change (even a rise in recent years). However I should mention that some of the domestic aviation emissions will be freight related, so the per passenger estimates might be a little high.

Car emissions per passenger km in 2017-18 were 154.5g/pkm, while bus was 79.0g/pkm and aviation 127.2g/pkm.

Of course the emissions per passenger kilometres of a bus or plane will depend on occupancy – a full aeroplane or bus will have likely have significantly lower emissions per passenger km. Indeed, the BITRE figures imply an average bus occupancy of around 9 people (typical bus capacity is around 70) – so a well loaded bus should have much lower emissions per passenger km. The operating environment (city v country) might also impact car and bus emissions. On the aviation side, BITRE report a domestic aviation average load factor of 79.3% in 2019-20.

Cost of transport

The final topic for this post is the real cost of transport. Here are headline real costs (relative to CPI) for Australia:

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.

The cost of private motoring has tracked relatively close to CPI, although it seems to be trending down since 2008, probably largely related to reductions in the price of automotive fuel (which peaked in 2008). The real cost of motor vehicles has plummeted since 1996, although that trend may have stopped in 2018. Urban transport fares have been increasing faster than CPI since the late 1970s, although they have grown slower than CPI (on aggregate) since 2013.

Here’s a breakdown of the real cost of private motoring and urban transport fares by city (note different Y-axis scales):

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 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.

I hope you have found this interesting.


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

Mon 7 December, 2020

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

About immigrant data

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

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

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

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

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

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

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

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

Immigrants and public transport mode share of journeys work

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

How did immigrants shift around Melbourne over time?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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


Update on Australian transport trends (December 2019)

Mon 30 December, 2019

Each year, just in time for Christmas, the good folks at the Australian Bureau of Infrastructure, Transport, and Regional Economics (BITRE) publish a mountain of data in their Yearbook. This post aims to turn those numbers (and some other data sources) into useful knowledge – with a focus on vehicle kilometres travelled, passenger kilometres travelled, mode shares, car ownership, driver’s licence ownership, greenhouse gas emissions, and transport costs.

There are some interesting new patterns emerging – read on.

Vehicle kilometres travelled

According to the latest data, road transport volumes actually fell in 2018-19:

Here’s the growth by vehicle type since 1971:

Light commercial vehicle kilometres have grown the fastest, curiously followed by buses (although much of that growth was in the 1980s).

Car kilometre growth has slowed significantly since 2004, and actually went down in 2018-19 according to BITRE estimates (enough to result in a reduction in total vehicle kilometres travelled).

On a per capita basis car use peaked in 2004, with a general decline since then. Here’s the Australian trend (in grey) as well as city level estimates to 2015 (from BITRE Information Sheet 74):

Technical note: “Australia” lines in these charts represent data points for the entire country (including areas outside capital cities).

Darwin has the lowest average which might reflect the small size of the city. The blip in 1975 is related to a significant population exodus after Cyclone Tracey caused significant destruction in late 1974 (the vehicle km estimate might be on the high side).

Canberra, the most car dependent capital city, has had the highest average car kilometres per person (but it might also reflect kilometres driven by people from across the NSW border in Queanbeyan).

The Australia-wide average is higher than most cities, with areas outside capital cities probably involving longer average car journeys and certainly a higher car mode share.

Passenger kilometres travelled

Overall, here are passenger kms per capital for various modes for Australia as a whole (note the log-scale on the Y axis):

Air travel took off (pardon the pun) in the late 1980s (with a lull in 1990), car travel peaked in 2004, bus travel peaked in 1990 and has been relatively flat since, while rail has been increasing in recent years.

It’s possible to look at car passenger kilometres per capita, which takes into account car occupancy – and also includes more recent estimates up until 2018/19.

Here’s a chart showing total car passenger kms in each city:

The data shows that Melbourne has now overtaken Sydney as having the most car travel in total.

Another interesting observation is that total car travel declined in Perth, Adelaide, and Sydney in 2018-19. The Sydney result may reflect a mode shift to public transport (more on that shortly), while Perth might be impacted by economic downturn.

While car passenger kilometres per capita peaked in 2004, there were some increases until 2018 in some cities, but most cities declined in 2019. Darwin is looking like an outlier with an increase between 2015 and 2018.

BITRE also produce estimates of passenger kilometres for other modes (data available up to 2017-18 at the time of writing).

Back to cities, here is growth in rail passenger kms since 2010:

Sydney trains have seen rapid growth in the last few years, probably reflecting significant service level upgrades to provide more stations with “turn up and go” frequencies at more times of the week.

Adelaide’s rail patronage dipped in 2012, but then rebounded following completion of the first round of electrification in 2014.

Here’s a longer-term series looking at per-capita train use:

Sydney has the highest train use of all cities. You can see two big jumps in Perth following the opening of the Joondalup line in 1992 and the Mandurah line in 2007. Melbourne, Brisbane and Perth have shown declines over recent years.

Here is recent growth in (public and private) bus use:

Darwin saw a massive increase in bus use in 2014 thanks to a new nearby LNG project running staff services.

In more recent years Sydney, Canberra, and Hobart are showing rapid growth in bus patronage.

Here’s bus passenger kms per capita:

Investments in increased bus services in Melbourne and Brisbane between around 2005 and 2012 led to significant patronage growth.

Bus passenger kms per capita have been declining in most cities in recent years.

Australia-wide bus usage is surprisingly high. While public transport bus service levels and patronage would certainly be on average low outside capital cities, buses do play a large role in carrying children to school – particularly over longer distances in rural areas. The peak for bus usage in 1990 may be related to deregulation of domestic aviation, which reduced air fares by around 20%.

