ABS census data tells us that Perth’s CBD experienced a massive 19% jump in the number of private transport commuter trips between 2016 and 2021. That’s over 5000 more journeys – mostly as car drivers – and is quite likely to have made traffic congestion worse.
So how did that happen? Where were these extra commuters travelling to? Were there particular changes in the modal mix in different parts of the CBD? Was this growth enabled by a big increase in car parking capacity? And what has been happening to car park pricing?
This post digs a little deeper following my last post that explored the impact of COVID on journey to mode shares in Australian cities in 2021.
A quick recap of overall changes in journey to work in the Perth CBD
Here’s the volume of Perth CBD commuters by main mode, including working at home in 2011, 2016, and 2021:
See my last post for my definition of the Perth CBD. A trip involving any public transport is classed as public, a trip that involves only walking or cycling is classed as active, and any other form of travel is classed as private.
At the 2021 census, Perth was COVID-free with relatively few restrictions on intra-state movement or activity.
Total employment in the CBD grew by a massive 26% from 82,214 in 2016 to 103,944 in 2021. Private transport trips increased by 19%, but because this was less growth than overall employment growth there was actually a commuter mode shift away from private transport of 1.6% (from 36.5% to 34.9%).
The biggest increase in CBD worker volumes was in those who worked at home.
Public transport commuting to the CBD increased by only 85 trips between 2016 and 2021, but still accounted for more trips than private transport.
My last post concluded there was likely a significant mode shift from public transport to remote working. There was some mode shift away from public transport and towards remote working and private transport for some middle age groups, although some of this shift is likely to be a normal trend seen as people age (and become parents). I was unable to identify occupations that saw a substantial mode shift from public transport to private transport, although some occupations saw a lot more private transport growth than public transport growth.
This post now takes that analysis a bit further by looking at spatial variations in the modal mix by workplace location.
Where were the extra private transport commuters working?
Here’s the change in private commuter trips for each destination zone around the Perth CBD:
Note: the circles aren’t always drawn in the middle of each destination zone, aren’t intended to highlight any particular location within each zone, and may not be representative of major car park locations.
There were both increases and decreases around the CBD. I’m going to focus in more detail on the following high-growth destination zones that I’ve arbitrarily named by a dominant building, precinct, or bordering streets:
Most of the zones that saw a big increase in private transport commuter trips also saw a big increase in public transport trips.
Capital Square saw jobs more than triple between 2016 and 2021 as a major new development was completed (including the new Woodside headquarters). It had the largest increase in private transport trips, but even more new trips were by public transport. The development includes five levels of car parking on a fairly large site (at least 659 car parks according to some planning documents). It also saw the largest growth in active transport commuter trips of any destination zone in the Perth CBD.
The zone I have labelled Kings Square (which includes Perth Arena and the new Shell and HBF buildings) saw only slightly more new public transport trips than new private transport trips, despite Perth train station being inside the zone.
The Royal Perth Hospital zone had almost all of its net job growth accounted for by private transport, some of which would have been shift workers. This is consistent with my last post that found a large increase in private transport commuters under the “carers and aids” and “health and welfare support” occupation groups. The hospital is directly adjacent to McIver train station, served by multiple train lines.
One mixed-use block between Terrace Road, Victoria Avenue, Adelaide Terrace, and Hill Street had an increase in private trips and a decrease in public trips. It’s difficult to speculate why this occurred due to the diverse mix of land uses.
The Elizabeth Quay zone saw more growth in private trips than public trips, despite being immediately adjacent to Elizabeth Quay train station. I’ve not been able to identify any large new car parks in the area. Car parks immediately north of the development site were offering $25 all-day car parking at the time of writing which I suspect the average employee might not consider particularly affordable.
The Brookfield Place and Central Park zones mostly saw a big increase in the number of remote workers.
Outside the CBD, the biggest decline in private trips was -1863 in a zone near West Leederville station where the Princess Margaret Hospital for Children closed in 2018 (replaced by the Perth Children’s Hospital in Nedlands).
Where was there a shift from public to private transport?
The following map shows destination zones where there was a decline in public transport trips and an increase in private transport trips (no zones showed the opposite flow):
Just under than half of the destination zones around the Perth CBD saw some sort of net shift to private transport, and most of these were very small numbers. In total these zones account for 492 trips within for my definition of the Perth CBD, about 0.5% of all workers. A net shift from public transport explains less than 10% of the total increase in private transport commuter trips.
This is consistent with analysis in my last post (which disaggregated by birth cohorts and occupations) and again suggests the growth in private trips was broadly in line with the overall growth in CBD employment. It also fits with the hypothesis that the biggest mode shift was from public transport to remote working.
Another way of analysing mode shift is to look the percentage change in private transport mode share by zone:
In the western part of the main CBD area there were many zones with a large mode shift away from private transport, and many of these zones had high employment density.
In fact, the next chart shows how employment density and private transport mode share changed between 2016 and 2021 in the Perth CBD, with the thin end of each ‘comet’ being 2016 and the thick end being 2021 (I’ve arbitrarily named several more destination zones based on major landmarks or surrounding streets).
Note: some destination zones include significant land that is not built up (eg parkland, water bodies, and/or freeway interchanges) and these will have understated employment density. This incudes Convention/Exhibition and Elizabeth Quay.
