Young adults are much more likely to use public transport (PT) than older adults.
Is it because younger adults are more likely to live and/or work near the city centre? Is it because they are more likely to live near train stations? Is it because they tend to live in higher density areas with better public transport? Is it because they are less likely to own a car? Is it because they are less likely to own a driver’s licence? Is it because they are less likely to be parents? Is it to do with their income? Is it related to how many of them are recent immigrants? Or is it a generational thing?
The answers are not as simple as you might expect. This is the first post in a series that aims to understand what influences mode choice across different ages. I’ll focus on (pre-COVID19) data about general travel and journeys to work in my home city of Melbourne, but I suspect the patterns will be similar in comparable cities.
About the data (boring but important)
My largest data source is the 2016 ABS census of population and housing which provides detailed demographic data about residents, captures the modes used for journeys to work, but doesn’t record travel for any other purposes and only covers a single day in August. There’s data on the travel choices of millions of people, and so it is possible to disaggregate data by several dimensions before you run into problems with small counts.
For general travel mode shares my data source is the Victoria Integrated Survey of Travel and Activity (VISTA) which is Victoria’s household travel survey, recording the transport and activity of a representative sample of Melbourne and Geelong residents across the whole year. The data set is smaller than the census (being a survey), but also contains rich demographic information and covers all travel purposes by people of all ages. I have used aggregated data over the survey years 2012-2018 to form a larger sample, so any underlying trends in behaviour over that period will be averaged out.
For this analysis I am filtering my data to either Greater Melbourne (for 2011 and 2016 census data) or otherwise the 31 local government areas (LGAs) that make up metropolitan Melbourne (all are entirely inside Greater Melbourne except Yarra Ranges).
All of this data was collected before the COVID19 pandemic, and of course travel patterns may well not return to similar patterns once the pandemic is over.
Consistent with elsewhere on this blog, I attribute
any journey involving a train, tram, bus, or ferry as a public transport journey (even if other modes were also used, including private transport),
a journey only involving walking and cycling as an active transport journey, and
any other journey as a private transport journey (mostly being car journeys).
This post mostly focuses on public transport – which I will often abbreviate to PT.
While the data sets I am using only identify sex as male or female, I want to acknowledge that not all people fit into binary classifications.
How does PT mode share vary by age and sex for general travel?
Here’s a chart showing public transport mode shares by age and gender using VISTA data for all travel purposes:
Public transport mode share peaked in the 15-19 age group – essentially around the later years of secondary school and early years of tertiary education or working life where people have more independence, may need to travel longer distances to get to school or university, and are too young and/or cannot afford private transport.
Public transport mode share then fell away with age, though the profile by gender is slightly different (some of this may be noise in the survey). Women under the age of 30 were more likely to use PT, but then they became less likely to use PT after age 30 – perhaps after the arrival of children.
Children under 10 years were least likely to use public transport, and there was only a small increase in public transport use amongst women aged over 65. Public transport use dropped considerably for those aged 85-89, and there wasn’t sufficient sample to confidently calculate mode shares for any older age groups.
How does PT mode share vary by age and sex for journeys to work?
Here is a chart of public transport mode share of journeys to work in Greater Melbourne by age and sex (using census data):
The chart shows women were much more likely to use public transport to get to work than men, particularly for young adults but also those in their 60s. Overall PT mode share was 17.7% for males and 20.3% for females. PT mode share peaked for females at age 26, and for males at age 24.
So what might explain the variations across age and gender? In this first post I’m going to explore the how mode share varies by home and work distance from the CBD.
For travel to/from the City of Melbourne, PT mode shares peaked around 50% for workers in their early 20s, and generally fell away with age, with females showing a higher PT mode share in all age groups (mode shares are only shown for ages 20-64 due to small samples of trips in other age groups).
For travel not to/from the City of Melbourne, PT mode shares peaked for teenagers and was very low for all other age groups, with only slightly higher mode shares for those in their 20s, early 30s, and early 80s.
With census journey to work data, we can increase the resolution to 2 year age-bands and dis-aggregate work destinations by distance bands from the CBD. The darker line of each colour is for females, the lighter for males.
Public transport mode share was much higher for workplaces near the CBD, and then declined with workplace distance from the CBD.
