Mapping Melbourne’s journeys to work

Mon 24 June, 2019

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.

Some observations:

  • 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.

You can see that Sydney has a lot more trips at lower private transport mode shares, and that workplaces outside the city centre start to show up earlier in the animation in Sydney – being the dense transit-orientated suburban employment clusters that are largely unique to Sydney (see: Suburban employment clusters and the journey to work in Australian cities).

If time permits, I may do similar analysis for Sydney and other cities in future posts.

6 Comments | Melbourne, Mode share, Sydney | Tagged: , | Permalink
Posted by chrisloader

How radial are journeys to work in Australian cities?

Fri 14 June, 2019

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:

very-radial-trips-by-mode-distance-from-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.

4 Comments | Australian Cities, Melbourne, Mode share, Sydney | Tagged: , , | Permalink
Posted by chrisloader

Are Australian cities sprawling with low-density car-dependent suburbs?

Wed 30 January, 2019

Many people talk about urban growth in Australian cities being car-dependent low-density suburban sprawl. But how true is that in more recent times? Are new greenfield density targets making a difference? Are cities growing around their rapid public transport networks? And how do growth areas compare to established areas at a similar distance out from city centres?

This post takes a look at what census data can tell us about outer urban growth areas in terms of population density, motor vehicle ownership, distance from train/busway stations, and journey to work mode shares.

How much of city population growth is in outer areas?

Firstly a recap, here is the percentage of annual population growth in each city that has occurred in “outer” areas (defined by groupings of SA3s around the edges of cities – refer my previous post for maps showing outer areas) for Greater Capital City Statistical Areas.

Sydney has had less than a third of its population growth in outer areas since around 2003, while Perth has mostly had the highest outer growth percentage (since 1996), and more recently pretty much all population growth in Perth has been on the fringe. You can see how the other cities sit in between.

However, not all of this “outer” population growth was in urban growth on the fringe. For that we need to distinguish between urban growth and infill development, even in “outer” areas. So we really need a better definition of outer growth areas.

How to define outer urban growth areas

I have built groupings of SA1s (Statistical Area Level 1) that try to represent outer urban greenfield residential development. SA1s are the smallest census geographic areas (average population 400) for which all census data variables are available.

I’ve selected 2016 SA1s that meet all of the following criteria:

  • Brand new SA1 or significant population growth: The 2016 SA1 is new and cannot be matched to a 2011 SA1 (by location/size and/or ABS correspondences), or if it can be matched, the population at least doubled between 2011 and 2016. Brand new SA1s are very common in urban growth areas as new SA1s are created to avoid oversized SA1s on last census boundaries (except this doesn’t always happen – more on that shortly).
  • In an SA2 with significant population growth: The SA2 (Statistical Area Level 2 – roughly suburb sized with typically 3,000 to 25,000 residents) that contains the SA1 had population growth of at least 1000 people between 2011 and 2016 (based on 2016 boundaries). That is, the general area is seeing population growth, not just one or two SA1s.
  • Are on – or close to – the urban fringe. I’ve filtered out particular SA2s that I’ve judged to be contain all or mostly in-fill development rather than greenfield development, or that are largely surrounded by existing urban areas and are not close to the urban fringe. I’ll be the first to admit that some of the inclusions/exclusions are a little arbitrary.

The criteria aren’t perfect, but it seems to work pretty well when I inspect the data. I’m calling these “Growth SA1s” or outer urban growth in this post.

For urban centres, I’m using Significant Urban Area 2016 boundaries (rather than Greater Capital City boundaries), and I’ve bundled Yanchep with Perth, Melton with Melbourne, and the Sunshine Coast and Gold Coast with Brisbane to form South East Queensland (SEQ).

Where are these outer urban growth areas?

What follows are maps for each city with the density of these growth SA1s shown by colour.

Melbourne’s northern and western growth areas:

Technical note: The maps do not show non-growth SA1s with fewer than 5 people per hectare, or “growth SA1s” with fewer than 1/hectare, although these SA1s are including in later analysis.

And the south of Melbourne:

Note: not shown on these Melbourne maps are isolated tiny growth SA1s in Rosebud and Mooroolbark.

Here are Sydney’s growth SA1s – all in the western suburbs:

Next up South East Queensland, starting in the north with the Sunshine Coast:

Northern Brisbane:

Outer urban growth is scattered in southern Brisbane and northern Gold Coast:

Gold Coast – Tweed Heads:

Perth’s northern and eastern growth areas:

Perth’s southern growth areas:

Note: Canning Vale East is an inclusion you could debate – the previous land use of the growth SA1s appear to have been rural based on satellite imagery.

Northern Adelaide:

Southern Adelaide:

And finally Canberra:

So how much of each cities’ population growth has been in outer growth areas?

Here’s a breakdown of the population growth for my six urban areas:

Over the five-year period, outer urban growth areas accounted for 19% of Sydney’s population growth, 43% of Melbourne’s, 37% of SEQ’s, 60% of Perth’s, 27% of Adelaide’s and 69% of Canberra’s.