The dominant pattern is that the zones with high and increasing employment density mostly saw declining private transport mode share, although the “Terrace / Hill / Victoria” block was a standout exception having increasing employment density and increasing private mode share.
How did the CBD absorb so many more car commuters?
It’s hard to know for sure but some possible explanations include:
New car parking supply: I’ve already mentioned the Capital Square development that included five levels of parking. Locals might know of other new large CBD car parks, but I’ve struggled to identify any large car parks on Parkopedia or Google Maps that didn’t already exist in 2016. Many new office buildings don’t appear to include large car parks.
Perth was in a “mining downturn” in 2016: The Perth CBD only added 1.7k jobs between 2011 and 2016, and there was no significant increase in private commuter trips. According to a Property Council report in August 2016, Perth was experiencing very high office vacancy rates (21.8%) and had been experiencing a decline in office space demand that started around 2013. So it seems quite plausible that car parking supply grew between 2011 and 2016, but commuter parking demand only grew strongly after 2016.
Reduced short-term parking demand? Perhaps there has been a decline in demand for short-term parking (through the normalisation of online business meetings) enabling more all-day parking. But I’m just speculating.
Someone reading this from the parking industry might be able to share some insights (please add comments).
What’s been happening to Perth CBD car parking prices?
Like Sydney and Melbourne, Perth has a CBD parking levy – an annual fee collected by government per space. Here’s what’s been happening to the levy prices in real terms:
The parking levy increased substantially in real terms in 2010 and again between 2014-2016, but in recent years has not been keeping up with inflation. Between 2016 and 2021 there was almost no real change in the levy.
So what’s been happening to car park prices?
The City of Perth itself operates a large number of CBD car parks and in 2021/22 parking revenue accounted for 36% of its total income (source: budget 2022-23).
Thanks to the incredible resource that is the Wayback Machine, I’ve been able to dig out prices at their CBD car parks right back to 2001-02. For the sake of manageable analysis I’ve focussed on four relatively large central CBD car parks – Concert Hall (399 spaces), Convention Centre (1428 spaces), Elder Street (1052 spaces) and Pier Street (680 spaces). Here’s how those prices have changed over time, in nominal and real terms:
The 2010 and 2015 jumps in the pricing levy were clearly reflected in retail parking prices.
In real terms, parking prices peaked around 2015-2017 and have been in decline since then. Prices for several car parks were cut substantially in 2017/18 – perhaps as a belated response to a reduction in office commuter demand during the mining downturn. Then parking prices were frozen from 2019 to 2022 – presumably due to the pandemic.
So despite the massive increase in CBD parking demand, the City of Perth reduced – rather than increased – all-day parking prices, and so has probably also missed out on significant additional revenue. This has arguably helped facilitate the big increase in commuter traffic volumes, along with the likely associated urban amenity impacts of more traffic in the CBD.
The City of Perth is a democratic local government so it’s probably not going to behave in an entirely economically rational way when it comes to price setting. Prices are also locked in for each financial year so are much less dynamic. So what have commercial parking operators been doing?
Unfortunately I’ve not been able to use the Internet Archive to find historical commercial car parking prices in the Perth CBD back to 2016. What I can tell you is that “flexi” online parking at the Wilson Parking run Central Park car park has risen from $19 in October 2021 to $26 in May 2023 – suggesting commercial operators are not afraid to change their pricing. That said, the Kings Complex car park (517 Hay Street, near Pier Street) has had no increase in its online daily rate between October 2021 and May 2023 ($18).
“This policy recognises that vehicular access to, from and within central Perth is a critical element in ensuring its continued economic and social viability. It also continues to recognise the need to preserve and enhance the city’s environment. The policy aims to address these needs by supporting the provision of a balanced transport network in order to manage congestion and provide for the efficient operation of the transport network to, from and within the city centre.”
I suspect the term “balanced transport” is indicative of not trying to shift travel towards more sustainable, non-car modes. And I guess it would also be hard for the City of Perth to start discouraging something that generates more than one third of its annual revenue. Although an increase in prices might increase revenue, even if it reduces demand.
Furthermore, the Western Australian government is also continuing to widen Perth’s freeways, in the hope this might reduce traffic congestion. I’m not sure many cities have succeeded with such strategies, but good luck Perth!
Wasn’t Perth public transport patronage below pre-pandemic levels in 2021?
I noted above that there were just 85 additional public transport commuters to Perth’s CBD in 2021 compared to 2016. But Perth’s overall public transport patronage in August 2021 was around 22%* below that in August 2016. If the number of CBD public transport commuters didn’t decline, the overall patronage decline must represent a mode shift away from public transport for trips to other destinations and/or for purposes other than travelling to work (and/or a decline in the number of such trips made by any mode).
*August 2016 had one more school weekday and one fewer Sunday than August 2021 which means we cannot directly compare total monthly patronage of the two months but they will be fairly close. It would be much cleaner to compare average school weekday patronage figures between months and years, but unfortunately few agencies publish such numbers (Victoria does now).
This is the second post in a series that explores why younger adults are more likely to use public transport (PT) than older adults, with a focus on the types of places where people live and work, including proximity to train stations, population density, job density, motor vehicle ownership and driver’s licence ownership.
