Each year, just in time for Christmas, the good folks at the Australian Bureau of Infrastructure, Transport, and Regional Economics (BITRE) publish a mountain of data in their Yearbook. This post aims to turn those numbers (and some other data sources) into useful knowledge – with a focus on vehicle kilometres travelled, passenger kilometres travelled, mode shares, car ownership, driver’s licence ownership, greenhouse gas emissions, and transport costs.
There are some interesting new patterns emerging – read on.
Vehicle kilometres travelled
According to the latest data, road transport volumes actually fell in 2018-19:
Here’s the growth by vehicle type since 1971:
Light commercial vehicle kilometres have grown the fastest, curiously followed by buses (although much of that growth was in the 1980s).
Car kilometre growth has slowed significantly since 2004, and actually went down in 2018-19 according to BITRE estimates (enough to result in a reduction in total vehicle kilometres travelled).
On a per capita basis car use peaked in 2004, with a general decline since then. Here’s the Australian trend (in grey) as well as city level estimates to 2015 (from BITRE Information Sheet 74):
Technical note: “Australia” lines in these charts represent data points for the entire country (including areas outside capital cities).
Darwin has the lowest average which might reflect the small size of the city. The blip in 1975 is related to a significant population exodus after Cyclone Tracey caused significant destruction in late 1974 (the vehicle km estimate might be on the high side).
Canberra, the most car dependent capital city, has had the highest average car kilometres per person (but it might also reflect kilometres driven by people from across the NSW border in Queanbeyan).
The Australia-wide average is higher than most cities, with areas outside capital cities probably involving longer average car journeys and certainly a higher car mode share.
Passenger kilometres travelled
Overall, here are passenger kms per capital for various modes for Australia as a whole (note the log-scale on the Y axis):
Air travel took off (pardon the pun) in the late 1980s (with a lull in 1990), car travel peaked in 2004, bus travel peaked in 1990 and has been relatively flat since, while rail has been increasing in recent years.
It’s possible to look at car passenger kilometres per capita, which takes into account car occupancy – and also includes more recent estimates up until 2018/19.
Here’s a chart showing total car passenger kms in each city:
The data shows that Melbourne has now overtaken Sydney as having the most car travel in total.
Another interesting observation is that total car travel declined in Perth, Adelaide, and Sydney in 2018-19. The Sydney result may reflect a mode shift to public transport (more on that shortly), while Perth might be impacted by economic downturn.
While car passenger kilometres per capita peaked in 2004, there were some increases until 2018 in some cities, but most cities declined in 2019. Darwin is looking like an outlier with an increase between 2015 and 2018.
BITRE also produce estimates of passenger kilometres for other modes (data available up to 2017-18 at the time of writing).
Back to cities, here is growth in rail passenger kms since 2010:
Sydney trains have seen rapid growth in the last few years, probably reflecting significant service level upgrades to provide more stations with “turn up and go” frequencies at more times of the week.
Adelaide’s rail patronage dipped in 2012, but then rebounded following completion of the first round of electrification in 2014.
Here’s a longer-term series looking at per-capita train use:
Sydney has the highest train use of all cities. You can see two big jumps in Perth following the opening of the Joondalup line in 1992 and the Mandurah line in 2007. Melbourne, Brisbane and Perth have shown declines over recent years.
Here is recent growth in (public and private) bus use:
Darwin saw a massive increase in bus use in 2014 thanks to a new nearby LNG project running staff services.
In more recent years Sydney, Canberra, and Hobart are showing rapid growth in bus patronage.
Here’s bus passenger kms per capita:
Investments in increased bus services in Melbourne and Brisbane between around 2005 and 2012 led to significant patronage growth.
Bus passenger kms per capita have been declining in most cities in recent years.
Australia-wide bus usage is surprisingly high. While public transport bus service levels and patronage would certainly be on average low outside capital cities, buses do play a large role in carrying children to school – particularly over longer distances in rural areas. The peak for bus usage in 1990 may be related to deregulation of domestic aviation, which reduced air fares by around 20%.
Melbourne has the lowest bus use of all the cities, but this likely reflects the extensive train and tram networks carrying the bulk of the public transport passenger task. Melbourne is different to every other Australian city in that trams provide most of the on-road public transport access to the CBD (with buses performing most of this function in other cities).
For completeness, here’s growth in light rail patronage:
Sydney light rail patronage increased following the Dulwich Hill extension that opened in 2014, while Adelaide patronage increased following an extension to the Adelaide Entertainment Centre in 2010.
We can sum all of the mass transit modes (I use the term “mass transit” to account for both public and private bus services):
Sydney is leading the country in mass transport use per capita and is growing strongly, while Melbourne, Brisbane, Perth have declined in recent years.
Mass transit mode share
We can also calculate mass transit mode share of motorised passenger kilometres (walking and cycling kilometres are unfortunately not estimated by BITRE):
Sydney has maintained the highest mass transit mode share, and in recent years has grown rapidly with a 3% mode shift in the three years 2016 to 2019, mostly attributable to trains. The Sydney north west Metro line opened in May 2019, so would only have a small impact on these figures.
Melbourne made significant gains between 2005 and 2009, and Perth also grew strongly 2007 to 2013.
Here’s how car and mass transit passenger kilometres have grown since car used peaked in 2004:
Mass transit use has grown much faster than car use in Australia’s three largest cities. In Sydney and Melbourne it has exceeded population growth, while in Brisbane it is more recently tracking with population growth.
Mass transit has also outpaced car use in Perth, Adelaide, and Hobart:
In Canberra, both car and mass transit use has grown much slower than population, and it is the only city where car growth has exceeded public transport growth.
The ABS regularly conduct a Motor Vehicle Census, and the following chart includes data up until January 2019.
Technical note: Motor Vehicle Census data (currently conducted in January each year, but previously conducted in March or October) has been interpolated to produce June estimates for each year, with the latest estimate being for June 2018.
In 2017-18 car ownership declined slightly in New South Wales, Victoria, and Western Australia, but there was a significant increase in the Northern Territory. Tasmania has just overtaken South Australia as the state with the highest car ownership at 63.1 cars per 100 residents.
Technical note: the ownership rate is calculated as the sum of car, motorbike and truck licenses – including learner and probationary licences, divided by population. Some people have more than one driver’s licence so it’s likely to be an over-estimate of the proportion of the population with any licence.
There’s been slowing growth over time, but Victoria has seen slow decline since 2011, and the ACT peaked in 2014.
Here’s a breakdown by age bands for Australia as a whole (note each chart has a different Y-axis scale):
There was a notable uptick in licence ownership for 16-19 year-olds in 2018. Otherwise licencing rates have increased for those over 40, and declined for those aged 20-39.
Licencing rates for teenagers (refer next chart) had been trending down in South Australia and Victoria until 2017, but all states saw an increase in 2018 (particularly Western Australia). The most recent 2019 data from NSW and Queensland shows a decline. The differences between states partly reflects different minimum ages for licensing.
The trends are mixed for 20-24 year-olds: the largest states of Victoria and New South Wales have seen continuing declines in licence ownership, but all other states and territories are up (except Queensland in 2019).
New South Wales, Victoria, and – more recently – Queensland are seeing downward trends in the 25-29 age bracket:
Licencing rates for people in their 70s are rising in all states (I suspect a data error for South Australia in 2016):
A similar trend is clear for people aged 80+ (Victoria was an anomaly before 2015):
[this emissions section updated on 8 January 2020 with BITRE estimates for 1975-2019]
According to the latest adjusted quarterly figures, Australia’s domestic non-electric transport emissions peaked in 2018 and have been slightly declining in 2019, which reflects reduced consumption of petrol and diesel. However it is too early to know whether this is another temporary peak or long-term peak.
Non-electric transport emissions made up 18.8% of Australia’s total emissions as at September 2019.
Here’s a breakdown of transport emissions:
A more detailed breakdown of road transport emissions is available back to 1990:
Here’s growth in transport sectors since 1975:
Road emissions have grown steadily, while aviation emissions took off around 1991. You can see that 1990 was a lull in aviation emissions, probably due to the pilots strike around that time.
In more recent years non-electric rail emissions have grown strongly. This will include a mix of freight transport and diesel passenger rail services – the most significant of which will be V/Line in Victoria, which have grown strongly in recent years (140% scheduled service kms growth between 2005 and 2019). Adelaide’s metropolitan passenger train network has run on diesel, but more recently has been transitioning to electric.
Here is the growth in each sector since 1990 (including a breakdown of road emissions):
Here are average emissions per capita for various transport modes in Australia, noting that I have used a log-scale on the Y-axis:
Per capita emissions are increasing for most modes, except cars. Total road transport emissions per capita peaked in 2004 (along with vehicle kms per capita, as above).
It’s possible to combine data sets to estimate average emissions per vehicle kilometre for different vehicle types (note I have again used a log-scale on the Y-axis):
Note: I suspect the kinks for buses and trucks in 2015, and motor cycles in 2011 are issues to do with assumptions made by BITRE, rather than actual changes.
The only mode showing significant change is cars – which have reduced from 281 g/km in 1990 to 243 g/km in 2019.
However, the above figures don’t take into account the average passenger occupancy of vehicles. To get around that we can calculate average emissions per passenger kilometre for the passenger-orientated modes:
Domestic aviation estimates go back to 1975, and you can see a dramatic decline between then and around 2004 – followed little change (even a rise in recent years). However I should mention that some of the domestic aviation emissions will be freight related, so the per passenger estimates might be a little high.
Car emissions per passenger km in 2018-19 were 154.5g/pkm, while bus was 79.4g/pkm and aviation 127.2g/pkm.
Of course the emissions per passenger kilometres of a bus or plane will depend on occupancy – a full aeroplane or bus will have likely have significantly lower emissions per passenger km. Indeed, the BITRE figures imply an average bus occupancy of around 9 people (typical bus capacity is around 60) – so a well loaded bus should have much lower emissions per passenger km. The operating environment (city v country) might also impact car and bus emissions. On the aviation side, BITRE report a domestic aviation average load factor of 78% in 2016-17.
Cost of transport
The final topic for this post is the real cost of transport. Here are headline real costs (relative to CPI) for Australia:
Technical note: Private motoring is a combination of factors, including motor vehicle retail prices and automotive fuel. Urban transport fares include public transport as well as taxi/ride-share.
The cost of private motoring has tracked relatively close to CPI, although it trended down between 2008 and 2016. The real cost of motor vehicles has plummeted since 1996. Urban transport fares have been increasing faster than CPI since the late 1970s, although they have grown slower than CPI (on aggregate) since 2013.
Here’s a breakdown of the real cost of private motoring and urban transport fares by city (note different Y-axis scales):
Note: I suspect there is some issue with the urban transport fares figure for Canberra in June 2019. The index values for March, June, and September 2019 were 116.3, 102.0, and 118.4 respectively.
Urban transport fares have grown the most in Brisbane, Perth and Canberra – relative to 1973.
However if you choose a different base year you get a different chart:
What’s most relevant is the relative change between years – eg. you can see Brisbane’s experiment with high urban transport fare growth between 2009 and 2017 in both charts.
Hopefully this post has provided some useful insights into transport trends in Australia.
Paid parking is often used when too many people want to park their car in the same place at the same time. Does it encourage people to cycle or use public transport instead of driving? Does that depend on the type of destination and/or availability of public transport? Are places with paid parking good targets for public transport upgrades?