Technical note: These “outer urban growth” figures are different to the chart at the top of this post which had a coarser definition of “outer” and used Greater Capital City boundaries. Some of my “outer urban growth” areas actually don’t quality as “outer” in the coarser definition, and I’ve also excluded several “outer” SA2s from “outer urban growth” where I’ve deemed the growth to be mostly infill. Hence the differences.

In case you are wondering, it’s not easy to create a longer-term time-series analysis about the proportion of population growth in “outer urban growth” areas because the classification of SA2s would have to change on a year-by-year basis which would be messy and somewhat arbitrary.

A challenge for density analysis: some SA1s are over-sized

You might have noticed some SA1s in the maps above are very large and show a low average density of 1-5 persons per hectare (I’ve coloured them in a light cyan). Many of these SA1s had thousands of residents in 2016, which is way more than the ABS guideline of 200 to 800 residents. Unfortunately what seems to have happened for 2011 and 2016 in some cities is that the ABS did not create enough SA1s to account for new urban areas. Some Melbourne SA1s had a population over 4000 in 2016. Many of these SA1s contain a combination or urban and rural land use, so their calculated density is rather misleading.

I’m designating any SA1s with more than 1000 residents and larger than 100 hectares as “oversized”, and I’ve exclude these from some density analysis below. Here’s a chart showing the proportion of outer growth area populations that are in oversized SA1s:

You can see it is a substantial problem in Sydney, Melbourne, Perth and South East Queensland, but miraculously not a problem at all in Adelaide or Canberra (I’m sure someone in ABS could explain why this is so!).

If you are interested, in 2011 it was a bigger problem in Melbourne, and only Canberra was fully clean.

So how dense are outer urban growth areas?

Firstly, I am excluding over-sized SA1s from this analysis for the reasons just mentioned.

Secondly, all cities will also have growth areas that were partially developed at the time of the census (ie some lots with occupied houses and other lots empty) so the densities measured here may be understated of the likely fully built-out density of these SA1s. That said, those areas perhaps are more likely to be in over-sized SA1s, but it’s hard to be sure. So keep this in mind when looking at growth area densities.

You can see dramatic differences, with Sydney, Canberra, and Melbourne showing higher densities, and South East Queensland with much lower densities. As we saw on the maps above, South East Queensland’s outer growth areas are very dispersed, so perhaps more of them are growing slowly and more of them are partially built-out? It’s hard to be sure.

But perhaps what is most remarkable is that Canberra had the highest densities in outer urban growth areas of any city – nothing like what you might consider suburban sprawl. Here’s what was 144.5 people per hectare in 2016 in Wright on Canberra’s new western growth front looks like:

(pic from Google Streetview, dated December 2016)

The densest SA1 in Sydney’s growth areas was 101 persons/ha. Nothing like this was seen in other cities.

Canberra’s outer growth areas are actually, on average, denser than the rest of Canberra (on a population weighted density measure):

The same was also true by a slim margin in both Perth and Adelaide, but they have relatively “suburban” densities for both growth and established areas. The growth areas of Sydney and Melbourne are more dense than Perth and Adelaide, but not compared to the rest of these cities as a whole. That’s probably got to do a lot with the large cities having dense inner suburbs.

So perhaps it is better to compare the urban growth areas with established areas a similar distance from city centres, which the following chart does (I’ve filtered out 5 km distance intervals without growth areas of at least 2000 population, and apologies for rather squashed Canberra label):

Technical note: for South East Queensland I’ve measured distances from the Brisbane CBD.

Outer growth areas were much more dense than the rest of each city at most distances from the city centre, except in Sydney.

One issue with the above chart is that different distance intervals have different populations – for example only 2,815 people were in growth SA1s at a distance of 45-50 km from the Perth CBD (just above my threshold of 2000), so the low population density of that interval is not hugely significant.

To get around that issue, I’ve calculated the overall population weighted density of non-growth SA1s that are within these 5 km distance intervals from the CBD (including all of SEQ beyond 15 km from the CBD). The following chart compares those calculations with the population weighted density of the growth areas overall:

This shows that urban growth areas are on average more dense than other parts of the city at similar distance from the CBD, except in South East Queensland. And remember, many of the growth SA1s will be partially built out, so their expected density is understated.

Are outer urban growth areas near rapid public transport?

The next chart shows the proportion of growth SA1 population by distance from the nearest train or busway station:

Technical notes: Distances are measured from the centroid of each SA1 to a point location defined for each station (sourced from August 2016 GTFS feeds). For oversized SA1s these distances might be a little longer than reality for the average resident. I haven’t excluded oversized SA1s because I want to see the population alignment of growth areas overall. Canberra excluded due to lack of separated rapid transit.

What sticks out clearly is that just over half the of the population in Perth’s outer growth areas was more than 5 km from a station in 2016. That is to say Perth has had the least alignment of outer urban growth areas and rapid public transport networks of all five cities. I’m not sure many urban planners would recommend such a strategy.

