How does travel vary across Melbourne and regional centres in Victoria?

Sun 19 June, 2011

What differences are there in car use by geography, income, household type, and age?

And could you do more to reduce car use by pushing population growth to regional cities instead of the fringe of Melbourne?

I thought I’d take a closer look at travel and trip distances using massive 2007-08 VISTA dataset, and see what factors lead to variations.

In this post I look at travel distances (total and by car) and mode splits across geographies, trip purposes, incomes, ages, and household types. And more.

While the results might not be too surprising, I hope you’ll find the evidence interesting.

How do travel distances vary by geography?

In a previous post I showed that people in the outer suburbs generally have a longer median travel distance:

The patterns were not uniform in the outer suburbs. Nillumbik is the second highest on 35.1 kms per person, while Hume is much lower on 19.6. Factors such as incomes and household types might explain this variation (more on that later).

Most of my analysis will deal with six geographic zones – four rings of Melbourne, Geelong and other regional cities in the VISTA sample combined (Ballarat, Bendigo, Shepparton and the Latrobe Valley). Here’s a map of the Melbourne zones:

Note: I’ve used “city” as shorthand for the central area, and “inner” as shorthand for the inner suburban ring.

Based on those zones, here is a simpler view of daily travel distances (total and by car):

This suggests little difference in total travel distance, but significant differences in car travel distances.

I’ve not used averages because some trips were extremely long (the longest trip by an inner city resident was 833 km) which can skew the averages.

But is median the right measure of travel distance? Probably not, if you look at the following chart of the cumulative distribution of all day travel distances:

How do you read this chart? A point on each line means Y% of people travelled up to X kms per day. Essentially the lower the curve on the chart, the longer distance those people travelled.

You can see differences between distributions are not straight forward:

  • The lower half of travel distances were quite similar.
  • The differences manifest in the top half of distances. You can see that people in outer Melbourne were much more likely to clock up longer travel distances that those in the inner city. For example, 30% of people in the outer suburbs travelled more than  , while only 15% of people who live in the inner city travelled more than 40 kms.
  • In fact, there were more long distance travellers in outer Melbourne than in Geelong or the other regional centres.
  • 24% of people in the outer suburbs of Melbourne did not travel at all, while only 15% of inner city residents did not travel on the survey day. This causes the distances to cross around the median.
  • There is greater diversity in travel distances of people in the outer suburbs, including about the quarter who did not travel.

Here is the same again for car distance travelled (probably the most important chart in this post):

The differences are much clearer here, with car use and travel distance increasing through Melbourne by distance from the city, and the outer Melbourne suburbs having the longest car travel distances. Distances in Melbourne’s outer suburbs are generally longer than in Geelong and the regional centres.

Interestingly, 48% of inner city residents made no car travel at all, hence the very low median. While the city, inner and middle lines converge at a longer distance, the outer suburbs still had 10% of people doing more than 80 kms in cars.

How does mode share vary by geography?

The distributions on car distance travelled reflect mode splits across the regions. Here is a chart of mode split for trips (using the ‘main’ mode for the trip, which means car+PT trips are counted as PT):

Active and public transport mode shares fell away with distance from the centre of Melbourne. I expect this will be a product of poorer service levels, and a smaller proportion of people travelling to Melbourne’s CBD (the main market where public transport dominates).

But here’s a slightly different take, the mode share of person travel distances:

There is much less variation in public transport mode share of kms travelled. This points to people in the outer suburbs of Melbourne, Geelong and other regional centres making much longer trips when they travelled by public transport. I expect many of these will be long distance rail trips to Melbourne.

The clear difference is that people in the outer suburbs and regional cities did a lot less walking/cycling and lot more travel by car.

What about mode share of very short trips?

Walking is a significant mode in the inner city, and many destinations are within walking distance. You might think that the regional centres are similar, because they are more compact in general.

Well, it appears not. Here is a chart of mode shares of trips under 1km (probably a walkable distance for most people).

Around half the short trips in the outer suburbs , Geelong and regional centres were made by private transport – essentially cars! Why did people drive for such short trips in these areas? Is it a lack of safe places to walk/cycle? Or is it a lack of disincentives to drive?

Digging deeper, even for recreational trips of less than 1km in the outer suburbs, 30% were made by car!

Does the number of trips made vary by geography?