In this post I’m going to try to answer the above questions. I’ll look at where there is paid parking in Melbourne, how transport mode shares vary for destinations across the city, and then the relationship between the two. I’ll take a deeper look at different destination types (particularly hospitals), explore the link between paid parking and employment density, and conclude with some implications for public transport planners. There’s a bit to get through so get comfortable.
This post uses data from around 158,000 surveyed trips around Greater Melbourne collected as part of a household travel survey (VISTA) between 2012 and 2018, as well as journey to work data from the 2016 ABS census.
Unfortunately the data available doesn’t allow for perfect analysis. The VISTA’s survey sample sizes are not large, I don’t have data about how much was paid for parking, nor whether other parking restrictions might impact mode choice (e.g. time limits), and I suspect some people interpreted survey questions differently. But I think there are still some fairly clear insights from the data.
Where is there paid parking in Melbourne?
I’m not aware of an available comprehensive car park pricing data set for Melbourne. Parkopedia tells you about formal car parks (not on street options) and doesn’t share data sets for free, while the City of Melbourne provides data on the location, fees, and time restrictions of on-street bays (only). So I’ve created my own – using the VISTA household travel survey.
For every surveyed trip involving parking a car, van, or truck, we know whether a parking fee was payable. However the challenge is that VISTA is a survey, so the trip volumes are small for any particular place. For my analysis I’ve used groups of ABS Destination Zones (2016 boundaries) that together have at least 40 parking trips (excluding trips where the purpose was “go home” as residential parking is unlikely to involve a parking fee). I’ve chosen 40 as a compromise between not wanting to have too small a sample, and not wanting to have to aggregate too many destination zones. In some cases a single destination zone has enough parking trips, but in most cases I have had to create groups.
I’ve tried to avoid merging different land uses where possible, and for some parts of Melbourne there are just not enough surveyed parking trips in an area (see appendix at the end of this post for more details). Whether I combine zones or use a single zone, I’m calling these “DZ groups” for short.
For each DZ group I’ve calculated the percentage of vehicle parking trips surveyed that involved someone paying a parking fee. The value will be low if only some circumstances require parking payment (eg all-day parking on weekdays), and higher if most people need to pay at most times of the week for both short and long stays (but curiously never 100%). The sample for each DZ group will be a small random sample of trips from different times of week, survey years, and durations. For DZ groups with paid parking rates above 20%, the margin of error for paid parking percentage is typically up to +/- 13% (at a 90% confidence interval).
Imperfect as the measure is, the following map shows DZ groups with at least 10% paid parking, along with my land use categorisations (where a DZ group has a specialised land use).
There are high percentages of paid parking in the central city, as you’d expect. Paid parking is more isolated in the suburbs – and mostly occurs at university campuses, hospitals, larger activity centres, and of course Melbourne Airport.
The next chart shows the DZ groups with the highest percentages of paid parking (together with the margin of error).
Technical note: the Y-axis shows the SA2 name, rather than the (unique but meaningless) DZ code(s), so you will see multiple DZ groups with the same SA2 name.
At the top of the chart are central city areas, major hospitals, several university campuses, and Melbourne Airport.
the area around Melbourne Zoo (Parkville SA2 – classified as “other”),
some inner city mixed-use areas,
two shopping centres – the inner suburban Victoria Gardens Shopping Centre in Richmond (which includes an IKEA store), and Doncaster (Westfield) – the only large middle suburban centre to show up with significant paid parking (many others now have time restrictions), and
some suburban industrial employment areas (towards the bottom of the chart) – in which I’ve not found commercial car parks.
These are mostly places of high activity density, where land values don’t support the provision of sufficient free parking to meet all demand.
While the data looks quite plausible, the calculated values not perfect, for several reasons:
Some people almost certainly forget that they paid for parking (or misinterpreted the survey question). For example, on the Monash University Clayton campus, 45% of vehicle driver trips (n = 126) said no parking fee was payable, 2% said their employer paid, and 12% said it was paid through a salary arrangement. However there is pretty much no free parking on campus (at least on weekdays), so I suspect many people forgot to mention that they had paid for parking in the form of a year or half-year permit (I’m told that very few staff get free parking permits).
Many people said they parked for free in an employee provided off-street car park. In this instance the employer is actually paying for parking (real estate, infrastructure, maintenance, etc). If this parking is rationed to senior employees only then other employees may be more likely to use non-car modes. But if employer provided is plentiful then car travel would be an attractive option. 22% of surveyed trips involving driving to the Melbourne CBD reported parking in an employer provided car park, about a quarter of those said no parking fee was required (most others said their employer paid for parking).
As already mentioned, the sample sizes are quite small, and different parking events will be at different times of the week, for different durations, and the applicability of parking fees may have changed over the survey period between 2012 and 2018.
The data doesn’t tell us how much was paid for parking. I would expect price to be a significant factor influencing mode choices.
Paid parking is not the only disincentive to travel by private car – there might be time restrictions or availability issues, but unfortunately VISTA does not collect such data (it would be tricky to collect).
How does private transport mode share vary across Melbourne?
The other part of this analysis is around private transport mode shares for destinations. As usual I define private transport as a trip that involved some motorised transport, but not any modes of public transport.
Rich data is available for journeys to work from the ABS census, but I’m also interested in general travel, and for that I have to use the VISTA survey data.
For much of my analysis I am going to exclude walking trips, on the basis that I’m primarily interested in trips where private transport is in competition with cycling and public transport. Yes there will be cases where people choose to walk instead of drive because of parking challenges, but I’m assuming not that many (indeed, around 93% of vehicle driver trips in the VISTA survey are more than 1 km). An alternative might be to exclude trips shorter than a certain distance, but then that presents difficult decisions around an appropriate distance threshold.
Here’s a map of private transport mode share of non-walking trips by SA2 destination:
Technical note: I have set the threshold at 40 trips per SA2, but most SA2s have hundreds of surveyed trips.The grey areas of the map are SA2s with fewer than 40 trips, and/or destination zones with no surveyed trips.
For all but the inner suburbs of Melbourne, private transport is by far the dominant mode for non-walking trips. Public transport and cycling only get a significant combined share in the central and inner city areas.
Where is private transport mode share unusually low? And could paid parking explain that?
The above chart showed a pretty strong pattern where private transport mode share is lower in the central city and very high in the suburbs. But are there places where private mode share in unusually low compared to surround land uses? These might be places where public transport can win a higher mode share because of paid parking, or other reasons.
Here’s a similar mode share map, but showing only DZ groups that have a private mode share below 90%:
If you look carefully you can see DZ groups with lower than 80% mode share, including some university/health campuses.
To better illustrate the impact of distance from the city centre, here’s a chart summarising the average private transport mode share of non-walking trips for selected types of places, by distance from the city centre:
Most destination place types are above 90% private transport mode share, except within the inner 5 km. The lowest mode shares are at tertiary education places, workplaces in the central city, secondary schools and parks/recreation. Up the top of the chart are childcare centres, supermarkets and kinders/preschool. Sorry it is hard to decode all the lines – but the point is that they are mostly right up the top.
The next chart brings together the presence of paid parking, distance from the CBD, destination place type, and private transport mode shares. I’ve greyed out DZ groups with less than 20% paid parking, and you can see they are mostly more than 3 km from the CBD. I’ve coloured and labelled the DZ groups with higher rates of paid parking. Also note I’ve used a log scale on the X-axis to spread out the paid DZ groups (distance from CBD).
Most of the DZ groups follow a general curve from bottom-left to top-right, which might reflect generally declining public transport service levels as you move away from the city centre.
The outliers below the main cloud are places with paid parking where private modes shares are lower than other destinations a similar distance from the CBD. Most of these non-private trips will be by public transport. The biggest outliers are university campuses, including Parkville, Clayton, Caulfield, Burwood, and Hawthorn. Some destinations at the bottom edge of the main cloud include university campuses in Kingsbury and Footscray, and parts of the large activity centres of Box Hill and Frankston.
Arguably the presence of paid parking could be acting as a disincentive to use private transport to these destinations.
Contrast these with other paid parking destinations such as hospitals, many activity centres, and Melbourne Airport. The presence of paid parking doesn’t seem to have dissuaded people from driving to these destinations.
Which raises a critical question: is this because of the nature of travel to these destinations means people choose to drive, or is this because of lower quality public transport to those centres? Something we need to unpack.
How strongly does paid car parking correlate with low private transport mode shares?
Here’s a chart showing DZ groups with their private transport mode share of (non-walking) trips and percent of vehicle parking trips involving payment.
Technical note: A colour has been assigned to each SA2 to help associate labels to data points, although there are only 20 unique colours so they are re-used for multiple SA2s. I have endeavoured to make labels unambiguous. It’s obviously not possible to label all points on the chart.
In the top-left are many trip destinations with mostly free parking and very high private transport mode share, suggesting it is very hard for other modes to compete with free parking (although this says nothing about the level of public transport service provision or cycling infrastructure). In the bottom-right are central city DZ groups with paid parking and low private transport mode share.
There is a significant relationship between the two variables (p-value < 0.0001 on a linear regression as per line shown), and it appears that the relative use of paid parking explains a little over half of the pattern of private transport mode shares (R-squared = 0.61). But there is definitely a wide scattering of data points, suggesting many other factors are at play, which I want to understand.
In particular it’s notable that the data points close to the line in the bottom-right are in the central city, while most of the data points in the top-right are mostly in the suburbs (they are also the same land use types that were an exception in the last chart – Melbourne Airport, hospitals, some university campuses, and activity centres).
As always, it’s interesting to look at the outliers, which I am going to consider by land use category.
The airport destination zone has around 62% paid parking and around 92% private transport mode share for general trips (noting the VISTA survey is only of travel by Melbourne and Geelong residents). The airport estimates 14% of non-transferring passengers use some form of public transport, and that 27% of weekday traffic demand is employee travel.
Some plausible explanations for high private mode share despite paid parking include:
shift workers travelling when public transport is infrequent or unavailable (I understand many airport workers commence at 4 am, before public transport has started for the day),
unreliable work finish times (for example, if planes are delayed),
longer travel distances making public transport journeys slower and requiring transfers for many origins,
travellers with luggage finding public transport less convenient,
highly time-sensitive air travellers who might feel more in control of a private transport trip,
active transport involving long travel distances with poor infrastructure, and
many travel costs being paid by businesses (not users).
It’s worth noting that the staff car park is remote from the terminal buildings, such that shuttle bus services operate – an added inconvenience of private transport. But by the same token, the public transport bus stops are a fairly long walk from terminals 1 and 2.
The destination zone that includes the airport terminals also includes industrial areas on the south side of the airport. If I aggregate only the surveyed trips with a destination around the airport terminals, that yields 69% paid parking, and 93% private mode share. Conversely, the industrial area south of the airport yields 6% paid parking, and 100% private mode share.
Almost all hospitals are above the line – i.e. high private mode share despite high rates of paid parking.
The biggest outliers are the Monash Medical Centre in Clayton, Austin/Mercy Hospitals in Heidelberg, and Sunshine Hospital in St Albans South.
The Heidelberg hospitals are adjacent to Heidelberg train station. The Monash Medical Centre at Clayton is within 10 minutes walk of Clayton train station where trains run every 10 minutes or better for much of the week, and there’s also a SmartBus route out the front. Sunshine Hospital is within 10 minutes walk of Ginifer train station (although off-peak services mostly run every 20 minutes).
It’s not like these hospitals are a long way from reasonably high quality public transport. But they are a fair way out from the CBD, and only have high quality public transport in some directions.