In the first post, we found younger adults in Melbourne were more likely to live and work close to the CBD, but this didn’t fully explain why they were more likely to use public transport.
This analysis uses 2016 ABS census data for Melbourne, and data for the years 2012-18 from Melbourne’s household travel survey (VISTA) – all being pre-COVID19. See the first post for more background on the data.
Proximity to train stations
Melbourne’s train network is the core mass rapid transit network of the city offering relatively car-competitive travel times, particularly for radial travel. It’s not Melbourne’s only high quality public transport, but for the want of a better metric, I’m going to use distance from train stations as a proxy for public transport modal competitiveness, as it is simple and easy to calculate.
In 2016 younger adults (and curiously the elderly) were more likely to live near train stations:
Almost 40% of people in their 20s lived within one km of a station. Could this partly explain why they were more likely to use public transport?
Well, maybe partly, but public transport mode shares of journeys to work were quite different between younger and older adults at all distances from train stations:
Public transport mode shares fell away with distance from stations, and age above 20 (the 15-19 age band being an exception).
With VISTA data we can look at general travel mode share by home distance from a train station:
There’s clearly a relationship between PT mode share and proximity to stations, but there’s also a strong relationship between age and PT use, at all home distance bands from train stations.
Younger adults were also more likely to work close to a train station. Indeed 46% of them worked within about 1 km of a station:
And unsurprisingly people who work near train stations are also more likely to live near train stations:
The chart shows around 70% of people who worked within 1 km of a station lived within 2 km of a station. Also, 37% of people who worked more than 5 km from a station, also lived more than 5 km from a station.
But again, journey to work PT mode shares varied by both age and workplace distance from a train station:
For completeness, here is another matrix-of-worms chart looking at journey to work PT mode shares by age for both work and home distances from train stations:
PT mode share declined with age for most distance combinations, but this wasn’t true for the 15-19 age band, particularly where both home and work were within a couple of kms of a station. We know from part one that teenagers are much less likely to work in the city centre, so this might represent teenagers who happen to live near a station, but work locally and can easily walk or cycle to work.
If we take age out for a moment, here is the relationship between PT mode share of journeys to work and both home and work distance from train stations:
The relationship between PT mode share and work distance from a train station is much stronger than for home distance from a station.
So while home and work proximity to train stations influenced mode shares, it doesn’t fully explain the variations across ages. So what if we combine…
Work distance from the CBD, home distance from a train station
Work distance from a station is strongly related to work distance from the CBD, as the CBD and inner city has a higher density of train stations:
I expect workplace proximity to a train station to be a weaker predictor of mode share when compared workplace distance from CBD. That’s pretty evident when looking at journey to work PT mode share by place of work on a map:
And even more evident when you look at PT mode shares for both factors (regardless of age):
So perhaps work distance from the CBD, and home distance from a train station might be two strong factors for mode share? If we control for these factors, is there still a difference in PT mode shares across ages?
Time for another matrix of worms:
The chart shows that even when you control for both home distance from a station, and work distance from the CBD, there is still a relationship with age (generally declining PT mode share with age, with teenagers sometimes an exception). So there must be other factors at play.
Consistent with proximity to train stations and the CBD, younger adults are more likely to live in denser residential areas:
Higher residential density often comes with proximity to higher quality public transport. Indeed, here is the distribution of population densities for people living at different distances from train stations:
The next chart shows the relationship between residential density and mode shares – split between adults aged 20-39 and those aged 40-69:
The chart shows that both age and residential density are factors for journey to work mode shares. Younger adults had higher public transport mode shares for journeys to work at all residential density bands.
Similarly, VISTA data also shows PT mode shares vary significantly by both age and population density for general travel:
Technical note: data only shown where age band and density combination had at least 400 trips in the survey.
Curiously, people in their 60s living in areas with densities of 50-80 persons/ha were more likely to use public transport to get to work than those in their 40s and 50s living in the same densities (maybe due the presence of children?). For lower densities, PT mode share generally declined with increasing age (from 20s onward).
Population density is also generally related to distance from the CBD:
And here is a chart showing how PT mode share of journeys to work varied across both:
The chart shows home distance from the CBD had a larger impact on mode shares than population density. Indeed population density only seemed to have a secondary impact for densities above 40 persons/ha. However, as we saw in the first post, people living closer to the CBD were more likely to work in the city centre, and therefore more likely to use public transport in their journey to work.
Young adults were more likely to work in higher density employment areas in 2016, where public transport is generally more competitive (with more expensive car parking):
But yet again, there is a difference in mode shares between age groups regardless of work location job density:
So job density doesn’t fully explain the difference in PT mode shares across age groups.
I should add that job density is also strongly related to workplace distance from the CBD:
and workplace distance from train stations:
And putting aside age, PT mode shares for journeys to work are related to both workplace distance from the CBD and job density:
PT mode shares are also related to both job density and workplace distance from stations:
You might be wondering about the dot of higher job density (200-300 workers/ha) that is between 3 and 4 km from a train station. It’s one destination zone that covers Doncaster Westfield shopping centre – a large shopping centre on a relatively small piece of land (almost all of the car parking is multistory – see Google Maps)
Motor vehicle ownership
Are younger adults more likely to use public transport because they are less likely to own motor vehicles?
