How did the journey to work change in Brisbane between 2011 and 2016?

Wed 25 April, 2018

Between 2011 and 2016, Greater Brisbane saw a 2% mode shift towards private motorised transport for journeys to work, the largest such shift of all large Australian cities. Was it to do with where jobs growth happened, or because public transport became less attractive over that time?

This post takes a more detailed look at the spatial changes in private transport mode shares, and then examines the relative impact on spatial variations in jobs growth compared to other factors.

Greater Brisbane main mode shares

Firstly for reference, here are the Brisbane Greater Capital City Statistical Area main mode shares and shifts for 2011 and 2016, measured by place of enumeration and place of work:

2011 2016 Change
Private Place of enumeration 80.0% 81.9% +1.9%
Place of work 79.1% 81.1% +2.0%
Public Place of enumeration 15.1% 13.5% -1.6%
Place of work 15.9% 14.2% -1.7%
Active Place of enumeration 4.9% 4.6% -0.3%
Place of work 5.0% 4.7% -0.3%

More information about main mode definitions and data in general is available at the appendix at the end of this post.

Mode shares and shifts by home location

Here are private transport mode shares by home location for 2006, 2011, and 2016:

(you might need to click on these charts to see them larger and more clearly)

You can see lower private mode shares around the central city and to some extent along the rail lines. In case you are wondering, the Redcliffe Peninsula railway opened in October 2016 – after the August 2016 census.

The changes between years are a little difficult to make out on the map above, so here are the mode shifts to private transport by home location at SA2 level:

Mode shifts to private transport can be seen over most parts of Brisbane, with the biggest being Auchenflower (+6%), Lawnton (+6%), Toowong (+5%), Norman Park (+5%), Strathpine – Brendale (+5%), Keperra (+5%), and Sandgate – Shorncliffe (+5%). Many of the large mode shifts to private transport were actually seen around the train network.

The Redland Islands area had a larger shift to public transport – but keep in mind this will include use of car ferries.

Here’s a map showing the mode split of net new trips by home SA2:

There were a lot of new trips from outer growth areas in the north, west and south, and the vast majority of these trips were by private transport (although the southern growth area of Springfield Lakes, where a rail line opened in 2010, had a relatively high 15% of new trips by public transport). Private transport mode shares of new new trips were also high in middle and most inner suburbs (unlike inner Melbourne).

To sum all that up, here are the changes in trip volumes by main mode and home distance from the CBD:

Private transport dominated most new trips, and there were net declines in public transport trips beyond 2 km from the CBD.

Here’s a look at the main mode split over time, by distance from the CBD:

Brisbane achieved significant mode shift away from private transport between 2006 and 2011, but that was pretty much reversed between 2011 and 2016.

Private transport mode shares dropped in 2011 but pretty much returned to 2006 values in 2016. On average, only the city centre saw a mode shift away from private transport between 2011 and 2016, and that’s only a tiny fraction of the Brisbane’s population.

Mode shares and shifts by work location

Here are workplace private transport mode shares for 2011 and 2016:

(more areas are coloured in 2016 because they reached my minimum density threshold of 4 jobs per hectare at destination zone level for inclusion on the map)

Low private mode share is only really seen around the city centre. Some lower mode share areas further out include St Lucia (UQ campus, 52% in 2016) and Nundah (74%), but most of the suburban jobs are dominated by private transport.

Here are the mode shifts by workplace location:

The biggest mode shifts to private transport were to workplaces in Wooloowin – Lutwyche (+7%), Spring Hill (just north of the CBD, +5%) and Jindalee – Mount Ommaney (+5%). The biggest shifts away from private transport were in Newstead – Bowen Hills (-6%), St Lucia (-4%, which includes the University of Queensland main campus), and West End (-3%).

Notably, the job rich Brisbane CBD had a 2% shift to private transport (with 3,135 more private transport trips in 2016).

Here’s a map of the net new jobs and their main mode splits:

And a zoom in on the inner city to separate the overlapping pie charts:

The SA2 with the biggest jobs growth was “Brisbane City” (covering the CBD) with 4584 new jobs – with 68% of this net increase attributable to private transport. North Lanes – Mango Hill in the northern suburbs was not far behind (4472 new jobs at 96% by private transport), followed by Newstead – Bowen Hills (4266 new jobs at 49% private transport) and Brisbane Airport (4197 new jobs at 95% private transport).

The distribution of jobs growth was not heavily concentrated in central Brisbane – in stark contrast to Melbourne where the central city jobs growth was much more signficant.

Here’s a clearer view of new jobs by workplace distance from the city centre and main mode:

At all distances from the CBD, private transport new trips outnumbered active and public transport new trips (and there was a decline in public transport trips to the very city centre). The vast majority of net new trips were to workplaces more than 4 km from the city centre, and by private transport.

So why was there an overall 2% mode shift to private transport?

The relative lack of jobs growth in the public transport rich city centre is very likely to have contributed to the mode shift to private transport. The vast majority of new jobs were in the suburbs where public transport is significantly less competitive (relative to the CBD).

Others will point to factors that have made public transport less attractive relative to private transport, including problems on the train network, extensive new motorway infrastructure, and public transport fares growing around twice the rate of inflation after 2010.

There was very rapid growth in fares between 2010 and 2015, but then fares were frozen in 2016 and substantially reduced in 2017:

Looking at people working in Greater Brisbane (Greater Capital City Statistical Area), there were 94,055 new private transport commutes, just 246 new public transport commutes, and 2,506 new active transport commutes. So around 97% of net new trips in 2016 were by private transport, much higher than the 2011 baseline private transport mode share of 79% of trips (measured for workplaces in Greater Brisbane), hence the overall 2% mode shift.

Looking at people living in Greater Brisbane, there were 61,557 new private transport commutes, a net reduction of 6,069 public transport commutes, and a net reduction of 54 active transport commutes. Thus every new commute was accounted for by private transport, and further to this there was mode shift away from active and public transport.

So how much of the mode shift can be explained by spatial changes in jobs distribution? If mode shares in each workplace SA2 had not changed between 2011 and 2016 then city level mode shares would be influenced only by spatial variations in jobs growth.

I’ve done the calculations at SA2 geography: if place of work mode shares in Brisbane had not changed between 2011 and 2016 (but volumes had), then the overall private transport mode share would have increased only 1.0% in 2016 (essentially because of higher jobs growth in the suburbs compared to the centre).

Actual private mode share increased by 2.0% (measured by place of work).

So this suggests only half of the mode shift can be explained the spatial variations in jobs growth. The other half will be explained by other factors, particularly changes in the relative attractiveness of modes.

Changes in the relative attractiveness of modes will include public transport service quality, public transport fares, fuel prices, toll prices, and infrastructure provision for private and active transport. Car ownership will undoubtedly be a factor, but I suspect many ownership decisions will be influenced by workplace locations and relative modal attractiveness. Other factors might include changes in real incomes, demographic changes, changes in employment density, and the locations of population growth. I’ll explore the last two in more detail.

What about the relationship between job density and mode share?

You could argue that if general public transport “attractiveness” had not changed, you could still expect a mode shift towards public transport in areas with both high and increasing job density, as car parking might struggle to grow at the same rate as jobs growth (as the land becomes increasingly valuable/scarce). This might particularly be the case in the city centre.

I’ve calculated weighted job density for each SA2 – that is, the average density of destination zones in the SA2, weighted by the number of jobs in each zone (similar to population weighted density, so that large areas within SA2s that house few jobs make little contribution to such scores).

Here’s how weighted job density and workplace private mode share changed in Brisbane for higher density SA2s:

While there is some relationship between job density and private mode share overall, there wasn’t a consistent negative correlation between changes in those values. If there was, you would expect all lines on the chart to be on a similar diagonal orientation (upper left – lower right).

South Brisbane and Upper Mount Gravatt saw increased density but little change in private mode share. Chermside, Auchenflower, and Woolloongabba (which incidentally is at the southern end of the Clem 7 motorway) saw increased job density but also increased private transport mode share (the opposite effect of what you might expect). Spring Hill had only a small drop in job density but a large increase in private mode share.

Newstead – Bowen Hills had the largest shift away from private transport, and also one of the largest increases in job density

You might be wondering how the Brisbane City SA2 (which includes the CBD) can have had an increase in total jobs, but a slight decline in weighted jobs density. It turns out that the 2016 SA2 boundary goes further into the Brisbane River than the 2011 boundary. Here’s a map generated on the ABS website, where blue lines are the 2011 boundaries and red the 2016 boundaries:

If you discounted the increase in area, you might expect a slight increase in job density (about 4% in unweighted average density) to result in a small mode shift away from private transport, quite the opposite of what actually happened. If increasing job density by itself might have pushed a mode shift away from private transport, it appears it was overpowered by factors working in the opposite direction.

The Brisbane City SA2 accounted for 12.5% of Brisbane’s jobs so its mode split impacts more than most on overall city mode shares.

So what might be the stand-alone impact of increased job density in the city centre on private mode share? It’s very hard to quantify. I can certainly look at other city centres, but there will be so many factors at play in those cities that it would be almost impossible to isolate the impact.

But as a rough stab, had Brisbane City SA2’s private mode share increased from 29.0% to 29.5% (instead of 30.6%), and all other things were the same, then the overall Brisbane private mode share would have been 0.14% lower.

While the actual impact is uncertain, it would only increase the influence of the “other factors” that are responsible for at least half of the 2% mode share towards private transport.

And what about the spatial distribution of population growth?

All other things being equal, if population growth had disproportionately occurred in places with high private transport mode share (eg the middle and outer suburbs), you might expect a mode shift to private transport. However I don’t think this was significant in Brisbane as there has also been inner city population growth.

Indeed, if the home-based private transport mode share of each SA2 had not changed between 2011 and 2016 (but population numbers had), then the overall Brisbane private mode share (by place of enumeration) would have increased only 0.1% (rather than 1.9%). So the overall mode shift doesn’t seem to have a lot to do with where population growth happened.

So what are these effects other cities? I’ll cover that in an upcoming post.

Appendix: about the data

Here’s how I have defined “main mode”:

Private (motorised) transport any journey to work involving car, motorcycle, taxi, truck and/or “other”, but not involving any mode of public transport (train, tram, bus, or ferry)
Public transport any journey involving train, bus, tram, or ferry (journeys could also involve private or active transport modes)
Active tranport journeys by walking or cycling only

I have extracted data from the ABS census for 2006, 2011, and 2016 for areas within the 2011 boundary of the Brisbane Significant Urban Area. The detailed maps are at the smallest available geography – Census Collector Districts (CD) for 2006 and Statistical Area Level 1 (SA1) for 2011 and 2016 for home locations, and Destination Zones (DZ) for workplaces in 2011 and 2016 (detailed workplace data is not readily available for 2006 for most cities). I’ve aggregated this data for distance from city centre calculations (filtered by 2011 Significant Urban Area boundaries), which means the small randomisations will have amplified slightly.

In 2011, a significant number of jobs were not assigned to a destination zone:

  • 3.8% of jobs were assigned to an SA2 but not a DZ – I’ve imputed these proportionately to the DZs in their SA2 based on modal volumes reported for each DZ (for want of something better).
  • 18,540 Queensland jobs (0.9%) were only known to be somewhere in Greater Brisbane.
  • 115,011 jobs (5.8%) were only known to be somewhere in Queensland (hopefully mostly outside Greater Brisbane!).

These special purpose codes are not present in the 2016 data – presumably the ABS did a much better job of coding jobs to DZs. It means that the volumes in 2011 may be slightly understated, and so growth between 2011 and 2016 might be slightly overstated.