The DZ containing Royal Melbourne Hospital, Royal Women’s Hospital, and Victoria Comprehensive Cancer Centre in Parkville is the exception below the line. It is served by multiple high frequency public transport lines, and serves the inner suburbs of Melbourne (also well served by public transport) which might help explain its ~45% private transport mode share.
The Richmond hospital DZ group is close to the line – but this is actually a blend of the Epworth Hospital and many adjacent mixed land uses so it’s not a great data point to analyse unfortunately.
So what might explain high private transport mode shares? I think there are several plausible explanations:
shift workers find public transport infrequent, less safe, or unavailable at shift change times (similar to the airport),
visitors travel at off-peak times when public transport is less frequent,
longer average travel distances (hospitals serve large population catchments with patients and visitor origins widely dispersed),
specialist staff who work across multiple hospitals on the same day,
patients need travel assistance when being admitted/discharged, and
visitor households are time-poor when a family member is in hospital.
The Parkville hospital data point above the line is the Royal Children’s Hospital. Despite having paid parking and being on two frequent tram routes, there is around 80% private transport mode share. This result is consistent with the hypotheses around time-poor visitor households, patients needing assistance when travelling to/from hospitals, and longer average travel distances (being a specialised hospital).
We can also look at census journey to work data for hospitals (without worrying about small survey sample sizes). Here’s a map showing the relative size, mode split and location of hospitals around Melbourne (with at least 200 journeys reported with a work industry of “Hospital”):
It’s a bit congested in the central city so here is an enlargement:
The only hospitals with a minority private mode share of journeys to work are the Epworth (Richmond), St Vincent’s (Fitzroy), Eye & Ear (East Melbourne), and the Aboriginal Health Service (Fitzroy) (I’m not sure that this is a hospital but it’s the only thing resembling a hospital in the destination zone).
Here’s another chart of hospitals showing the number of journeys to work, private transport mode share, and distance from the Melbourne CBD:
Again, there’s a very strong relationship between distance from the CBD and private transport mode share.
Larger hospitals more than 10 km from the CBD (Austin/Mercy, Box Hill, Monash) seem to have slightly lower private mode shares than other hospitals at a similar distance, which might be related to higher parking prices, different employee parking arrangements, or it might be that they are slightly closer to train stations.
The (relatively small) Royal Talbot Hospital is an outlier on the curve. It is relatively close to the CBD but only served by ten bus trips per weekday (route 609).
To test the public transport quality issue, here’s a chart of journey to work private mode shares by distance from train stations:
While being close to a train station seems to enable lower private transport mode shares, it doesn’t guarantee low private transport mode shares. The hospitals with low private transport mode shares are all in the central city.
So perhaps the issue is as much to do with the public transport service quality of the trip origins. The hospitals in the suburbs largely serve people living in the suburbs which generally have lower public transport service levels, while the inner city hospitals probably more serve inner city residents who generally have higher public transport service levels and lower rates of motor vehicle ownership (see: What does the census tell us about motor vehicle ownership in Australian cities? (2006-2016)).
Indeed, here is a map showing private transport mode share of non-walking trips by origin SA2:
Technical notes: grey areas are SA1s (within SA2s) with no survey trips.
Finally for hospitals, here is private transport mode share of journeys to work (from the census) compared to paid parking % from VISTA (note: sufficient paid parking data is only available for some hospitals, and we don’t know whether staff have to pay for parking):
There doesn’t appear to be a strong relationship here, as many hospitals with high rates of paid parking also have high private transport mode shares.
The distance of a hospital from the CBD seems to be the primary influence on mode share.
Specialised hospitals with larger catchments (eg Children’s Hospital) might have higher private transport mode shares.
The quality of public transport to the hospital seems to have a secondary impact on mode shares.
Suburban activity centres such as Frankston, Box Hill, Dandenong, and Springvale have high private mode shares, which might reflect lower public transport service levels than the inner city (particularly for off-rail origins).
Box Hill is the biggest outlier for activity centres in terms of high private mode share despite paid parking. But compared to other destinations that far from the Melbourne CBD, it has a relatively low private transport mode share. It is located on a major train line, and is served by several frequent bus routes.
In general, there are fewer reasons why increased public transport investment might not lead to higher public transport mode share compared to airports and hospitals. Travel distances are generally shorter, many people will be travelling in peak periods and during the day, there are probably few shift workers (certainly few around-the-clock shift workers).
The biggest university outliers above the line (higher private mode shares and higher paid parking %) are Deakin University (Burwood) and La Trobe University (Kingsbury). Furthermore, private transport also has a majority mode share for Monash University Clayton, Victoria University Footscray Park, Monash University (Caulfield) and Swinburne University (Hawthorn).
As discussed earlier, I suspect the rates of paid parking may be understated for university campuses because people forget they have purchased long-term parking permits.
The following chart shows the full mode split of trips to the University DZ groups in various SA2s (this time including walking trips):
Of the campuses listed, only Hawthorn and Caulfield are adjacent to a train station. Of the off-rail campuses:
Parkville (Melbourne Uni, 43% public transport) is served by multiple frequent tram routes, plus a high frequency express shuttle bus to North Melbourne train station. In a few years it will also have a train station.
Burwood (Deakin, 19% PT) is on a frequent tram route, but otherwise moderately frequent bus services (its express shuttle bus service to Box Hill train station – route 201 – currently runs every 20 minutes)
Footscray (Park) (Victoria Uni, 14% PT) has bus and tram services to Footscray train station but they operate at frequencies of around 15 minutes in peak periods, and 20 minutes inter-peak.
Kingsbury (La Trobe Uni, 13% PT) has an express shuttle bus service from Reservoir station operating every 10 minutes on weekdays (introduced in 2016).
The success of high frequency express shuttle bus services to Parkville and Clayton may bode well for further public transport frequency upgrades to other campuses.
University campuses are also natural targets for public transport as university students on low incomes are likely to be more sensitive to private motoring and parking costs.
However university campuses also have longer average travel distances which might impact mode shares – more on that shortly.
Most central city DZ groups are in the bottom-right of the scatter plot, but there are some notable exceptions:
A Southbank DZ around Crown Casino has 65% paid parking and 70% private transport mode share. This was also an exception when I analysed journey to work (see: How is the journey to work changing in Melbourne? (2006-2016)) and might be explained be relatively cheap parking, casino shift workers, and possibly more off-peak travel (eg evenings, weekends).
Similarly, a Southbank DZ group around the Melbourne Convention and Exhibition Centre / South Wharf retail complex has 62% paid parking and around 74% private mode share. Many parts of this area are a long walk from public transport stops, and also there are around 2,200 car parks on site (with $17 early bird parking at the time of writing).
Albert Park – a destination zone centred around the park – has around 54% paid parking and 87% private transport mode share. Most of the VISTA survey trips were recreation or sport related, which may include many trips to the Melbourne Sports and Aquatic Centre. The park is surrounded by tram routes on most sides, but is relatively remote from the (rapid) train network.
Northern Docklands shows up with around 50% paid parking and around 88% private transport mode share, despite being very close to the Melbourne CBD. While this area is served by multiple frequent tram routes, it is a relatively long walk (or even tram ride) from a nearby a train station (from Leven Avenue it is 16 minutes by tram to Southern Cross Station and around 18 minutes to Flagstaff Station, according to Google). The closest train station is actually North Melbourne, but there is currently no direct public transport or pedestrian connection (the E-gate rail site and future Westgate Tunnel road link would need to be crossed).
Some places to the bottom-left of the cloud on the chart include inner suburban areas such as South Yarra, Fitzroy, Richmond, Abbotsford, Brunswick, and Collingwood. While paid parking doesn’t seem to be as common, private transport mode shares are relatively low (even when walking trips are excluded). These areas typically have dense mixed-use activity with higher public transport service levels, which might explain the lower private transport mode shares. These areas probably also have a lot of time-restricted (but free) parking.
What is the relationship between paid parking and journey to work mode shares?
For journeys to work we thankfully have rich census data, with no issues of small survey sample sizes.
The following chart combines VISTA data on paid parking, with 2016 census data on journey to work mode shares (note: the margin of error on the paid parking percentage is still up to +/-12%).
The pattern is very similar to that for general travel, and the relationship is of a similar strength (r-squared = 0.59).
There are more DZ groups below the line on the left side of the chart, meaning that the private transport mode share of journeys to work is often lower than for general travel.
Indeed, here is a chart comparing private transport mode share of general travel (VISTA survey excluding walking and trips to go home) with journeys to work (ABS census):
Note the margin of error for private transport mode shares is around +/-10% because of the small VISTA sample sizes.
For most DZ groups of all types, private transport mode shares are lower for journeys to work compared to general travel (ie below the diagonal line). This might reflect public transport being more competitive for commuters than for visitors – all-day parking might be harder to find and/or more expensive. This suggests investment in public transport might want to target journeys to work.
The DZ groups above the line include Flemington Racecourse (census day was almost certainly not a race day so there was probably ample parking for employees, while many VISTA survey trips will be from event days), Deakin Uni (Burwood), and a few others. Some of these DZ groups are dominated by schools, where workers (teachers) drive while students are more likely to cycle or catch public transport.
What about public transport mode shares?
The following chart shows VISTA public transport mode shares (for general travel) against paid parking percentages:
There are similar patterns to the earlier private transport chart, but flipped. The outliers are very similar (eg hospitals and Melbourne Airport in the bottom-right), although the top-left outliers include some destinations in socio-economically disadvantaged areas (eg Braybrook, Broadmeadows, Dandenong).
The DZ group in Blackburn South with no paid parking but 22% public transport mode share contains several schools but otherwise mostly residential areas, and the survey data includes many education related trips.
Are shift workers less likely to use public transport?
Shift workers at hospitals, Melbourne Airport, and the casino might be less likely to use public transport because of the inconvenience of travelling at off-peak shift change times, when service levels may be lower or non-existent.
Here’s a chart showing the mode split of VISTA journeys to work by destination type categories, and also type of working hours:
For hospitals, rostered shifts had a lower public transport mode share, compared to fixed and flexible hours workers, so this seems to support (but not prove) the hypothesis.
Public transport use is actually higher for rostered shift workers at other destination types, but I suspect these are mostly not around-the-clock shifts (eg retail work), and are more likely to be lower paid jobs, where price sensitivity might contribute more to mode choice.
Unfortunately there are not enough VISTA journey to work survey responses for Melbourne Airport to get sensible estimates of mode shares for different work types.
Do longer travel distances result in lower public transport mode shares?
Another earlier hypothesis was that destinations that attract longer distance trips (such as universities, hospitals, and airports) are more likely to result in private transport mode choice, as public transport journeys are more likely to require one or more transfers.
Trip distances to specialised places such as airports, suburban employment areas, universities and hospitals are indeed longer. But the central city also rates here and that has low private transport mode shares.
Digging deeper, here are median travel distances to DZ groups around Melbourne:
The central city has higher median trip distances but low private mode shares, while many suburban destinations (particularly employment/industrial areas, universities, and hospitals) have similar median travel distances but much higher public transport mode shares.
I think a likely explanation for this is that public transport to the central city is generally faster (often involving trains), more frequent, and involves fewer/easier transfers. Central city workers are also more likely to live near radial public transport lines. On the other hand, the trip origins for suburban destinations are more likely to be in the suburbs where public transport service levels are generally lower (compared to trip origins in the inner suburbs).
Cross-suburban public transport travel will often require transfers between lower frequency services, and will generally involve at least one bus leg. Very few Melbourne bus routes are currently separated from traffic, so such trips are unlikely to be as fast as private motoring (unless parking takes a long time to find), but they might be able to compete on marginal cost (if there is more expensive paid parking).