With census data, it is possible to measure motor vehicle ownership on an SA1 area basis by adding up household motor vehicles and persons aged 18-84 (as an approximation of driving aged people) and calculating the ratio. Of course individual households within these areas will have different levels of motor vehicle ownership.
Using this metric, young adults were indeed more likely to live in areas which have lower levels of motor vehicle ownership (in 2016):
But yet again, the PT journey to work mode shares varied between younger and older adults regardless of the levels of motor vehicle ownership of the area (SA1) in which they live:
Using VISTA data, we can calculate motor vehicle ownership at a household level. I’ve classified households by the ratio of motor vehicles to adults.
VISTA data shows PT mode shares strongly related to both age and motor vehicle ownership (I’ve shown the most common ratios):
You might be wondering why I didn’t calculate motor vehicle ownership at the household level for census data. Unfortunately it’s not possible for me to calculate the ratio of household motor vehicles to number of adults because ABS TableBuilder doesn’t let me combine the relevant data fields (for some reason).
The best I can do is the ratio of household motor vehicles to the usual number of residents (of any age). The usual residents may or may not include children under driving age – we just don’t know.
Nevertheless the data is still interesting. Here is how public transport mode shares of journeys to work varied across different vehicle : occupant combinations for households in Greater Melbourne:
Yes that’s a lot of squiggly lines – but for most combinations (excluding those with zero motor vehicles) there was a peak of PT mode share in the early 20s, and then a decline with increasing age.
The lines with green and yellow shades – where the ratio is around 1:2 or 1:3 – show a sharp drop around the mid 20s. I expect these lines are actually a mix of working parents with younger children, and working adult children living with their (older) parents. The high mode shares for those in their early 20s could represent many adult children living with their parents (but without their own car), while those in their 30s and 40s are more likely to be parents of children under the driving age. So the sharp drop is probably more to do with a change in household age composition.
If we want to escape the issue of children, the highest pink line is for households with one motor vehicle and one person (so no issues about the age of children because there are none present) – and that line has a peak in PT mode share in the mid 30s and then declines with age, suggesting other age-related factors must be in play.
But motor vehicle ownership levels aren’t only related to age. They are strongly related to population density,
..home distance from the CBD,
..and home distance from train stations:
And public transport mode shares are related to both motor vehicle ownership rates and population density (with motor vehicle ownership probably being the stronger factor):
Technical note: for these charts I’ve excluded data points with fewer than 5 qualifying SA1s to remove anomalous exceptions.
Public transport mode shares are also related to both motor vehicle ownership and home distance from the CBD:
And shares are also related to both motor vehicle ownership and home distance from a train station:
In all three cases, PT mode shares fell with increasing levels of motor vehicle ownership, but this effect mostly stopped once there were more motor vehicles than persons aged 18-84.
Drivers licence ownership
I’ve previously shown on this blog that people without a full car driver’s licence are much more likely to use public transport, which will surprise no one. So are younger adults less likely to have a driver’s licence?
VISTA data shows us that younger adults are indeed less likely to have a car driver’s licence, with licence ownership peaking around 97% for those in their late 40s and early 50s, and only dropping to 91% by age 75 (there is a little noise in the data):
So the lack of a driver’s licence by many young adults will no doubt partly explain why they are more likely to use public transport.
Consistent with VISTA, data from the BITRE yearbooks also shows that younger adults have become less likely to own a licence over time:
At the same time, those aged 60-79 have been more likely to own a licence over time.
But do public transport mode shares vary by age, even for those with a solo driver’s licence? (by solo, I mean full or probationary licence). The following chart shows public transport mode shares for age bands and licence ownership levels (data points only shown where 400+ trips exist in the survey data).
PT mode shares peaked for age band 23-29 for most licence ownership levels, including no licence ownership (there isn’t enough survey data for people older than 22 with red probationary licences – the licence you have for your first year of solo driving).
As an aside, there is a curious increase in public transport mode share for those aged over 60 without a drivers licence – this may be related to these people being eligible for concession fares and occasional free travel with a Seniors Card (if they work less than 35 hours per week).
So even younger adults who own a driver’s licence are more likely to use public transport.
But is this because they don’t necessarily have a car available to them? Let’s put the two together…
Motor vehicle and driver’s licence ownership
For the following chart I’ve classified households as:
“Limited MVs” if there were more licensed drivers than motor vehicles attached to the household,
“Saturated MVs” if there was at least as many motor vehicles as licensed drivers, and
“No MVs” if there were no motor vehicles associated with the household.
If there were any household motor vehicles I’ve further disaggregated by individuals with a solo licence and those without a solo licence (the latter may have a learner’s permit). I’ve only shown data points with at least 400 trip records in the category to avoid small sample noise (I am reliant on VISTA survey data).
Except for households with no motor vehicles, public transport mode share peaked for age band 18-22 or 23-29 and then declined with increasing age. So again there must be other age-related factors. However the impact of age is smaller than that of motor vehicle ownership and licence ownership.
Unfortunately driver’s licence ownership data is not collected by the census, so it is not possible to combine it with other demographic variables from the census.
So, what have we learnt in part two:
Younger adults are more likely to work and live near train stations, but that only partly explains why younger adults are more likely to use public transport.
Workplace distance from the CBD has a much bigger impact on public transport mode shares for journeys to work than home distance from a train station.