The following chart shows the distribution of the number of trips made. In VISTA, a trip is defined as travel between two activities.

People in the inner city generally made more trips, and those in the middle and outer suburbs made fewer trips. This will also be influencing the total distance travelled per day.

Note: very few people make only 1 trip in a day because it essentially means you start and finish you day in different locations (within the VISTA definitions of a day at least).

How do trip lengths vary?

Here is a distribution chart of lengths of trips (for any purpose):

By almost any measure, those in the outer suburbs of Melbourne made the longest trips. They were followed by people who live in the middle suburbs of Melbourne and Geelong. This means that either people choose to partake in activities that were further away, or (more likely) those activities were further away from home.

What about trip distances for different purposes?

First up, median trip distances by purpose:

Work related trip distances were clearly the longest, especially in the outer suburbs. The “median” person living in the outer suburbs of Melbourne travelled 16 kms for work (note that not all “work related” trips are to/from home).

Here’s a closer look at the distribution of trip lengths between home and work:

The differences when looking only at home to work and work to home trips is much more stark, with the outer suburbs of Melbourne fairing worst by a long way.

The median distances in Geelong and the other regional centres were actually less than the inner suburbs of Melbourne, however they have a long tail with over 10% of trips in Geelong more than 50km.

Back to the previous chart, social trips also get longer as you move to the outer suburbs of Melbourne, which suggests that outer suburbs are not as self-contained for social destinations.

Most other trips purposes had a median around 3-4 kms, although this was more like 2-3 kms in the inner city, and distances increase in the outer suburbs. Chauffeuring trips (pick up or drop off someone) show the least variability (many of these would be taking kids to/from school).

Trips to education were longest in the inner suburbs, possibly reflecting children from wealthy families attending private schools that are further away.

How does travel time vary by trip purpose?

You can see:

  • Work trips take the most time in Melbourne, but there isn’t a lot of variation. This supports the hypothesis that people have a commuting travel time budget, and generally find work within that budget.
  • Work travel times were highest in the inner suburbs (perhaps related to slower road speeds) and outer suburbs (much longer distances).
  • Education trip times were longer in the inner and middle suburbs (perhaps related to congestion and/or longer trips to private schools by children in wealthy families)
  • 10 minutes was the most common median trip time – which actually shows up as 9 minutes in the chart, owing to the way I calculate medians in Excel (sorry, not perfect, but Excel doesn’t do medians in pivot tables).

Here’s a closer look at work-home trip time distributions:

You can see big steps at the multiples of five minutes, as people tend to round estimated trip times to the nearest 5 minutes. Median trip times in Melbourne are all around 30 minutes, and much lower in Geelong and regional centres. People in the inner suburbs were least likely to have commute trips less than 20 minutes, while the outer suburbs were most likely to have trip times over 30 minutes.

How does travel speed vary by trip purpose?

As you might expect, trips were faster in the outer suburbs, probably because a combination of less congestion and more roads designed primarily to move vehicles quickly (freeways and divided arterials).

Education trips didn’t speed up as much in the outer suburbs, perhaps because they were more likely to be on public transport. Which brings us to…

How does mode share vary by trip purpose?

Around half of education trip kms were by public transport overall, although this was curiously lower in the inner city and outer suburbs.

Work trips had the next highest public transport mode share, which fell away towards the outer suburbs.

Other trip types mostly had slightly higher public transport mode shares closer to the centre of Melbourne. Note: I have not excluded very long trips from this analysis, so they might throw the figures slightly.

Here is another view, private transport mode share:

You can see more significant trends across Melbourne, as people in the inner city and suburbs were more likely to travel by active transport.

What other factors influence travel distance and mode split?

Different households will have different travel needs, and the distribution of household types across Melbourne is not even:

And median per person travel distance varied by household type:

You can see that the household types more prevalent in the outer suburbs (couples with or without kids) have the highest median car travel distances. So this will be impacting longer travel distances in the outer suburbs. You can also see that couples with kids have the highest car mode share, which is no big surprise!

Here’s a look at the distribution of total travel distance for people living in households that were couples + kids, one of the most common household type:

Couples with kids in the inner city certainly travel less distance, and while the bottom half of people were similar for other regions, the travel distances were much longer in the upper half of such people, suggesting geography still had a big impact.