However, Perth’s MetroNet program appears to be trying to rectify this with new lines and stations proposed near urban growth areas such as Yanchep, Canning Vale East, Ellenbrook, Byford, and Karnup (Golden Bay). It will however take some time to get to them all built and open.

South East Queensland was second to Perth in terms of urban growth remote from stations, with a lot of the growth scattered rather than concentrated around rail corridors. I haven’t included the Gold Coast light rail in my proximity calculation – it runs at an average speed of 27 km/h (which is slower than most train networks) and doesn’t serve outer urban growth areas.

Sydney and Adelaide had the highest proximity of growth areas to stations.

Around half of Melbourne’s growth SA1s that were more than 5km from a train station were in Mernda and Doreen, a corridor in which a rail extension opened in 2018. Many of the rest are not in the current designated growth corridors, or are where future train stations are planned. Melbourne’s current designated urban growth corridors are fairly well aligned to its train network. From a transport perspective this is arguably a better kind of sprawl than what Perth has been experiencing.

Adelaide’s outer growth areas more than 5 km from a station were in Mount Barker (satellite town to the east) and Aldinga (on the far south coast of Adelaide).

Are the outer urban growth areas better aligned to rapid public transport stations than non-growth areas at the same distance from city centres? Here’s the chart as above but with an extra column for non-growth areas within the same distance intervals from the CBD (as before).

The populations of urban growth areas are less likely to be within a couple of kilometres of a station (most of that land probably has long-established urban development), but curiously in Adelaide and South East Queensland the urban growth areas are more likely to be within 5 kilometres of a station than the non-growth areas, suggesting better rapid public transport alignment than older urban growth areas. Older urban areas in other cities are more closely aligned to stations, particularly in Perth.

As an interesting aside, here’s a breakdown over the last three censuses of population by distance from train/busway stations (operational in 2016 – so it overstates 2006 and 2011 slightly):

You can really see how Perth has had much population growth remote from its rapid public transport network, which probably goes some way to explaining the overall 1.2% journey to work mode shift towards private transport between 2011 and 2016.

So how did people in these outer growth areas get to work?

Technical note: The figures here for “private transport” are for journeys involving only private transport modes – i.e. they exclude journeys involving both private and public transport (eg car+train).

While private transport (mostly car driver only journeys) dominated journeys to work from almost all growth areas, Melbourne and Sydney were the only cities to see significant numbers of residents in outer growth areas with private transport mode shares below 80%.

South East Queensland’s outer urban growth areas were the most reliant on private transport to get to work, with an overall private transport mode share of 93%, followed by Adelaide on 92%, Canberra on 91%, Perth on 90%, Melbourne on 86%, and Sydney on 81%.

Here’s how the growth area mode shares compare to other areas a similar distance from city centres (note: the Y-axis is not zero-based):

Significantly, the growth areas of Sydney and Melbourne had lower private transport mode shares of journeys to work than other parts of the city a similar distance out – even though they are generally further away from train or busway stations (as we saw above)! That’s not to say they didn’t drive themselves to a train station to get to work.

Similar to population density, here is a summary of growth areas compared to other areas in the same distance interval from the CBD:

There’s really not a huge amount of difference within cities. Sydney’s growth areas had a mode share 1.5% lower than non-growth areas, while Canberra’s growth areas had a mode share 2.5% higher.

What are motor vehicle ownership rates like in the outer growth areas?

My preferred measure is household motor vehicles per persons aged 18-84 (roughly people of driving age).

Motor vehicle ownership rates are generally very high across the growth areas – with the notable exceptions of Melbourne and Canberra where around a quarter of the growth area population had a motor vehicle ownership rate of less than 80 (although that is still pretty high!). (I explored this in more detail in an earlier post on Melbourne)

South East Queensland, Perth, and Adelaide outer urban growth areas had the highest motor vehicle ownership rates. Perth’s urban growth areas overall averaged 96.7 motor vehicles per persons aged 18-84 – pretty close to saturation.

How does motor vehicle ownership compare to established areas a similar distance from the city centre? The following chart compares motor vehicle ownership between urban growth and other areas at the same distance from the CBD (note: the Y-axis is not zero-based):

Motor vehicle ownership tends to increase with distance from the CBD, and in Sydney and South East Queensland the growth areas have higher ownership compared to non-growth areas. But the opposite is true in Melbourne, Perth and Canberra.

The population at each distance interval varies considerably, so here is a summary of the data across all distance intervals that have growth SA1s for each city:

The growth areas of Melbourne, Perth and Canberra had slightly lower motor vehicle ownership than other areas a similar distance from the city, while the opposite was true in other cities. That said, motor vehicle ownership rates are very high across all cities.

 

Finally, I’ll look at the relationships between these measures for growth areas (see another post for analysis for whole cities).

How does motor vehicle ownership relate to distance from stations?