I’ve also extracted the data at SA2 (Statistical Area Level 2) based on 2016 boundaries for the purposes of calculating mode shifts and changes in trip volumes at SA2 level (to avoid aggregating small random adjustments ABS applies). However this wasn’t possible for jobs where 2011 SA2s were split into smaller SA2s in 2016 – because some 2011 jobs were assigned an SA2 but not a DZ, so we cannot map those to a specific 2016 SA2 (I aggregated imputed DZ numbers to 2016 SA2 boundaries instead).

I also extracted data at the Brisbane Greater Capital City Statistical Area level, as noted (the boundary did not change between 2011 and 2016).

I have not counted jobs that were reported to have no fixed address in my place of work analysis. I’ve also excluded people who worked at home, did not go to work on census day, or did not provide information about their mode(s) of travel. These workers are also excluded from job density calculations.


Introducing a census journey to work origin-destination explorer, with Melbourne examples

Sun 28 January, 2018

The Australian census provides incredibly rich data about journeys to work, with every journey classified by origin, destination, and mode(s) of transport. So you can ask questions such as “where did workers living in X commute to and how many used public transport?” or “where did workers in Y commute from and what percentage used private transport?”, or “What percentage of people in each home location work in the central city?”.

It’s very possible to answer these questions with census data, but near-impossible to produce an atlas of maps that would answer most questions.

But thanks to new data visualisation platforms, it’s now possible to build interactive tools that allow exploration of the data. I’ve built one in Tableau Public, using both 2011 and 2016 census data for all of Australia at the SA2 geography level (SA2s are roughly suburb sized). This means you can look at each census year, as well and the changes between 2011 and 2016.

I’m going to talk through what I’ve built with plenty of interesting examples from my home city Melbourne.

I hope you find exploring the data as fascinating and useful as I do. I also hope this tool makes it easier to inform transport discussions with evidence.

Also, a warning that this is a longer post, so get comfortable.

About the data (boring but important)

The census asks people which modes they used in the journey to work, and the data is encoded for up to three modes.

I’ve extracted a count of the number of trips between all SA2s within each state, by “main mode” for both 2011 and 2016. I’ve aggregated all responses into one of the following “main mode” categories:

  • Private (motorised) transport only – any journey involving car, truck, motorbike or taxi, but no modes of public transport, or people who only responded with “other”. Around 89% of journeys in this category were simply “car as driver”.
  • Walking/cycling only (or “active transport”) – journeys by walking or cycling only.
  • Public transport – any journey involving any public transport mode (train, tram, bus, and/or ferry). These journeys might also involve private motorised transport and/or cycling.

There are 466,597 rows of data all up – so you will need to be a little patient while Tableau prepares charts for you.

Things to note:

  • I’ve had to extract each state separately to stop the number of possible origin-destination combinations getting too large. This means that interstate journeys to work are not included in the data. I have however combined New South Wales (NSW) and the small Australian Capital Territory (ACT), as many people commute between Queanbeyan (NSW) and Canberra (ACT). Apologies to other areas near state borders!
  • When you ask the ABS for the number of people meeting certain criteria, the answer will never be 1 or 2. The ABS randomly adjust small numbers to protect privacy, and it’s not a good idea to add up lots of small randomly adjusted figures. That’s another reason why I haven’t gone smaller than SA2 geography and why I’ve aggregated mode combinations to just three modal categories. You will still see counts of 3 or 4, which need to be treated with caution.
  • Not all SA2s are the same size in terms of residential population, and particularly in terms of working population. The biggest source of commuters for a work area might simply be an SA2 with a larger total residential population.
  • The ABS change the SA2 boundaries between censuses. With each census some SA2s are split into smaller SA2s, particularly in fast growing areas. If you want to compare 2011 and 2016 figures, it is necessary to aggregate the 2016 data to 2011 boundaries, which the tool does where required. Some visualisation pages will give you the option of aggregating 2016 data to 2011 boundaries to make it easier to compare 2011 and 2016 data.
  • I’ve only counted journeys where the origin, destination and mode are known. Anyone who didn’t go to work on census day, didn’t state their mode(s) of travel, or didn’t state a fixed land-based work location are excluded.
  • Assigning “other” only trips as private transport might not be perfect, as it might include non-motorised modes like skateboards and foot scooters. It will also count air travel, and it’s arguable whether that is private or public transport (it’s certainly not low-carbon transport). However, overall numbers are quite small – 0.81% of all journeys with a stated mode in Australia.

Mode share maps to/from a location

First up, you can produce maps showing the main mode share of commuters from all home SA2 for a particular work SA2, or all workplaces for a particular home SA2.

Here is a map of private transport mode shares for journeys to work from Point Cook North:

Private transport dominates most middle and outer work destinations (even local trips), with many at 100%. Lower shares are evident for central city destinations, although Southbank next to the CBD is relatively high at 65%, and 100% of commuters who travelled to Fishermans Bend did so by private transport.

You can also look at it the other way around. Here’s private transport mode share for commutes to Parkville (just north of the CBD):

There was a low private transport mode share from the city centre and Brunswick to the north, roughly 40-50% mode shares from the south-eastern suburbs (accessible by train), but very high mode shares from the middle and outer suburbs to the north and west (public transport access more difficult). The new Metro Tunnel could make a dent in these mode shares, with a new train station in Parkville.

Here is a map of private transport only mode share for journeys to the “Melbourne” SA2 (which represents the Melbourne CBD):

Private transport (only) mode shares were lower than 30% for most areas, as public and active transport options are generally cheaper and more convenient for travel to the CBD. However you can see corridors with higher private transport mode share, including Kew – Bulleen – Doncaster – Warrandyte, and Keilor East – Keilor – Greenvale (around Melbourne Airport). These corridors are more remote from heavy rail lines. Other patches of higher private mode share include Rowville – Lysterfield, Altona North, and Point Cook East (including Sanctuary Lakes).

A high private transport mode share does not necessary mean a flood of private vehicles are coming from these areas. Kinglake is the rich orange area in the north-east of the above map, and according the 2016 census, 57% of people commuted to the Melbourne CBD by private transport only. Except that 57% is actually just 23 out of just 40 people making that commute – which is pretty small number in whole scheme of things.

Which leads me to…

Journey volume and mode split maps

These maps show the volume (size of pie) and mode split for journeys from/to a selected SA2.

The following map shows the volume and mode split of journeys to the “Melbourne” SA2 in 2016:

As I discussed in a recent post, not many people actually commute from the outer suburbs to the central city. Indeed, only 767 people commuted from Rowville to the Melbourne CBD in 2016, which is less than one train full.

Unfortunately all the pie charts in the inner city tend to overlap, while the pie charts in the outer suburbs are tiny. Here’s a zoomed in map for the inner suburbs with a lot less overlap:

You can see large green wedges in the inner city, where walking or cycling to the CBD is practical. You can also see that almost everywhere the blue wedges (public transport) are much larger than the red (private transport).

What does stand out more in this map is Kew – where 716 people travelled to the Melbourne CBD by private transport (highest of any SA2) – with a relatively high 41% mode share for a location so close to the city, despite it being connected to the CBD by four frequent tram and bus lines. Kew is also a quite wealthy area, so perhaps parking costs do not trouble such commuters (maybe employers are paying?). Other home SA2s with high volumes and relatively high private mode shares are Essendon – Alberfeldie (521 journeys, 28% private mode share), Brighton (493, 33%), Keilor East (419, 41%), Toorak (404, 35%) and Balwyn North (396, 35%). Most of these are wealthy suburbs, with the notable exception of Keilor East, which does not have a nearby train station.

Here is the same for Parkville:

The home areas with significant numbers of Parkville commuters are in the inner northern suburbs, and active and public transport were the dominant mode share for these trips. While 92% of commuters from Burnside Heights to Parkville were by private transport, there were only 35 such trips. The overall private transport mode share for Parkville as a destination was 50%.

Here is the same type of map for Fishermans Bend (Port Melbourne Industrial), which is just south-west of the CBD:

Private transport dominates mode share, and you can see a slight bias towards the western suburbs. Which means a lot of cars driving over the Westgate Bridge.

Around 30,000 people travelled to work in Clayton in Melbourne’s south-east. Here’s a map showing the origins of those commutes:

Almost half of the workers who both live and work in Clayton walked or cycled (only) to work, of which I suspect many work at Monash University. The public transport mode shares are higher towards the north-west, particularly around the Dandenong train line that connects to Clayton. Very few people put themselves through the pain of commuting from Melbourne’s western and northern suburbs to Clayton.

Over 60,000 people commuted to Dandenong in 2016, which includes the large Dandenong South industrial area. Here are the volumes and mode splits for where they came from:

You can see a significant proportion of the workforce lived to the south-east, and much less to the north and west. You can also see private transport dominates travel from all directions (despite there being two train lines through the Dandenong activity centre, and a north-south SmartBus route through the industrial area).

Here‘s a look at people who commuted to work at Melbourne Airport:

You can see that airport workers predominantly came from the nearby suburbs, and the vast majority commuted by private transport. The most common home locations of airport workers include Sunbury South (543), Gladstone Park – Westmeadows (411), and Greenvale – Bulla (351 – note Greenvale has a much higher population than Bulla).

The largest public transport volume actually came from the CBD (48 out of 67 commuters, which is a 72% mode share), probably using staff discount tickets on SkyBus. The biggest trip growth 2011 to 2016 was from Craigieburn – Mickelham: 367 more trips of which 355 were by private transport only.

The data can also be filtered to only show a particular main mode. For example, here is a map of the origins for private transport trips to the Melbourne CBD (ie who drives to work in the CBD):

Which can also be shown as a sorted bar chart:

The most common sources of private transport trips to the CBD were generally very wealthy suburbs, where many people are probably untroubled by the cost of car parking (they can easily afford it, or someone else is paying). However bear in mind that not all SA2s have the same population so larger SA2s will be higher on the list (all other things being equal).

This data can also be viewed the other way around. Here are the volumes and mode splits of journeys from Point Cook South in 2016. The Melbourne CBD was the biggest destination (994 journeys) with 69% public transport mode share followed by Docklands (342 journeys) with 64% public transport mode share.

Here is yet another way to look at this data, which is particularly relevant for the central city…

Percentage of commuters who travel to selected workplace SA2s

Here is a map showing the proportion of commuters in each home SA2 who work in the Melbourne, Southbank or Docklands SA2s (the tool allows selection of up to three workplace SA2s):

There are some interesting patterns in this map. Generally the percentage of people commuting to central Melbourne declined with distance from the CBD. There are however some outlier SA2s that had relatively high percentages of people travelling to central Melbourne, despite being some distance from the city centre.

In fact, here is a chart showing distance from the CBD, and the percentage of commuters travelling to the central city:

Tableau has labelled some of the points, but not all (interact with the data in Tableau to explore more). The outliers above the curve are generally west or north of the city, with Point Cook South being the most significant outlier. Further from the city, the commuter towns of Macedon, Riddells Creek and Gisborne have unusually high percentage of commuters travelling to the central city for that distance from the city (made possible by upgraded V/Line train services).  Many of the outliers below the curve are less wealthy areas, where people were less likely to work in the central city.

The previous map showed the proportion of all commuters that went to the central city. The tool can also filter that by mode. Here’s a map showing the percentage of public transport commuters who had a destination of Melbourne, Docklands or Southbank:

Typically around two-thirds of public transport journeys to work from most parts of Greater Melbourne are to Melbourne, Docklands, or Southbank SA2s. The lowest percentages were in the local jobs rich SA2s of Clayton (49%) and Dandenong (40%).

Adding Carlton and East Melbourne to the above three central city SA2s roughly takes the proportion up to around 70%. That’s a lot of public transport commutes to other destinations, but still a vast majority are focussed on the central city.