Equally household incomes were not consistent across Melbourne:

Equivalised household income is a measure that allows income comparisons across different household sizes. It is calculated as household income divided by a measure of householders: the first person is assigned a value of 1.0, subsequent persons over 15 years are 0.5, and any children are 0.3.

Curiously, the inner city has the lowest income profile in Melbourne (note that VISTA 2007 did not include Southbank and Docklands residents), while the wealthy live in the inner suburbs.

It will come as little surprise that household income is a driver of total – and car-based – travel distance:

Do rich people shun public transport?


No, only the very-rich seem to shun public transport. According to the VISTA numbers (which are weighted to census 2006 demographics), only 10% of people live in households with an equivalised income over $2000.

The highest concentration of wealthy people is in the inner suburbs, travel distances are generally shorter (although income might explain longer travel distances in relatively wealthy Nillumbik).

To isolate household income, here is a distribution chart for people in households with an equivalised income of between $500 and $750 per week (the largest $250 bracket overall):

Again the lower half exhibits very little difference, while the outer suburbs of Melbourne has much longer distances in the upper half. (note the inner city line is quite jagged, because the sample size in this instance is only 204).

What about age:

People aged 20-64 certainly travelled longer distances. Looking at the distribution of ages, there were more people aged 20-74 living in the inner city and inner suburbs, compared to middle and outer Melbourne. There is very little difference in the percentage of the population between 25 and 64 across the regions (those with the largest car travel distances).

And yes, public transport mode share is lowest amongst very young children and the middle-aged (the later group often being the decisions makers!):

And finally (without going through all the detail here) people who work full-time tend to travel more, but they become less prevalent as you move away from the centre of Melbourne.

So, should we encourage population growth in regional centres instead of Melbourne’s outer suburbs?

Well, it’s probably the wrong question to ask! People in inner Melbourne do a lot less car travel than anywhere else. This analysis clearly shows that encouraging people to move into inner Melbourne would probably do the most to reduce car travel per capita.

People currently living in the outer suburbs of Melbourne travel more and do more car kms than those in regional cities. The main problem is that their work and social trips are much longer.

The evidence suggests putting people into regional cities would generate less car travel than putting people on the fringe of Melbourne.

However, there are several points worth considering:

  • If you can generate jobs in the outer suburbs of Melbourne, you might be able to reduce work travel distances. Easy to say, but it defies agglomeration economies that cause jobs to co-locate in the inner city and suburbs. If Melbourne’s Central Activities Areas (formerly Central Activities Districts (formerly Transit Cities)) can become significant employment destinations then that will certainly help.
  • If you do encourage people to settle in regional cities, will they have the same transport profile as existing residents? I would guess that there would be a significant difference between people living in the centre of regional cities, and those living on the fringe. The reduced car travel advantages of regional cities are probably largely eroded on the fringes of the regional cities. However, encouraging higher density in the inner areas of the regional cities would probably generate less car kms.
  • If you increase the population in regional cities without also increasing employment opportunities, you’ll create unemployment problems and/or force people to travel further to get to work. This would cancel out some of the benefits of locating people in regional centres. It may also increase demand on long distance V/line commuter trains into Melbourne (which currently consume valuable metropolitan train paths with low passenger density).

It doesn’t seem like there is much difference between the outer suburbs and regional cities. But there is a much bigger difference when you compare these with the inner suburbs of Melbourne.

If we really want to reduce car use, we’ll need to do relatively easy things like:

  • Locate people in inner city and suburban areas, where travel distances are short and there is viable high quality public transport (though it will probably require capacity upgrades)
  • Increase public transport service levels in existing outer suburbs and regional cities, with a particular focus on efficiently connecting people to employment areas by public transport.
  • Break down the barriers to walking and cycling in the outer suburbs and regional cities. Footpaths and safe places to ride would be a good start!

Notes about the data:

  • Wherever possible I have used person weightings in VISTA, which are for all week travel and align VISTA data with 2006 census data on demographics.
  • I have determined trip purpose by looking at the destination purpose of each trip. If the destination is not home, then I assign the destination purpose as the trip purpose. If the destination purpose is home, then I assign the origin purpose as the trip purpose. This gets around the common problem of nearly half of all trips having “go home” as the trip purpose, which costs you half your data when analysing by trip purpose.

2 Comments | Melbourne, Mode share, Urban Planning | Tagged: | Permalink
Posted by chrisloader

What does Melbourne’s urban density look like?