Technical note: for scatter plots I’ve filtered out SA1s with less than 50 population as they are more likely to have outlier results (one person can change a measure by 2% or more).

Lower rates of motor vehicle ownership are generally only found close to train/busway stations (and are dominated by Melbourne examples), but close proximity to a station does not guarantee lower rates of motor vehicle ownership. Quite a few Adelaide SA1s are found the top middle part of the chart – these are all in Mount Barker which has frequent peak period express buses to the Adelaide CBD operating along the South East Freeway – which is similar to rapid transit although without a dedicated right of way.

How do journey to work mode shares relate to distance from stations?

Here’s a scatter plot of private transport mode shares of journeys to work and distance from train/busway station:

This shows that lower private transport mode shares are only generally seen within proximity of train or busway stations, and areas remote from stations are very likely to have high private transport mode shares. But also that proximity to a station does not guarantee lower private transport mode shares of journeys to work (particularly in SEQ).

Technical aside: You might have noticed that almost no SA1s report 99% private mode share. How can that be? The ABS make random adjustments to small figures to avoid identification of individuals which means you never see counts of 1 and 2 in their data. To get a mode share of 99% you’d need at least 300 journeys to work with “3” being non-private (or a similar but larger ratio). Very few SA1s have 300+ journeys to work, and even for over-sized SA1s, they are very unlikely to have only 3 or 4 non-private journeys to work. A mode share of 100% is much easier because you can get that no matter the total number of journeys.

How does population density relate to distances from train/busway stations?

Densities above 45 persons/ha were mostly only found within 5 km of stations, and almost entirely in Sydney and Melbourne. The highest densities were very close to train stations in Sydney. In the middle area of the chart you can see quite a few Perth SA1s that are around 30-40 persons/ha but remote from stations. These are all in the Ellenbrook area of Perth’s north-east, generating a lot of car traffic.

How does motor vehicle ownership relate to private transport mode shares of journeys work to work?

For interest, here is the relationship as a scatter plot:

There is certainly a relationship, but it’s not strong (r-squared = 0.22). Other factors are at play.

Conclusions

  • Perth and Canberra are seeing most of their population growth on the fringe, with Sydney, Adelaide, Melbourne, and South East Queensland seeing most of their population growth in established areas.
  • Growth areas in Sydney, Melbourne, and Canberra have higher than traditional urban densities, indeed Sydney and Canberra have a few very high density greenfield developments. Perth, Adelaide, and particularly South East Queensland have urban growth at relatively low densities. In fact, SEQ is the only major urban centre where growth areas are measured as less dense than non-growth areas at similar distances from the CBD.
  • Perth’s urban growth areas are largely remote from rapid transit stations, and this is likely contributing directly to very high and increasing rates of motor vehicle ownership and private transport mode shares. Melbourne’s current urban growth corridors are closely aligned to train stations (thanks to the opening of the Mernda line), and this is also largely true of Sydney and Adelaide.
  • Almost all outer urban growth areas had high rates of motor vehicle ownership. Overall, Melbourne, Perth, and Canberra’s outer urban growth areas had slightly lower rates of motor vehicle ownership compared to other areas at the same distance from the CBD. Only Sydney, Melbourne and Canberra have some growth areas with lower motor vehicle ownership and/or lower private transport mode shares of journeys to work – and these were all close to train or busway stations.

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

For a similar and more detailed analysis around these topics, see this excellent 2013 BITRE research report on changes between 2001 and 2006.

1 Comment | Australian Cities, Brisbane, Car ownership, Melbourne, Mode share, Mode shift, Sydney, Urban density, Urban Planning | Tagged: , , | Permalink
Posted by chrisloader

What explains variations in journey to work mode shares between and within Australian cities?

Thu 6 December, 2018

Private and public transport journey to work mode shares vary considerably both between Australian cities and within them. Are these differences related to factors such as population density, motor vehicle ownership, employment density, proximity to train stations, proximity to busway stations, jobs within walking distance of homes, and distance from the city centre?

This posts sheds some light on those relationships for Australia’s six largest cities. I’m afraid it isn’t a short post (so get comfortable) but it’s fairly comprehensive (over 30 charts).

I should stress up front that a strong relationship between a certain factor and high or low mode shares does not imply causation. There are complex relationships between many of these factors, for example motor vehicle ownership rates are generally lower in areas of higher residential density (which I will also explore), and more factors beyond what I will explore here.

If you are interested in seeing spatial mode share patterns, see previous posts for Melbourne, Brisbane, and Sydney. You might also be interested in my analysis explaining the mode shifts between 2011 and 2016.

Population density

Higher population densities are commonly associated with higher public transport use. This stands to reason, as high density areas have more potential users per unit of area, but also higher density is likely to mean high land prices, which in turn increases the cost of residential parking. But higher public transport mode share can only happen if government’s invest in higher service levels, and this isn’t guaranteed to happen (although it often does, through pressures of overcrowding).