But within each workplace distance band from the CBD there was also a generally declining PT mode share with age, flattening out somewhat for ages above 45. While there was a difference between genders at all workplace distance bands, it was generally smaller than the overall mode share difference between genders for journeys to work.
How can these lines have quite a different curve shape to overall PT mode share by age/sex? Well, here is a chart showing the proportion of Greater Melbourne workers at every age who worked within 4 kms – and within 10 kms – of the Melbourne CBD:
Young adults were much more likely to work closer to the CBD than older adults, and women even more so (although they are not actually a majority of workers close to the CBD).
Teenagers were least likely to work in the City of Melbourne, which likely reflects their lack of qualifications for high-skill jobs that tend to locate in the central city.
The curves for men and women peaked at different ages, with younger adult women more likely to work in the City of Melbourne than younger adult men, which then flipped for ages 38+. This isn’t because of stay-at-home mums because the data only counts people who travelled to work.
Here’s another look at that data – showing the distribution of work locations from the CBD for age bands. Around 40% of young adult workers worked within 6 km of the CBD:
And flipping that, workplaces closer to the CBD have a higher proportion of younger adults:
Public transport mode shares for general travel (in the VISTA survey) were related to both age and trip destination distance from the CBD, with those in their 50s least likely to use PT for destinations within 5 km of the CBD:
So a major explanation why younger adults were more likely to use public transport in their journey to work is that they were more likely to work in the central city. However, when you control for travel proximity to the CBD there is still a significant relationship between PT mode share and age – other factors must be at play.
I’m curious – is the fact that younger adults (particularly women) were more likely to work in the city centre related to their…
Well, younger adults turn out to have the highest educational qualifications of any age group, with those in their early 30s generally being the most qualified (as at 2016):
Note: Supplementary codes includes people with no educational attainment.
Furthermore, younger women are generally more qualified than younger men, which could explain why a higher proportion of younger women work in the City of Melbourne, and therefore have a higher public transport mode share overall.
[As an aside: I find that chart fascinating – there’s been a generational shift in educational attainment which will continue to work it’s way up the age brackets in the decades ahead, resulting in an increasingly skilled workforce over time. Part of this will be skilled migration, part may be temporary migrants (eg international postgraduate students), and another part presumably reflects greater access to higher education in recent decades.]
Looking to the future perhaps this cohort of highly educated young adults will continue to work in the inner city as they age, along with younger skilled graduates, leading to more centralisation of employment in Melbourne as we become more of a “knowledge economy”? Or maybe the recent mass working-from-home experience of highly skilled workers during the COVID-19 pandemic will see more workers based in the central city but travelling to their workplace less often.
But back to the how education levels impact work location and mode choice…
People with higher educational attainment are more likely to work closer to the CBD:
The chart shows around half of workers with postgraduate degrees worked within 4 km of the CBD, whereas those who didn’t complete secondary education were much more likely to work in the suburbs.
We know that workplace distance from the CBD impacts PT mode shares, but does varying educational qualifications explain the differences in mode share between ages?
The following animated chart shows how PT mode shares for journeys to work vary by age for people with the same level of educational qualifications and working the same distance from the Melbourne CBD. I have animated the chart across workplace distance from the CBD bands.
If you watch and study the chart, you’ll see that there is a relationship between age and PT mode share for most combinations of educational attainment and workplace distance from the CBD. That is, age is significant in itself, or there is some other explanation for mode share difference by ages.
You’ll also see that public transport mode shares were generally higher for higher levels of educational attainment, with postgraduate degrees mostly showing the highest public transport mode share.
Here’s an alternative, non-animated view of that data. It’s a matrix of mini line charts showing PT mode share by age, for each combination of workplace distance from the CBD and highest level of educational attainment. You could call it a matrix of worms. The light horizontal line within each matrix box represents a 50% PT mode share, and the colours give you a rough sense of age (refer legend). I don’t expect you to be able read the mode share values for any age band on any line, but it does show PT mode shares falls with rising age for all education levels and workplace distances from the CBD (except some further out where PT mode share is just very low for all ages).
Here is yet another view: the relationship between PT mode share, workplace distance from the CBD, and highest level of educational attainment. I have roughly sorted the education levels by PT mode share, rather than ordering by level of qualification.
PT mode shares were not directly proportional to education levels, but I suspect this will be partly related to occupations – eg Certificate III and IV qualifications often related to trades where driving to non-centralised work sites is a more convenient option.