Melbourne has the lowest bus use of all the cities, but this likely reflects the extensive train and tram networks carrying the bulk of the public transport passenger task. Melbourne is different to every other Australian city in that trams provide most of the on-road public transport access to the CBD (with buses performing most of this function in other cities).

For completeness, here’s growth in light rail patronage:

Sydney light rail patronage increased following the Dulwich Hill extension that opened in 2014, while Adelaide patronage increased following an extension to the Adelaide Entertainment Centre in 2010.

We can sum all of the mass transit modes (I use the term “mass transit” to account for both public and private bus services):

Sydney is leading the country in mass transport use per capita and is growing strongly, while Melbourne, Brisbane, Perth have declined in recent years.

Mass transit mode share

We can also calculate mass transit mode share of motorised passenger kilometres (walking and cycling kilometres are unfortunately not estimated by BITRE):

Sydney has maintained the highest mass transit mode share, and in recent years has grown rapidly with a 3% mode shift in the three years 2016 to 2019, mostly attributable to trains. The Sydney north west Metro line opened in May 2019, so would only have a small impact on these figures.

Melbourne made significant gains between 2005 and 2009, and Perth also grew strongly 2007 to 2013.

Here’s how car and mass transit passenger kilometres have grown since car used peaked in 2004:

Mass transit use has grown much faster than car use in Australia’s three largest cities. In Sydney and Melbourne it has exceeded population growth, while in Brisbane it is more recently tracking with population growth.

Mass transit has also outpaced car use in Perth, Adelaide, and Hobart:

In Canberra, both car and mass transit use has grown much slower than population, and it is the only city where car growth has exceeded public transport growth.

Car ownership

The ABS regularly conduct a Motor Vehicle Census, and the following chart includes data up until January 2019.

Technical note: Motor Vehicle Census data (currently conducted in January each year, but previously conducted in March or October) has been interpolated to produce June estimates for each year, with the latest estimate being for June 2018.

In 2017-18 car ownership declined slightly in New South Wales, Victoria, and Western Australia, but there was a significant increase in the Northern Territory. Tasmania has just overtaken South Australia as the state with the highest car ownership at 63.1 cars per 100 residents.

Victorian car ownership has been in decline since 2011, which is consistent with a finding of declining motor vehicle ownership in Melbourne from census data (see also an older post on car ownership).

Driver’s licence ownership

Thanks to BITRE Information Sheet 84, the BITRE Yearbook 2019, and some useful state government websites (NSW, SA, Qld), here is motor vehicle licence ownership per 100 persons (of any age) from June 1971 to June 2018 or 2019 (depending on data availability):

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.

There’s been slowing growth over time, but Victoria has seen slow decline since 2011, and the ACT peaked in 2014.

Here’s a breakdown by age bands for Australia as a whole (note each chart has a different Y-axis scale):

There was a notable uptick in licence ownership for 16-19 year-olds in 2018. Otherwise licencing rates have increased for those over 40, and declined for those aged 20-39.

Licencing rates for teenagers (refer next chart) had been trending down in South Australia and Victoria until 2017, but all states saw an increase in 2018 (particularly Western Australia). The most recent 2019 data from NSW and Queensland shows a decline. The differences between states partly reflects different minimum ages for licensing.

The trends are mixed for 20-24 year-olds: the largest states of Victoria and New South Wales have seen continuing declines in licence ownership, but all other states and territories are up (except Queensland in 2019).

New South Wales, Victoria, and – more recently – Queensland are seeing downward trends in the 25-29 age bracket:

Licencing rates for people in their 70s are rising in all states (I suspect a data error for South Australia in 2016):

A similar trend is clear for people aged 80+ (Victoria was an anomaly before 2015):

See also an older post on driver’s licence ownership for more detailed analysis.

Transport greenhouse gas emissions

[this emissions section updated on 8 January 2020 with BITRE estimates for 1975-2019]

According to the latest adjusted quarterly figures, Australia’s domestic non-electric transport emissions peaked in 2018 and have been slightly declining in 2019, which reflects reduced consumption of petrol and diesel. However it is too early to know whether this is another temporary peak or long-term peak.

Non-electric transport emissions made up 18.8% of Australia’s total emissions as at September 2019.

Here’s a breakdown of transport emissions:

A more detailed breakdown of road transport emissions is available back to 1990:

Here’s growth in transport sectors since 1975:

Road emissions have grown steadily, while aviation emissions took off around 1991. You can see that 1990 was a lull in aviation emissions, probably due to the pilots strike around that time.

In more recent years non-electric rail emissions have grown strongly. This will include a mix of freight transport and diesel passenger rail services – the most significant of which will be V/Line in Victoria, which have grown strongly in recent years (140% scheduled service kms growth between 2005 and 2019). Adelaide’s metropolitan passenger train network has run on diesel, but more recently has been transitioning to electric.

Here is the growth in each sector since 1990 (including a breakdown of road emissions):

Here are average emissions per capita for various transport modes in Australia, noting that I have used a log-scale on the Y-axis:

Per capita emissions are increasing for most modes, except cars. Total road transport emissions per capita peaked in 2004 (along with vehicle kms per capita, as above).

It’s possible to combine data sets to estimate average emissions per vehicle kilometre for different vehicle types (note I have again used a log-scale on the Y-axis):

Note: I suspect the kinks for buses and trucks in 2015, and motor cycles in 2011 are issues to do with assumptions made by BITRE, rather than actual changes.

The only mode showing significant change is cars – which have reduced from 281 g/km in 1990 to 243 g/km in 2019.