We can also look at this data from the origin end…

Where do people from a particular area commute to?

As an example, here is a map showing the percentage of commuters from Point Cook – South (a new and relatively wealthy area in Melbourne’s south-west) who worked in each work SA2 (destinations with less than 20 workers excluded):

You can see that 20% worked in the Melbourne CBD, followed by 7% in Docklands, and 6% in each of Point Cook North and Point Cook South (local). The largest nearby employment area is the industrial areas of Laverton, but this industrial area only attracted 4% of commuters from Point Cook South.

Here is a map for “Rowville – Central” SA2:

You can see that journeys to work are very scattered, with only 6% travelling to the Melbourne CBD.

(these maps can also be filtered by mode)

Another way to look at that data is a…

List of top commuter destinations

Here’s a chart showing the top work destinations from Rowville – Central in 2016, split by mode (this is a screenshot so the scroll bar doesn’t work):

You can see local trips are most numerous, and are dominated by private transport (although there were 48 active transport local trips). Dandenong was the second most common destination, with 97% private transport mode share, followed by Melbourne CBD with 40% private transport mode share (137 private transport journeys). The only other destination with high public transport mode share was Docklands at 59% (48 private transport journeys).

Changes between 2011 and 2016

We’ve so far looked at volumes and mode shares, but of course we can also look at the changes in volumes and mode share between 2011 and 2016.

There were around 15,000 more commutes to Dandenong in 2016 compared to 2011. Here are the changes in volumes by main mode for home SA2s with the largest total number of journeys:

You can see almost all of the new journeys to work were by private transport, no doubt putting a lot of pressure on the road network. A lot of the growth was from the suburbs to the east and south-east, none of which had a direct public transport connection to the Dandenong South industrial area at the time of the 2016 census. That’s now changed, with new bus route 890 linking the Cranbourne train line at Lynbrook with the Dandenong South industrial area (it operates every 40 minutes).

Note: a row with no figure or bar drawn (quite common in the Active only column) means that there were no such trips in either 2011 and/or 2016. Unfortunately the tool doesn’t show the change in volume in such circumstances (I’ll try to fix this in the future).

Contrast this with Parkville:

Brunswick is Parkville’s biggest source of workers, and there were many more such workers coming in by public and active transport, and a decline in workers who commuted by private transport. However there was an increase in private transport from places further out like Coburg and Pascoe Vale.

Of course you can do this the other way around too. Here‘s the new trips from Tarneit, a major growth area in Melbourne’s south-west where a train station opened in 2015:

Access to the Melbourne CBD by public transport improved significantly with the new train station, and 527 more people did that trip in 2016 compared to 2011. But the number of people who drove declined by only 35. The train line didn’t reduce the number of people driving out of Tarneit in total, but there probably would have been a lot more had it not opened. In the case of the Melbourne CBD, there were simply a lot more CBD workers living in Tarneit in 2016 (some CBD workers may have moved to Tarneit, and people otherwise in Tarneit were probably more likely to choose the CBD for work).

Here is a map of private transport mode shifts for journeys to the Melbourne CBD (were blue is mode shift to private transport and orange is mode shift away from private transport):

The biggest shifts away from private transport include Narre Warren North (-19%, but small volumes), Tarneit (-17%, with a train station opening in 2015), Wyndham Vale (-15%, also new train station), Don Vale – Park Orchards (-15%, with buses being primary mode for access to the CBD), Melton (-13%), and then -12% in Point Cook (new train station and bus upgrades in 2013), West Footscray – Tottenham, Sunbury (rail electrification 2012), South Morang (new train station), and Warrandyte – Wonga Park (SmartBus to city).

The biggest mode shifts to private transport were in low volume areas, including Monbulk – Silvan (+14%, which is an extra 5 trips), Keilor (+8%, 8 extra trips), Tullamarine (+8%, 16 extra trips), Lysterfield (+7%, 4 extra trips), Panton Hill – St Andrews (+7%, 4 extra trips) and more surprisingly Coburg North (+6%, up from 47 to 97 trips).

Again, you can see the problem with mode share and mode shift figures is that the volumes may be inconsequential. The map doesn’t show regions with less than 30 travellers, or less than 4 travellers by the selected mode. There was an overwhelming mode shift away from private transport for travel to the Melbourne CBD.

Here’s another view of the data: the change in the number of private transport trips to the Melbourne CBD, mapped:

That’s a peculiar mix of increases in decreases, but most of the volume changes are relatively small (note the scale).

The biggest increase was +142 trips from Truganina, a growth area with two nearby train stations built between 2011 and 2016. If that sounds alarming, it should be compared with an increase of 555 public transport trips from Truganina to the Melbourne CBD.

The larger declines were from suburbs like:

  • -85 from Doncaster East (bus upgrades),
  • -67 from Donvale – Park Orchards (bus upgrades),
  • -66 from Templestowe (also bus upgrades), and
  • -61 from Deer Park – Derrimut (also bus and train service upgrades).

Curiously, there was an increase of 71 private transport journeys to work entirely within the Melbourne CBD (to a new total of 236). Why anyone living and working in the CBD would go by private transport is almost beyond me – it’s very walkable and the trams are now free. Digging deeper…in 2016: 137 drove a car, 20 were a car passenger, 17 used motorbike/scooter, 13 a taxi, and 31 were “other” (okay, some of those 31 might have been skateboards or kick scooters, but we don’t know).

We can do the same by home location. Here are the net new trip destinations from Wyndham Vale in Melbourne’s outer south-west:

Wyndham Vale added more trips to the Melbourne CBD than trips to local workplaces.

Find your own stories

As mentioned, I’ve built interactive visualisations for all of this data, in Tableau Public, which you can use for free.

If you have a reasonably large screen, you might want to start with one of these four “dashboards” that show you volumes and mode shares, or volume changes and mode shifts. Choose a state, then an SA2, then you might need to zoom/pan the maps to show the areas of interest (unfortunately I can’t find a way to change the map zoom to be relevant to your selected SA2). The good thing about these dashboards is that you see mode shares and volumes on the same page.

Play around with the various filtering options to get different views of the data, including an option to turn on/off labels (which can overlap a lot when you zoom out), and change the colour scheme for mode share maps.

If you want more detail and/or have a smaller screen, then you might want to use one of the following links to a single map/chart:

Journey volumes by mode on a map to selected work location from selected home location
on a bar chart to selected work location from selected home location
Mode share on a map to selected work location from selected home location
on a bar chart to selected work location from selected home location
Percent of journeys on a map to selected work location(s) from selected home location
on a box chart to selected work location from selected home location
Journey volume change 2011 to 2016 on a map to selected work location from selected home location
on a bar chart to selected work location from selected home location
Mode shift
2011 to 2016
on a map to selected work location from selected home location
on a bar chart to selected work location from selected home location

Once you have the tool open in Tableau Public you can switch between the dashboards and worksheets with the tabs at the top (note: it will reset if you don’t use it for a while). You can mouse over the data to see more details (I’ve tried to list relevant data for each area), and often your filtering selections will apply to related tabs.

Finally remember to be careful in your analysis:

  • A large mode share or mode shift might not be for a significant volume.
  • A large change in volume might not be a significant mode shift.

Have fun!

[This post and the Tableau tool were updated 3 February 2018 with better label positions on maps. For larger SA2s, label positions better reflect the centre of residential or working population, as appropriate to the type of map. The Tableau tool should also be faster to load]

Changes in Melbourne’s journey to work – by mode (2006-2016)

Sun 10 December, 2017

Post updated 24 March 2018. See end of post for details.

My last post looked at the overall trends in journeys to work in Melbourne, with a focus on public and private transport at the aggregate level. This post dives down to look at particular modes or modal combinations, including mode shares, mode shifts and the origins and destinations of new trips.


Here’s mode share for journeys involving train by home location (journeys may also include other modes):

The highest train mode shares can be seen mostly along the train lines, which will surprise no one.

In fact, we can measure what proportion of train commuters live close to train stations. The following chart looks at how far commuters live from train stations, for commuters who use only trains, used trains and possible other modes, and for all commuters.

Melbourne train and all commuters by distance from trains station 2016

This chart shows that almost 60% of people who only used train (and walking) to get to work lived within 1 km of a station, and almost three-quarters were within 1.5 km. But around 8% of people only reporting train in their journey to work were more than 3 km from a train station. That’s either a long walk, or people forgot to mention the other modes they used (a common problem it seems).

For journeys involving train, 50% were from within 1 km of a station, but around a quarter were from more than 2 km from a station.

Interestingly, around a third of all Melbourne commuters lived within 1 km of a train station, but a majority of them did not actually report train as part of their journey to work.

So where were the mode shifts to and from train (by home location)?

There were big mode shifts to train around new stations including Wyndham Vale, Tarneit, Lynbrook, South Morang, and Williams Landing. Other bigger shifts were in West Footscray – Tottenham, South Yarra – East, Brighton, Viewbank – Yallambie, Yarrville, Footscray, Kensington, and Pascoe Vale (some of which might be gentrification leading to more central city workers?).

There was also a significant shift to trains in Point Cook, which doesn’t have a train station, but is down the road from the new Williams Landing Station. Almost 28% of commuters from Point Cook South work in the Melbourne CBD, Docklands or Southbank, and most of those journeys were by public transport.

We can also look at mode shares by work location. Here is train mode share by workplace location for 2011 and 2016 (I’ve zoomed into inner Melbourne as the mode shares are negligible elsewhere, and I do not have equivalent data for 2006 sorry):

Melbourne Train mode share 2011 2016 work.gif

The highest shares are in the CBD, Docklands and East Melbourne. Notable relatively high suburban shares include the pocket of Footscray containing State Trustees office tower (30.7% in 2016),  a pocket of Caulfield including a Monash University campus (29.5%), Box Hill (up to 19.6%), Swinburne University in Hawthorn (37.4%), and 17.5% in a pocket of Yarraville.

The biggest workplace mode shifts to train were in Docklands (+8.6%), Southbank (+5.5%), Abbotsford (+5.5%), Richmond (+5.3%),  Collingwood (+5.1%), Parkville (+4.9%), and South Yarra – East (+4.8%).


Across Melbourne, bus mode share had a significant rise from 2.6% in 2006 to 3.3% in 2011, and then a small rise to 3.4% in 2016. Here’s how it looks spatially for any journey involving bus:

The highest bus mode shares are in the Kew-Doncaster corridor, around Clayton (Monash University), in the Footscray – Sunshine corridor, a pocket of Heidelberg West, around Box Hill and in Altona North. These are areas of Melbourne with higher bus service levels (and most lack train and tram services).

Here’s a map showing mode shift 2011 to 2016 at the SA2 level:

Outside the Kew – Doncaster corridor there were small mode shifts in pockets that received bus network upgrades between 2011 and 2016, including Point Cook, Craigieburn, Epping – West, Mernda, Port Melbourne, and Cairnlea.

There was also a shift to buses in Ormond – Glenhuntly, which can be largely explained by Bentleigh and Ormond Stations being closed on census day due to level crossing removal works, with substitute buses operating.

There were larger declines in Dandenong, Footscray, and Abbotsford.

In terms of workplaces, Westfield Doncaster topped Melbourne with 14.4% of journeys involving bus, followed by Monash University Clayton with 12.8% (remember this figure does not include students who didn’t also work at the university on census day), 13.3% at Northland Shopping Centre, and 12.3% in a pocket of Box Hill.


“SmartBus” services operate from 5 am to midnight weekdays, 6 am to midnight Saturdays, and 7 am to 9 pm Sundays, with services every 15 minutes or better on weekdays from 6:30 am to 9 pm, and half-hourly or better services at other times. These are relatively high service levels by Melbourne standards.