Those with postgraduate degrees generally showed the highest public transport mode share at each distance interval.
So we’ve explored work distance from the CBD, but what about…
Home distance from CBD
Younger adults were more likely to live closer to the Melbourne CBD compared to other age groups:
Public transport service quality is generally higher closer to the CBD, so does the fact that younger adults were more likely to live closer to the city explain their higher PT mode shares?
The following chart shows how public and active transport journey to work mode shares vary by home distance from the Melbourne CBD:
Public transport mode shares show a relationship with both home distance from the CBD and age – with mode shares peaking for ages 20-39, and dropping with older age bands. I’ve plotted active transport mode shares as well (for interest), which shows teenage workers much more likely to get to work by active transport – which makes sense as many of them will be below driving age and/or unable to afford private transport. Curiously those aged 70-79 who live in the suburbs are slightly more likely to walk to work.
Okay, but people who live closer to the CBD are more likely to work closer to the CBD, as the following chart shows:
The following chart looks at mode shares for those who worked within 2 km of the CBD:
PT mode shares for these commuters were relatively high and flat for workers who live more 5 km from the CBD (those closer are more likely to use active transport – as per the bottom half of the chart). PT mode shares rose slightly with distance from the CBD for home distances 25-40 km from the CBD. But there was still a difference between age bands, with younger adults more likely to have used PT to get to work, regardless of how far from the CBD they lived.
So are there still PT mode share differences by age if you control for both home and work distance from the CBD?
Home AND work distance from the CBD
Firstly, here are PT mode shares for journeys to work by home and work distance from the CBD, animated over age bands:
Technical notes: the chart only shows mode shares where at least 200 people fell within the age and distance bands – which is quite a low threshold so there is a little noise – so please try not to get distracted by small differences in numbers. For teenagers and those aged 60-69, many combinations failed this threshold so are left blank.
The chart shows that work distance from the CBD is a very strong driver of mode shares at all age bands. Home distance from the CBD is much weaker driver of PT mode share – and only really significant for those living within 5 km of the CBD, and those under 40 years within 15 km of the CBD.
If the animation is hard to follow, here’s another matrix-of-worms chart. It shows PT mode share by age band – for every combination of home and work distance from the CBD.
The thin horizontal lines within each square of the matrix represent 50% PT mode share. While you cannot read off the PT mode shares for any age and distance combination, you can see that within each pane PT mode share either generally fell with increasing age, or were very low for all ages. That is to say, that home and workplace distance from the CBD doesn’t fully explain the relationship between age and PT mode shares for journeys to work. Other factors must be at play.
The above chart makes it hard to compare mode shares for the different work distances from the CBD, so here is a transposed version with work distances as rows and home distances as columns:
There’s not a lot of difference between home distance bands for each work distance band, except for younger adults living closer to the CBD and working in the city centre. This confirms the earlier finding that work distance from the CBD is a much stronger determinate in PT mode shares.
So in summary, younger adults are more likely to live and work closer to the CBD, and that is likely related to them generally having higher educational qualifications. While these factors generally lead to higher public transport use, we’ve found they don’t fully explain why younger adults have higher public transport mode shares.
Further posts in this series will look at other demographic factors that may explain these differences. Read on to part two.
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.
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.
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):
[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.
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.
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.
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.
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.
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.
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).
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.
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.
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).
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?
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).
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.
In part 1 of this series, I looked at the radialness of general travel around Melbourne based on the VISTA household travel survey. This part 2 digs deeper into radialness by time of the day and week, and maps radialness and mode share for general travel around Melbourne.
A brief recap on measuring radialness: I’ve been measuring the difference in angle between the bearing of a trip, and a straight line to the CBD from the trip endpoint that is furthest from the CBD (origin or destination). An angle of 0° means the trip is perfectly radial (directly towards or away from the CBD) while 90° means the trip is entirely orbital relative to the CBD. An average angle in the low 40s means that there isn’t really any bias towards radial travel. I’ve been calling this two-way off-radial angle. Refer to part 1 if you need more of a refresher.
How does trip radialness vary by time of week?
The first chart shows the average two-way off-radial angle for trips within Greater Melbourne by time and type of day, for private transport, public transport, and walking.