Younger adults are more likely to live in areas with higher residential density, but this only partly explains why they are more likely to use public transport.
Younger adults are more likely to work in areas with higher job density but this is highly correlated with workplace distance from the CBD, which is a stronger factor influencing mode shares.
Younger adults are more likely to live in areas with lower motor vehicle ownership (these areas are generally also have higher residential density and are closer to the city centre and to train stations), but this again only partly explains why they are more likely to use public transport. Motor vehicle ownership appears to be a stronger factor influencing mode shares than population density, distance from stations, or distance from the city.
Younger adults are less likely to have a driver’s licence, but again this only partly explains why they are more likely to use public transport.
While this analysis confirms younger adults tend to align with known factors correlating with higher public transport use, we are yet to uncover a factor or combination of factors that mostly explain the differences in public transport use between younger and older adults. That is, when we control for these factors we still see differences in public transport use between ages.
The next post in this series will explore the impacts on public transport use of parenting responsibilities, generational factors (birth years), and year of immigration to 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.
Private and public transport journey to work mode shares vary considerably both between Australian cities and within them. Are these differences related to factors such as population density, motor vehicle ownership, employment density, proximity to train stations, proximity to busway stations, jobs within walking distance of homes, and distance from the city centre?
This posts sheds some light on those relationships for Australia’s six largest cities. I’m afraid it isn’t a short post (so get comfortable) but it’s fairly comprehensive (over 30 charts).
I should stress up front that a strong relationship between a certain factor and high or low mode shares does not imply causation. There are complex relationships between many of these factors, for example motor vehicle ownership rates are generally lower in areas of higher residential density (which I will also explore), and more factors beyond what I will explore here.
Higher population densities are commonly associated with higher public transport use. This stands to reason, as high density areas have more potential users per unit of area, but also higher density is likely to mean high land prices, which in turn increases the cost of residential parking. But higher public transport mode share can only happen if government’s invest in higher service levels, and this isn’t guaranteed to happen (although it often does, through pressures of overcrowding).
My preferred measure is population weighted density, which is the weighted average density of land parcels in a city, weighted by their population (this gets around problems of including sparsely populated urban land). I’ve measured it at census district (CD) geography for 2006 and Statistical Area Level 1 (SA1) geography for 2011 and 2016, using 2011 Significant Urban Area boundaries to define cities. The 2006 density figures are not perfectly comparable with 2011 and 2016 because CDs are slightly larger than SA1s, so the density values will be calculated as slightly smaller.
Here is the relationships at city level (the thin end of each worm is 2006 and the thick end 2016, with 2011 in the middle):
The relationship is very strong for Melbourne and Sydney over time. Between 2011 and 2016, Perth and Brisbane saw increased population density but reduced public transport mode share (mostly because of changes in the distribution of jobs between the centre and the suburbs).
Brisbane was a bit of an outlier in 2006 and 2011 with high public transport mode share relative to its lower population density.
Canberra is also perhaps a bit of an outlier, with much lower public transport mode share compared to similarly low density cities. This might be explained by the smaller total population, lower jobs density, and lack of rapid public transport services segregated from traffic.
But Canberra does have higher active transport mode share, so it’s worth doing the same analysis with private transport mode shares:
Brisbane was still an outlier in the relationship in 2006 and 2011, but Canberra is more in line with other data points.
Another interesting note is that Canberra went from being the least dense city in 2006 to the third most dense in 2016.
Drilling down to SA2 geography (SA2s are roughly the size of a suburb), here’s a chart showing all SA2s in all cities across the three census years (filtered for CDs and SA1s with at least 5 persons per hectare). I’ve animated it to highlight one city at a time so you can compare the cities, and I’ve used a log scale on the X-axis to spread out the data points (only the Sydney and Melbourne CBDs go off the chart to the right).
(if these animated GIF charts are not clear on your screen, you can click to enlarge the image, then use “back” to come back to this page).
You can see a fairly strong relationship, although it is very much a “cloud” rather than a tight relationship – there are other factors at play.
What I find interesting is that Sydney has had a lot of SA2s with population weighted densities around 50-100 but private mode shares over 55% (toward the upper-right part of the cloud of data points) – which are rare in all other cities. That’s a lot of traffic generation density, which cannot be great for road congestion. In a future post I might focus in on the outlier SA2s that are in the top right of these charts (can public transport do better in those places?).
In case you are wondering about the Brisbane SA2 with low density and low private transport mode share (middle left of chart) it is the Redland Islands where car-carrying ferries are essential to get off an island, and are counted as public transport in my methodology. The Canberra outlier in the bottom left is Acton (which is dominated by the Australian National University).
I’ve calculated a weighted job density in the same way I’ve calculated population weighted density, but using Destination Zones (which can actually be quite large so it certainly isn’t perfect). Weighted job density is a weighted average of job densities of all destination zones, weighted by the number of jobs in each zone. In a sense it is the density at which the average person works
(technical notes: I’ve actually only counted jobs as people who travelled on census day and reported their mode(s) of travel. Unfortunately I only have 2006 data for Sydney and Melbourne)
This chart suggests a very strong relationship at the city level, with all cities either moving up and left (Adelaide, Perth and Brisbane) or down and right (Sydney, Melbourne, Canberra).