However, the above figures don’t take into account the average passenger occupancy of vehicles. To get around that we can calculate average emissions per passenger kilometre for the passenger-orientated modes:

Domestic aviation estimates go back to 1975, and you can see a dramatic decline between then and around 2004 – followed little change (even a rise in recent years). However I should mention that some of the domestic aviation emissions will be freight related, so the per passenger estimates might be a little high.

Car emissions per passenger km in 2018-19 were 154.5g/pkm, while bus was 79.4g/pkm and aviation 127.2g/pkm.

Of course the emissions per passenger kilometres of a bus or plane will depend on occupancy – a full aeroplane or bus will have likely have significantly lower emissions per passenger km. Indeed, the BITRE figures imply an average bus occupancy of around 9 people (typical bus capacity is around 60) – so a well loaded bus should have much lower emissions per passenger km. The operating environment (city v country) might also impact car and bus emissions. On the aviation side, BITRE report a domestic aviation average load factor of 78% in 2016-17.

Cost of transport

The final topic for this post is the real cost of transport. Here are headline real costs (relative to CPI) for Australia:

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.

The cost of private motoring has tracked relatively close to CPI, although it trended down between 2008 and 2016. The real cost of motor vehicles has plummeted since 1996. Urban transport fares have been increasing faster than CPI since the late 1970s, although they have grown slower than CPI (on aggregate) since 2013.

Here’s a breakdown of the real cost of private motoring and urban transport fares by city (note different Y-axis scales):

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 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.

Hopefully this post has provided some useful insights into transport trends in Australia.


What impact does paid car parking have on travel mode choice in Melbourne?

Thu 3 October, 2019

Paid parking is often used when too many people want to park their car in the same place at the same time. Does it encourage people to cycle or use public transport instead of driving? Does that depend on the type of destination and/or availability of public transport? Are places with paid parking good targets for public transport upgrades?

In this post I’m going to try to answer the above questions. I’ll look at where there is paid parking in Melbourne, how transport mode shares vary for destinations across the city, and then the relationship between the two. I’ll take a deeper look at different destination types (particularly hospitals), explore the link between paid parking and employment density, and conclude with some implications for public transport planners. There’s a bit to get through so get comfortable.

This post uses data from around 158,000 surveyed trips around Greater Melbourne collected as part of a household travel survey (VISTA) between 2012 and 2018, as well as journey to work data from the 2016 ABS census.

Unfortunately the data available doesn’t allow for perfect analysis. The VISTA’s survey sample sizes are not large, I don’t have data about how much was paid for parking, nor whether other parking restrictions might impact mode choice (e.g. time limits), and I suspect some people interpreted survey questions differently. But I think there are still some fairly clear insights from the data.

Where is there paid parking in Melbourne?

I’m not aware of an available comprehensive car park pricing data set for Melbourne. Parkopedia tells you about formal car parks (not on street options) and doesn’t share data sets for free, while the City of Melbourne provides data on the location, fees, and time restrictions of on-street bays (only). So I’ve created my own – using the VISTA household travel survey.

For every surveyed trip involving parking a car, van, or truck, we know whether a parking fee was payable. However the challenge is that VISTA is a survey, so the trip volumes are small for any particular place. For my analysis I’ve used groups of ABS Destination Zones (2016 boundaries) that together have at least 40 parking trips (excluding trips where the purpose was “go home” as residential parking is unlikely to involve a parking fee). I’ve chosen 40 as a compromise between not wanting to have too small a sample, and not wanting to have to aggregate too many destination zones. In some cases a single destination zone has enough parking trips, but in most cases I have had to create groups.

I’ve tried to avoid merging different land uses where possible, and for some parts of Melbourne there are just not enough surveyed parking trips in an area (see appendix at the end of this post for more details). Whether I combine zones or use a single zone, I’m calling these “DZ groups” for short.

For each DZ group I’ve calculated the percentage of vehicle parking trips surveyed that involved someone paying a parking fee. The value will be low if only some circumstances require parking payment (eg all-day parking on weekdays), and higher if most people need to pay at most times of the week for both short and long stays (but curiously never 100%). The sample for each DZ group will be a small random sample of trips from different times of week, survey years, and durations. For DZ groups with paid parking rates above 20%, the margin of error for paid parking percentage is typically up to +/- 13% (at a 90% confidence interval).

Imperfect as the measure is, the following map shows DZ groups with at least 10% paid parking, along with my land use categorisations (where a DZ group has a specialised land use).

There are high percentages of paid parking in the central city, as you’d expect. Paid parking is more isolated in the suburbs – and mostly occurs at university campuses, hospitals, larger activity centres, and of course Melbourne Airport.

The next chart shows the DZ groups with the highest percentages of paid parking (together with the margin of error).

Technical note: the Y-axis shows the SA2 name, rather than the (unique but meaningless) DZ code(s), so you will see multiple DZ groups with the same SA2 name.

At the top of the chart are central city areas, major hospitals, several university campuses, and Melbourne Airport.

Further down the chart are:

  • larger activity centres – many inner suburban centres plus also Dandenong, Frankston, Box Hill, and curiously Springvale (where some controversial parking meters were switched off in 2017),
  • the area around Melbourne Zoo (Parkville SA2 – classified as “other”),
  • some inner city mixed-use areas,
  • two shopping centres – the inner suburban Victoria Gardens Shopping Centre in Richmond (which includes an IKEA store), and Doncaster (Westfield) – the only large middle suburban centre to show up with significant paid parking (many others now have time restrictions), and
  • some suburban industrial employment areas (towards the bottom of the chart) – in which I’ve not found commercial car parks.

These are mostly places of high activity density, where land values don’t support the provision of sufficient free parking to meet all demand.