Technical notes: I’ve had to aggregate weekend data into two hour blocks to avoid issues with small sample sizes. I’m only showing data where there are at least 100 trips for a mode and time (that’s still not a huge sample size so there is some “noise”). Trips times are assigned by the clock hour of the middle of the trip duration. For example, a trip starting at 7:50 am and finishing at 9:30 am has a mid-trip time of 8:40 am and therefore is counted in 8 – 9 am for one hour intervals, and 8 – 10 am for two hour intervals.
You can see:
Public transport trips are much more radial at all times of the week, but most particularly in the early AM peak and in the PM commuter peak. They are least radial in the period 3-4 pm on weekdays (PM school peak), which no doubt reflects school student travel, which is generally less radial.
Private transport trips are more radial before 8 am on weekdays, and in the early morning and late evening on weekends. Curiously private transport trips in the PM peak don’t show up as particularly radial, possibly because there is more of a mix of commuter and other trips at that time.
Walking trips show very little radial bias, except perhaps in the commuter peak times on weekdays.
When I drill down into specific modes, the sample sizes get smaller, so I have used 2 hour intervals on weekdays, and 3 hour intervals on weekends. Also to note is that VISTA assigns a “link mode” to each trip, being the most important mode used in the journey (generally train is highest, followed by tram, bus, vehicle driver, vehicle passenger, bicycle, walking only). I am using this “link mode” in the following charts.
Train trips are the most radial, followed by tram trips (no surprise as these networks are highly radial).
Bicycle trips are generally the third most radial mode, except at school times.
Public bus trips are more radial in the commuter peak periods, and much less radial in the middle of the day on weekdays. The greater radialness in commuter peaks will likely reflect people using buses in non-rail corridors to travel to the city centre (particularly along the Eastern Freeway corridor). Most of Melbourne’s bus routes run across suburbs, rather than towards the city centre, which will likely explain bus-only trips being less radial than train and tram, particularly off-peak.
How does radialness vary by trip purpose and time of week?
The following chart shows the average two-way off-radial angle of trips by trip purpose (at destination) and time of day:
Work related trips are generally the most radial, particularly in the AM peak (as you might expect), but less so on weekdays afternoons.
Weekday education trips are the next most radial (excluding trips to go home in the afternoon and evening), except at school times (school travel being less radially biased than tertiary education travel).
Social trips become much more radial late at night on weekends, probably reflecting inner city destinations.
Recreational trips are the least radial on weekends.
Otherwise most other trip purposes average around 35-40° – which is only slightly weighted towards radial travel.
What is the distribution of off-radial angles by time of day?
So far my analysis has been looking at radialness, without regard to whether trips are towards or away from the CBD. I’ve also used average off-radial angles which hides the underlying distribution of trip radialness.
I’m curious as to whether modes are dominated by inbound or outbound trips at any times of the week (particularly private transport), and the distribution of trips across various off-radial angles.
So to add the inbound/outbound component of radialness, I am going to use a slightly different measure, which I call the “one-way off-radial angle”. For this I am using a scale of 0° to 180°, with 0° being directly towards the CBD, and 180° being directly away from the CBD, and 90° being a perfectly orbital trip with regard to the CBD. For inbound trips, the one-way off-radial angle will be the same as the two-way off-radial angle, but outbound trips will instead fall in the 90° to 180° range.
One-way off-radial angles are still calculated relative to the trip end point (origin or destination) that is furthest from the CBD. I explained this in part 1.
Here is the distribution of one-way off-radial angles by time of day for trips where train was the main mode:
A reminder: only time intervals with a sample of at least 100 trips are shown.
In the morning, trips are very much inbound radial, with around three-quarters being angles of 0°-10°. Likewise in the PM peak, almost three-quarters of train trips are very outbound radial with angles 170°-180°.
As per the second chart in this post, train trips remain very radial throughout the day. But there is slightly more diversity in off-radial angles 3-4 pm on weekdays, when many school students use trains for journeys home from school that are less radially biased. Less radial trips could be a result of using two train lines, using bus in combination with train, or using a short section of the train network that isn’t as radial (eg Eltham to Greensborough, Williamstown to Newport, or a section of the Alamein line).
On weekends it’s interesting to see that there are many more inbound than outbound journeys between 12 pm and 2 pm on weekends. The “flip time” when outbound journeys outnumber inbound journeys is probably around 2 pm. This is consistent with CBD pedestrian counters that show peak activity in the early afternoon.