SmartBus includes four routes that connect the city to the Manningham/Doncaster region via the Eastern Freeway, three orbital routes, and a couple of other routes in the middle south-eastern suburbs. All routes are relatively direct and none are particularly short. Seven of these routes serve the Manningham region.

To assist analysis, I’ve created a “SmartBus zone” which includes all SA1 and CD areas which have a centroid within 600 m of a SmartBus route numbered 900-908. These routes were all introduced between 2006 and 2011, generally replacing existing routes that operated at lower service levels (I’ve excluded SmartBus route 703 because it was not significant upgraded between 2006 and 2016).

Here are mode shares inside and outside the SmartBus zone:

In 2006 the SmartBus zone already had double the bus mode share of the rest of Melbourne, as existing routes had relatively good service levels, including Eastern Freeway services. Following SmartBus (and other bus) upgrades between 2006 and 2011, there was a 2.5% mode shift to bus in the SmartBus zone, and a 1.3% mode shift to bus elsewhere. The SmartBus zone had a further 0.5% shift between 2011 and 2016 while the shift was only 0.2% in the rest of Melbourne.

Here’s an animated look at bus mode shares for just the SmartBus zone.

You can see plenty of mode shift in the Manningham area (where many SmartBus routes overlap), but also some mode shifts along the others routes – particularly in the south-east.


  • the SmartBus zone includes overlaps with some other high service bus routes – those pockets generally had higher starting mode shares in 2006.
  • The orbital SmartBus routes do overlap with trains and/or trams which provide radial public transport at high service levels, negating the need or bus as a rail feeder mode (still useful for cross-town travel).
  • I haven’t excluded sections of SmartBus freeway running from the SmartBus zone. Sorry, I know that’s not perfect analysis, particularly along the Eastern Freeway.

Train + bus

Journeys involving train and bus rose from 1.1% in 2006 to 1.5% in 2011 and 1.7% in 2016, which is fairly large growth off a small base and represents around half of all journeys involving bus. I suspect there might be some under-reporting of bus in actual bus-train journeys, as we saw many people a long way from train stations only reporting train as their travel mode.

Here’s a map showing train + bus mode share. Note the mode shares are very small, and I’m not willing to calculate a mode share where less than 6 trips were reported but they result in more than 3% mode share (I’ve shaded those zones grey):

Large increases are evident around the middle eastern suburbs (particularly around SmartBus routes), the Footscray-Sunshine corridor (which have frequent bus services running to frequent trains at Footscray Station), Point Cook (where relatively frequent bus routes feeding Williams Landing Station were introduced in 2013, resulting in 750 train+bus journeys in 2016), Craigieburn (again bus service upgrades with strong train connectivity), and Wollert (likewise).

Ormond – Glen Huntly shows up in 2016 because of the rail replacement bus services at Bentleigh and Ormond Stations at the time (as previously mentioned).

If you look closely, you’ll see higher shares in the Essendon – East Keilor corridor, where bus route 465 provides high peak frequencies meeting just about every train (service levels have not changed between 2006 and 2016)


Here’s a map of tram mode shares, overlaid on the 2016 tram network (there haven’t been any significant tram extensions since 2005).

Melbourne tram share

Higher tram mode shares closely follow the tracks, with the highest shares in Brunswick, North Fitzroy, St Kilda, Richmond, and Docklands.

It’s also interesting to note that several outer extremities of the tram network have quite low tram mode shares – including East Brighton, Vermont South, Box Hill, Camberwell / Glen Iris (where the Alamein line crosses tram 75), Carnegie, and to a lesser extent Airport West and Bundoora. These areas have overlapping train services and/or are a long travel time from the CBD.

Overall tram mode share increased from 4.0% in 2006 to 4.6% in 2011 and 4.8% in 2016. Here’s a map of tram mode shift 2011 to 2016 by home SA2:

The biggest mode shift was +13% in Docklands, followed by +10% in the CBD. This no doubt reflects the introduction of the free tram zone across these areas. Walk-only journey to work mode share fell 4% in Docklands and 6% in the CBD.

Abbotsford had a 9% mode shift to trams, which possibly reflects the extension of route 12 to Victoria Gardens, providing significantly more capacity along Victoria Street (the only tram corridor serving Abbotsford).

There were small mode share declines in many suburbs, although this does not necessarily mean a reduction in the number of journeys by tram. In Port Melbourne there was a shift from tram to bus and bicycle.

Here are tram mode shares by workplace for 2011 and 2016:

Melbourne tram share workplace

The highest workplace tram mode shares were in the CBD, along St Kilda Road south of the CBD, Carlton, Fitzroy, Parkville, Albert Park, South Melbourne, and St Kilda.


Cycling mode share increased from 1.5% in 2006 to 1.8% in 2011 and 1.9% in 2016. These are low numbers, but the bicycle mode share was anything but uniform across Melbourne.

Firstly here’s a map of cycling mode share by home location:

There’s not much action outside the inner city, so let’s zoom in:

The highest mode shares are in the inner northern suburbs (pockets around 25%) where there has been considerable investment in cycling infrastructure.

Here’s a chart showing the mode shift at SA2 level:

The biggest mode shift were 2% in Brunswick West and South Yarra West. However aggregating to SA2 level hides some of the other changes. If you study the detailed map you can see larger mode shifts in more isolated pockets and/or corridors (including a corridor out through Footscray).

Here is the growth in bicycle trips between 2011 and 2016 by home distance from the city centre:

Significant growth was only seen for homes within 10km of the city centre. Here are those new trips mapped, with Brunswick SA2 showing the largest growth:

What about cycling mode shares by workplaces? I’ve gone straight to the inner city so you can see the interesting detail:

The highest workplace mode shares are in the inner northern suburbs, including Parkville (9%) and Fitzroy North (8%).

You’ll note the CBD does not have a high cycling mode share (3.8%) compared to the inner northern suburbs. But if you look at the concentration of cycling commuter workplaces, you get quite a different story:

This shows the CBD having the highest concentrations of commuter cycling destinations, although there were also relatively high densities at the Parkville hospitals and the Alfred Hospital. The highest concentration of commuter cyclists in 2016 was a block bound by Lonsdale Street, Exhibition Street, Little Lonsdale Street and Spring Street (it had a mode share of 4.3%).

However if you look at the increase in bicycle commuter trips between 2011 and 2016 by workplace distance from the city, the biggest growth was for destinations 1-4 km from the city centre:

Note: I am using a different scale for charts by workplace distance from the CBD.

How has walking changed?

Overall walking-only mode share in Melbourne as measured by the census has hardly changed, from 3.6% in 2006 to 3.5% in both 2011 and 2016. However there are huge spatial variations.

Here’s walking by home location:

The highest walking mode shares are around the central city with mode shares above 40% in parts of the CBD, Southbank, Carlton, Docklands, North Melbourne, and Parkville. Outside the city centre relatively high mode shares are seen around Monash University Clayton, the Police Academy in Glen Waverley, Box Hill, and Swinburne University in Hawthorn. Walking-only trips are very rare in most other parts of the city.

Here are walking mode shares by workplace location:

The highest walking shares by SA2 in 2016 were in St Kilda East, Prahran – Windsor, South Yarra, Carlton, Carlton North, Fitzroy, and Elwood. There were also smaller pockets of high walking mode share in Yarraville, Footscray, Flemington, Northcote, Ormond – Glenhuntly, Richmond, and Box Hill.

The biggest mode shifts away from walking were in the CBD (-7.3%) and Docklands (-4.0%), which also had big shifts to tram – probably due to the new Free Tram Zone.

Overall, the biggest increase in walking journeys was seen within 5km of the city centre:

For workplaces, the biggest growth in walking was to jobs between 2-4 km from the CBD (be aware of different X-axis scales):

Most common non-car mode

Here is a map showing the most common non-car mode in 2016*. Note the most common non-car mode might still have a very small mode share so interpret this map with caution.

*actually, I’ve not checked motorbike/scooter, taxi, or truck on the basis they are very unlikely to be the most common.

Train dominates most parts of Melbourne, with notable exceptions of the Manningham region (served by buses but not trains), several tram corridors that are remote from trains, and walking around the city centre.

The southern Mornington Peninsula is a mix of bus and walking, plus some SA1s where no one travelled to work by train, tram, bus, ferry, bicycle, or walk-only!

The next map zooms into the inner suburbs, showing the tram network underneath:

Generally tram is only the dominant mode in corridors where trains do no overlap (we saw lower tram mode shares in these areas above). In most of the inner south-eastern suburbs served by trams and trains, train is the dominant non-car mode.

If you look carefully, there are a few SA1s where bicycle is the dominant non-car mode.

In case you are wondering, there are places in Melbourne where train, tram, or walking-only trumped car-only as the most common mode. They are all on this map:

Mode with the most growth

Finally, another way to look at the data is the mode with the highest growth in trips.

Here is a map showing the mode (out of car, train, tram, bus, ferry, bicycle, walk-only) that had the biggest increase in number of trips between 2011 and 2016, by SA2:

Car trips dominated new trips in most outer suburbs (particularly in the south-east), but certainly not all of Melbourne. Train was most common in many middle suburbs (and even some outer suburbs).

Bicycle was the most common new journey mode in Albert Park (+56 journeys), South Yarra – West (+54), Carlton North – Princes Hill (+80), Fitzroy North (+162) and Brunswick West (+158).

Walking led Fitzroy (+147) and Keilor Downs (+15, with most other modes in small decline, so don’t get too excited).

Bus topped SA2s in the Doncaster corridor, but also Port Melbourne (+176), Vermont South (+30), Kings Park (+10) and Ormond – Glen Huntly (+275 with rail replacement buses operating on census day in 2016).

Tram topped several inner SA2s including the CBD, Docklands and Southbank.

A caution on this map: the contest might have been very close between modes and the map doesn’t tell you how close.

Want to explore the data in Tableau?

I’ve built visualisations in Tableau Public where you can choose your mode of interest, year(s) of interest, and zoom into whatever geography you like.

By home location:

By work location:

Have fun exploring the data!

This post was updated on 24 March 2018 with improved maps. Also, data reported at SA2 level is now as extracted at SA2 level for 2011 and 2016, rather than an aggregation of CD/SA1/DZ data (each of which has small random adjustment for privacy reasons, which amplifies when you aggregate, also some work destinations seem to be coded to an SA2 but not a specific DZ). This does have a small impact, particularly for mode shifts, and mode with the most growth.

How is the journey to work changing in Melbourne? (2006-2016)

Tue 5 December, 2017

Post last updated 7 April 2018. See end of post for details.

While journeys to work only represents around a quarter of all trips in Melbourne, they represent around 39% of trips in the AM peak (source: VISTA 2012-13). Thanks to the census there is incredibly detailed data available about the journey to work, and who doesn’t like exploring transport data in detail?

Between 2006 and 2016, Melbourne has seen mode shifts away from private transport and walking, and towards public transport and cycling. The following measures are by place of enumeration (and 2011 Significant urban area boundaries):

2006 2011 2016
Public transport (any) 14.16% 16.34% 18.15%
+2.18% +1.82%
Private transport (only) 80.43% 78.16% 76.20%
-2.28% -1.96%
Walk only 3.63% 3.46% 3.47%
-0.18% +0.01%
Bicycle only 1.34% 1.56% 1.63%
+0.23% +0.06%

This post unpacks where mode shifts and trip growth is happening, by home locations, work locations, and home-work pairs. It tries to summarise the spatial distribution of journeys to work in Melbourne. It will also look at the relationship between car parking, job density and mode shares.