While the data looks quite plausible, the calculated values not perfect, for several reasons:

  • Some people almost certainly forget that they paid for parking (or misinterpreted the survey question). For example, on the Monash University Clayton campus, 45% of vehicle driver trips (n = 126) said no parking fee was payable, 2% said their employer paid, and 12% said it was paid through a salary arrangement. However there is pretty much no free parking on campus (at least on weekdays), so I suspect many people forgot to mention that they had paid for parking in the form of a year or half-year permit (I’m told that very few staff get free parking permits).
  • Many people said they parked for free in an employee provided off-street car park. In this instance the employer is actually paying for parking (real estate, infrastructure, maintenance, etc). If this parking is rationed to senior employees only then other employees may be more likely to use non-car modes. But if employer provided is plentiful then car travel would be an attractive option. 22% of surveyed trips involving driving to the Melbourne CBD reported parking in an employer provided car park, about a quarter of those said no parking fee was required (most others said their employer paid for parking).
  • As already mentioned, the sample sizes are quite small, and different parking events will be at different times of the week, for different durations, and the applicability of parking fees may have changed over the survey period between 2012 and 2018.
  • The data doesn’t tell us how much was paid for parking. I would expect price to be a significant factor influencing mode choices.
  • Paid parking is not the only disincentive to travel by private car – there might be time restrictions or availability issues, but unfortunately VISTA does not collect such data (it would be tricky to collect).

How does private transport mode share vary across Melbourne?

The other part of this analysis is around private transport mode shares for destinations. As usual I define private transport as a trip that involved some motorised transport, but not any modes of public transport.

Rich data is available for journeys to work from the ABS census, but I’m also interested in general travel, and for that I have to use the VISTA survey data.

For much of my analysis I am going to exclude walking trips, on the basis that I’m primarily interested in trips where private transport is in competition with cycling and public transport. Yes there will be cases where people choose to walk instead of drive because of parking challenges, but I’m assuming not that many (indeed, around 93% of vehicle driver trips in the VISTA survey are more than 1 km). An alternative might be to exclude trips shorter than a certain distance, but then that presents difficult decisions around an appropriate distance threshold.

Here’s a map of private transport mode share of non-walking trips by SA2 destination:

Technical note: I have set the threshold at 40 trips per SA2, but most SA2s have hundreds of surveyed trips. The grey areas of the map are SA2s with fewer than 40 trips, and/or destination zones with no surveyed trips.

For all but the inner suburbs of Melbourne, private transport is by far the dominant mode for non-walking trips. Public transport and cycling only get a significant combined share in the central and inner city areas.

Where is private transport mode share unusually low? And could paid parking explain that?

The above chart showed a pretty strong pattern where private transport mode share is lower in the central city and very high in the suburbs. But are there places where private mode share in unusually low compared to surround land uses? These might be places where public transport can win a higher mode share because of paid parking, or other reasons.

Here’s a similar mode share map, but showing only DZ groups that have a private mode share below 90%:

If you look carefully you can see DZ groups with lower than 80% mode share, including some university/health campuses.

To better illustrate the impact of distance from the city centre, here’s a chart summarising the average private transport mode share of non-walking trips for selected types of places, by distance from the city centre:

Most destination place types are above 90% private transport mode share, except within the inner 5 km. The lowest mode shares are at tertiary education places, workplaces in the central city, secondary schools and parks/recreation. Up the top of the chart are childcare centres, supermarkets and kinders/preschool. Sorry it is hard to decode all the lines – but the point is that they are mostly right up the top.

The next chart brings together the presence of paid parking, distance from the CBD, destination place type, and private transport mode shares. I’ve greyed out DZ groups with less than 20% paid parking, and you can see they are mostly more than 3 km from the CBD. I’ve coloured and labelled the DZ groups with higher rates of paid parking. Also note I’ve used a log scale on the X-axis to spread out the paid DZ groups (distance from CBD).

Most of the DZ groups follow a general curve from bottom-left to top-right, which might reflect generally declining public transport service levels as you move away from the city centre.

The outliers below the main cloud are places with paid parking where private modes shares are lower than other destinations a similar distance from the CBD. Most of these non-private trips will be by public transport. The biggest outliers are university campuses, including Parkville, Clayton, Caulfield, Burwood, and Hawthorn. Some destinations at the bottom edge of the main cloud include university campuses in Kingsbury and Footscray, and parts of the large activity centres of Box Hill and Frankston.

Arguably the presence of paid parking could be acting as a disincentive to use private transport to these destinations.

Contrast these with other paid parking destinations such as hospitals, many activity centres, and Melbourne Airport. The presence of paid parking doesn’t seem to have dissuaded people from driving to these destinations.

Which raises a critical question: is this because of the nature of travel to these destinations means people choose to drive, or is this because of lower quality public transport to those centres? Something we need to unpack.

How strongly does paid car parking correlate with low private transport mode shares?

Here’s a chart showing DZ groups with their private transport mode share of (non-walking) trips and percent of vehicle parking trips involving payment.

Technical note: A colour has been assigned to each SA2 to help associate labels to data points, although there are only 20 unique colours so they are re-used for multiple SA2s. I have endeavoured to make labels unambiguous. It’s obviously not possible to label all points on the chart.

In the top-left are many trip destinations with mostly free parking and very high private transport mode share, suggesting it is very hard for other modes to compete with free parking (although this says nothing about the level of public transport service provision or cycling infrastructure). In the bottom-right are central city DZ groups with paid parking and low private transport mode share.