My preferred measure is population weighted density, which is the weighted average density of land parcels in a city, weighted by their population (this gets around problems of including sparsely populated urban land). I’ve measured it at census district (CD) geography for 2006 and Statistical Area Level 1 (SA1) geography for 2011 and 2016, using 2011 Significant Urban Area boundaries to define cities. The 2006 density figures are not perfectly comparable with 2011 and 2016 because CDs are slightly larger than SA1s, so the density values will be calculated as slightly smaller.

Here is the relationships at city level (the thin end of each worm is 2006 and the thick end 2016, with 2011 in the middle):

The relationship is very strong for Melbourne and Sydney over time. Between 2011 and 2016, Perth and Brisbane saw increased population density but reduced public transport mode share (mostly because of changes in the distribution of jobs between the centre and the suburbs).

Brisbane was a bit of an outlier in 2006 and 2011 with high public transport mode share relative to its lower population density.

Canberra is also perhaps a bit of an outlier, with much lower public transport mode share compared to similarly low density cities. This might be explained by the smaller total population, lower jobs density, and lack of rapid public transport services segregated from traffic.

But Canberra does have higher active transport mode share, so it’s worth doing the same analysis with private transport mode shares:

Brisbane was still an outlier in the relationship in 2006 and 2011, but Canberra is more in line with other data points.

Another interesting note is that Canberra went from being the least dense city in 2006 to the third most dense in 2016.

Drilling down to SA2 geography (SA2s are roughly the size of a suburb), here’s a chart showing all SA2s in all cities across the three census years (filtered for CDs and SA1s with at least 5 persons per hectare). I’ve animated it to highlight one city at a time so you can compare the cities, and I’ve used a log scale on the X-axis to spread out the data points (only the Sydney and Melbourne CBDs go off the chart to the right).

(if these animated GIF charts are not clear on your screen, you can click to enlarge the image, then use “back” to come back to this page).

You can see a fairly strong relationship, although it is very much a “cloud” rather than a tight relationship – there are other factors at play.

What I find interesting is that Sydney has had a lot of SA2s with population weighted densities around 50-100 but private mode shares over 55% (toward the upper-right part of the cloud of data points) – which are rare in all other cities. That’s a lot of traffic generation density, which cannot be great for road congestion. In a future post I might focus in on the outlier SA2s that are in the top right of these charts (can public transport do better in those places?).

In case you are wondering about the Brisbane SA2 with low density and low private transport mode share (middle left of chart) it is the Redland Islands where car-carrying ferries are essential to get off an island, and are counted as public transport in my methodology. The Canberra outlier in the bottom left is Acton (which is dominated by the Australian National University).

Employment density

I’ve calculated a weighted job density in the same way I’ve calculated population weighted density, but using Destination Zones (which can actually be quite large so it certainly isn’t perfect). Weighted job density is a weighted average of job densities of all destination zones, weighted by the number of jobs in each zone. In a sense it is the density at which the average person works

(technical notes: I’ve actually only counted jobs as people who travelled on census day and reported their mode(s) of travel. Unfortunately I only have 2006 data for Sydney and Melbourne)

This chart suggests a very strong relationship at the city level, with all cities either moving up and left (Adelaide, Perth and Brisbane) or down and right (Sydney, Melbourne, Canberra).

So is the relationship as strong when you break it down to the Destination Zone level? The next chart shows jobs density and private mode share for all destination zones for 2016. Note that there is a log scale on the x-axis, and Adelaide dots are drawn on top of other cities in the top left which explains why that dense cloud of dots appears mostly green.

There’s clearly a strong relationship, although again the data points form a large cloud rather than tightly bunch around a line, so other factors will be at play.

It’s also interesting to see that the blue Sydney dots are generally lower than other cities at all job densities. That is, Sydney generally has lower private transport mode shares than other cities, regardless of employment density.

Which leads us to the next view: the private transport mode shares for jobs in different density ranges in each city for 2011 and 2016.

(click to enlarge if the chart appears blurry)

You can see a fairly consistent relationship between weighted job density and mode shares across all cities in both 2011 and 2016.

At almost all job density ranges, Sydney had the lowest average private transport mode share, while Adelaide and Perth were generally the highest (data points are not shown when there are fewer than 5 destination zones at a density range for a city). This shows that something other than jobs density is impacting private transport mode shares in Sydney. Is it walking catchment, public transport quality & quantity, or something else?

For more on the relationship between job density and mode share, see this previous post.

Proximity to public transport

Trains generally provide the fastest and most punctual public transport services (being largely separated from road traffic and having longer stop spacing), and are the most common form of rapid transit in Australian cities. So you would expect higher public transport mode shares around train stations.

Here is a chart showing average journey to work public transport mode shares by home distance from train stations. It’s animated over the three census years, with a longer pause on 2016.

Technical note: A limitation here is that I’ve measured all census years against train stations that were operational in 2016 – so the 2006 and 2011 mode shares will be under-stated for the operational stations of those years. For example, in Melbourne the following stations opened between 2011 and 2016: Williams Landing, South Morang, Lynbrook, and Cardinia Road.