One problem with the chart above is that volumes of train travel vary considerably across the day. So here’s the same data, but as (estimated) average daily trips:
You can see the intense peak periods on weekdays, and a gradual switch from inbound trips to outbound trips around 1 pm on weekdays. There’s also a mini-peak in the “contra-peak” directions (outbound trips in the AM peak and inbound trips in the PM peak).
The weekend volumes are for two hour intervals so not directly comparable to weekdays (which are calculated for one hour intervals), but you can see higher volumes of inbound trips until around 2 pm, and then outbound trip volumes are higher.
Those results for trains were probably not surprising, but what about private vehicle driver trips?
There is much more diversity in off-radial angles at all times of the day, and a less severe change between inbound and outbound trips across the day.
On both weekday and weekend mornings there is a definite bias towards inbound travel. Afternoons and evenings are biased towards outbound travel, but not nearly as much (it’s much stronger late at night). This is consistent with the higher average two-way off-radial angle seen for private transport in the PM peak compared to the AM peak.
Here is the same data again but in volumes:
This shows the weekday AM peak spread concentrated between 8 and 9 am, while the PM peak is more spread over three hours (beginning with the end of school).
Here are the same two charts for tram trips (the survey sample is smaller, so we can only see results for weekdays):
Again there is a strong bias to inbound trips in the morning and outbound in the afternoon, with slightly more diversity in the PM school peak, and early evening.
Next up public bus (a separate category to school buses, however many school students do travel on public buses):
There is a lot more diversity in off-radial angles (particularly 2-4 pm covering the end of school), but also the same trend of more inbound trips in the morning and outbound trips in the afternoon.
Next up, bicycle:
There’s a fair amount of diversity, across the day, with inbound trips dominating the AM peak and outbound trips in the PM commuter peak (but not as strongly in the PM school peak). Weekend late afternoon trips show a little more diversity than morning and early afternoon trips, but the volumes are relatively small.
Next is walking trips:
There is considerable diversity in off-radial angles across most of the week, although outbound trips have a larger share in the late evening.
Walking volumes on weekdays peak at school times. On weekends walking seems to peak between 10 am and 12 pm and again 4 pm to 6 pm, but not considerably compared to the rest of the day time.
Mapping mode shares and radialness
So far I’ve been looking at radialness for modes by time of day. This section next section looks at radialness and mode shares by origins and destinations within Melbourne.
In recent posts I’ve had fun mapping journeys to work from census data (see: Mapping Melbourne’s journeys to work), so I’ve been keep to explore what’s possible for general travel.
VISTA is only a survey of travel (rather than a census), so if you want to map mode shares of trips around the city, you unfortunately need to lose a lot of geographic resolution to get reasonable sample sizes.
The following map shows private transport mode shares for journeys between SA3s (which are roughly the size of municipalities), where there were at least 80 surveyed trips (yes, that is a small sample size so confidence intervals are wider, but I’m also showing mode shares in 10% ranges). Dots indicate trips within an SA3, and lines indicate trips between SA3s. I’ve animated the map to make try to make it slightly easier to call out the high and low private mode shares.
You can see lower private transport mode shares for radial travel involving the central city (Melbourne City SA3), particularly from inner and middle suburbs (less so from outer suburbs). Radial travel that doesn’t go to the city centre generally has high private transport mode shares.
I also have origin and destination SA1s for surveyed trips. Here is a map showing all SA1-SA1 survey trip combinations by main mode, animated to show intervals of two-way off-radial angles:
It’s certainly not a perfect representation because of the all the overlapping lines (I have used a high degree of transparency). You can generally see more blue lines (public transport) in the highly radial angles, and almost entirely red (private transport) and short green lines (active transport) for larger angle ranges. This is consistent with charts in my last post (see: How radial is general travel in Melbourne? (Part 1)).
You can also see that few trips fall into the 80-90° interval, which is because I’m measuring radialness relative to the trip endpoint furthest from the CBD. An angle of 80-90° requires the origin and destination to be about the same distance from the CBD and for the trip to be relatively short.
So there you go, almost certainly more than you ever wanted or needed to know about the radialness of travel in Melbourne. I suspect many of the patterns would also be found in other cities, although some aspects – such the as the geography of Port Phillip Bay – will be unique to Melbourne.
Again, I want to the thank the Department of Transport for sharing the full VISTA data set with me to enable this analysis.