Of course this is not to suggest that cross-suburban public transport cannot be improved. More direct routes, higher frequencies, and separation from traffic can all make public transport more time-competitive.
How does parking pricing relate to employment density?
The following chart compares weighted job density (from census 2016) and paid parking percentages (from VISTA):
Technical notes: Weighted job density is calculated as a weighted average of the job densities of individual destination zones in a DZ group, with the weighting being the number of jobs in each zone (the same principle as population weighted density). I have used a log-scale on the X-axis, and not shown DZ groups with less than 1 job/ha as they are not really interesting
There appears to be a relationship between job density and paid parking – as you would expect. The top right quadrant contains many university campuses, hospitals, and central city areas with high job density and high paid parking percentages.
In the bottom-right are many large job-dense shopping centres that offer “free” parking. Of course in reality the cost of parking is built into the price of goods and services at the centres (here’s a thought: what if people who arrive by non-car modes got a discount?). An earlier chart showed us that employees are less likely to commute by private transport than visitors.
The outliers to the top-left of the chart are actually mostly misleading. An example is Melbourne Airport where the density calculation is based on a destination zone that includes runways, taxiways, a low density business park, and much green space. The jobs are actually very concentrated in parts of that zone (e.g. passenger terminals) so the density is vastly understated (I’ve recommended to the ABS that they create smaller destination zones around airport terminal precincts in future census years).
Inclusion of significant green space and/or adjacent residential areas is also an issue at La Trobe University (Kingsbury data point with just under 50% mode share), RMIT Bundoora campus (Mill Park South), Royal Children’s Hospital (Parkville), Sunshine Hospital (St Albans South), Victoria University (Footscray (Park)), Albert Park (the actual park), and Melbourne Polytechnic Fairfield campus / Thomas Embling Hospital (Yarra – North).
I am at a loss to explain paid parking in Mooroolbark – the only major employer seems to be the private school Billanook College.
Can you summarise the relationship between paid parking and mode shares?
I know I’ve gone down quite a few rabbit holes, so here’s a summary of insights:
Distance from the Melbourne CBD seems to be the strongest single predictor of private transport mode share (as origin or destination). This probably reflects public transport service levels generally being higher in the central city and lower in the suburbs. Destinations further from the central city are likely to have trip origins that are also further from the central city, for which public transport journeys are often slower.
Paid parking seems to be particularly effective at reducing private transport mode shares at university campuses, and the impact is probably greater if there are higher quality public transport alternatives available.
There’s some evidence to suggest paid parking may reduce private transport mode shares at larger activity centres such as Box Hill and Frankston.
Most hospitals have very high private transport mode shares, despite also having paid parking. Hospitals with better public transport access have slightly lower private transport mode shares.
Destinations with around-the-clock shift workers (e.g. hospitals and airports) seem generally likely to have high private transport mode shares, as public transport services at shift change times might be infrequent or unavailable.
Suburban destinations that have longer median travel distances (such as hospitals, airports and industrial areas) mostly have higher private transport mode shares.
Even if there isn’t much paid parking, destinations well served by public transport tend to have lower private transport mode shares (although this could be related to time-restricted free parking).
Are places with paid parking good targets for public transport investments?
Many of my recent conversations with transport professionals around this topic have suggested an hypothesis that public transport wins mode share in places that have paid parking. While that’s clearly the case in the centre of Melbourne and at many university campuses, this research has found it’s more of a mixed story for other destinations.
While this post hasn’t directly examined the impact of public transport investments on mode shares in specific places, I think it can inform the types of destinations where public transport investments might be more likely to deliver significant mode shifts.
Here’s my assessment of different destination types (most of which have paid parking):
Suburban hospitals may be challenging due to the presence of shift workers, patients needing assistance, visitors from time-poor households, and long average travel distances making public transport more difficult for cross-suburban travel. There’s no doubt many people use public transport to travel to hospitals, but it might not include many travellers who have a private transport option.
Larger activity centres with paid parking show lower private transport mode shares. Trips to these centres involve shorter travel distances that probably don’t require public transport transfers, and don’t suffer the challenges of around-the-clock shift workers, so they are likely to be good targets for public transport investment.
Universities are natural targets for public transport, particularly as many students would find the cost of maintaining, operating and parking a car more challenging, or don’t have access to private transport at all (around 35% of full time university/TAFE students do not have a full or probationary licence according to the VISTA sample). Universities do attract relatively higher public transport mode shares (even in the suburbs) and recent investments in express shuttle services from nearby train stations appear to have been successful at growing public transport patronage.
Melbourne Airport has high rates of paid parking and private transport mode share. It is probably a challenging public transport destination for employees who work rostered shifts. However already public transport does well for travel from the CBD, and this will soon be upgraded to heavy rail. Stations along the way may attract new employees in these areas, but span of operating hours may be an issue.
Job dense central city areas that are not currently well connected to the rapid public transport network could be public transport growth opportunity. In a previous post I found the largest journey to work mode shifts to public transport between 2011 and 2016 were in SA2s around the CBD (see: How is the journey to work changing in Melbourne? (2006-2016)). The most obvious target to me is northern Docklands which is not (yet) conveniently connected its nearby train station. Public transport is also gaining patronage in the densifying Fishermans Bend employment area (buses now operate as often as every 8 minutes in peak periods following an upgrade in October 2018).
Lower density suburban employment/industrial areas tend to have free parking, longer travel distances, and very high private transport mode shares. These are very challenging places for public transport to win significant mode share, although there will be some demand from people with limited transport options.
An emerging target for public transport might be large shopping centres that are starting to introduce paid or time-restricted car parking (particularly those located adjacent to train stations, e.g. Southland). That said, Westfield Doncaster, which has some paid parking (around 19%), has achieved only 6% public transport mode share in the VISTA survey (n=365), athough this may be growing over time. Meanwhile, Dandenong Plaza has around 16% public transport mode share despite only 6% paid parking.
Upgraded public transport to shopping centres might be particularly attractive for workers who are generally on lower incomes (we’ve already seen staff having lower private transport mode shares than visitors). Also, customer parking may be time-consuming to find on busy shopping days, which might make public transport a more attractive option, particularly if buses are not delayed by congested car park traffic.
There’s a lot going on in this space, so if you have further observations or suggestions please comment below.
Appendix: About destination group zones
Here is a map showing my destination zone groups in the central city area which have 15% or higher paid parking. Each group is given a different colour (although there are only 20 unique colours used so there is some reuse). The numbers indicate the number of surveyed parking trips in each group:
Some of the DZ groups have slightly less than 40 parking trips, which means they are excluded from much of my analysis. In many cases I’ve decided that merging these with neighbouring zones would be mixing disparate land uses, or would significantly dilute paid parking rates to not be meaningful (examples include northern Abbotsford, and parts of Kew and Fairfield). Unfortunately that’s the limitation of the using survey data, but there are still plenty of qualifying DZ groups to inform the analysis.
I have created destination zone groups for most destination zones with 10%+ paid parking, and most of the inner city area to facilitate the DZ group private transport mode share chart. I haven’t gone to the effort of creating DZ groups across the entire of Melbourne, as most areas have little paid parking and are not a focus for my analysis.
In part 1 of this series, I looked at the radialness of general travel around Melbourne based on the VISTA household travel survey. This part 2 digs deeper into radialness by time of the day and week, and maps radialness and mode share for general travel around Melbourne.
A brief recap on measuring radialness: I’ve been measuring the difference in angle between the bearing of a trip, and a straight line to the CBD from the trip endpoint that is furthest from the CBD (origin or destination). An angle of 0° means the trip is perfectly radial (directly towards or away from the CBD) while 90° means the trip is entirely orbital relative to the CBD. An average angle in the low 40s means that there isn’t really any bias towards radial travel. I’ve been calling this two-way off-radial angle. Refer to part 1 if you need more of a refresher.
How does trip radialness vary by time of week?
The first chart shows the average two-way off-radial angle for trips within Greater Melbourne by time and type of day, for private transport, public transport, and walking.
Technical notes: I’ve had to aggregate weekend data into two hour blocks to avoid issues with small sample sizes. I’m only showing data where there are at least 100 trips for a mode and time (that’s still not a huge sample size so there is some “noise”). Trips times are assigned by the clock hour of the middle of the trip duration. For example, a trip starting at 7:50 am and finishing at 9:30 am has a mid-trip time of 8:40 am and therefore is counted in 8 – 9 am for one hour intervals, and 8 – 10 am for two hour intervals.
You can see:
Public transport trips are much more radial at all times of the week, but most particularly in the early AM peak and in the PM commuter peak. They are least radial in the period 3-4 pm on weekdays (PM school peak), which no doubt reflects school student travel, which is generally less radial.
Private transport trips are more radial before 8 am on weekdays, and in the early morning and late evening on weekends. Curiously private transport trips in the PM peak don’t show up as particularly radial, possibly because there is more of a mix of commuter and other trips at that time.
Walking trips show very little radial bias, except perhaps in the commuter peak times on weekdays.
When I drill down into specific modes, the sample sizes get smaller, so I have used 2 hour intervals on weekdays, and 3 hour intervals on weekends. Also to note is that VISTA assigns a “link mode” to each trip, being the most important mode used in the journey (generally train is highest, followed by tram, bus, vehicle driver, vehicle passenger, bicycle, walking only). I am using this “link mode” in the following charts.
Train trips are the most radial, followed by tram trips (no surprise as these networks are highly radial).
Bicycle trips are generally the third most radial mode, except at school times.
Public bus trips are more radial in the commuter peak periods, and much less radial in the middle of the day on weekdays. The greater radialness in commuter peaks will likely reflect people using buses in non-rail corridors to travel to the city centre (particularly along the Eastern Freeway corridor). Most of Melbourne’s bus routes run across suburbs, rather than towards the city centre, which will likely explain bus-only trips being less radial than train and tram, particularly off-peak.
How does radialness vary by trip purpose and time of week?
The following chart shows the average two-way off-radial angle of trips by trip purpose (at destination) and time of day:
Work related trips are generally the most radial, particularly in the AM peak (as you might expect), but less so on weekdays afternoons.
Weekday education trips are the next most radial (excluding trips to go home in the afternoon and evening), except at school times (school travel being less radially biased than tertiary education travel).
Social trips become much more radial late at night on weekends, probably reflecting inner city destinations.
Recreational trips are the least radial on weekends.
Otherwise most other trip purposes average around 35-40° – which is only slightly weighted towards radial travel.
What is the distribution of off-radial angles by time of day?
So far my analysis has been looking at radialness, without regard to whether trips are towards or away from the CBD. I’ve also used average off-radial angles which hides the underlying distribution of trip radialness.
I’m curious as to whether modes are dominated by inbound or outbound trips at any times of the week (particularly private transport), and the distribution of trips across various off-radial angles.
So to add the inbound/outbound component of radialness, I am going to use a slightly different measure, which I call the “one-way off-radial angle”. For this I am using a scale of 0° to 180°, with 0° being directly towards the CBD, and 180° being directly away from the CBD, and 90° being a perfectly orbital trip with regard to the CBD. For inbound trips, the one-way off-radial angle will be the same as the two-way off-radial angle, but outbound trips will instead fall in the 90° to 180° range.
One-way off-radial angles are still calculated relative to the trip end point (origin or destination) that is furthest from the CBD. I explained this in part 1.
Here is the distribution of one-way off-radial angles by time of day for trips where train was the main mode:
A reminder: only time intervals with a sample of at least 100 trips are shown.