Sat 2 April, 2011

Transport planners love to talk about urban density, but what does Melbourne’s urban density actually look like? Google for a Melbourne urban density map and you won’t find much.

The ABS publication Melbourne.. A Social Atlas has a density map (see pages 12-13) at the Census Collection District (CCD) level, but only has five colour graduations so subtleties are quickly lost.

So I’ve decided to draw one myself.

Arguably the best source of data for housing density is the ABS’s experimental mesh blocks, which are smaller than Census Collection Districts (CCD). Mesh blocks are designed to have more uniform land use, which gets around the problem of a CCD which might contain a mix of residential, parkland and commercial land use showing up as low density. But I’ll come back to this.

So here is a 2006 population density map of Melbourne at the mesh block level:

(I’m using people per square km, which is 100 times larger than people per hectare if you need to convert).

You’ll need to click to zoom in, and you might want to then zoom in again with your favourite image viewer to see the detail.

Some observations:

  • Many areas on the very fringe show as low density, but this might be because that area was under development at the time of the census, and only some people had moved in.
  • Everyone talks about low density sprawl on the fringe, but even back in 2006 there was evidence of higher density development in the outer suburbs. Have a look at the Craigieburn area in the north or around Narre Warren and you will see many patches of green. New blocks on the urban fringe are now actually quite small in places compared to those in the middle suburbs. Two storey townhouses are actually not uncommon in new estates.
  • In the north-west (around Delahey/Sydenham), you can see a north-south divide where there is higher density on the eastern side. This corresponds with the municipal boundary between Brimbank and Melton. Presumably they’ve had different urban development policies.
  • The biggest clumps of density are in the inner city, particularly Carlton and Carlton North, Fitzroy, St Kilda, Richmond, and Kensington (the western side of which enjoys a 5½ days per week route 404 bus service).
  • Looking at the Central Activities Districts (CADs), there are clumps of density near the Dandenong and Box Hill CADs. But nothing to speak of inside Ringwood, Frankston, or Broadmeadows CADs (in 2006).
  • Other curious pockets of density in the suburbs include west of Highpoint Shopping Centre, Sunshine, Glenhuntly/Carnegie, and Glen Iris.
  • The lowest density suburbs in Melbourne are found in the middle and outer eastern suburbs (particularly Upwey/Belgrave), and in the north-east around well off areas such as Eltham, Toorak and Eaglemont. North west Reservoir seems to be a problem area – high socio-economic disadvantage and low density (not to mention a bus route that runs 5½ days a week).
  • Interesting to see relatively higher densities south of the Dandenong rail line.

For comparison purposes, I’ve also created a version based on larger Census Collection Districts (CCDs):

(note: this map doesn’t show anything outside the Melbourne SD)

What’s the difference you ask? You cannot see a great deal of difference, though the CCD map makes Melbourne look a little less dense.

But if you zoom in you can spot differences in some areas where a CCD is part residential, part not. Here’s an example in the Black Rock/Beaumaris area:

The CCD map on the left shows a few darker red blocks next to the whitespace, but that low density is not visible in the mesh blocks on the right, because the  mesh blocks split the parkland and houses. You can also see that the CCDs run to the shoreline, while the beach area has been split into separate mesh blocks.

The advantage of the mesh block map is that it pretty much shows housing density, as most pieces of land that are not residential have been removed (including suburban parks).

But the advantage of CCD density is that it includes local parkland, which is a measure of open space within and immediately surrounding residential areas.

A better way of looking at the density equation is a cumulative distribution chart, as created by Fedor Manin on his blog We Alone on Earth (also referenced on Human Transit).Rather than having to worry about whether low density areas on the fringe are “urban” or not, you can just look at density by population share, and the fringe areas will quickly tail out anyway. On this basis the problems of using an administrative boundary of a city (which often contains a large areas of rural land) largely go away, but then you don’t get a single number.

I’ve lined up all mesh blocks and CCDs in the Melbourne SD in order of density, and created a cumulative profile of density for each.

You can see a big difference between CCDs and mesh blocks (note the X axis is logarithmic). On a mesh block basis, about half of Melbourne’s population lives at a density of greater than 3200/km2, whereas on a CCD basis, only 30% of Melbourne’s population lives at a density greater than 3200/km2. Take note anyone doing a comparison between cities!