There is a significant relationship between the two variables (p-value < 0.0001 on a linear regression as per line shown), and it appears that the relative use of paid parking explains a little over half of the pattern of private transport mode shares (R-squared = 0.61). But there is definitely a wide scattering of data points, suggesting many other factors are at play, which I want to understand.

In particular it’s notable that the data points close to the line in the bottom-right are in the central city, while most of the data points in the top-right are mostly in the suburbs (they are also the same land use types that were an exception in the last chart – Melbourne Airport, hospitals, some university campuses, and activity centres).

As always, it’s interesting to look at the outliers, which I am going to consider by land use category.

Melbourne Airport

The airport destination zone has around 62% paid parking and around 92% private transport mode share for general trips (noting the VISTA survey is only of travel by Melbourne and Geelong residents). The airport estimates 14% of non-transferring passengers use some form of public transport, and that 27% of weekday traffic demand is employee travel.

Some plausible explanations for high private mode share despite paid parking include:

  • shift workers travelling when public transport is infrequent or unavailable (I understand many airport workers commence at 4 am, before public transport has started for the day),
  • unreliable work finish times (for example, if planes are delayed),
  • longer travel distances making public transport journeys slower and requiring transfers for many origins,
  • travellers with luggage finding public transport less convenient,
  • highly time-sensitive air travellers who might feel more in control of a private transport trip,
  • active transport involving long travel distances with poor infrastructure, and
  • many travel costs being paid by businesses (not users).

It’s worth noting that the staff car park is remote from the terminal buildings, such that shuttle bus services operate – an added inconvenience of private transport. But by the same token, the public transport bus stops are a fairly long walk from terminals 1 and 2.

The destination zone that includes the airport terminals also includes industrial areas on the south side of the airport. If I aggregate only the surveyed trips with a destination around the airport terminals, that yields 69% paid parking, and 93% private mode share. Conversely, the industrial area south of the airport yields 6% paid parking, and 100% private mode share.

Hospitals

Almost all hospitals are above the line – i.e. high private mode share despite high rates of paid parking.

The biggest outliers are the Monash Medical Centre in Clayton, Austin/Mercy Hospitals in Heidelberg, and Sunshine Hospital in St Albans South.

The Heidelberg hospitals are adjacent to Heidelberg train station. The Monash Medical Centre at Clayton is within 10 minutes walk of Clayton train station where trains run every 10 minutes or better for much of the week, and there’s also a SmartBus route out the front. Sunshine Hospital is within 10 minutes walk of Ginifer train station (although off-peak services mostly run every 20 minutes).

It’s not like these hospitals are a long way from reasonably high quality public transport. But they are a fair way out from the CBD, and only have high quality public transport in some directions.

The DZ containing Royal Melbourne Hospital, Royal Women’s Hospital, and Victoria Comprehensive Cancer Centre in Parkville is the exception below the line. It is served by multiple high frequency public transport lines, and serves the inner suburbs of Melbourne (also well served by public transport) which might help explain its ~45% private transport mode share.

The Richmond hospital DZ group is close to the line – but this is actually a blend of the Epworth Hospital and many adjacent mixed land uses so it’s not a great data point to analyse unfortunately.

So what might explain high private transport mode shares? I think there are several plausible explanations:

  • shift workers find public transport infrequent, less safe, or unavailable at shift change times (similar to the airport),
  • visitors travel at off-peak times when public transport is less frequent,
  • longer average travel distances (hospitals serve large population catchments with patients and visitor origins widely dispersed),
  • specialist staff who work across multiple hospitals on the same day,
  • patients need travel assistance when being admitted/discharged, and
  • visitor households are time-poor when a family member is in hospital.

The Parkville hospital data point above the line is the Royal Children’s Hospital. Despite having paid parking and being on two frequent tram routes, there is around 80% private transport mode share. This result is consistent with the hypotheses around time-poor visitor households, patients needing assistance when travelling to/from hospitals, and longer average travel distances (being a specialised hospital).

We can also look at census journey to work data for hospitals (without worrying about small survey sample sizes). Here’s a map showing the relative size, mode split and location of hospitals around Melbourne (with at least 200 journeys reported with a work industry of “Hospital”):

It’s a bit congested in the central city so here is an enlargement:

The only hospitals with a minority private mode share of journeys to work are the Epworth (Richmond), St Vincent’s (Fitzroy), Eye & Ear (East Melbourne), and the Aboriginal Health Service (Fitzroy) (I’m not sure that this is a hospital but it’s the only thing resembling a hospital in the destination zone).

Here’s another chart of hospitals showing the number of journeys to work, private transport mode share, and distance from the Melbourne CBD:

Again, there’s a very strong relationship between distance from the CBD and private transport mode share.

Larger hospitals more than 10 km from the CBD (Austin/Mercy, Box Hill, Monash) seem to have slightly lower private mode shares than other hospitals at a similar distance, which might be related to higher parking prices, different employee parking arrangements, or it might be that they are slightly closer to train stations.

The (relatively small) Royal Talbot Hospital is an outlier on the curve. It is relatively close to the CBD but only served by ten bus trips per weekday (route 609).

To test the public transport quality issue, here’s a chart of journey to work private mode shares by distance from train stations:

While being close to a train station seems to enable lower private transport mode shares, it doesn’t guarantee low private transport mode shares. The hospitals with low private transport mode shares are all in the central city.

So perhaps the issue is as much to do with the public transport service quality of the trip origins. The hospitals in the suburbs largely serve people living in the suburbs which generally have lower public transport service levels, while the inner city hospitals probably more serve inner city residents who generally have higher public transport service levels and lower rates of motor vehicle ownership (see: What does the census tell us about motor vehicle ownership in Australian cities? (2006-2016)).