You can see that public transport shares went up between 2006 and 2011 in most cities at all distances from train stations. In both Perth and Brisbane there were new train lines opened between 2006 and 2011, which will explain some of that growth.

But if you watch carefully you will see public transport mode shares near train stations fell in both Brisbane and Perth between 2011 and 2016. That is, there was a mode shift away from public transport, even for people living close to train stations. As discussed previously, this is most likely related to there being only small jobs growth in the CBDs of those cities between 2011 and 2016, compared to suburban locations.

You can also see that public transport mode shares aren’t that much higher for areas near train stations in Adelaide (I’ll come back to that).

We can do the same for train mode shares (any journey involving train):

Again, Sydney’s train stations seem to have the biggest pulling power, while Adelaide’s have the least.

Busways are the other major form of rapid transit in Australian cities, with major lines in Brisbane, Sydney and Adelaide. Here is a chart of public transport mode share by distance from busway stations, excluding areas also within 1.5 km of a train station:

Note for Adelaide this data only considers suburban stations on the O-bahn, and not bus stops in the CBD. For Sydney all “T-Way” station are included, plus the four busway stations on the M2 motorway for which buses run into the CBD (but not the relatively short busway along Anzac Parade in Moore Park). Sydney’s north west T-Ways opened in 2007

Proximity to a busway station appears to influence public transport mode share strongly in Brisbane and Adelaide, where busways are mostly located in the inner and middle suburbs and cater for trips to the CBD. Sydney’s busway stations are in the “outer” western suburbs, feeding Blacktown, Parramatta, but also relatively long distance services to the Sydney CBD via the M2.

Curiously, public transport mode shares were higher in places between 3 and 5 km from busway stations in Sydney, compared to immediately adjacent areas. I’m not sure that I can explain that easily, but it suggests equally attractive public transport options exist away from busway and train stations.

The station proximity influence appears to extend around 1 km, which possibly reflects the fact that few busway stations have park and ride facilities, and are therefore more dependent on walking as an access mode (although cycling may be another station access mode).

Over time Sydney public transport mode share lifted at all distances from busway stations, while in Brisbane it rose in 2011 and then fell again in 2016, in line with other city mode shares.

So are busway stations similar to train stations in their impact on public transport mode share? To answer this I’ve segmented cities into areas near train stations, near busway stations, near both, and near neither. I’ve used 1.5 km as a proximity threshold that might represent an extended walking catchment.

In Sydney, train stations appear to have a much stronger influence on public transport mode shares than busway stations, but the opposite is true in Brisbane and Adelaide. This possibly reflects the much higher service frequencies on Adelaide and Brisbane busways compared to their trains, and the fact Sydney’s busway stations are so far from the CBD (and thus have fewer workers travelling to the CBD where public transport dominates mode share).

Also of note in this chart is that for areas more than 1.5 km from a train or busway station, Sydney had a much higher public transport mode share compared to the other cities. These areas will be served mostly by on-road buses, but also some ferries and one light rail line. Adelaide has the least difference between mode share for areas near and not-near train or busway stations.

We can do the a similar analysis for workplaces:

The most curious pattern here is Adelaide – where public transport mode share was highest for jobs between 1.5-2.5 kms from train stations. This distance band is dominated by the centre of the Adelaide CBD (the station being on the edge, arguably a “corner”), for which bus was the dominant public transport access mode. Also, there was no destination zone small enough near Adelaide central train station to register as 0 – 0.5 km away, and only one that is 0.5 – 1 km away (I use distances between station data points and destination zone centroids). So the results might look slightly different if smaller destination zones were drawn in the Adelaide CBD.

In all other cities there was a very strong relationship between train station proximity and public transport mode share, as you would expect. And Sydney again stands out with high public transport mode shares for workplaces more distant from train stations.

If you are wondering, the bump in Sydney at 2.5 to 3 km includes the Kensington / Randwick area which has high employment density and a strong bus connection to the central city (partly assisted by the Anzac Parade busway). And the relatively high figure for Melbourne at 1 – 1.5 km includes parts of Docklands, Parkville, Southbank, and St Kilda Road, which all have high tram service levels.

Unfortunately destination zones around busway stations are generally too large to provide meaningful insights so I’m not presenting such data.

Motor vehicle ownership

It will come as little surprise that there is a relationship between household motor vehicle ownership and journey to work mode shares.

Here’s a summary chart for each city for the 2006, 2011 and 2016 censuses:

There appears to be a fairly strong relationship between the two factors at city level.

Sydney and Melbourne have seen the largest mode shift away from private transport, but only Melbourne has also seen declining motor vehicle ownership rates.

Canberra saw only weak growth in motor vehicle ownership between 2011 and 2016, and at the same time there was a shift away from private transport (and a large increase in population weighted density).