So is the relationship as strong when you break it down to the Destination Zone level? The next chart shows jobs density and private mode share for all destination zones for 2016. Note that there is a log scale on the x-axis, and Adelaide dots are drawn on top of other cities in the top left which explains why that dense cloud of dots appears mostly green.
There’s clearly a strong relationship, although again the data points form a large cloud rather than tightly bunch around a line, so other factors will be at play.
It’s also interesting to see that the blue Sydney dots are generally lower than other cities at all job densities. That is, Sydney generally has lower private transport mode shares than other cities, regardless of employment density.
Which leads us to the next view: the private transport mode shares for jobs in different density ranges in each city for 2011 and 2016.
(click to enlarge if the chart appears blurry)
You can see a fairly consistent relationship between weighted job density and mode shares across all cities in both 2011 and 2016.
At almost all job density ranges, Sydney had the lowest average private transport mode share, while Adelaide and Perth were generally the highest (data points are not shown when there are fewer than 5 destination zones at a density range for a city). This shows that something other than jobs density is impacting private transport mode shares in Sydney. Is it walking catchment, public transport quality & quantity, or something else?
For more on the relationship between job density and mode share, see this previous post.
Proximity to public transport
Trains generally provide the fastest and most punctual public transport services (being largely separated from road traffic and having longer stop spacing), and are the most common form of rapid transit in Australian cities. So you would expect higher public transport mode shares around train stations.
Here is a chart showing average journey to work public transport mode shares by home distance from train stations. It’s animated over the three census years, with a longer pause on 2016.
Technical note: A limitation here is that I’ve measured all census years against train stations that were operational in 2016 – so the 2006 and 2011 mode shares will be under-stated for the operational stations of those years. For example, in Melbourne the following stations opened between 2011 and 2016: Williams Landing, South Morang, Lynbrook, and Cardinia Road.
You can see that public transport shares went up between 2006 and 2011 in most cities at all distances from train stations. In both Perth and Brisbane there were new train lines opened between 2006 and 2011, which will explain some of that growth.
But if you watch carefully you will see public transport mode shares near train stations fell in both Brisbane and Perth between 2011 and 2016. That is, there was a mode shift away from public transport, even for people living close to train stations. As discussed previously, this is most likely related to there being only small jobs growth in the CBDs of those cities between 2011 and 2016, compared to suburban locations.
You can also see that public transport mode shares aren’t that much higher for areas near train stations in Adelaide (I’ll come back to that).
We can do the same for train mode shares (any journey involving train):
Again, Sydney’s train stations seem to have the biggest pulling power, while Adelaide’s have the least.
Busways are the other major form of rapid transit in Australian cities, with major lines in Brisbane, Sydney and Adelaide. Here is a chart of public transport mode share by distance from busway stations, excluding areas also within 1.5 km of a train station:
Note for Adelaide this data only considers suburban stations on the O-bahn, and not bus stops in the CBD. For Sydney all “T-Way” station are included, plus the four busway stations on the M2 motorway for which buses run into the CBD (but not the relatively short busway along Anzac Parade in Moore Park). Sydney’s north west T-Ways opened in 2007
Proximity to a busway station appears to influence public transport mode share strongly in Brisbane and Adelaide, where busways are mostly located in the inner and middle suburbs and cater for trips to the CBD. Sydney’s busway stations are in the “outer” western suburbs, feeding Blacktown, Parramatta, but also relatively long distance services to the Sydney CBD via the M2.
Curiously, public transport mode shares were higher in places between 3 and 5 km from busway stations in Sydney, compared to immediately adjacent areas. I’m not sure that I can explain that easily, but it suggests equally attractive public transport options exist away from busway and train stations.
The station proximity influence appears to extend around 1 km, which possibly reflects the fact that few busway stations have park and ride facilities, and are therefore more dependent on walking as an access mode (although cycling may be another station access mode).
Over time Sydney public transport mode share lifted at all distances from busway stations, while in Brisbane it rose in 2011 and then fell again in 2016, in line with other city mode shares.
So are busway stations similar to train stations in their impact on public transport mode share? To answer this I’ve segmented cities into areas near train stations, near busway stations, near both, and near neither. I’ve used 1.5 km as a proximity threshold that might represent an extended walking catchment.
In Sydney, train stations appear to have a much stronger influence on public transport mode shares than busway stations, but the opposite is true in Brisbane and Adelaide. This possibly reflects the much higher service frequencies on Adelaide and Brisbane busways compared to their trains, and the fact Sydney’s busway stations are so far from the CBD (and thus have fewer workers travelling to the CBD where public transport dominates mode share).
Also of note in this chart is that for areas more than 1.5 km from a train or busway station, Sydney had a much higher public transport mode share compared to the other cities. These areas will be served mostly by on-road buses, but also some ferries and one light rail line. Adelaide has the least difference between mode share for areas near and not-near train or busway stations.
We can do the a similar analysis for workplaces:
The most curious pattern here is Adelaide – where public transport mode share was highest for jobs between 1.5-2.5 kms from train stations. This distance band is dominated by the centre of the Adelaide CBD (the station being on the edge, arguably a “corner”), for which bus was the dominant public transport access mode. Also, there was no destination zone small enough near Adelaide central train station to register as 0 – 0.5 km away, and only one that is 0.5 – 1 km away (I use distances between station data points and destination zone centroids). So the results might look slightly different if smaller destination zones were drawn in the Adelaide CBD.