In the morning, trips are very much inbound radial, with around three-quarters being angles of 0°-10°. Likewise in the PM peak, almost three-quarters of train trips are very outbound radial with angles 170°-180°.
As per the second chart in this post, train trips remain very radial throughout the day. But there is slightly more diversity in off-radial angles 3-4 pm on weekdays, when many school students use trains for journeys home from school that are less radially biased. Less radial trips could be a result of using two train lines, using bus in combination with train, or using a short section of the train network that isn’t as radial (eg Eltham to Greensborough, Williamstown to Newport, or a section of the Alamein line).
On weekends it’s interesting to see that there are many more inbound than outbound journeys between 12 pm and 2 pm on weekends. The “flip time” when outbound journeys outnumber inbound journeys is probably around 2 pm. This is consistent with CBD pedestrian counters that show peak activity in the early afternoon.
One problem with the chart above is that volumes of train travel vary considerably across the day. So here’s the same data, but as (estimated) average daily trips:
You can see the intense peak periods on weekdays, and a gradual switch from inbound trips to outbound trips around 1 pm on weekdays. There’s also a mini-peak in the “contra-peak” directions (outbound trips in the AM peak and inbound trips in the PM peak).
The weekend volumes are for two hour intervals so not directly comparable to weekdays (which are calculated for one hour intervals), but you can see higher volumes of inbound trips until around 2 pm, and then outbound trip volumes are higher.
Those results for trains were probably not surprising, but what about private vehicle driver trips?
There is much more diversity in off-radial angles at all times of the day, and a less severe change between inbound and outbound trips across the day.
On both weekday and weekend mornings there is a definite bias towards inbound travel. Afternoons and evenings are biased towards outbound travel, but not nearly as much (it’s much stronger late at night). This is consistent with the higher average two-way off-radial angle seen for private transport in the PM peak compared to the AM peak.
Here is the same data again but in volumes:
This shows the weekday AM peak spread concentrated between 8 and 9 am, while the PM peak is more spread over three hours (beginning with the end of school).
Here are the same two charts for tram trips (the survey sample is smaller, so we can only see results for weekdays):
Again there is a strong bias to inbound trips in the morning and outbound in the afternoon, with slightly more diversity in the PM school peak, and early evening.
Next up public bus (a separate category to school buses, however many school students do travel on public buses):
There is a lot more diversity in off-radial angles (particularly 2-4 pm covering the end of school), but also the same trend of more inbound trips in the morning and outbound trips in the afternoon.
Next up, bicycle:
There’s a fair amount of diversity, across the day, with inbound trips dominating the AM peak and outbound trips in the PM commuter peak (but not as strongly in the PM school peak). Weekend late afternoon trips show a little more diversity than morning and early afternoon trips, but the volumes are relatively small.
Next is walking trips:
There is considerable diversity in off-radial angles across most of the week, although outbound trips have a larger share in the late evening.
Walking volumes on weekdays peak at school times. On weekends walking seems to peak between 10 am and 12 pm and again 4 pm to 6 pm, but not considerably compared to the rest of the day time.
Mapping mode shares and radialness
So far I’ve been looking at radialness for modes by time of day. This section next section looks at radialness and mode shares by origins and destinations within Melbourne.
In recent posts I’ve had fun mapping journeys to work from census data (see: Mapping Melbourne’s journeys to work), so I’ve been keep to explore what’s possible for general travel.
VISTA is only a survey of travel (rather than a census), so if you want to map mode shares of trips around the city, you unfortunately need to lose a lot of geographic resolution to get reasonable sample sizes.
The following map shows private transport mode shares for journeys between SA3s (which are roughly the size of municipalities), where there were at least 80 surveyed trips (yes, that is a small sample size so confidence intervals are wider, but I’m also showing mode shares in 10% ranges). Dots indicate trips within an SA3, and lines indicate trips between SA3s. I’ve animated the map to make try to make it slightly easier to call out the high and low private mode shares.
You can see lower private transport mode shares for radial travel involving the central city (Melbourne City SA3), particularly from inner and middle suburbs (less so from outer suburbs). Radial travel that doesn’t go to the city centre generally has high private transport mode shares.
I also have origin and destination SA1s for surveyed trips. Here is a map showing all SA1-SA1 survey trip combinations by main mode, animated to show intervals of two-way off-radial angles:
It’s certainly not a perfect representation because of the all the overlapping lines (I have used a high degree of transparency). You can generally see more blue lines (public transport) in the highly radial angles, and almost entirely red (private transport) and short green lines (active transport) for larger angle ranges. This is consistent with charts in my last post (see: How radial is general travel in Melbourne? (Part 1)).
You can also see that few trips fall into the 80-90° interval, which is because I’m measuring radialness relative to the trip endpoint furthest from the CBD. An angle of 80-90° requires the origin and destination to be about the same distance from the CBD and for the trip to be relatively short.
So there you go, almost certainly more than you ever wanted or needed to know about the radialness of travel in Melbourne. I suspect many of the patterns would also be found in other cities, although some aspects – such the as the geography of Port Phillip Bay – will be unique to Melbourne.
Again, I want to the thank the Department of Transport for sharing the full VISTA data set with me to enable this analysis.
In a recent post I found that journeys to work are generally quite radial relative to city CBDs. But what about travel for other purposes, travel on different days of the week, and travel to different types of places?
This post explores the radialness of general travel around Melbourne using data from Melbourne’s household travel survey (VISTA – the Victorian Integrated Survey of Travel and Activity), which captures all types of personal travel by residents.
In this post (part 1), I will look at measuring radialness, radialness of weekday and weekend trips, radialness of total distance travelled, and how radialness varies by mode, distance from the CBD, different places, ages and sex.
Part 2 of this analysis will look at radialness at different times of day and also visualising radialness on maps.
Measuring radialness for general travel
Unlike the census of population and housing that only captures journeys from home to work, VISTA measures trips in all directions, including to and from survey home locations, so I need a slightly different measure to my previous post (see: How radial are journeys to work in Australian cities?).
When considering the radialness of a trip, I want to compare the difference between the trip’s alignment and a trip that would head directly towards (or away from) the CBD. I am calling the difference between these alignments the off-radial angle.
So, in the following simple example, the off-radial angle is the difference between the bearing from the origin to destination and the bearing from the origin to the CBD.
The trip is quite radial and the off-radial angle is small. A perfectly radial trip would have an off-radial angle of 0°, while a perfectly orbital trips would have an off-radial angle of 90°.
Unfortunately it’s not always that simple. Let’s consider an trip that is the exact opposite of the example above. If I don’t care about whether the trip is towards or away from the CBD, then I want to get the same radialness measure as the first example because it is equally as radial.
To get the same measure as the first example, I need to measure the off-radial angle with respect to the destination rather the origin. If I measure the off-radial angle for the second trip with respect to its origin then I’d get an angle of around 100°, suggesting a very non-radial trip, which isn’t really the case.
So for this post I am always going to measure the off-radial with respect to the trip end that is furthest from the CBD, whether than be the origin or destination. When I don’t care whether the trip is inbound or outbound, the off-radial angle will always be in the range 0° to 90°.
Just in case you need more convincing, consider the following trip:
The origin is so close to the CBD that this is a very radial trip so I want a small angle, but the off-radial angle with respect to the origin is almost 90°.
If you think about a trip originating in the CBD, the off-radial angle with respect to the origin has everything to do with the location within the CBD compared to the GPO (the actual point I am measuring against), when really a trip from anywhere in the CBD to the suburbs is very radial.
This approach does introduce a slight bias towards smaller off-radial angles. Using the trip end that is furthest from the CBD means that even if you had a completely random distribution of trip origins and destinations, there would be more trips with smaller off-radial angles and fewer trips with angles near 90°. In fact, to get close to a 90° angle, the origin and destination would have to be almost exactly the same distance from the CBD, and the trip be not be very long – an unlikely scenario. So for a truly random distribution of trip directions the off-radial angles will be slightly biased towards smaller angles and the average would be less than 45°. I don’t expect this bias would be large, and we will get a feel for this bias shortly.
Another slight complication is that a short trip within the CBD will have a fairly arbitrary off-radial angle which isn’t very meaningful or relevant. So I am not going to bother considering trips that start and finish within (an arbitrary) 1.5 km of the GPO.
I should point out that a “trip” in VISTA is considered a journey between two places of activity for a person. It may have multiple stops along the way for the purposes of changing mode (e.g. bus to train), but for this post I’m looking at the geometry of the end-to-end trip.
Finally, to differentiate this radialness measure from a slightly different radialness measure I will introduce in part 2 of my analysis, I’m calling it the “two-way off-radial angle” (two-way because I don’t care whether the trip is inbound or outbound with respect to the CBD).
How radial is travel on weekdays and weekends?
This first chart looks at the distribution of trips by mode and two-way off-radial angle interval, with a histogram for each day type:
Technical note: As per usual, I’ve classified any journey involving public transport as “Public”, any journey not involving motorised transport as “Active”, and any journey involving a motorised road-based vehicle as “Private”. With VISTA a quite small number of trips are classified as “other”, which I have excluded.
You can see that on all day types, the most common two-way off-radial angle group is 0°-10° – which are very radial trips. The most radial day type is a school holiday weekday, where there are still many work trips to the central, but a lot fewer non-radial trips to schools.
You can also see that active transport trips are fairly well distributed across the angle intervals (with only a slightly bias towards radial trips), while public transport trips are highly radial on all day types.
Public transport mode share is only really significant for highly radial trips. This probably reflects most (but not all) high quality public transport lines being highly radial and running to the central city where car parking costs are generally much higher.
What surprised me a little is that weekend trips are only slightly less radial than weekdays, even for private transport trips (good quality roads exist in multiple travel directions in most of Melbourne).
What is different between weekday and weekend travel?
Here is a VISTA destination density map of travel around Melbourne by private transport (excluding trips to places of accommodation – such as homes) animated to alternate between school weekdays and weekends. The red areas have the highest concentration of trip destinations.
The central city area dominates weekday destination density, but major suburban shopping centres are also significant destinations on weekends (as you might expect).
Here is a chart showing the distribution of destinations by distance from the CBD by day type:
You can see that trips on non-school weekdays are more likely to be to destinations closer to the CBD (indeed around 19% were to destinations within 2 km of the GPO). This reduces for school weekdays, and further for Saturdays and Sundays.
But as we saw in the first chart, there are still a large number of very radial trips on weekends, so where are these trips going? Are they still mostly going to the CBD, even though fewer people are travelling to the CBD when compared to weekdays?
The following chart is similar to the last, but is filtered for very radial trips – being those with off-radial angles 0° to 10°:
Destinations within 0-2 km of the CBD are the largest category for very radial trips, but actually a minority of (non-home) destinations, even on weekdays. On weekends around two-thirds of very radial trips have a (non-home) destination more than 2 km from the CBD. There are lots of very radial trips on weekdays and weekends, but most of them are to destinations more than 2 km from the CBD.
So are these very radial trips shorter on weekends? Here’s a chart showing the median and average distance of trips by two-way off-radial angle:
Technical note regarding weighted v unweighted: VISTA trips are weighted so that they can be summed to represent travel by the total residential population – with survey home types that are under-represented given a larger weighting. In all my averages I’ve used this weighting, but unfortunately at the time of writing Tableau cannot calculate weighted medians (or percentiles), so I’ve had to calculate unweighted medians instead (I’ve manually checked the weighted medians for 0-10° and they calculate as 9.98 km on weekdays and 7.99 km on weekends – fairly close to the unweighted medians).