I’m afraid this isn’t a short post. So get comfortable, there is much fascinating data to explore about commuting in Melbourne.

Public transport share by home location

Here’s an animated public transport mode share map 2006 to 2016 – you might want to click to enlarge, or view this map in Tableau (be patient it can take some time to load and refresh). For those with some colour-blindness, you can also get colour-blind friendly colour scales in Tableau.

The higher mode shares pretty clearly follow the train lines and the areas covered by trams, with mode share growing around these lines. Public transport mode shares of over 50% can be found in a sizeable patch of Footscray, and pockets of West Footscray, Glenroy, Ormond – Glen Huntly, Murrumbeena, Flemington, Docklands, Carlton, and South Yarra. Larger urban areas with very low public transport mode share can be found around the outer east and south-east of the city, particularly those remote from the rail network.

Here’s a map showing mode shift at SA2 level:

(explore in Tableau)

The biggest shifts to public transport in the middle and outer suburbs were in Wyndham Vale, Tarneit, South Morang, Lynbrook/Lyndhurst, Sanctuary Lakes (Point Cook – East), Truganina / Williams Landing, Rockbank, Pascoe Vale, and Glenroy. That’s almost a roll call of all the new train stations opened between 2011 and 2016. The exceptions are Rockbank (a small community at present which received significantly more frequent trains in 2015), Point Cook East (a bus service was introduced in 2015), and Pascoe Vale / Glenroy (where more people are commuting to the city centre and increasingly by public transport).

Inner suburban areas with high mode shifts include West Footscray, Yarraville, Seddon – Kingsville, Collingwood, Abbotsford, Kensington, Flemington, South Yarra – East, and Brighton. The Melbourne CBD itself had a 13% shift to public transport – and actually a 6% mode shift away from walking (which probably reflects the new Free Tram Zone in the CBD area).

The biggest mode shifts away from public transport (of 1 to 2%) were at Ardeer – Albion, Coburg North, Chelsea – Bonbeach, Seaford, Frankston, Dandenong, Hampton Park – Lynbrook, and Lysterfield. At the 2016 census there were no express trains operating on the Frankston railway line due to level crossing removal works, which might have slightly impacted public transport demand in Frankston, Seaford and Chelsea – Bonbeach. I’m not sure of explanations for the others, but these were not large mode shifts.

Here’s a chart showing mode split over time, by home distance from the CBD:

Public transport mode share by work location

Here’s a map showing work location public transport mode share (Destination Zones with less than 5 travellers per hectare not shown):

It’s no surprise that public transport mode share is highest in the CBD and surrounding area, and lower in the suburbs. But note the scale – public transport mode share falls away extremely quickly as you move away from the city centre.

Private transport mode shares are very high in the middle and outer suburbs:

Large areas of Melbourne have near saturation private transport mode share. In most suburban areas employee parking is likely to be free and public transport would struggle to compete with car travel times, even on congested roads (particularly for buses that are also on those congested roads).

There are some isolated pockets of relatively high public transport mode share in the suburbs, including

  • 34% in a pocket of Caulfield – North (right next to Caulfield Station),
  • 33% in a pocket of Footscray (includes the site of the new State Trustees office tower near the station),
  • 25% in a pocket of Box Hill near the station, and
  • 17% at the Monash University Clayton campus.

Explore the data yourself in Tableau.

Here’s an enlargement of the inner city area:

And here’s a map showing the mode shift between 2011 and 2016 by workplace location (for SA2s with at least 4 jobs per hectare):

The biggest shifts to public transport were in the inner city. The biggest shifts away from public transport were 1.4% in Ormond – Glen Huntly (rail stations temporarily closed) and North Melbourne.

Here’s a closer look at the inner city:

Docklands had the highest mode shift to public transport of 9% (almost all of it involving train) followed by Collingwood with 7%, and Parkville, Southbank, and Abbotsford with 6%.

North Melbourne saw a decline of 1.4% – at the same time private transport mode share and active (only) mode shares increased by 1%.

Another way to slice this data is by distance from the CBD. Here are main mode shares by workplace distance from the centre, over time:

For this and several upcoming pieces of analysis, I have aggregated journeys into three “main mode” categories:

  • Public transport (any trip involving public transport)
  • Private transport (any journey involving private transport that doesn’t also involve public transport)
  • Active transport only (walking or cycling)

Here are the mode shifts by workplace distance from the centre between 2006 and 2016:

The biggest mode shift from private to public transport was for distances of 1-2km from the city centre, which includes Docklands, East Melbourne, most of Southbank, and southern Carlton and Parkville (see here for a reference map). A mode shift to public transport (on average) was seen for workplaces up to 40km from the city centre. The biggest mode shift to active transport was for jobs 2-4 km from the city centre (but do keep in mind that weather can impact active transport mode shares on census day).

What about job density?

Up until now I’ve been looking at mode shifts by geography – but the zones can have very different numbers of commuters. What matters more is the overall change in volumes for different modes. A big mode shift for a small number of journeys can be a smaller trip count than a small mode shift on a large number of journeys.

Firstly, here’s a map of jobs per hectare in Melbourne (well, jobs where someone travelled on census day and stated their mode, so slight underestimates of total employment density):

Outside the city centre, relatively high job density destination zones include:

  • Heidelberg (Austin/Mercy hospitals with 10.2% PT mode share),
  • Monash Medical Centre in Clayton (8.3% PT mode share),
  • Northern Hospital (3.8% PT mode share),
  • Victoria University Footscray Park campus (21.1% PT mode share),
  • Swinburne University Hawthorn (39.8% PT mode share),
  • a pocket of Box Hill (19.9% PT mode share),
  • a zone including the Coles head office in Tooronga (11.2% PT mode share),
  • an area near Camberwell station (26.8% PT mode share),
  • a pocket of Richmond on Church Street (27.8% PT mode share), and
  • a pocket of Richmond containing the Epworth Hospital (39.5% PT mode share).

Explore this map in Tableau.

You’ll probably not be very surprised to see that there is a very strong negative correlation between job density and private transport mode share. The following chart shows the relationship between the two for each Melbourne SA2 with the thin end of each “worm” being 2006 and the thick end 2016 (note: the job density scale is exponential):

Correlation of course is not necessarily causation – high job density doesn’t automatically trigger improved public and active transport options. But parking is likely to be more expensive and/or less plentiful in areas with high employment density, and many employers will be attracted to locations with good public transport access so they can tap into larger labour pools.

The Melbourne CBD SA2 is at the bottom right corner of the chart, if you were wondering.

The Port Melbourne Industrial and Clayton SA2s are relatively high density employment areas with around 90% private transport mode shares.

Here’s a zoom in on the “middle” of the above chart, with added colour and labels to help distinguish the lines:

Not only is there a strong (negative) relationship between job density and private transport mode share, most of these SA2s are moving down and to the right on the chart (with the exception of North Melbourne which saw only small change between 2011 and 2016). However the correlation probably reflects many new jobs being created in areas with good public and active transport access, particularly as Melbourne grows its knowledge economy and employers want access to a wide labour market.

How does private transport mode share relate to car parking provision?

Do more people drive to work if parking is more plentiful where they work?

Thanks to the City of Melbourne’s Census of Land Use and Employment, I can create a chart showing the number of non-residential off-street car parks per 100 employees in the City of Melbourne (which I will refer to as “parking provision” as shorthand):

(see a map of CLUE areas)

Car parking provision per employee has increased in Carlton, North Melbourne and Port Melbourne and decreased in Docklands, West Melbourne (industrial), and Southbank. Docklands had the highest car parking provision in 2002 but this has fallen dramatically and land has been developed for employment usage. Southbank, which borders the CBD, has relatively high car park provisioning – much higher than Docklands and East Melbourne.

Here’s the relationship between parking provision and journey to work private transport mode share between 2006 and 2016:

It’s little surprise to see a strong relationship between the two, although Carlton is seeing increasing parking provision but decreasing private transport mode share (maybe those car parks aren’t priced for commuters?).

If all non-resident off street car parks were used by commuters, then you would expect the private transport mode share to be the same as the car parks per employee ratio.

Private transport mode shares were much the same as parking provision rates in Melbourne CBD, Docklands, and Southbank, suggesting most non-residential car parks are being used by commuters (with the market finding the right price to fill the car parks?). Private transport mode share was higher than car parking provision in East Melbourne, Parkville, South Yarra, North Melbourne, and West Melbourne (industrial). This might be to do with on-street parking and/or more re-use of car parks by shift workers (eg hospital workers).

Port Melbourne parking provision is very high (there is also lots of on-street parking). It’s possible some people park in Port Melbourne and walk across Lorimer Street (the CLUE border) to work in “Docklands” (which includes a significant area just north of Lorimer Street). It’s also likely that many parking spaces are reserved for visitors to businesses. Carlton similarly had higher parking provision than private transport mode share (again, could be priced for visitors).

(Data notes: For 2011, I have taken the average of 2010 and 2012 data as CLUE is conducted every even year. I’ve done a best fit of destinations zones to CLUE areas, which is not always a perfect match)

Where are the new jobs and how did people get to them?

Here’s a map showing the relative number of new jobs per workplace SA2, and the main mode used to reach them:

The biggest growth in jobs was in the CBD (+31,438), followed by Docklands (+22,993), Dandenong (+11,136), and then Richmond (+6,242).

And here’s an enlargement of the inner city:

(explore this data in Tableau)

The CBD added 31,438 jobs, and almost all of those were accounted for by public transport journeys, although 2,630 were by active transport, and only 449 new jobs by private transport (1%).

Likewise most of the growth in Docklands and Southbank was by public transport, and then in several inner suburbs private transport was a minority a new trips.

However, Southbank still has a relatively high private transport mode share of 46% for an area so close to the CBD. The earlier car parking chart showed that Southbank has about one off-street non-residential car park for every two employees. These include over 5000 car parks at the Crown complex alone (with $16 all day commuter parking available as at November 2017). It stands to reason that the high car parking provision could significantly contribute to the relatively high private transport mode share, which is in turn generating large volumes of radial car traffic to the city centre on congested roads. Planning authorities might want to consider this when reviewing applications for new non-residential car parks in Southbank.

Here’s a chart look looking at commuter volumes changes by workplace distance from the CBD (see here for a map of the bands).

(Note: the X-axis is quasi-exponential)

Public transport dominated new journeys to work up to 2km from the city centre and only just outnumbered private transport between 2 and 4 km. Private transport dominated new journeys to workplaces more than 4km from the city centre – however that doesn’t necessarily mean a mode shift away from public transport if the new trips have a higher public transport mode share than the 2011 trips. Indeed there was a mode shift towards public transport for workplaces in most parts of Melbourne.

Here is a map showing the private transport mode share of net new journeys to work by place of work:

Private transport had the lowest mode share of new jobs in the inner city. As seen on the map, some relative anomalies for their distance from the CBD include Box Hill (64%), Hampton (57%), Brunswick East (34%), Dingley Village (28%), and Albert Park (6%). Explore the data in Tableau.

Where did the new commuters come from and what mode did they use?

Here’s a map showing the (relative) net volume change of private transport journeys to work, by home location:

As you can see many of the new private transport journeys to work commenced in the growth areas, although there were also some substantial numbers from inner suburbs such as South Yarra, Richmond, Braybrook, Maribyrnong and Abbotsford.

There are many middle suburban SA2s with declines. These are also suburbs where there has been population decline – which I suspect are seeing empty nesting (adult children moving out) and people retiring from work. For example Templestowe generated 566 fewer private transport trips, 28 fewer active transport only trips, but only 70 new public transport trips.