In all other cities there was a very strong relationship between train station proximity and public transport mode share, as you would expect. And Sydney again stands out with high public transport mode shares for workplaces more distant from train stations.
If you are wondering, the bump in Sydney at 2.5 to 3 km includes the Kensington / Randwick area which has high employment density and a strong bus connection to the central city (partly assisted by the Anzac Parade busway). And the relatively high figure for Melbourne at 1 – 1.5 km includes parts of Docklands, Parkville, Southbank, and St Kilda Road, which all have high tram service levels.
Unfortunately destination zones around busway stations are generally too large to provide meaningful insights so I’m not presenting such data.
Motor vehicle ownership
It will come as little surprise that there is a relationship between household motor vehicle ownership and journey to work mode shares.
Here’s a summary chart for each city for the 2006, 2011 and 2016 censuses:
There appears to be a fairly strong relationship between the two factors at city level.
Sydney and Melbourne have seen the largest mode shift away from private transport, but only Melbourne has also seen declining motor vehicle ownership rates.
Canberra saw only weak growth in motor vehicle ownership between 2011 and 2016, and at the same time there was a shift away from private transport (and a large increase in population weighted density).
Perth and Brisbane saw increasing private transport mode share and increasing motor vehicle ownership between 2011 and 2016.
Here’s a more detailed look at the relationship over time for Melbourne at SA2 geography:
The outliers on the upper left are generally less-wealthy middle-outer suburban areas (lower motor vehicle ownership but high private mode share), while the outliers to the lower-right are wealthy inner suburbs where people can afford to own plenty of motor vehicles, but they didn’t use them all to get to work.
In the bottom left of the chart are inner city SA2s with declining private mode share and declining motor vehicle ownership. For motor vehicle ownership rates around 70-80 (motor vehicles per persons aged 18-84), there are many SA2s with private mode shares that declined 2006 to 2016, but not significantly lowering motor vehicle ownership rates. That suggests that just because people own many motor vehicles, they don’t necessarily use them to drive them to work.
Here is the same data for Sydney:
There are many SA2s with motor vehicle ownership rates around 50 to 70 where the private mode shares are dropping faster than motor vehicle ownership. But there are also many areas with high private mode shares and increasing rates of motor vehicle ownership.
How do the other cities compare? Here are all the SA2s for all cities on the same chart, with alternating highlighted cities:
You can see big differences between the cities, but also that Brisbane and Perth have many SA2s with very high private mode share and rapidly increasing motor vehicle ownership (ie moving up and right, although it’s a little difficult to see with so many lines overlapping). Melbourne and Sydney have plenty of SA2s moving down and left – reducing motor vehicle ownership and declining private transport mode share (which may make some planners proud).
Of course there will be a relationship between motor vehicle ownership and where people choose to live and work. People working in the central city may prefer to live near train stations so they can avoid driving in congested traffic to expensive car parks. People who prefer not to drive might choose to live close to work and/or a frequent public transport line. People who are happy to drive to work in the suburbs might avoid higher priced real estate near train stations or the inner city.
As an aside, we can compare total household motor vehicles to the number of people driving to work, to estimate the proportion of household motor vehicles actually used in the journey to work. Here is Melbourne:
SA2s with a lower estimate are generally nearer the CBD, are wealthier areas, have reasonable public transport accessibility, and/or might be areas with a higher proportion of people not in the workforce (for whatever reason). The areas where the highest proportion of motor vehicles are required for the journey to work are relatively new outer suburbs on the fringe (perhaps suggesting forced car ownership), where adult workforce participation is probably high and public transport accessibility is lower.
The proportion of cars used in the journey to work declined on average in many parts of Melbourne. Given that motor vehicle ownership rates in Melbourne barely changed between 2011 and 2016, this probably represents people mode shifting, rather than people acquiring more motor vehicles even though they don’t need them to drive to work.
Jobs within walking distance of home
It stands to reason that people would be more likely to walk to work if there were more work opportunities within walking distance of their home.
For every SA1 I’ve measured how many jobs are approximately within 1 km as a notional walking catchment (measured as the sum of jobs in destination zones whose centroid are within 1 km of the centroid of each home SA1, so it is not perfect). Here’s the relationship with walking mode share:
(there are a lot of dots overlapping in the bottom left-corner and Adelaide dots have been drawn on top so try not to get thrown by that).
You don’t have to have a lot of nearby jobs to get a higher walking mode share, but if you do, you are very likely to get a high walking more share. The exceptions (many jobs, but low walking share) include many parts of Parramatta (Sydney), and areas separated from nearby jobs by water bodies or other topographical barriers (eg Kangaroo Point in Brisbane).
Workplace distance from the city centre
As was seen in a previous post, workplaces closer to city centres had much lower private transport mode shares, which is unsurprising as these are locations with generally the best public transport accessibility, high land values that can lead to higher car parking prices (which impact commuters who pay them), and often higher traffic congestion.
Here is a chart showing private transport mode share by workplace distance from the city centre. I’ve used faded lines to show 2011 and 2006 results (2006 only available for Sydney and Melbourne).