For very radial trips (0°-10°), weekend trips are shorter by both measures, but for the next interval (10°-20°) weekend trips are curiously longer. There’s not a huge difference with subsequent angle intervals and I don’t think we should get too excited about them because there will be some noise in the sample.
How radial are trips by distance from the city centre?
If we want to measure radialness against multiple variables, then more histogram charts aren’t going to be practical. So instead I’m going to calculate a single summary statistic: the average two-way off-radial angle.
If the average is small, then trips are very radial, whereas an average near 40-45° would suggest no radial bias.
Here’s a look at radialness by origin distances from the CBD, main mode, and weekdays v weekends:
(in case you are wondering, this chart looks very similar if the X-axis is destination distance from the CBD)
Trips starting closer to the CBD are much more radial for all modes (although much less so for walking), suggesting the CBD is dominant for travel from the inner city.
Public transport trips are the most radial compared to other modes (as we also saw earlier). They are followed by bicycle trips, which probably reflects relatively better cycling infrastructure in the central city, and the fact bicycles can be ridden and parked in the central city for free (unlike public and private transport).
Walking trips are the least radial on average, with almost no radial-bias showing at all. Earlier in this post I pointed out that two-way off-radial angles measured will be slightly biased towards smaller angles, even if trips were truly randomly orientated. I think there’s a good chance that walking trips in the middle and outer suburbs have no radial bias, and the fact they mostly have an average angle of between 40° and 45° suggests the inherent measurement bias to smaller angles is not particularly strong.
In the chart you can also see that within each main mode there is little difference between weekday and weekend trips (for walking trips the smaller weekend sample size is likely introducing some noise).
Furthermore, average two-way off-radial angles are mostly flat for each mode between beyond 10 km from the CBD, with two exceptions:
The weekend public transport average two-way off-radial angle for 40-45 km from the CBD was 23°, influenced by many local non-work trips (and there is insufficient sample size for public transport trips commencing further out).
Trips in outer Melbourne (55+ km) are actually slightly biased towards non-radial travel – i.e. average two-way off-radial angle higher than 45°. It turns out most of them are on the Mornington Peninsula where trip bearings are heavily influenced by the shape of the peninsula.
To visualise this, here is a map showing the average two-way off-radial angle for trip origin SA2s across Greater Melbourne:
Technical note: the map is drawn using SA1 areas, and only SA1s with a trip origin are included, which explains the many small gaps.
Point Nepean (at the bottom of the map) has an average off-radial angle of 52°, which reflects the thin non-radial geography of the end of the peninsula.
Some relative outliers of interest include:
Melbourne Airport has a relatively low average angle of 23°, probably reflecting higher travel volumes to the CBD and wealthy inner city and south-east suburbs where regular air travellers might be more likely to live. However this would be offset by airport workers who tend to live nearby, although those in Sunbury are actually making fairly radial trips. Note that non-resident airport users are not included in VISTA, and I’ve filtered out flights (because almost all of them don’t stay within Greater Melbourne).
Wandin – Seville in the outer east has an average off-radial angle of 18°, probably reflecting that many of the urban settlements in this area are along the east-west (radial) Warburton Highway.
Melton and Melton South have a high average angle of 42° – probably reflecting many local trips within the township that would have no radial bias.
Mount Dandenong – Olinda has a higher average angle of 42° also – probably reflecting some north-south geographic barriers in the area (ie steeper mountain slopes on the western edge).
Several bay-side SA2s have higher radialness, probably reflecting the coast line being fairly radially aligned with the CBD, and the coast being a natural barrier to non-radial travel.
How does radialness vary by trip purpose?
Firstly, here’s a chart showing average two-way off-radial angles by (destination) trip purpose for weekdays and weekends:
Work related trips are generally the most radial, followed by social and then education trips (except weekend education trips, of which there are few).
The least radial trip type is recreational trips on weekends.
Curiously, weekend trips to accompany, pick up or drop off someone were more radial than weekdays, perhaps because more people are not working and able to do this for others, and/or weekend public transport service levels are lower.
VISTA designates a “link mode” to each trip – being generally the highest ranked mode in the journey (trains being highest ranked, followed by trams, buses, vehicle driver, vehicle passenger, bicycle, walking). Actual trips may involve multiple modes, and the off-radial angle is measured for the end-to-end trip, not the part of the trip that used the “link mode”.
The following table shows average two-way off-radial angles for combinations of link mode and trip purpose:
Technical note: gaps in the table are where there were insufficient trips sampled of that trip purpose and mode combination (less than 100).
The table shows that:
Train trips are the most radial, followed by trams, taxis, motorcycles, buses, bicycles, private vehicle travel and finally walking.
Education trips by train and tram are less radial than other trip purposes on trains and trams, I suspect because many will be to schools and universities not in the central city.
How does radialness vary by different destination types?
Here is a chart of average two-way off-radial angles for trips to most VISTA destination place types (those with a sample of at least 100 trips):
Trips to tourist places were the most radial, with most of those destinations in the central city.
Markets attract quite radial trips, probably because many popular markets are in the inner city (eg Queen Victoria, South Melbourne, Prahran, Footscray).
Trips to the bay or beach are the least radial – which makes sense given much of the coastlines are radial in orientation.
Within education places, primary schools have the least radialness, and tertiary institutions the most, with secondary schools in between.
I’ll let you make your own further observations.
How does radialness vary by age and sex?
I expect the patterns are related to the most common occupations and trip purposes for males and females at different ages. For example, 20-24 year-olds might be more likely to be studying at university or working (both resulting in more radial trips), while children are more likely to travel to school which is not generally particularly radial. Females over 30 are less likely to be in the workforce so more likely to make local trips that are not radially biased.
How radial is total distance travelled?
Not all trips are the same length or duration. Longer trips will have more impact on the transport network. So what does radialness look like in terms of total distance travelled?
Here’s a similar chart to the first chart, but the Y-axis is the proportion of distance travelled on that day type, rather than proportion of trips:
A much larger proportion of distance travelled is accounted for by very radial trips (angles 0°-10°) compared to the proportion of trips. In fact it is around 42% on school weekdays, 35% on Saturdays and 37% on Sundays. On this measure weekend travel is again less radial than weekdays, but still heavily biased towards radial travel.
Why is travel so radial in Melbourne?
This analysis has found that travel in Melbourne is biased towards radial trips, even on weekends. I think a few things can explain the more radial nature of general travel, including:
The central and inner city is a major destination for many trips, including those to work, universities, entertainment, and medical facilities. Also, many specialised activities and services are only available in the central and inner city. Trips to these destinations will inherently be more radial.
Other large population-serving destinations such as hospitals, large shopping centres, and universities are more likely to be located in the inner and middle suburbs, resulting in fairly radial travel to them from the outer suburbs.
Melbourne’s urban form (like many cities) has several outer radial corridors, often orientated around train lines. Local travel within these corridors is going to be more radial on average because there are fewer non-radial trip destinations with the urbanised area.
The coastlines of Port Phillip Bay are largely radial in orientation relative to the CBD. In areas near the coastline, it is possible to make radial trips but non-radial trips are restricted by the coastline (as we saw above).
Part 2 of this analysis will focus on radialness of trips at different times of day for different travel purposes and modes, as well as visualising radialness on maps.
Finally, I want to thank the Victorian Department of Transport for sharing the VISTA data set with me and allowing me to publish this analysis.
The unwritten rules of mapping data include avoiding too much data and clutter, and not using too much colour. This blog often violates those rules, and when it comes to visualising journeys to work, I think we can learn a lot about cities with somewhat cluttered colourful animated maps.
This post maps journeys to work in Melbourne, using data from the 2016 census. I will look at which types of home-work pairs have different public, private and active transport mode shares and volumes.
Although this post will focus on Melbourne, I will include a brief comparison to Sydney at the end.
Where are public transport journeys to work in Melbourne?
First I need to explain the maps you are about to see.
So that I can show mode shares, I’ve grouped journeys between SA2s (which are roughly the size of a suburb). Lines are drawn from the population centroid of the home SA2 (thin end) to the employment centroid of the work SA2 (thicker end). Centroids are calculated as the weighted average location residents/jobs in each SA2 (using mesh block / destination zone data). This generally works okay for urban areas, but be aware that actual trips will be distributed across SA2s, and some SA2s on the urban fringe are quite large.
The thickness of each line at the work end is roughly proportional to the number of journeys by the mode of interest between the home-work pair (refer legends), but it’s difficult to use a scale that is meaningful for smaller volumes. Unfortunately there’s only so much you can do on a 2-D chart.
I’ve not drawn lines where there are fewer than 50 journeys in total (all modes), or where there were no journeys of the mode that is the subject of the map. This threshold of 50 isn’t perfect either as SA2s are not consistently sized within and between cities, so larger SA2s are more likely to generate lines on the map.
To try to help deal with the clutter, I’ve made the lines somewhat transparent, and also animated the map to highlight trips with different mode share intervals. For frames showing all lines, the lines with highest mode share are drawn on top.
So here is an animated map showing public transport journeys to work in Melbourne, by different mode share ranges and overall:
Technical note: I have included journeys to work that are internal to an SA2. Usually these appear as simple circles, but sometimes they appear as small teardrops where the population and employment centroids are sufficiently far apart.
You can see that the highest PT shares and largest PT volumes are for journeys to the central city, and generally from SA2s connected to Melbourne CBD by train (including many outer suburbs).
As the animation moves to highlight lower PT mode share ranges, the lines become a little less radial, a little shorter on average, and the lowest (non-zero) PT mode shares are mostly for suburban trips.
A notable exception is journeys to Port Melbourne Industrial SA2 (also known as Fishermans Bend), which is located at the junction of two major motorways and is remote from rapid public transport (it does however have a couple of high frequency bus lines from the CBD).
The lowest PT mode shares are seen for trips around the outer suburbs. The maps above unfortunately aren’t very good at differentiating small volumes. The following animated map shows public transport journeys with a filter progressively applied to remove lines with small numbers of public transport journeys (refer blue text in title):
You can see that most of the outer suburban lines quickly disappear as they have very small volumes. Inter-suburban lines with more than 50 public transport journeys go to centres including Dandenong, Clayton, Box Hill, and Heidelberg.
Here’s another animation that builds up the map starting with low public transport mode share lines, and then progressively adds lines with higher public transport mode shares:
As an aside, here is a chart showing journeys to work by straight line distance (between SA2 centroids), public transport mode share, work distance from the CBD and home-work volume:
The black dots represent journeys to the inner 5km of the city, and you can see public transport has a high mode share of longer trips. Public transport mode share falls away for shorter journeys to the inner city as people are more likely to use active transport. A dot on the top left of the curve is 8,874 journeys from Docklands to Melbourne – which benefits from the free tram zone and the distances can be 1-2 km. Most of the longer journeys with low public transport mode share are to workplaces remote from the CBD (coloured dots).
Another way to deal with the clutter of overlapping lines around the CBD is to progressively remove lines to workplaces in and around the CBD. Here is another animated map that does exactly so that you can better see journeys in the nearby inner and middle suburbs.
As you strip away the CBD and inner suburbs, you lose most lines with high public transport mode shares and volumes. However some high public transport mode share lines remain, including the following outbound journeys:
Melbourne (CBD) to Melbourne Airport: 72% of 64 journeys
Melbourne (CBD) to Box Hill: 66% of 76 journeys
Melbourne (CBD) to Clayton: 57% of 82 journeys
South Yarra – East to Clayton: 57% of 173 journeys
Just keep in mind that these are all very small volumes compared to total journeys in Melbourne.