Here’s a chart on the same data showing a population distribution across densities, using mesh blocks and CCDs:

You can see the most common density for mesh blocks is slightly higher than for CCDs. The peak for mesh blocks is between 2818-3162 people/km2 on my intervals. That’s an funny sounding interval because I’ve used logarithmic intervals (if you use even intervals of 100 people/km2, the peak is between 2900 and 3300 people/km2)

So what is the average density of Melbourne?

What is Melbourne? Should we include satellite urban areas around the city? For example, is Sunbury part of Melbourne? It is within the Melbourne SD (Statistical District) but not within the Melbourne “Urban Centre” as defined by ABS. Do you want to include non-residential areas (urban density), or not? (residential density)

Here are six very different measures of the urban density of Melbourne, including some measures that have minimum density threshold to restrict the calculation to “residential” areas. The maps above use 1000 people/km2 as a threshold for colouring, and this appears to include all “residential” areas, except for some very large block estates.

Geography Area (km2) Population Density (pop/km2)
Mesh blocks within all Urban Centres/Localities within Melbourne SD 2,357 3,506,207 1,488
“Melbourne” Urban Centre 2,153 3,368,069 1,564
CCDs within Melbourne SD, with population density > 100 people/km2 2,151 3,514,658 1,634
Meshblocks within Melbourne SD, with population density > 100 people/km2 1,566 3,511,982 2,242
Meshblocks within “Melbourne” Urban Centre, with population density > 100 people/km2 1,350 3,358,317 2,487
Meshblocks within Melbourne SD, with population density > 1000 people/km2 1,084 3,316,516 3,060

You can quickly see why trying to calculate an average density is a fraught exercise! Though the first two are trying to measure “urban density”, while the later are attempting to measure “residential density” (and note the threshold for residential density makes a big difference).

A density profile chart (as above) is clearly a good way to get around the defined area problem. But you still need to be consistent in the land parcel size you use when comparing cities. Not easy when comparing cities with different statistics agencies.

Land use map of Melbourne

Before I finish up, the other beauty of the mesh block data is that it contains a land use classification for each mesh block.

So it is really easy to produce a land use map of Melbourne (and Geelong for good measure):

What are those two black blobs I hear you ask? Essendon and Moorabbin Airports. Tullamarine and Avalon airports are actually classified agricultural.

And you will see residential areas stretching a fair way east of Frankston, and north of Craigieburn – though these are not actually developed. So it’s not perfect.

In fact, according to the data, there is a mesh block in Melbourne with 358 people living in an area of 420 square metres (852,700 people/km2). That’s 1.17 square metres of land space per person. Really? No, what appears to have happened is that almost every resident of the Burnside Retirement Village was registered to one tiny parcel of land. I suppose that’s census data for you!

4 Comments | Melbourne, Urban Planning | Tagged: | Permalink
Posted by chrisloader

Melbourne urban sprawl and consolidation

Sat 10 April, 2010

[Last updated September 2011, first posted April 2010]

How much is Melbourne sprawling? Is urban densification happening? I thought it worth looking at ABS population data to find out.

While this isn’t transport data, urban growth has obvious impacts on transport demand and mode share.

Population growth

The first chart shows net annual population growth by regions of Melbourne (see below for definitions of regions and note that the areas have different sizes).

As you can see, Melbourne’s population growth accelerated dramatically in recent years, although it eased to 2.0% growth in 2009-10 (down from 2.5% in 2008-09). There were a net 79,014 new residents in 2009/10, an average of about 1500 per week.

The chart also shows a dramatic switch in 2010 from urban consolidation in established areas to growth on the fringe. Probably a record annual number of new residents settled in the growth areas, while population growth was down significantly in established areas of Melbourne. This bucks a trend between 2005 and 2009 of higher rates of urban consolidation in inner, middle and established outer areas.

Perhaps this partly explains the slowdown in public transport patronage growth in 2009-10 (more population growth in car dependent areas).

The chart below shows that the growth areas are now taking 58% of new residents, a level not seen since 2004. This is a significant deviation from the aims of the (now defunct) Melbourne 2030 strategy. More on that further below.

Note that not all greenfields sites are in outer growth areas – the “outer” areas also include some smaller greenfields developments.

Growth compared to forecasts

The Victorian government periodically makes projections of population growth in all local government areas (LGAs). The following chart shows the ABS population estimates exceed both Victoria In Future (VIF) forecasts made in 2004 and 2008.