Perth and Brisbane saw increasing private transport mode share and increasing motor vehicle ownership between 2011 and 2016.

Here’s a more detailed look at the relationship over time for Melbourne at SA2 geography:

The outliers on the upper left are generally less-wealthy middle-outer suburban areas (lower motor vehicle ownership but high private mode share), while the outliers to the lower-right are wealthy inner suburbs where people can afford to own plenty of motor vehicles, but they didn’t use them all to get to work.

In the bottom left of the chart are inner city SA2s with declining private mode share and declining motor vehicle ownership. For motor vehicle ownership rates around 70-80 (motor vehicles per persons aged 18-84), there are many SA2s with private mode shares that declined 2006 to 2016, but not significantly lowering motor vehicle ownership rates. That suggests that just because people own many motor vehicles, they don’t necessarily use them to drive them to work.

Here is the same data for Sydney:

There are many SA2s with motor vehicle ownership rates around 50 to 70 where the private mode shares are dropping faster than motor vehicle ownership. But there are also many areas with high private mode shares and increasing rates of motor vehicle ownership.

How do the other cities compare? Here are all the SA2s for all cities on the same chart, with alternating highlighted cities:

You can see big differences between the cities, but also that Brisbane and Perth have many SA2s with very high private mode share and rapidly increasing motor vehicle ownership (ie moving up and right, although it’s a little difficult to see with so many lines overlapping). Melbourne and Sydney have plenty of SA2s moving down and left – reducing motor vehicle ownership and declining private transport mode share (which may make some planners proud).

Of course there will be a relationship between motor vehicle ownership and where people choose to live and work. People working in the central city may prefer to live near train stations so they can avoid driving in congested traffic to expensive car parks. People who prefer not to drive might choose to live close to work and/or a frequent public transport line. People who are happy to drive to work in the suburbs might avoid higher priced real estate near train stations or the inner city.

As an aside, we can compare total household motor vehicles to the number of people driving to work, to estimate the proportion of household motor vehicles actually used in the journey to work. Here is Melbourne:

SA2s with a lower estimate are generally nearer the CBD, are wealthier areas, have reasonable public transport accessibility, and/or might be areas with a higher proportion of people not in the workforce (for whatever reason). The areas where the highest proportion of motor vehicles are required for the journey to work are relatively new outer suburbs on the fringe (perhaps suggesting forced car ownership), where adult workforce participation is probably high and public transport accessibility is lower.

The proportion of cars used in the journey to work declined on average in many parts of Melbourne. Given that motor vehicle ownership rates in Melbourne barely changed between 2011 and 2016, this probably represents people mode shifting, rather than people acquiring more motor vehicles even though they don’t need them to drive to work.

Jobs within walking distance of home

It stands to reason that people would be more likely to walk to work if there were more work opportunities within walking distance of their home.

For every SA1 I’ve measured how many jobs are approximately within 1 km as a notional walking catchment (measured as the sum of jobs in destination zones whose centroid are within 1 km of the centroid of each home SA1, so it is not perfect). Here’s the relationship with walking mode share:

(there are a lot of dots overlapping in the bottom left-corner and Adelaide dots have been drawn on top so try not to get thrown by that).

You don’t have to have a lot of nearby jobs to get a higher walking mode share, but if you do, you are very likely to get a high walking more share. The exceptions (many jobs, but low walking share) include many parts of Parramatta (Sydney), and areas separated from nearby jobs by water bodies or other topographical barriers (eg Kangaroo Point in Brisbane).

Workplace distance from the city centre

As was seen in a previous post, workplaces closer to city centres had much lower private transport mode shares, which is unsurprising as these are locations with generally the best public transport accessibility, high land values that can lead to higher car parking prices (which impact commuters who pay them), and often higher traffic congestion.

Here is a chart showing private transport mode share by workplace distance from the city centre. I’ve used faded lines to show 2011 and 2006 results (2006 only available for Sydney and Melbourne).

Here’s a chart that shows the mode shifts between 2011 and 2016:

Inner Melbourne had the biggest mode shifts away from private transport (particularly in Docklands that falls into the 1-2 km range, which saw significant employment and tram service growth), but Sydney had more consistent mode shifts across most distances from the city centre. Adelaide and Canberra saw mode shifts away from private transport in the inner city but towards private transport further out.

Brisbane and Perth saw – on average – a mode shift to private transport across almost all distances from the city centre, with the highest mode shift to private transport in Brisbane actually for the CBD itself(!).

Home distance from the city centre

There’s unquestionably a relationship here too, and it’s probably mostly driven by public transport service levels being roughly proportional to distance from the CBD, but also the proportion of the population who work in the CBD being much higher for homes nearer the CBD.

Sydney had the lowest average private transport mode share at all distances up to 20 km from the CBD, followed by Melbourne and Brisbane, in line with overall mode shares.