Here’s a chart that shows the mode shifts between 2011 and 2016:
Inner Melbourne had the biggest mode shifts away from private transport (particularly in Docklands that falls into the 1-2 km range, which saw significant employment and tram service growth), but Sydney had more consistent mode shifts across most distances from the city centre. Adelaide and Canberra saw mode shifts away from private transport in the inner city but towards private transport further out.
Brisbane and Perth saw – on average – a mode shift to private transport across almost all distances from the city centre, with the highest mode shift to private transport in Brisbane actually for the CBD itself(!).
Home distance from the city centre
There’s unquestionably a relationship here too, and it’s probably mostly driven by public transport service levels being roughly proportional to distance from the CBD, but also the proportion of the population who work in the CBD being much higher for homes nearer the CBD.
Sydney had the lowest average private transport mode share at all distances up to 20 km from the CBD, followed by Melbourne and Brisbane, in line with overall mode shares.
The trends over time are also interesting. Brisbane saw mode shifts towards private transport at all distances more than 2 km from the city centre between 2011 and 2016. However there were not significant shifts for Perth outside the city centre – that is: modes shares by geography didn’t change very much. The mode shift away from public transport in Perth is best explained by the shift in jobs balance away from the city centre.
Here are public transport mode shares by home distance from city centres:
In most cities, public transport mode share peaked at a few kilometres from the city (as active transport has a higher mode in the central city).
Here are public transport mode shifts by distance from the city centre between 2011 and 2016:
The significant shift in central Melbourne is likely to be largely explained by the Free Tram Zone introduced in 2015. Outside of the city centre the mode shifts are surprisingly uniform across each city.
Here’s the same chart for 2006-2011, and you can clearly see the impact of the opening of the Mandurah railway line in Perth with significant mode shift beyond 30 km:
Curiously there was a massive shift to public transport for CBD residents in Melbourne (and this is before the free tram zone was introduced).
So which factors best explain the patterns in mode shares across cities?
What we’ve clearly seen is that higher public transport mode shares are seen for journeys to work…
to higher density workplaces
from areas of lower motor vehicle ownership
to workplaces closer to train stations
from higher density residential areas
from areas around train and busway stations
to and from areas closer to city centres (except from the central city where walking takes over)
from less wealthy areas (while I haven’t tested this directly, wealth seems to explain a lot of the outliers in the scatter plots)
I’ve listed these roughly in order of the strength of the relationships seen in the data, but I haven’t put them all in a regression model (yet, sorry).
Of course most of these factors are inter-related, so we cannot isolate causation factors. I’m going to run through many of them, because they are often interesting: (note I have sometimes used log scales)
Population density is roughly related to distance from the city centre:
Motor vehicle ownership has a strong relationship with population density (see this post for more analysis):
Motor vehicle ownership has a weaker relationships with distance from the city centre:
Motor vehicle ownership is related to home distance from train stations, except in Adelaide:
Technical note: For this chart (and some below) I’ve calculated average quantities for the variable on the Y axis, as there would otherwise there are too many data points on the chart and it becomes very hard to see the relationship (I would need to show all SA1s because SA2s are too large in terms of distance from stations). The downside is that these style of charts don’t indicate the strength of relationships.
Population weighted density is related to distance from train stations, especially in Melbourne and Sydney, but not at all in Adelaide:
There is a relationship – although not strong – between weighted job density and distance from city centres:
There’s some relationship between average weighted jobs density and distance from train stations, except in Adelaide:
Here’s the same data, but as a scatter plot with a point for each destination zone, scaled by the number of journeys to each destination zone, and a linear Y-axis:
Technical note: the X-axis appears green mostly because Adelaide data points are drawn on top of other cities, but those data points aren’t of much interest.
In most cities, destination zones with high jobs density (over 700 jobs/ha) were only found within 1 km of a train station – with the notable major exception of Adelaide (again!).
(If you are curious, the large Melbourne zone at 1.4 km from a train station and 659 jobs/ha is the Parkville hospital precinct – where incidentally a train station is currently under construction).
There is a relationship between motor vehicle ownership and proximity to busway stations, but it varies between cities:
But there’s not much relationship between population density and proximity to busway stations (except in the immediate vicinity of busway stations in Brisbane):
Final remarks: there’s something about Adelaide’s train network
A few key observations come through clearly about the catchments around Adelaide’s train stations:
In aggregate they do not have higher population density, unlike other cities.
In aggregate they do not have particularly high public transport mode shares, unlike other cities.
In aggregate they do not have lower rates of motor vehicle ownership, unlike other cities.
They do not include the area of highest job density in the CBD (a longer walk or transfer to tram or bus is required), unlike other cities.
Few cities have spare land corridors available for new at-grade rapid public transport lines, and so transport planners generally want to make maximum use of the ones they’ve got, before opting for expensive and/or disruptive tunnelling or viaducts solutions. It looks like Adelaide’s rail corridors are not reaching their people-moving potential.
By contrast, Adelaide’s “O-Bahn” busway does go into the job dense heart of the CBD and the busway station catchments do have higher public transport mode share and lower motor vehicle ownership. However they do not have higher population density, possibly because the stations are surrounded by car parks, green space, and one large shopping centre (Tea Tree Plaza).
Mode shares, population densities, and motor vehicle ownership rates would quite probably change significantly if Adelaide could address the fourth issue by building a train station near the centre of the CBD.