Growth in dwellings

Data on dwelling approvals is published by the Department of Planning and Community Development.

The following chart shows a spike in dwelling approvals in 2009-10, after three years of tracking just below VIF 2008 forecasts.

Impacts on household sizes

The following chart shows the ratio of population growth to dwelling growth. In 2008-09, there was one new dwelling approved for every 3 new residents, but this dropped one new dwelling for every 2 new residents in 2009-10 thanks to the surge of dwelling approvals in 2009-10. Earlier in the decade, the ratio was one dwelling for every 1.5 new residents.

The chart also shows the VIF 2008 forecast of average household size (of occupied dwellings), and forecast ratio of population growth to dwelling growth. The forecast was for slowly declining average household size (following a recent trend).

Until 2010, population growth outstripped dwelling growth which would suggest that actual average household sizes have been forced upwards. Given the surge in dwelling approvals in 2009-10, maybe the housing “crisis” has eased?

Curiously, the ratio of new residents to dwelling approvals was only 1.5 in the early parts of the decade, much lower than average household sizes. Does this reflect small dwelling sizes approved in those years, or a housing glut? I’ll leave that to the housing experts.

Note that not all dwelling approvals represent an increase in available housing stock for permanent residents. The RBA has estimated that around 15% of dwelling approvals replace demolished dwellings, and around 8% are second homes or holiday homes.

Measuring progress against the Melbourne 2030 urban consolidation target

Melbourne doesn’t have population targets for different regions, but there was a target for dwellings growth in the now defunct Melbourne 2030. It stated the aim to:

reduce the overall proportion of new dwellings in greenfield sites from the current figure of 38 per cent to 22 per cent by 2030

The greenfield sites in Melbourne 2030 were mostly (but not entirely) located in the designated growth areas. As “greenfields” dwelling approval data isn’t readily available, I have used dwelling approvals in the designated outer growth LGAs as a proxy (from DPCD’s Residential Land Bulletin).

The dashed red line is a straight line interpolation of the Melbourne 2030 target for greenfields dwelling share. The outer growth LGA’s share of dwelling approvals has been higher than the target, but fluctuates a fair bit, and curiously has been taking a dive since June 2010.

The Melbourne 2030 target share of dwelling growth in greenfields areas is not being met, at least as a share of much higher than forecast population growth.

(Note: The growth LGAs’ share early in the decade was much lower. This may reflect urban growth that was still occurring in areas I have classified as “outer” (as opposed to “outer-growth”) before the Melbourne 2030 plan was released in 2002.)

However, if you look at absolute volumes of population growth in established areas, the story is very different. The next chart shows the VIF 2004 forecasts for population growth by region (I use VIF 2004 because it came out soon after Melbourne 2030):

Urban consolidation in Melbourne has vastly exceeded the VIF 2004 forecasts, even with the slowdown in 2010, as the following chart attests:

I cannot comment on whether the 2004 forecasts were too conservative.

Unfortunately the available data doesn’t tell us whether this urban consolidation has occurred in designated activity centres, or it is spread throughout the urban area. Changes in population density as measured by the census would illuminate this topic in more detail – although the last census was 2006, early in the consolidation trend (and census districts boundaries regularly change size).

Appendix: Definitions of regions

I have allocated local government areas to regions as follows:

Centre = Melbourne, Yarra, Port Phillip

Inner = Hobsons Bay, Maribyrnong, Moonee Valley, Moreland, Darebin, Banyule, Boroondara, Stonnington, Glen Eira, Bayside

Middle = Brimbank, Manningham, Whitehorse, Monash, Kingston, Greater Dandenong (all but one in the east)

Outer = Nillumbik, Maroondah, Yarra Ranges, Knox, Frankston, Mornington Peninsular (all in the east and south-east)

Outer growth = Wyndham, Melton, Hume, Whittlesea, Casey, Cardinia

Here is a map of Melbourne with the regions shaded (dotted white are indicates within the 2006 urban growth boundary, sorry the colours don’t match exactly).

Here is a reference map for those unfamiliar with Melbourne LGAs. You’ll need to click to enlarge so you can read the text.

5 Comments | Melbourne, Urban Planning | Permalink
Posted by chrisloader

Car ownership and public transport

Sun 17 January, 2010

 

Is there a link between good quality public transport and car ownership rates? Will high density urban develop around good quality public transport lead to significant increases in car ownership?