The trends over time are also interesting. Brisbane saw mode shifts towards private transport at all distances more than 2 km from the city centre between 2011 and 2016. However there were not significant shifts for Perth outside the city centre – that is: modes shares by geography didn’t change very much. The mode shift away from public transport in Perth is best explained by the shift in jobs balance away from the city centre.

Here are public transport mode shares by home distance from city centres:

In most cities, public transport mode share peaked at a few kilometres from the city (as active transport has a higher mode in the central city).

Here are public transport mode shifts by distance from the city centre between 2011 and 2016:

The significant shift in central Melbourne is likely to be largely explained by the Free Tram Zone introduced in 2015. Outside of the city centre the mode shifts are surprisingly uniform across each city.

Here’s the same chart for 2006-2011, and you can clearly see the impact of the opening of the Mandurah railway line in Perth with significant mode shift beyond 30 km:

Curiously there was a massive shift to public transport for CBD residents in Melbourne (and this is before the free tram zone was introduced).

So which factors best explain the patterns in mode shares across cities?

What we’ve clearly seen is that higher public transport mode shares are seen for journeys to work…

  • to higher density workplaces
  • from areas of lower motor vehicle ownership
  • to workplaces closer to train stations
  • from higher density residential areas
  • from areas around train and busway stations
  • to and from areas closer to city centres (except from the central city where walking takes over)
  • from less wealthy areas (while I haven’t tested this directly, wealth seems to explain a lot of the outliers in the scatter plots)

I’ve listed these roughly in order of the strength of the relationships seen in the data, but I haven’t put them all in a regression model (yet, sorry).

Of course most of these factors are inter-related, so we cannot isolate causation factors. I’m going to run through many of them, because they are often interesting: (note I have sometimes used log scales)

Population density is roughly related to distance from the city centre:

Motor vehicle ownership has a strong relationship with population density (see this post for more analysis):

Motor vehicle ownership has a weaker relationships with distance from the city centre:

Motor vehicle ownership is related to home distance from train stations, except in Adelaide:

Technical note: For this chart (and some below) I’ve calculated average quantities for the variable on the Y axis, as there would otherwise there are too many data points on the chart and it becomes very hard to see the relationship (I would need to show all SA1s because SA2s are too large in terms of distance from stations). The downside is that these style of charts don’t indicate the strength of relationships.

Population weighted density is related to distance from train stations, especially in Melbourne and Sydney, but not at all in Adelaide:

There is a relationship – although not strong – between weighted job density and distance from city centres:

There’s some relationship between average weighted jobs density and distance from train stations, except in Adelaide:

Here’s the same data, but as a scatter plot with a point for each destination zone, scaled by the number of journeys to each destination zone, and a linear Y-axis:

Technical note: the X-axis appears green mostly because Adelaide data points are drawn on top of other cities, but those data points aren’t of much interest.

In most cities, destination zones with high jobs density (over 700 jobs/ha) were only found within 1 km of a train station – with the notable major exception of Adelaide (again!).

(If you are curious, the large Melbourne zone at 1.4 km from a train station and 659 jobs/ha is the Parkville hospital precinct – where incidentally a train station is currently under construction).

There is a relationship between motor vehicle ownership and proximity to busway stations, but it varies between cities:

But there’s not much relationship between population density and proximity to busway stations (except in the immediate vicinity of busway stations in Brisbane):

Final remarks: there’s something about Adelaide’s train network

A few key observations come through clearly about the catchments around Adelaide’s train stations:

  • In aggregate they do not have higher population density, unlike other cities.
  • In aggregate they do not have particularly high public transport mode shares, unlike other cities.
  • In aggregate they do not have lower rates of motor vehicle ownership, unlike other cities.
  • They do not include the area of highest job density in the CBD (a longer walk or transfer to tram or bus is required), unlike other cities.

Few cities have spare land corridors available for new at-grade rapid public transport lines, and so transport planners generally want to make maximum use of the ones they’ve got, before opting for expensive and/or disruptive tunnelling or viaducts solutions. It looks like Adelaide’s rail corridors are not reaching their people-moving potential.

By contrast, Adelaide’s “O-Bahn” busway does go into the job dense heart of the CBD and the busway station catchments do have higher public transport mode share and lower motor vehicle ownership. However they do not have higher population density, possibly because the stations are surrounded by car parks, green space, and one large shopping centre (Tea Tree Plaza).

Mode shares, population densities, and motor vehicle ownership rates would quite probably change significantly if Adelaide could address the fourth issue by building a train station near the centre of the CBD.

In fact, Auckland had a very similar problem with its previous main city station being located away from the centre of the CBD. They fixed that with Britomart station opening in 2003 and train patronage soon rose quite dramatically (off a very low base, and also helped by service upgrades, subsequent electrification, and many other investments).

Should Adelaide do the same? It would certainly not be cheap and you would have to weigh up the costs and benefits.

1 Comment | Australian Cities, Brisbane, Car ownership, Employment density, Melbourne, Mode share, Mode shift, Sydney, Urban density | Tagged: , , , | Permalink
Posted by chrisloader

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