Indeed, here is a map showing private transport mode share of non-walking trips by origin SA2:

Technical notes: grey areas are SA1s (within SA2s) with no survey trips.

Finally for hospitals, here is private transport mode share of journeys to work (from the census) compared to paid parking % from VISTA (note: sufficient paid parking data is only available for some hospitals, and we don’t know whether staff have to pay for parking):

There doesn’t appear to be a strong relationship here, as many hospitals with high rates of paid parking also have high private transport mode shares.

In summary:

  • The distance of a hospital from the CBD seems to be the primary influence on mode share.
  • Specialised hospitals with larger catchments (eg Children’s Hospital) might have higher private transport mode shares.
  • The quality of public transport to the hospital seems to have a secondary impact on mode shares.

Activity centres

Suburban activity centres such as Frankston, Box Hill, Dandenong, and Springvale have high private mode shares, which might reflect lower public transport service levels than the inner city (particularly for off-rail origins).

Box Hill is the biggest outlier for activity centres in terms of high private mode share despite paid parking. But compared to other destinations that far from the Melbourne CBD, it has a relatively low private transport mode share. It is located on a major train line, and is served by several frequent bus routes.

In general, there are fewer reasons why increased public transport investment might not lead to higher public transport mode share compared to airports and hospitals. Travel distances are generally shorter, many people will be travelling in peak periods and during the day, there are probably few shift workers (certainly few around-the-clock shift workers).

University campuses

The biggest university outliers above the line (higher private mode shares and higher paid parking %) are Deakin University (Burwood) and La Trobe University (Kingsbury). Furthermore, private transport also has a majority mode share for Monash University Clayton, Victoria University Footscray Park, Monash University (Caulfield) and Swinburne University (Hawthorn).

As discussed earlier, I suspect the rates of paid parking may be understated for university campuses because people forget they have purchased long-term parking permits.

The following chart shows the full mode split of trips to the University DZ groups in various SA2s (this time including walking trips):

Of the campuses listed, only Hawthorn and Caulfield are adjacent to a train station. Of the off-rail campuses:

  • Parkville (Melbourne Uni, 43% public transport) is served by multiple frequent tram routes, plus a high frequency express shuttle bus to North Melbourne train station. In a few years it will also have a train station.
  • Clayton (Monash, 22% PT) is also served by a high frequency express shuttle bus service to Huntingdale train station.
  • Burwood (Deakin, 19% PT) is on a frequent tram route, but otherwise moderately frequent bus services (its express shuttle bus service to Box Hill train station – route 201 – currently runs every 20 minutes)
  • Footscray (Park) (Victoria Uni, 14% PT) has bus and tram services to Footscray train station but they operate at frequencies of around 15 minutes in peak periods, and 20 minutes inter-peak.
  • Kingsbury (La Trobe Uni, 13% PT) has an express shuttle bus service from Reservoir station operating every 10 minutes on weekdays (introduced in 2016).

The success of high frequency express shuttle bus services to Parkville and Clayton may bode well for further public transport frequency upgrades to other campuses.

University campuses are also natural targets for public transport as university students on low incomes are likely to be more sensitive to private motoring and parking costs.

However university campuses also have longer average travel distances which might impact mode shares – more on that shortly.

Central city

Most central city DZ groups are in the bottom-right of the scatter plot, but there are some notable exceptions:

  • A Southbank DZ around Crown Casino has 65% paid parking and 70% private transport mode share. This was also an exception when I analysed journey to work (see: How is the journey to work changing in Melbourne? (2006-2016)) and might be explained be relatively cheap parking, casino shift workers, and possibly more off-peak travel (eg evenings, weekends).
  • Similarly, a Southbank DZ group around the Melbourne Convention and Exhibition Centre / South Wharf retail complex has 62% paid parking and around 74% private mode share. Many parts of this area are a long walk from public transport stops, and also there are around 2,200 car parks on site (with $17 early bird parking at the time of writing).
  • Albert Park – a destination zone centred around the park – has around 54% paid parking and 87% private transport mode share. Most of the VISTA survey trips were recreation or sport related, which may include many trips to the Melbourne Sports and Aquatic Centre. The park is surrounded by tram routes on most sides, but is relatively remote from the (rapid) train network.
  • Northern Docklands shows up with around 50% paid parking and around 88% private transport mode share, despite being very close to the Melbourne CBD. While this area is served by multiple frequent tram routes, it is a relatively long walk (or even tram ride) from a nearby a train station (from Leven Avenue it is 16 minutes by tram to Southern Cross Station and around 18 minutes to Flagstaff Station, according to Google). The closest train station is actually North Melbourne, but there is currently no direct public transport or pedestrian connection (the E-gate rail site and future Westgate Tunnel road link would need to be crossed).

Inner suburbs

Some places to the bottom-left of the cloud on the chart include inner suburban areas such as South Yarra, Fitzroy, Richmond, Abbotsford, Brunswick, and Collingwood. While paid parking doesn’t seem to be as common, private transport mode shares are relatively low (even when walking trips are excluded). These areas typically have dense mixed-use activity with higher public transport service levels, which might explain the lower private transport mode shares. These areas probably also have a lot of time-restricted (but free) parking.

What is the relationship between paid parking and journey to work mode shares?

For journeys to work we thankfully have rich census data, with no issues of small survey sample sizes.

The following chart combines VISTA data on paid parking, with 2016 census data on journey to work mode shares (note: the margin of error on the paid parking percentage is still up to +/-12%).

The pattern is very similar to that for general travel, and the relationship is of a similar strength (r-squared = 0.59).