You might have noticed on the western edge of the map some yellow and orange lines from the Wyndham area (south-west Melbourne) that go off the map towards the south west. These journeys go to Geelong.
Here’s a map showing journeys around Geelong and between Geelong and Greater Melbourne (journeys entirely within Greater Melbourne excluded):
You can see very high public transport mode shares for journeys from the Geelong and Bellarine region to the Melbourne CBD and Docklands (and fairly large volumes), but no lines to Southbank, East Melbourne, Parkville or Carlton – all more remote from Southern Cross Station, the city terminus for regional trains.
(The other purple lines to the CBD are from Ballarat, Bacchus Marsh, Daylesford, Woodend, Kyneton, Castlemaine, Kilmore-Broadford and Warragul, with at least 60 journeys each.)
You can also see those orange and yellow lines from the Wyndham area to central Geelong, being mode shares of 20-40%. The Geelong train line provides frequent services between Tarneit, Wyndham Vale, and Geelong, and has proved reasonably popular with commuters to Geelong (frequency was significantly upgraded in June 2015 with the opening of the Regional Rail Link, just 14 months before the census of August 2016).
However, public transport mode shares for travel within Greater Geelong are very small – even for SA2 that are connected by trains. This might reflect Geelong train station being on the edge of its CBD, relatively cheap parking in central Geelong, limited stopping frequency at some stations (many at 40 minute base pattern), and/or limited walk-up population catchments at several of Geelong’s suburban train stations.
Does public transport have significant mode share for cross-suburban journeys to work?
To search for cross-suburban journeys with relatively high public transport mode shares, here is a map that only shows lines with public transport mode shares above 20% between homes and workplaces both more than 5 km from the CBD (yes, those are arbitrary thresholds):
Of these journeys, the highest mode shares are for journeys from the inner northern suburbs to St Kilda and Hawthorn. There’s also a 49% mode share from Footscray to Maribyrnong (connected by frequent trams and buses).
The tear drop to the north of the city is 114 people who used PT from Coburg to Brunswick (connected by two tram routes and one train route).
Most of the other links on this map are fairly well aligned with train, tram, or SmartBus routes, suggesting high quality services are required to attract significant mode shares.
But these trips are a tiny fraction of journeys to work around Melbourne. In fact 3.0% of journeys to work in Melbourne were by public transport to workplaces more than 5 km from the CBD. The same statistic for Sydney is more than double this, at 7.3%.
What about private transport journeys?
Firstly, here’s a map showing private transport mode shares and volumes, building up the map starting with low private mode share lines.
The links with lowest private transport mode shares are very radial as you might expect (pretty much the inverse of the public transport maps). Progressively less radial lines get added to the map before there is a big bang when the final private transport mode share band of 95-100% gets added, with large volumes of outer suburban trips.
For completeness, here’s an animation that highlights each mode share range individually.
There are some other interesting stories in this data. The following map shows private transport mode share of journeys to work, excluding workplaces up to 10 km from the CBD to remove some clutter.
If you look carefully you’ll see that there is a much lower density of trips that cross the Yarra River (which runs just south of Heidelberg and Eltham). There are limited bridge crossings, and this is probably inhibiting people considering such journeys.
The construction of the North East Link motorway will add considerable cross-Yarra road capacity, and I suspect it may induce more private transport journeys to work across the Yarra River (although tolls will be a disincentive).
What about active transport journeys?
Next is a map for active transport journeys, but this time I’ve progressively added a filter for the number of active transport journeys, as most of the lines on the full chart are for very small volumes.
As soon as the filter reaches a minimum of 50 active journeys most of the lines between SA2s in the middle and outer suburbs disappear. Note that journeys between SA2s are not necessarily long, they might just be a short trip over the boundary.
Then at minimum 200 journeys you can only see central city journeys plus intra-SA2 journeys in relatively dense centres such as Hawthorn, Heidelberg, Box Hill, Clayton, Frankston, Mornington, Footscray, and St Kilda. The large volume in the south of the map that hangs around is Hastings – Somers, where 882 used active transport (probably mostly walking to work on the HMAS Cerebus navy base).
Active transport journeys are mostly much shorter than private and public transport journeys – as you might expect as most people will only walk or ride a bicycle so far. But there are people who said they made very long active transport journeys to work – the map shows some journeys from Point Nepean, Torquay, Ballarat, Daylesford, and Castlemaine to Melbourne. That’s some keen cyclists, incredible runners, people who changed jobs in the week of the census (the census asks for work location the prior week, and modes used on census day), and/or people who didn’t fill in their census forms accurately. The volumes of these trips are very small (mostly less than 5).
That map is very congested around the central city, so here is a map zoomed into the inner suburbs and this time animated by building up the map starting with high active transport mode share lines.
The highest active transport mode shares are for travel within Southbank and from Carlton to Parkville, followed by journeys to places like the CBD, Docklands, South Yarra, South Melbourne, Carlton, Fitzroy, Parkville, and Carlton.
Then you see a lot of trips added from the inner northern suburbs, which are connected to the central city by dare-I-say “above average” cycling infrastructure across some relatively flat terrain. In particular, a thick red line on the map is for 471 active transport journeys from Brunswick to Melbourne (CBD) with a mode share of 17%. A second thick red line is Richmond to Melbourne (CBD) being 589 journeys with 16% active mode share.
Another way of summarising mode shares by work and home distance from the CBD
I’ve experimented with another visualisation approach to overcome the clutter issues. The next charts have home distances from the CBD on the Y axis, work distances from the CBD on the X axis, bubble size representing number of journeys, and colour showing mode shares. I’m drawing smaller journey volumes on top, and I’ve used some transparency to help a little with the clutter.
Firstly here is public transport (animated to show each mode share range individually):
The chart is roughly a V-shape with many trips on the left edge and along a diagonal (mostly representing intra-SA2 journeys), then with several vertical stripes being major suburban employment destinations (including Dandenong at 31 km, Clayton at 19 km, and Frankston at 40 km). Trips above the diagonal are roughly inbound, while trips below the diagonal are roughly outbound.
The diagonal line (mostly local journeys) has very low public transport mode shares (sometimes zero).
Higher PT mode shares are only seen on the far left and bottom left hand corner of the chart. Some outliers include Richmond to Box Hill (32%), Clayton to Malvern East (32%), and South Yarra – East to Clayton (57%).
PT mode shares of 80+% are only seen for journeys to the CBD from home SA2s at least 11 km out (with one exception of Melbourne CBD to St Kilda with 80% PT share).
Home-work pairs with zero public transport journeys are scattered around the middle and outer suburbs, most being longer distance journeys (home and work at different distances from the CBD).
Here’s the same chart for private transport:
The lowest private transport shares are seen for journeys to the CBD. The diagonal has many mode shares in the 80-90% range.
And here is active transport:
The highest active transport mode shares are seen in the central city area, followed by the diagonal mostly representing local journeys (with generally higher shares closer to the CBD). Some notable outliers include local trips within Clayton (1,298 active trips / 46% active mode share), Box Hill (914 / 40%), Hastings – Somers (1,762 / 27%), Flinders (240 / 24%), Glen Waverley – West (308 / 21%), and Mentone (226 / 23%).
How does Sydney compare to Melbourne?
Here is a chart with private transport mode share maps for both Melbourne and Sydney, animated in tandem to progressively add higher mode share journeys.
In almost every city, hordes of people commute towards the city centre in the morning and back away from the city in the evening. This largely radial travel causes plenty of congestion on road and public transport networks.
But only a fraction of commuters in each city actually work in the CBD. So just how radial are journeys to work? How does it vary between cities? And how does it vary by mode of transport?
This post takes a detailed look at journey to work data from the ABS 2016 Census for Melbourne, Sydney, and to a less extent Brisbane, Perth, Adelaide and Canberra. I’ve included some visualisations for Melbourne and Sydney that I hope you will find interesting.
How to measure radialness?
I’m measuring radialness by the difference in degrees between the bearing of the journey to work, and a direct line from the home to the CBD of the city. I’m calling this the “off-radial angle”.
So an off-radial angle of 0° means the journey to work headed directly towards the CBD. However that doesn’t mean the workplace was the CBD, it might be have been short of the CBD or even on the opposite side of the CBD.
Similarly, an off-radial angle of 180° means the journey to work headed directly away from the CBD. And a value of 90° means that the trip was “orbital” relative to the CBD (a Melbourne example would be a journey from Box Hill that headed either north or south). And then there are all the angles in between.
To deal with data on literally millions of journeys to work, I’ve grouped journeys by home and work SA2 (SA2s are roughly the size of a suburb), and my bearing calculations are based on the residential centroid of the home SA2 and the employment centroid of the work SA2.
So it is certainly not precise analysis, but I’ve also grouped off-radial angles into 10 degree intervals, and I’m mostly looking for general trends and patterns.
So how radial are trips in Melbourne and Sydney?
Here’s a chart showing the proportion of 2016 journeys to work at different off-radial angle intervals:
Technical note: As per all my posts, I’ve designated a main mode for journeys to work: any journey involving public transport is classed as “Public”, any journey not involving motorised transport is classed as “Active”, and any other journey is classed as “Private”.
In both cities over 30% of journeys to work were what you might call “very radial” – within 10 degrees of perfectly radial. It was slightly higher in Melbourne.
You can also see that public transport trips are even more radial, particularly in Melbourne. In fact, around two-thirds of public transport journeys to work in 2016 had a destination within 2 km of the CBD.
Melbourne’s “mass transit” system (mostly trains and trams) is very radial, so you might be wondering why public transport accounts for less than half of those very radial journeys (41% in fact).
Here are Melbourne’s “very radial” journeys broken down by workplace distance from the Melbourne CBD:
Public transport dominates very radial journeys to workplaces within 2 km of the centre of the CBD, but is a minority mode for workplaces at all other distances. Many of these highly radial journeys might not line up with a transit line towards the city, and/or there could well be free parking at those suburban workplaces that make driving all too easy. I will explore this more shortly.
Sydney however had higher public transport mode shares for less radial journeys to work. I think this can be explained by Sydney’s large and dense suburban employment clusters that achieve relatively high public transport mode shares (see: Suburban employment clusters and the journey to work in Australian cities), the less radial nature of Sydney’s train network, and generally higher levels of public transport service provision, particularly in inner and middle suburbs.
Visualising radialness on maps
To visualise journeys to work it is necessary to simplify things a little so maps don’t get completely cluttered. On the following maps I show journey to work volumes between SA2s where there are at least 50 journeys for which the mode is known. The lines between home and work SA2s get thicker at the work end, and the thickness is proportional to the volume (although it’s hard to get a scale that works for all scenarios).
First up is an animated map that shows journeys to work coloured by private transport mode share, with each frame showing a different interval of off-radial angle (plus one very cluttered view with all trips):
(click/tap to enlarge maps)
I’ve had to use a lot of transparency so you have a chance at making out overlapping lines, but unfortunately that makes individual lines a little harder to see, particularly for the larger off-radial angles.
You can see a large number of near-radial journeys, and then a smattering of journeys at other off-radial angles, with some large volumes across the middle suburbs at particular angles.
The frame showing very radial trips was rather cluttered, so here is an map showing only those trips, animated to strip out workplaces in the CBD and surrounds so you can see the other journeys:
Private transport mode shares of very radial trips are only very low for trips to the central city. When the central city jobs are stripped out, you see mostly high private transport mode shares. Some relative exceptions to this include journeys to places like Box Hill, Hawthorn, and Footscray. More on that in a future post.