Here’s a similar map showing change in public transport journeys:

The biggest increases were from the inner city, with the CBD itself generating the largest number of new public transport trips (including almost 2500 journeys involving tram). However there were a number of new public transport trips from the Wyndham area in the south-west (where new train stations opened).

Here’s a map of the total new trip volume and main mode split:

(explore in Tableau)

You can see that private transport dominates new journeys from the outer suburbs, but less so in the south-west where a new train line was opened. The middle and inner suburbs are hard to see on that map, so here is a zoomed in version:

You can see many areas where private transport accounted for a minority of new trips. Also, around half of new trips in several middle northern suburbs were by public transport.

Here’s how it looks by distance from the city centre:

Public transport dominated new journeys to work for home locations up until 10km from the city centre, was roughly even with private transport from 10km to 20km (hence a net mode shift to public transport). However private transport dominated new commuter journeys beyond 20km – most of which is from urban growth areas. The 24-30 km band covers most of the western and northern growth areas, while the 40km+ band is almost entirely the south-east growth areas.

Here is a view of the private transport mode share of net new trips:

(explore in Tableau)

The pink areas had a net decline in the number of private transport trips (or total trips) generated, so calculating a mode share doesn’t make a lot of sense. There are some areas with 100%+ which means more new private transport trips were generated than total new trips – ie active and/or public transport trips declined.

You can again see that private transport dominated new trips in the most outer suburbs, with notable exceptions in the west:

  • Wyndham in the south-west where two new train stations opened. 41% of new trips from Wyndham Vale and 30% of new trips from Tarneit were by public transport.
  • Sunbury in the north-west, to which the Metro train network was extended in 2012.  Around 37% of new trips from Sunbury -South were by public transport (that’s 307 trips).

How has the distribution of home and work locations in Melbourne changed by distance from the city?

Here’s a chart showing the number of journey to work origins and destinations by distance from the city centre by year. Note the distance intervals are not even, so look for the vertical differences in this chart:

You can see most of the worker population growth (origins) has been in the outer suburbs. The destination (job) growth was much more concentrated in the inner city between 2006 and 2011, but then more evenly distributed across the city in 2016.

The median distance of commuter home locations from the city centre increased from 18.2 km in 2006 to 18.6 km in 2016. The median distance from the city centre of commuter workplaces decreased from 13.3 km in 2006 to 12.8 km in 2011 but then increased back to 13.3 km in 2016.

Here’s another way at looking at the task. I’ve split Melbourne by SA2 distance from the CBD (to create 10km wide rings) for home and work locations (and further split out the CBD as a place of work) to create a matrix. Within each cell of the matrix is a pie chart – the size of which represents the relative number of commuter trips between that home and work ring, and the colours showing the main mode. I’ve then animated it over 2011 and 2016 (to make it five dimensional!).

I think this chart fairly neatly summarises journeys to work in Melbourne:

  • Private transport dominates all journeys that stay more than 5km from the city centre (all but top left corner)
  • Active transport is only significant for commuters who work and live in the same ring (diagonal top left – bottom right), or for trips entirely within 15 km of the centre (six cells in top left corner)
  • Public transport dominates journeys to the CBD, no matter how far away people’s homes are, but the number of such journeys falls away rapidly with home distance from the CBD. Very few people commute from the outer suburbs to the CBD.
  • Private transport commuters are mostly travelling between middle suburbs, not to the CBD or even the to within 5 km of the city. However on average they are travelling towards the centre. This will become clearer shortly.
  • Public transport otherwise only gets 15% or better mode share for trips to within 5 km of the centre or the relatively small number of outward trips from the inner 5km.

Here’s a look at the absolute change in number of trips between the rings:

You can see:

  • A significant growth in private transport trips, particularly within 5 – 25 km from the CBD.
  • A significant growth in public transport trips, mostly to the CBD and areas within 5 km from the CBD.

Where are commuters headed on different modes?

This next analysis looks at the distribution of origins and destinations for people using particular modes, which can be compared to all journeys.

The next chart looks at the distributions of work destinations by main mode for each census year (using a higher resolution set of distances from the CBD).

On the far right is the distribution of jobs across Melbourne (with roughly equal numbers in each distance interval), and then to the left you can see the distribution of workplace locations for people who used particular modes. You can see how different modes are more prominent in different parts of the city.

You might need to click to enlarge to read the detail.

In 2016, trips to within 2km of the city centre accounted for 19% of all journeys, but 62% of public transport journeys, 31% of walking journeys, and only 7% of private transport only journeys.

Train, tram, and bicycle journeys are biased towards the inner city, while private transport only journeys are biased to the outer suburbs. Walking and bus journeys are only slightly biased towards the inner city. This should come as no surprise given the maps above showing high public transport mode shares in the inner city and very high private transport mode shares in most of the rest of the city.

Over time, public transport journeys to work became less likely to be to the central city as public transport gained more trips to the suburbs. However bus journeys to work became more likely to be in the city centre (this probably reflects the significant upgrades in bus services between the Doncaster area and city centre).

Notes on the data:

  • Unless a mode is labelled “only”, then I’ve counted journeys that involved that mode (and possibly other modes).
  • Sorry I don’t have public transport mode specific data for 2006 so there are some blank columns.

Where do commuters using different modes live?

Here’s the same breakdown, but by home distance from the city centre:

Private transport commuters were slightly more likely to come from the middle and outer suburbs. Tram and bicycle commuters were much more likely to come from the inner city. Bus commuters were over-represented in the 15-25 km band – probably dominated by the Doncaster area. Train commuters were over-represented in distances 5-25 km from the city, and under-represented in distances 35 km and beyond. Journeys by both public and private transport were more likely to come from the middle suburbs.

51% of people walking to work live within 5 km of the city centre, and the growth in walking journeys to work has been much stronger in the inner city.

Here’s a chart showing the most common home-work pairs for distance rings from the CBD for public transport journeys. It’s like a pie chart, but rectangular, larger and much easier to label (I haven’t labelled the small boxes in the bottom right hand corner):

You can see the most common combination is from 5-15 kms to 0-5 kms. This is followed by 15-25 to 0-5 kms and 0-5 to 0-5 kms.

Here’s the same for private transport only journeys:

There is a much more even distribution.

Finally, here is the same for active-only journeys to work:

This is much more polarised, with almost 40% of active transport trips being entirely within 5 km of the city centre. The second most common journey is within 5-15km of the city followed by from 5-15 km to 0-5 km.

In future posts I will look at more specific mode shares and shifts in more detail, the relationship between motor vehicle ownership and journey to work mode shares, and much more!

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

(note: this post uses data re-issued in December 2017 after ABS pulled the original Place of Work data in November 2017 due to quality concerns)

This post was updated on 24 March 2018 with improved maps. Also, data reported at SA2 level is now as extracted at SA2 level for 2011 and 2016, rather than an aggregation of CD/SA1/DZ data (each of which has small random adjustment for privacy reasons, which amplifies when you aggregate, also some work destinations seem to be coded to an SA2 but not a specific DZ). This does have a small impact, particularly for mode shifts and mode shares of new trips. On 7 April 2018 this post was updated to count journeys by “Other” and “Bicycle, Other” as private transport to ensure completeness of total mode share (we don’t actually know what modes “Other” is, so this isn’t perfect).

Trends in journey to work mode shares in Australian cities to 2016 (second edition)

Tue 24 October, 2017

[Updated 1 December 2017 with reissued Place of Work data]

The ABS has now released all census data for the 2016 journey to work. This post takes a city-level view of mode share trends. It has been expanded and updated from a first edition that only looked at place of work data.

My preferred measure of mode share is by place of enumeration – ie how did you travel to work based on where you were on census night (see appendix for discussion on other measures).

I’m using Greater Capital City Statistical Areas (GCCSA) geography for 2011 and 2016 and Statistical Divisions for earlier years. For Perth, Melbourne, Adelaide, Brisbane and Hobart the GCCSAs are larger than the Statistical Divisions used for earlier years, but then those cities have also grown over time. See appendix 1 for more discussion.

Some of my data goes back to 1976 – I’ll show as much history as I have for each mode/modal combination.

Public transport mode share

Sydney continues to have the largest public transport mode share, and the largest shift of the big cities. Melbourne also saw significant positive mode shift, but Perth and particularly Brisbane had mode shift away from public transport.

There’s so much to unpack behind these trends, particularly around the changing distribution of jobs in cities that I’m going to save that lengthy discussion for another blog post.

But what about the…

Massive mode shift to “public transport” in Darwin?!?

[this section updated 26 Oct 2017]

Yes, I have triple-checked I downloaded the right data. “Public transport” mode share increased from 4.3% to 10.9%. The number of people reporting bus-only journeys went from 1648 in 2011 to 5661 in 2016, which is growth of 244%. There has also been a spike in the total number of journeys to work in 2011, 30% higher than in 2011, while population growth was 13%.

Initially I thought this might have been a data error, but I’ve since learnt that there is a large LNG gas project just outside Darwin, and up to 180 privately operated buses are being used to transport up to 4700 workers to the site. This massive commuter task is swamping the usage of public buses.

Here’s the percentage growth in selected journey types between 2011 and 2016:

Bus + car as driver grew from 74 to 866 journeys, which reflects the establishment of park and ride sites around Darwin for the special commuter buses. Bus only journeys increased from 1953 to 5744. So it looks like most workers are getting the bus from home and/or forgot to mention the car part of their journey (in previous censuses I’ve seen many people living kilometres from a train station saying they got to work by train and walking only).

So this new project has swamped organic trends, although it is quite plausible that some people have shifted from cycling/walking to local jobs to using buses to commute to the LNG project (which is outside urban Darwin). When I look at workplaces within the Darwin Significant Urban Area (2011 boundary), public transport mode share is 6.0%, in 2016, still an increase from 4.4% in 2011. More on that in a future post.


Sydney saw the fastest train mode share growth, followed by Melbourne, while Brisbane and Perth went backwards.


Darwin just overtook Sydney for top spot thanks to the LNG project. Otherwise only Sydney, Canberra and Melbourne saw growth in bus mode share. Melbourne’s figure remains very low, however it is important to keep in mind that trams provide most of the on-street inner suburban radial public transport function in Melbourne.

Train and bus

Sydney comes out on top, with a large increase in 2016 (although much of this is still concentrated around Bondi where there are high bus frequencies and no fare penalties for transfers – more on that in an upcoming post). Melbourne is seeing substantial growth (perhaps due to improvements in modal coordination), while Perth, Adelaide and Brisbane had declines in terms of mode share (Brisbane and Adelaide were also declines on raw counts, not just mode share). I’m sure some people will want to comment about degrees of modal integration in different cities.

Train and bicycle

Some cities are also trying to promote the bicycle and train combination as an efficient way to get around (they are the fastest motorised and (mostly)non-motorised surface modes because they can generally sail past congested traffic). The mode shares are still tiny however:

Sydney and Melbourne are growing but the other cities are in decline in terms of mode share.

As this modal combination is coming off an almost zero base, it’s also probably worth looking at the raw counts:

The downturns in Brisbane and Perth are not huge in raw numbers, and probably reflect the general mode shift away from public transport (which is probably more to do with changing job distributions than bicycle facilities at train stations).


I have a longer time-series of bicycle-only mode share, compared to “involving bicycle”, so two charts here:


  • Darwin lost top placing for cycling to work with a large decline in mode share (refer discussion above about the massive shift to bus).
  • Canberra took the lead with more strong growth.
  • Melbourne increased slightly between 2011 and 2016 (note: rain was forecast on census day which may have suppressed growth, more on that in a moment).
  • Hobart had a big increase in 2016, following rain in 2011.
  • Sydney remains at the bottom of the pack and declined in 2016.