There are more DZ groups below the line on the left side of the chart, meaning that the private transport mode share of journeys to work is often lower than for general travel.

Indeed, here is a chart comparing private transport mode share of general travel (VISTA survey excluding walking and trips to go home) with journeys to work (ABS census):

Note the margin of error for private transport mode shares is around +/-10% because of the small VISTA sample sizes.

For most DZ groups of all types, private transport mode shares are lower for journeys to work compared to general travel (ie below the diagonal line). This might reflect public transport being more competitive for commuters than for visitors – all-day parking might be harder to find and/or more expensive. This suggests investment in public transport might want to target journeys to work.

The DZ groups above the line include Flemington Racecourse (census day was almost certainly not a race day so there was probably ample parking for employees, while many VISTA survey trips will be from event days), Deakin Uni (Burwood), and a few others. Some of these DZ groups are dominated by schools, where workers (teachers) drive while students are more likely to cycle or catch public transport.

What about public transport mode shares?

The following chart shows VISTA public transport mode shares (for general travel) against paid parking percentages:

There are similar patterns to the earlier private transport chart, but flipped. The outliers are very similar (eg hospitals and Melbourne Airport in the bottom-right), although the top-left outliers include some destinations in socio-economically disadvantaged areas (eg Braybrook, Broadmeadows, Dandenong).

The DZ group in Blackburn South with no paid parking but 22% public transport mode share contains several schools but otherwise mostly residential areas, and the survey data includes many education related trips.

Are shift workers less likely to use public transport?

Shift workers at hospitals, Melbourne Airport, and the casino might be less likely to use public transport because of the inconvenience of travelling at off-peak shift change times, when service levels may be lower or non-existent.

Here’s a chart showing the mode split of VISTA journeys to work by destination type categories, and also type of working hours:

For hospitals, rostered shifts had a lower public transport mode share, compared to fixed and flexible hours workers, so this seems to support (but not prove) the hypothesis.

Public transport use is actually higher for rostered shift workers at other destination types, but I suspect these are mostly not around-the-clock shifts (eg retail work), and are more likely to be lower paid jobs, where price sensitivity might contribute more to mode choice.

Unfortunately there are not enough VISTA journey to work survey responses for Melbourne Airport to get sensible estimates of mode shares for different work types.

Do longer travel distances result in lower public transport mode shares?

Another earlier hypothesis was that destinations that attract longer distance trips (such as universities, hospitals, and airports) are more likely to result in private transport mode choice, as public transport journeys are more likely to require one or more transfers.

Trip distances to specialised places such as airports, suburban employment areas, universities and hospitals are indeed longer. But the central city also rates here and that has low private transport mode shares.

Digging deeper, here are median travel distances to DZ groups around Melbourne:

The central city has higher median trip distances but low private mode shares, while many suburban destinations (particularly employment/industrial areas, universities, and hospitals) have similar median travel distances but much higher public transport mode shares.

I think a likely explanation for this is that public transport to the central city is generally faster (often involving trains), more frequent, and involves fewer/easier transfers. Central city workers are also more likely to live near radial public transport lines. On the other hand, the trip origins for suburban destinations are more likely to be in the suburbs where public transport service levels are generally lower (compared to trip origins in the inner suburbs).

Cross-suburban public transport travel will often require transfers between lower frequency services, and will generally involve at least one bus leg. Very few Melbourne bus routes are currently separated from traffic, so such trips are unlikely to be as fast as private motoring (unless parking takes a long time to find), but they might be able to compete on marginal cost (if there is more expensive paid parking).

Of course this is not to suggest that cross-suburban public transport cannot be improved. More direct routes, higher frequencies, and separation from traffic can all make public transport more time-competitive.

How does parking pricing relate to employment density?

My previous research has confirmed a strong relationship between job density and lower journey to work private transport mode shares (see: What explains variations in journey to work mode shares between and within Australian cities?). Can this be explained by more paid parking in areas with higher job density?

The following chart compares weighted job density (from census 2016) and paid parking percentages (from VISTA):

Technical notes: Weighted job density is calculated as a weighted average of the job densities of individual destination zones in a DZ group, with the weighting being the number of jobs in each zone (the same principle as population weighted density). I have used a log-scale on the X-axis, and not shown DZ groups with less than 1 job/ha as they are not really interesting

There appears to be a relationship between job density and paid parking – as you would expect. The top right quadrant contains many university campuses, hospitals, and central city areas with high job density and high paid parking percentages.

In the bottom-right are many large job-dense shopping centres that offer “free” parking. Of course in reality the cost of parking is built into the price of goods and services at the centres (here’s a thought: what if people who arrive by non-car modes got a discount?). An earlier chart showed us that employees are less likely to commute by private transport than visitors.

The outliers to the top-left of the chart are actually mostly misleading. An example is Melbourne Airport where the density calculation is based on a destination zone that includes runways, taxiways, a low density business park, and much green space. The jobs are actually very concentrated in parts of that zone (e.g. passenger terminals) so the density is vastly understated (I’ve recommended to the ABS that they create smaller destination zones around airport terminal precincts in future census years).

Inclusion of significant green space and/or adjacent residential areas is also an issue at La Trobe University (Kingsbury data point with just under 50% mode share), RMIT Bundoora campus (Mill Park South), Royal Children’s Hospital (Parkville), Sunshine Hospital (St Albans South), Victoria University (Footscray (Park)), Albert Park (the actual park), and Melbourne Polytechnic Fairfield campus / Thomas Embling Hospital (Yarra – North).

I am at a loss to explain paid parking in Mooroolbark – the only major employer seems to be the private school Billanook College.

Can you summarise the relationship between paid parking and mode shares?