Obviously not a new topic, but in this post I hope to at least illuminate the state of play in Melbourne (as per the 2006 census).

The Australian Census provides very detailed data on car ownership to a high resolution. The data includes the number of 0, 1, 2, 3 and 4 or more car households in each Census Collection District (which average around 225 dwellings). Most spatial representations of car ownership show the number of households with 0,1,2,3, or 4 or more cars. This is fine, except it ignores household size. Single occupant households are unlikely to have 3 cars, while large family households with grown up children are more likely to have 4 cars.

So I have looked at the data differently in two ways:

  • Rather than cars per household, I’ve used cars per 100 adults for an area (I define “adults” below). This removes household size from the equation. Essentially what I did was add up the number of reported cars in each CCD, which for 0,1,2 and 3 car households is straight forward. 5.1% of households in Victoria reported “4 or more” motor vehicles and a further 3.7% did not specify the number of motor vehicles. In the absence of more detailed information, I have assumed an average of 4.2 motor vehicles for households reporting “4 or more” and zero motor vehicles present where households did not respond. While this means there is a small level of uncertainty as to the actual total number of motor vehicles in each census collection district, these represent small percentages of the total, and it is still possible to compare relative levels of car ownership between areas. Hence I refer to the car ownership rates as estimate.
  • I divide the number of cars by the number of “adults” – ie people who are generally of driving age. As the census reports age in 5 year blocks, I’ve used 20-74 as the age range where most people would be eligible and confident to obtain a drivers license. This is fairly arbitrary I agree. People under 20 can drive and own cars, as can those over 74, but they are perhaps less likely to do so.

So the calculations are not an exact science, but give a pretty good idea of the car ownership rates for different areas. It also allows me to show car ownership rates on a single map, rather than the need for multiple maps. In the maps below I have only shown urban areas that come within a minimum urban residential density, so the boundaries between urban and regional areas are clearer.

Superimposed on the map are high quality public transport routes that existed in Melbourne in 2006. I’ve used all train lines (stations are marked), all tram routes, and selected bus routes with “high” service levels – reasonable frequency and long span (at most a 16 minute headway on weekdays inter-peak). It’s hard to define a perfect threshold for bus routes as some have good frequency but poor span of hours in 2006. Again not a perfect science.

Here are the maps for greater Melbourne and inner Melbourne (click on them for higher resolution – you may need to click again to zoom in).

 

 

Observations and Analysis

Pockets of low car ownership rates are generally found:

  • In lower socio-economic areas (eg around Broadmeadows, St Albans, Dandenong)
  • Near large activity centres with public transport hubs (eg Ringwood, Box Hill, Dandenong, Frankston)
  • Where there is a dense grid of high quality public transport – ie you can catch public transport in multiple directions relatively easily to get to a range of destinations. This includes areas where the high frequency transport is provided by buses and not trams (eg the Footscray to Sunshine Corridor).
  • Residential colleges near universities (eg Clayton, Caulfield, Bundoora, Maribyrnong)
  • Army bases (eg near Watsonia)
  • Prisons (you can see a couple in the west)
  • Areas of higher income generally have higher car ownership. Higher than local trend car ownership can be seen in areas like (west) Kew, Toorak, Brighton and Greenvale.

Note that this exercise does not aim to fully explain the reason for rates of car ownership in Melbourne. There is extensive literature available about this subject. We have primarily set out to highlight car ownership rates in Melbourne to inform debate.

Conclusions and Commentary

  • The maps show low levels of car ownership in many places where there is a dense network of high quality public transport. That suggest that high frequency public transport routes operating from early to late in multiple directions is an enabler for people to choose not to own a car.
  • Increasing population in areas with dense networks of high quality public transport is therefore less likely to result in high levels of car ownership and use.
  • The tram network provides the radial links in most cases, while bus routes are needed to provide links across the tram routes. By upgrading a few more inner city bus routes, a larger area could support higher populations with low car ownership rates and high liveability.
  • Even those people who do bring cars with them will probably leave them parked most of the time as walking, cycling or public transport will be an easier option for most trips. High rates of car ownership do not necessarily translate into high rates of car use.

This analysis was the subject a media story in The Age, in November 2009.

1 Comment | Car ownership, Melbourne, Urban Planning | Permalink
Posted by chrisloader

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