Walking and cycling mode share is likely to be impacted by weather. Here’s a summary of recent census weather conditions for most cities (note: Canberra minimums were -3 in 2001, -7 in 2006, 0 in 2011 and -1 in 2016):

Perth had rain on all of the last four census days, while Adelaide had significant rain only in 2001 and 2011 (and indeed 2006 shows up with higher active transport mode share). Hobart had significant rain in 2011, which appears to have suppressed active transport mode share that year.

But perhaps equally important is the forecast weather as that could set people’s plans the night before. Here was the forecast for the 2016 census day,  from the BOM website the night before:

Note that it didn’t end up raining in Melbourne, Adelaide, or Hobart.

The census is conducted in winter – which is the best time to cycle in Darwin (dry season) and not a great time to cycle in other cities. However the icy weather in Canberra clearly hasn’t stopped it getting the highest and fastest growing cycling mode share of all cities!

Indeed here is a chart from VicRoads showing the seasonality of cycling in Melbourne at their bicycle counters:

And in case you are interested, here are the (small) mode shares of journeys involving bicycle and some other modes (other than walking):

Walking only

Canberra was the only city to have a big increase, while there were declines in Darwin, Perth, Adelaide, Brisbane, and Sydney.

The smaller cities had the highest walking share, perhaps as people are – on average – closer to their workplace, followed by Sydney – the densest city. But city size doesn’t seem to explain cycling mode shares.


The following chart shows the proportion of journeys to work made by car only (either as driver or passenger):

Sydney has the lowest car only mode share and it declined again in 2016. It was followed by Melbourne in 2016. Brisbane and Perth had large increases in car mode share in 2016 (in line with the PT decline mentioned above). Darwin also shows a big shift away from the car to public transport (although the total number of car trips still increased by 24%). Adelaide hit top spot, followed by Hobart and Perth.

Here is car as driver only:

And here is car as passenger only:

Car as passenger declined in all cities again in 2016, but was more common in the smaller cities, and least common in the bigger cities. I’m not sure why car as passenger declines paused for Perth and Sydney in 2006.

We can calculate an implied notional journey to work car occupancy by comparing car driver only and car passenger only journeys. This is not actual car occupancy, because it excludes people not travelling to work and excludes journeys that involved cars and other modes. However it does provide an indication of trends in car pooling for journeys to work.

There were further significant decreases in car commuter occupancy, in line with increasing car ownership and affordability.

Private transport

Here is a chart summing all modal combinations involving cars (driver or passenger), motorcycle/scooter, taxis, and trucks, but excluding any journeys that also include public transport.

The trends mirror what we have seen above, and are very similar to car-only travel.


Overall mode split

Here’s an overall split of journeys to work by “main mode” (click to enlarge):

Note: the 2001 data includes estimated splits of aggregated modes based on 2006 data.

I assigned a ‘main mode’ based on a hierarchy as follows:

  • Any journey involving train is counted with the main mode as train
  • Any other journey involving bus is counted with the main mode as bus
  • Any other journey involving tram and/or ferry is counted as “tram/ferry”
  • Any other journey involving car as driver, truck or motorbike/scooter is counted as “vehicle driver”
  • Any other journey involving car as passenger or taxi is counted as “vehicle passenger”
  • Any other journey involving walking or cycling only as “active”

How different are “place of work” and “place of enumeration” mode shares?

[this section updated 1 December 2017 with re-issued Place of Work data. See new Appendix 3 below for analysis of the changes]

The first edition of this post reported only “place of work” data, as place of enumeration data wasn’t released until 11 November 2017. This second edition now focuses on place of enumeration – where people were on census night.

The differences are not huge, as most people who live in a city also work in that city, but there are still a number of people who leave or enter cities’ statistical boundaries to go to work. Here’s an animation showing the main mode split by place of work and enumeration so you can compare the differences (you’ll need to click to enlarge). The animation dwells longer on place of work data.

Public + active transport main mode shares are generally higher for larger cities with place of work data, and smaller for smaller cities.

Here’s a closer look at the 2016 public transport mode shares by the two measures:

See also a detailed comparison in Appendix 1 below for 2011 Melbourne data.

I’d like to acknowledge Dr John Stone for assistance with historical journey to work data.

Appendix 1 – How to measure journey to work mode share

Firstly, I exclude people who did not work, worked at home, or did not state how they worked. The first two categories generate no transport activity, and if the actual results for “not stated” were biased in any way we would have no way of knowing how.

I prefer to use “place of enumeration” data (ie where people were on census night). “Place of usual residence” data is also available, but is unfortunately contaminated by people who were away from home on census day. The other data source is “Place of work”.

Some people might prefer to measure mode shares on Urban Centres which excludes rural areas within the larger blobs that are Greater Capital City Statistical Areas and Statistical Divisions (use this ABS map page to compare boundaries). However, “place of work” data is not readily available for that geography, and this method also excludes satellite urban centres that might be detached from the main urban centre, but are very much part of the economic unit of the city.

Another option is “Significant Urban Area”, which includes more fringe areas, and some more satellite towns, and in Canberra’s case crosses the NSW border to capture Queanbeyan.

What difference does it make?

Here’s a comparison of public transport mode shares for the different methods for 2011.

If you look closely, you’ll notice:

  • The more than you remove non-urban areas, the higher your public transport mode share, which makes sense, as those non-urban areas are mostly not served by public transport.
  • Place of usual residence tends to increase public transport mode shares for smaller cities (people probably visiting larger cities) and depresses public transport mode share in larger cities (people visiting smaller cities and towns).
  • Place of work is only readily available for Greater Capital City Statistical Areas. For the bigger cities it tends to inflate PT mode share where people might be using good inter-urban public transport options, or driving to good public transport options on the edges of cities (eg trains). However it has the opposite impact in Darwin and Canberra, where driving into the city is probably easier.

But I think the main point is that for any time series trend analysis you should use the same measure if possible.

If you want to compare the two, I’ve created a Tableau Public visualisation that has a large number of mode shares by both place of work and place of enumeration.

Appendix 2 – Estimating pre-2006 mode shares from aggregated data

For 2006 onwards, ABS TableBuilder provides counts for every possible combination of up to three modes (other than walking, which is assumed to be part of every journey). For example, in Melbourne in 2006, 36 people went to work by taxi, car as driver, and car as passenger (or so they said!). Unfortunately for years before 2006 data is not readily available with a full breakdown.

The 2001 data includes only aggregated counts for the following categories:

  • train and other (excluding bus)
  • bus and other (excluding train)
  • other two modes (no train or bus)
  • train and two other modes
  • bus and two other modes (excluding train)
  • three other modes (no train or bus)

Together these accounted for 3.7% of journeys in Melbourne and 4.5% of journeys in Sydney.

However all but two of those aggregate categories definitely involve train and/or bus, so can be included in public transport mode share calculations.

Journeys in the aggregate categories “Other two modes” and “Other three modes” might involve tram and/or ferry trips (if such modes exist in a city), but we don’t know for sure.

I’ve used the complete modal data for 2006 to calculate the percentage of 2006 journeys that fit into these two categories that are by public transport. I’ve then assumed these same percentage apply in 2001 to estimate total public transport mode shares for 2001 (for want of a better method).

Here are the 2001 relevant stats for each city:

(note: totals do not add perfectly due to rounding)

The estimates add up to 0.2% to the total public transport mode shares in cities with significant modes beyond train and bus (namely ferry and tram in Sydney, tram in Melbourne, ferry in Brisbane, tram and Adelaide). This almost entirely comes from “other two modes” category while “other three modes” is tiny. For these categories, almost no journeys in Perth, Canberra and Hobart actually involved a public transport mode.

In the past I have knowingly ignored public transport journeys that might be part of these categories, which almost certainly means public transport mode share is underestimated (I suspect most other analysts have too). By including some assumed public transport journeys my estimate should be closer to the true value, which I think is better than an underestimate.

But are these reasonable estimates? Are the 2001 modal breakdowns for these categories likely to be the same as 2006? Maybe not exactly, but because we are multiplying small numbers by small numbers, the impact of slightly inaccurate estimates is unlikely to shift the total by more than 0.1%. I tested the methodology between 2006 and 2011 results (eg using 2011 full breakdown against created 2006 aggregate categories and vice versa) and the estimated total mode shares were almost always exactly the same as the perfectly calculated shares (at worst there was a difference of 0.1% when rounding to one decimal place).

In the first edition of this post I had to estimate 2016 place of work mode shares in a similar way for public and private transport, but I wasn’t confident enough to estimate mode share of journeys involving cycling.

I now have the final data and I promised to see how I went, so here’s a comparison:

If you round to one decimal place, the estimates were no different for public and private transport and out by up to 0.1% for cycling (which is relatively significant for the small cycling mode shares).

I’ve applied a similar approach to estimate several other mode share types, and these are marked on charts.

Appendix 3 – How different is the re-issued place of work data?

In December 2017, ABS re-issued Place of Work data due to data quality issues. This is how they described it:

**The place of work data for the 2016 Census has been temporarily removed from the ABS website so an issue can be corrected. There was a discrepancy in the process used to transform detailed workplace location information into data suitable for output. The ABS will release the updated information in TableBuilder on December 2. The Working Population Profiles will be updated on December 13.**

I have loaded the new data, and here are differences in public transport and private transport mode shares for capital cities:

You can see differences of up to 0.3% (Melbourne PT mode share), but mostly quite small.

Update on trends in Australian transport

Sat 28 January, 2017

This post charts some key Australian transport trends based on the latest available official data estimates as at January 2017 (including the Bureau of Infrastructure, Transport, and Regional Economics 2016 Yearbook).

Car use per capita has continued to decline in most Australian cities (the exceptions being Adelaide and Brisbane, but still well down on the peak of 2004):


Mass transit’s share of motorised passenger kms was very slightly in decline in most cities in 2014-15 (the exceptions being Sydney and Adelaide)


(note: “mass transit” includes trains, trams, ferries, and both public and private buses)

At the same time, estimated total vehicle kilometres in Australian cities has been increasing:


However, mass transit use has outpaced growth in car usage since 2003-04 across the five big cities:


In terms of percentage annual growth, car use growth only exceeded mass transit in 2009-10, and 2012-13.

Car ownership has still been slowly increasing (note the Y axis scale):


Australia’s domestic transport greenhouse gas emissions actually ever-so-slightly declined in 2015-16:


Here is driver licence ownership by age group for Australia:


(note: the rate is calculated as the sum of car, motorbike and truck licenses – including learner and probationary licences, divided by population. Some people have more than one driver’s licence so it’s not a perfect measure)

From June 2014 to June 2015, license ownership rates increased in all age groups except 30-39, 60-69 and 80+.

2015 saw a change in the trend on licence ownership rates for teenagers, with a slight increase after four years of decline. However the trends are quite different in each state:


(note: in most states 16 is the age where people are able to obtain a learner’s permit)

I’m really not sure why Western Australia has such a low licence ownership rate compared to the other states (maybe the data doesn’t actually include learner permits).

And finally, here are licence ownership rates for people aged 20-24, showing quite different trends in different states:


I’ll aim to elaborate more on these trends in updates to subject-specific posts when I get time.

What does the census tell us about cycling to work?

Mon 27 January, 2014

Who is cycling to work? Where do they live? Where do they work? How old are they? What work do they do? Do men commute by bicycle more than women? How far are cyclists commuting? What other modes are cyclists using?

The census provides some answer to these questions for the entire Australian working population, albeit for one winter’s day every five years.

This post builds on material I presented at the Bike Futures 2013 conference, using census data from across Australian with a little more detail on capital cities and my home city Melbourne.