I know I’ve gone down quite a few rabbit holes, so here’s a summary of insights:

  • Distance from the Melbourne CBD seems to be the strongest single predictor of private transport mode share (as origin or destination). This probably reflects public transport service levels generally being higher in the central city and lower in the suburbs. Destinations further from the central city are likely to have trip origins that are also further from the central city, for which public transport journeys are often slower.
  • Paid parking seems to be particularly effective at reducing private transport mode shares at university campuses, and the impact is probably greater if there are higher quality public transport alternatives available.
  • There’s some evidence to suggest paid parking may reduce private transport mode shares at larger activity centres such as Box Hill and Frankston.
  • Most hospitals have very high private transport mode shares, despite also having paid parking. Hospitals with better public transport access have slightly lower private transport mode shares.
  • Destinations with around-the-clock shift workers (e.g. hospitals and airports) seem generally likely to have high private transport mode shares, as public transport services at shift change times might be infrequent or unavailable.
  • Suburban destinations that have longer median travel distances (such as hospitals, airports and industrial areas) mostly have higher private transport mode shares.
  • Even if there isn’t much paid parking, destinations well served by public transport tend to have lower private transport mode shares (although this could be related to time-restricted free parking).

If you’d like more on factors influencing mode shares, I’ve also explored this more broadly elsewhere on this blog, with employment density (related to parking prices), cycling infrastructure quality, proximity to rapid public transport, and walking catchment density found to be significant factors (see: What explains variations in journey to work mode shares between and within Australian cities?).

Are places with paid parking good targets for public transport investments?

Many of my recent conversations with transport professionals around this topic have suggested an hypothesis that public transport wins mode share in places that have paid parking. While that’s clearly the case in the centre of Melbourne and at many university campuses, this research has found it’s more of a mixed story for other destinations.

While this post hasn’t directly examined the impact of public transport investments on mode shares in specific places, I think it can inform the types of destinations where public transport investments might be more likely to deliver significant mode shifts.

Here’s my assessment of different destination types (most of which have paid parking):

  • Suburban hospitals may be challenging due to the presence of shift workers, patients needing assistance, visitors from time-poor households, and long average travel distances making public transport more difficult for cross-suburban travel. There’s no doubt many people use public transport to travel to hospitals, but it might not include many travellers who have a private transport option.
  • Larger activity centres with paid parking show lower private transport mode shares. Trips to these centres involve shorter travel distances that probably don’t require public transport transfers, and don’t suffer the challenges of around-the-clock shift workers, so they are likely to be good targets for public transport investment.
  • Universities are natural targets for public transport, particularly as many students would find the cost of maintaining, operating and parking a car more challenging, or don’t have access to private transport at all (around 35% of full time university/TAFE students do not have a full or probationary licence according to the VISTA sample). Universities do attract relatively higher public transport mode shares (even in the suburbs) and recent investments in express shuttle services from nearby train stations appear to have been successful at growing public transport patronage.
  • Melbourne Airport has high rates of paid parking and private transport mode share. It is probably a challenging public transport destination for employees who work rostered shifts. However already public transport does well for travel from the CBD, and this will soon be upgraded to heavy rail. Stations along the way may attract new employees in these areas, but span of operating hours may be an issue.
  • Job dense central city areas that are not currently well connected to the rapid public transport network could be public transport growth opportunity. In a previous post I found the largest journey to work mode shifts to public transport between 2011 and 2016 were in SA2s around the CBD (see: How is the journey to work changing in Melbourne? (2006-2016)). The most obvious target to me is northern Docklands which is not (yet) conveniently connected its nearby train station. Public transport is also gaining patronage in the densifying Fishermans Bend employment area (buses now operate as often as every 8 minutes in peak periods following an upgrade in October 2018).
  • Lower density suburban employment/industrial areas tend to have free parking, longer travel distances, and very high private transport mode shares. These are very challenging places for public transport to win significant mode share, although there will be some demand from people with limited transport options.

An emerging target for public transport might be large shopping centres that are starting to introduce paid or time-restricted car parking (particularly those located adjacent to train stations, e.g. Southland). That said, Westfield Doncaster, which has some paid parking (around 19%), has achieved only 6% public transport mode share in the VISTA survey (n=365), athough this may be growing over time. Meanwhile, Dandenong Plaza has around 16% public transport mode share despite only 6% paid parking.

Upgraded public transport to shopping centres might be particularly attractive for workers who are generally on lower incomes (we’ve already seen staff having lower private transport mode shares than visitors). Also, customer parking may be time-consuming to find on busy shopping days, which might make public transport a more attractive option, particularly if buses are not delayed by congested car park traffic.

There’s a lot going on in this space, so if you have further observations or suggestions please comment below.

Appendix: About destination group zones

Here is a map showing my destination zone groups in the central city area which have 15% or higher paid parking. Each group is given a different colour (although there are only 20 unique colours used so there is some reuse). The numbers indicate the number of surveyed parking trips in each group:

Some of the DZ groups have slightly less than 40 parking trips, which means they are excluded from much of my analysis. In many cases I’ve decided that merging these with neighbouring zones would be mixing disparate land uses, or would significantly dilute paid parking rates to not be meaningful (examples include northern Abbotsford, and parts of Kew and Fairfield). Unfortunately that’s the limitation of the using survey data, but there are still plenty of qualifying DZ groups to inform the analysis.

I have created destination zone groups for most destination zones with 10%+ paid parking, and most of the inner city area to facilitate the DZ group private transport mode share chart. I haven’t gone to the effort of creating DZ groups across the entire of Melbourne, as most areas have little paid parking and are not a focus for my analysis.