Here are the same maps for Sydney:
Across both of these maps you can find Sydney’s suburban employment clusters which have relatively low private transport mode shares. I explore this, and many other interesting ways to visualise journeys to work on maps in another post.
What about other Australian cities?
To compare several cities on one chart, I need some summary statistics. I’ve settled on two measures that are relatively easy to calculate – namely the average off-radial angle, and the percent of journeys that are very radial (up to 10°).
The ACT (Canberra) actually has the most radial journeys to work of these six cities, despite it being something of a polycentric city. Adelaide has the next most radial journeys to work, but there’s not a lot of difference in the largest four cities, despite Sydney being much more a polycentric city than the others. Note the two metrics do not correlate strongly – summary statistics are always problematic!
Here are those radialness measures again, but broken down by main mode:
Sydney now looks the least radial of the cities on most measures and modes, particularly by public transport.
The Australian Capital Territory (Canberra) has highly radial private and active journeys to work, but much less-radial public transport journeys than most other cities. This probably reflects Canberra’s relatively low cost parking (easy to drive to the inner city), but also that the public transport bus network is orientated around the suburban town centres that contain decent quantities of jobs.
Adelaide has the most radial journeys to work when it comes to active and public transport.
What about other types of travel?
In a future post, I’ll look at the radialness of general travel around Melbourne using household travel survey data (VISTA), and answer some questions I’ve been pondering for a while. Is general travel around cities significantly less radial than journeys to work? Is weekend travel less radial than weekday travel?
Follow the blog on twitter or become an email subscriber (see top-right of this page) to get alerted when that comes out.
Australian cities are growing in population as a result of international migration, internal migration, and births outnumbering deaths. But which of these factors are most at play in different parts of the country?
Thanks to ABS publishing data on the components of population growth with their Regional Population Growth product, we now have estimates of births, deaths, internal/international arrivals, and internal/international departures right down to SA2 geography for 2016-17 and 2017-18.
This post aims to summarise the main explanation for population change in different parts of the country.
This post isn’t much about transport, but I hope you also find the data interesting. That said, it’s possible that immigrants from transit-orientated countries might be more inclined to use public transport in Australia, and that might impact transport demand patterns. We know that recent immigrants are more likely to travel to work by public transport than longer term residents, but that probably also has a lot to do with where they are settling.
How is population changing in bigger and smaller cities?
First up, I’ve divided Australia into Capital Cities (Greater Capital City Statistical Areas), Large regional cities (Significant Urban Areas with population 100,000+, 2016 boundaries), small regional cities (Significant Urban Areas, with population 10,000 to 100,000, 2016 boundaries ) and “elsewhere”.
Here’s a chart showing the total of the six components of population change in each of those four place types. I’ve animated the chart (and most upcoming charts) to show changes in the years to June 2017 and June 2018, with a longer pause on 2018.
There were significant internal movements in all parts of Australia (shown in green) – even more so in 2018. These include people moving between any SA2s, whether they adjacent within a city or across the country.
International arrivals and departures were much larger in capital cities and there were more arrivals than departures in all four place types. International arrivals declined between 2017 and 2018, while international departures increased slightly between 2017 and 2018.
Births also outnumbered deaths in all place categories in both years.
Here’s a look at the larger capital cities individually:
The chart shows Sydney, Perth, and Adelaide had more internal departures than arrivals. These cities only grew in total population because of natural increase and net international immigration. Melbourne and Brisbane had a net increase from internal movements in both 2017 and 2018, while Canberra has been a lot more even.
International arrivals outnumbered international departures and births significantly outnumbered deaths in all cities. Melbourne and Canberra were the only cities to see a significant increase in international arrivals between 2017 and 2018.
Here is the same chart but for medium sized cities:
Again, there were much larger volumes of internal migration in 2017-18 compared to 2016-17.
The Gold Coast is the only medium-sized city to have significant volumes of international movements. The fast population growth of the Gold and Sunshine Coasts is mostly coming from internal arrivals.
What is the dominant explanation for population change in different parts of Australia?
As mentioned the ABS data goes down to SA2 statistical geography which allows particularly fine grain analysis, with six measures available for each SA2. However it is difficult to show those six components spatially. They can be consolidated into three categories: net natural increase, net internal arrivals/departures, and net international arrivals/departures, but that is still three different metrics for all SA2s.
One way to look at this is simply the component with the largest contribution to population growth (or decline). Here is a map showing that for each SA2 in Melbourne:
You can see that international arrivals dominated population growth in most inner and middle suburbs, while internal arrivals dominated population growth in most outer suburbs. There are also some SA2s where births dominated (often low growth outer suburbs).
This representation is quite simplified, and doesn’t show what else might be happening. For example, here is a summary of the population changes in Sunshine for the year to June 2018:
Overseas arrivals dominated population growth (net +313), but the otherwise hidden story here is that they were largely offset by net internal departures of 279.
So to add more detail to the analysis, I’ve created a slightly more detailed classification system that looks at the largest component and often a secondary component, as per the following table.
Growth – mostly births replacing locals
Net internal departures more than 50 and net internal departures more than net overseas departures
Growth – mostly births
Net internal and overseas departures of no more than 50
Growth – mostly immigrants replacing locals
Net overseas arrivals
Net internal departures of at least 50 and/or natural decrease of at least 50.
Growth – mostly immigration
Net overseas arrivals
Net internal departures less than 50 (or net arrivals).
Net natural decrease of 50 or more, and bigger than net overseas departures
Growth – mostly internal arrivals
Net internal arrivals
Net internal arrivals greater than net overseas arrivals and natural increase
Decline – mostly internal departures
Net internal departures
Natural increase and net overseas arrivals both less than 50
Decline – mostly internal departures partly offset by births
Net internal departures
Natural increase of at least 50, and natural increase larger than net overseas arrivals.
Decline – mostly internal departures partly offset by immigrants
Net internal departures
Net overseas arrivals of at least 50, and net overseas arrivals larger than natural increase.
Decline – mostly deaths
There are no SA2s where net international departures was the major explanation for population change.
Here’s what these summary explanations look like in Melbourne (again, animated to show years to June 2017 and June 2018):
Technical notes: On these maps I’ve omitted SA2s where there was population change of less than 50 people, or where no components of population change were more than 1% of the population. Not all classifications are present on all maps.
You can now see that in most middle suburbs there has been a net exodus of locals, more than offset by net international arrivals (light purple). Also, many of the outer suburbs with low growth actually involve births offsetting internal departures (light blue).
Turning near-continuous data into discrete classifications is still slightly problematic. For example the summary explanations don’t tell you by how much one component was larger than the others. For example if there were 561 net international arrivals and 560 net internal arrivals, it would be classified as “Growth – mostly immigration”. Also, SA2s are not consistently sized across Australia (see: How is density changing in Australian cities?), so my threshold of 50 is not perfect. At the end of the post I provide a link to Tableau where you can inspect the data more closely for any part of Australia.
The inner city area of Melbourne was a little congested with data marks on the above map, so here is a map zoomed into inner and middle Melbourne:
You can see significant population growth in the Melbourne CBD and surrounding SA2s, particularly in 2017. The main explanation for inner city growth is international immigration, although internal arrivals came out on top in Southbank in 2018. Curiously, net internal arrivals were larger the international migration in Brunswick East in both years. And natural increase was dominant in Newport in the inner-west.
Zooming out to include the bigger regional centres of Victoria (note: many regional SA2s don’t show up because of very little population change):
In most regional Victorian cities, internal arrivals account for most of the population growth, although the net growth in “Shepparton – North” of +222 in 2017 and +152 in 2018 was mostly made up of international arrivals. The only other SA2s to show international arrivals as the main explanation were in inner Geelong.
(I haven’t shown all of Victoria because few SA2s outside the above map had significant population change).
Heading up to Sydney, the picture is fairly similar to Melbourne:
Like Melbourne, internal arrivals accounted for most of the population growth in outer growth areas.
International immigration dominated the inner and middle suburbs in 2017, but in 2018 immigration eased off, and births became the main explanation for population growth in more SA2s.
The middle SA2s of Homebush Bay – Silverwater and Botany are noticeable exceptions to the pattern, dominated by internal arrivals.
Zooming out to New South Wales:
Central Newcastle, central Wollongong, Armidale and Griffith saw mostly international immigration led population growth. Most larger regional towns saw growth from internal arrivals, but further inland there was population decline – mostly from internal departures.
Next up, Brisbane:
Population growth in Brisbane’s inner suburbs is much more of a mix of internal and overseas arrivals. There are also more SA2s where births dominate population growth. There were also some SA2s with slight population decline for various reasons.
Zooming out to South East Queensland:
International arrivals dominated areas on the Gold Coast closer to the coastline, but much less so on the Sunshine Coast and in Toowoomba.
Looking at other parts of Queensland:
There was population decline in several areas, including Mackay and Mount Isa. Rockhampton and Cairns saw population growth mostly through internal arrivals. Townsville was dominated by internal arrivals in 2017, and births in 2018.
Airlie – Whitsundays stands out as having population growth mostly from international arrivals in both years.
Next up, Perth:
Like other cities, population growth in the outer suburbs was dominated by internal arrivals. There were a lot more SA2s showing population decline, and this was largely due to internal departures, partly offset by natural increases or net overseas arrivals.
Zooming out to Western Australia:
Population growth on the south-west coast was mostly dominated by internal arrivals, while in many other centres around the state there was population decline, mostly due to internal departures, however in many areas this was offset partly by births.
Next up, Adelaide:
Firstly, keep in mind that there has been relatively slow population growth in Adelaide (the scale is adjusted). The inner and middle suburbs mostly show population growth from international arrivals (often offsetting net internal departures), and the outer growth areas were again mostly about internal arrivals.
Zooming out to South Australia:
In 2017 there was considerable population decline in Whyalla and Port Augusta. Murray Bridge is another rare regional centre where population growth was largely driven by almost 400 overseas arrivals each year.
Next is Tasmania:
Note the circle size scale is even smaller. Overseas arrivals dominated population growth in central Hobart and Newman – Mayfield in Launceston (possibly related to university campuses), while internal migration dominated most other areas.
Here is Canberra:
International immigrants dominated population growth around Civic and the inner north. Internal arrivals dominated Kingston and Griffith and most outer growth areas. The outer suburbs saw a mixture of births and internal arrivals as the dominant explanation.
And finally, Darwin, which actually saw net population decline in the year to June 2018:
Palmerston South saw the largest population growth – mostly from internal arrivals. International arrivals were significant around Darwin city in 2017, but were much less significant in 2018. Most of the northern suburbs saw population decline in the year to June 2018.
Didn’t see your area, or want to explore further? You can view this data interactively in Tableau (you might want to filter by state as that will change the scale of circle sizes).
Where were international arrivals most significant?
I’ve calculated the ratio of international arrivals to population for each SA2. The SA2s where international arrivals in the year to June 2018 make up a significant portion of the 2018 population are all near universities and/or CBDs. Namely:
Melbourne CBD and neighbouring Carlton at 20% (Melbourne Uni, RMIT, and others)
Brisbane CBD at 18% and neighbouring Spring Hill at 20% (QUT and others)
Clayton in Melbourne at 18% (Monash Uni)
Sydney – Haymarket – The Rocks at 15% and neighbouring Pyrmont – Ultimo at 17% (near to UTS, Sydney Uni, and various others)
Acton (ACT) at 17% (ANU)
Kingsford (in Sydney) at 16% (UNSW)
St Lucia (Brisbane) at 15% (UQ)
I hope you’ve found this interesting. In a future post I might look at internal migration origin-destination flows, including how people are moving within and between cities.