It’s not a short post, so settle in for 13 charts and 17 maps of data analysis.

How has cycling mode share changed over time?

The first chart shows the proportion of journeys to work by bicycle (only) in Australia’s capital cities.

Cyclcing only mode share for cities time series

Darwin appears to the capital of cycling to work, although it is quickly losing ground to Canberra (unfortunately I don’t have figures for Darwin pre-1996).  The census is conducted in Darwin’s dry season, but other data suggests there is little difference in bicycle activity between the wet and dry seasons.

Melbourne has shown very strong growth since 2001 and Sydney showed strong growth between 2006 and 2011. Cycling mode share has grown in all cities since 1996.

Mode shares collapsed in Adelaide, Sydney, Brisbane, and Melbourne between 1991 and 1996, which many people have attributed to the introduction of mandatory helmet laws (Alan Davies has a good discussion about this issue on his blog).

But as I pointed out at the start, census data is only good for one winter’s day every five years. Does the weather on these days impact the results?

Here is a chart roughly summarising the weather in (most of) the capital cities for 2001, 2006 and 2011 in terms of minimum temperature, maximum temperature and rainfall. It doesn’t cover wind, nor what time of day it rained (although perhaps some fair-weather cyclists might avoid riding on any forecast rain). It also fails to show the sub-zero minimums in Canberra (involves asking too much from Excel).

Census day weather

You can see that 2011 was wetter in Adelaide and Hobart than previous years, and this coincides with lower cycling mode shares in these cities in 2011. So census data is quite problematic from a weather point of view. That said, most cities had very little or no rain on the last three census days.

Where were the commuter cyclists living and working?

Other posts on this blog have also covered some of these maps, but not for all cities.

Some of the following maps are animated to show both 2006 and 2011 results, and note that the colour scales are not the same for all maps. I’ve sometimes zoomed into inner city areas when these are the only places with significant cycling mode share. White sections on maps represent areas with low density, or where the number of overall commuters was very small (sorry I haven’t gone to the effort of making every map 100% consistent, but rest assured the areas in white are less interesting). Click on the maps to see more detail.


Firstly home locations:

ACT 2011 bicycle

The cycling commuters mostly appear to be coming from the inner northern suburbs. I don’t know Canberra intimately, but Google maps doesn’t show a higher concentration of cycling infrastructure in this area compared to the rest of Canberra.

Secondly, bicycle mode share by work destination (at the larger SA2 geography):

Canberra 2011 SA2 dest bicycle any

The highest mode share was 12% in the SA2 of Acton, which is dominated by the Australian National University. Perhaps a lot of the university staff live in the inner northern suburbs of Canberra?


By home location:

Melb bicycle any zoom

Cycling mode share is highest for origins in the inner northern suburbs and has grown strongly since 2006. There’s also been some growth in the Maribyrnong  and Port Phillip council areas off a lower base.

By work location (note: this data is at the smaller destination zone geography):

bicycle mode share DZ Melbourne inner

Cycling to work boomed in inner Melbourne between 2006 and 2011, particularly to workplaces in the inner north. Princess Hill had the highest bike share of 14% in 2011 (possibly dominated by Princess Hill Secondary College employees), followed by a pocket of south-west Carlton that jumped from around 5% to 13%. Apart from the inner north, there were notable increases in Richmond, Balaclava, Yarraville and Southbank. Cycling rates within the CBD are relatively low, perhaps reflecting limited cycling infrastructure on CBD most streets in 2006 and 2011.


Firstly, by home:

Adl bicycle any zoom

Adelaide appears to lack any major concentrations of cycling, although slightly higher levels are found just outside the parkland surrounding the CBD.

Secondly, bicycle mode share by work destination at the (larger) SA2 geography:

Adl 2011 SA2 dest bicycle

The numbers are all small, with 3% in the (large) Adelaide CBD. I imagine a map based on destination zones might show some pockets with higher mode share, but that data isn’t freely available unfortunately.


By home location:

Perth cycling inner

The inner northern and western suburbs, and south of Fremantle seem to be the main areas of cycling growth.

For workplaces at the larger SA2 geography:

Perth 2011 dest SA2 bicycle

The highest mode share was in ‘Swanbourne – Mount Claremont’, only slightly ahead of ‘Nedlands – Dalkeith – Crawley’ – which contains the University of Western Australia. The Fremantle SA2 (with 3% bicycle mode share by destination) includes of Rottnest Island where around 20% of the 73 resident commuters cycled to work, but the result will be easily dominated by the mainland Fremantle section.

Again, I suspect some smaller pockets would have had higher mode shares if I had access to destination zone data.


By home location:

Bris cycling

There was significant growth in cycling from the West End, and around the University of Queensland/St Lucia – which may be related to the opening of the Eleanor Schonell Bridge (after the 2006 census) which only carries pedestrians, cyclists and buses.

By work location (at larger SA2 geography):

Bris 2011 dest bicycle

The highest share was in St Lucia – which is probably dominated by the University of Queensland. Neighbouring Fairfield – Dutton Park came in second. These two areas are directly joined by the Eleanor Schonell Bridge which provides cycling a major advantage over private transport. It seems to have been quite successful at promoting cycling in these areas.


First by home location:

Sydney cycling zoom

There were quite noticeable shifts to cycling in the inner south and around Manly.

By work location (by smaller destination zone geography):

Syd dest bicycle

There was strong growth, again in the inner southern suburbs. In 2011 bicycle mode share was highest in Everleigh (11.5%) following by the University of NSW (Paddington) at 7.9% (excluding travel zones with less than 200 employees who travelled).

Rural Australia

Here’s a map showing bicycle share by SA2 workplace location for all of Australia, which gives a sense of bicycle mode shares in rural areas.

Australia 2011 dest bicycle mode share

Higher regional/rural bicycle mode shares are evident in southern Northern Territory (Petermann – Simpson), Katherine (NT), the Exmouth region, the Otway SA2 on the Great Ocean Road in western Victoria, and Longford – Loch Sport in eastern Victoria. I’ll let other people explain those.

The SA2s in Australia with the highest cycling mode shares in 2011 (by home location) were:

  • Lord Howe Island, NSW: 21%
  • Acton, ACT (covering Australian National University): 12%
  • Port Douglas, Queensland: 10%
  • Parkville, Victoria (covering the University of Melbourne main campus): 8%
  • East Side, Northern Territory (Alice Springs): 8%
  • St Lucia, Queensland (covering the University of Queensland): 8%

How far did people cycle to work? (in Melbourne)

It is difficult to get precise distances for journeys to work, but approximations are possible. I’ve calculated the approximate distance for each journey to work by measuring the straight line distance between the centroid of the home and work SA2s and then rounded to the nearest whole km. To give a feel for how this looks, here is a map showing inner Melbourne SA2s and the approximate distances between selected SA2s:

SA2 distances sample map

This distance measure generally works well in inner city areas. However in the outer suburbs SA2s are often much larger in size, and sometimes only partially urbanised. However as we’ve seen above the volumes of cycling journeys to work are very low in these places, so that hopefully won’t skew the results signficantly.

2011 Melb JTW cycling distances

Two-thirds of cycling journeys to work in Melbourne were approximately 5km or less, with 80% less than 7 km, and 30% were 2 km or less.

The longest commute recorded within Greater Melbourne was approximately 44km.

Was cycling combined with other modes?

The following chart shows that bicycles were seldom combined with other modes:

cycling - presence of other modes 2006 2011

Around 16-17% of cycling commuters in the four largest cities in 2011 involved another mode. Use of other modes with cycling grew in all cities between 2006 and 2011

The next chart shows what these other modes were:

Other modes with cycling 2011

Sydney, Melbourne, Brisbane and Perth had high rates of bicycle use with trains, while combining car and bicycle was more common in the smaller cities.

The next chart shows the number of trips involving bicycle and trains in 2006 and 2011:

JTW bicycle + train raw numbers

The chart shows the relative success of Melbourne Parkiteer program of introducing high quality bicycle cages at train stations, which has helped boost the number of people access the train network by bicycle by around 600 between 2006 and 2011. I understand a similar project has been undertaken in Perth which saw growth of around 250.

In Melbourne, the home locations for people using bicycle and train are extremely scattered – the following map shows a seemingly random smattering:

Melb bicycle + train

How does commuter cycling vary by age and sex?

bicycle mode share by age sex

This chart shows remarkably clear patterns. Males were much more likely to cycle to work. Teenage boys (particularly those under driving age) had the highest cycling mode shares (with teenage girls much less likely to cycle). The next peak for men was around the mid thirties, and women’s mode share peaked around ages 28-32.

Where are women more likely to cycle to work?

Women are sometimes talked about as the “indicator species” for cycling – ie if you have large numbers of women cycling compared to men then maybe you have good cycling infrastructure that attracts a broader range of people.

The census data can shed some light on this. For each SA2 in Melbourne, I have calculated the male and female cycling mode shares both as a home origin, and as a work destination (this analysis looks at people who only used bicycle (and walking) in their journey to work). I’ve then calculated the ratio of male mode share to female mode for each area (SA2).

I’ve used the ratio of mode shares in preference to the straight gender split of cycling commuters – as female workforce participation is generally lower and there can be spatial variations in the gender split of the workforce. 46% of all journeys within Greater Melbourne in the 2011 census were by females, but only 28% of cycling journeys to work were by females.

The following map shows the ratio of male to female cycling mode shares by home location for SA2s (with more than 50 commuter cyclists, and where the bicycle mode share is above 1%):

Melb 2011 cycling gender ratio home

Areas attracting comparable female and male bicycle shares include the inner northern suburbs and – curiously – Toorak (probably many using the off-road Gardiners Creek and Yarra Trails to access the city centre).

Here’s a similar map, but by workplace areas:

Melb 2011 cycling share gender ratio WP

The patterns are much more pronounced. Six SA2s had higher female mode shares than male: Yarraville, Fitzroy North, Brunswick East, Ascot Vale, Carlton North – Princes Hill, and Elsternwick.

The areas with near-1 ratios of male to female mode shares were similar to the areas with higher total cycling mode shares. The following chart confirms this relationship (note areas with cycling mode shares below 1% not shown):

gender ratio and overal mode share

What this also shows is that home-area mode shares reach much higher values than workplace-area mode shares. Perhaps the secret is in the home-area cycling infrastructure? Or perhaps it’s more to do with the residential demographics?

See the Bicycle Network Victoria website for more data about female cycling rates in Melbourne.

Do women cycle the same distances as men?

Again using the approximate straight line commuting distances (as explained above) the following chart shows that women’s cycling commutes are a little shorter than men’s, but not by much:

commute distance and gender

The median female cycling commute was approximately 1.8 km shorter than for males.

What types of workers are more likely to cycle to work?

Firstly, I’ve looked at the differences between public and private sector employees.

Before I dive into the data, it’s important to recognise that different types of workers are not evenly spread across Australia. Some types of jobs concentrate in city centres while others might be more likely to be found in the suburbs or the country. Therefore many of the following charts show results for Australia as a whole, but also for people working in central Melbourne (the SA2s of Melbourne, Carlton, Docklands, East Melbourne, North Melbourne and Southbank), which has a relatively high rate of cycling to work.

The data suggests public servants were much more likely to cycle to work:

cycling by employer type

The local government result has prompted me to calculate the cycling mode shares for local government workers across Australia (assuming workers work within the council for which they work). Here are bicycle mode shares for the top 20 councils for employee cycling mode share in the census:

Council State Bicycle mode share
Tumby Bay (DC) SA 23.5%
Kent (S) WA 18.8%
Carnamah (S) WA 16.0%
Central Highlands (M) Qld 14.3%
Uralla (A) NSW 13.8%
Wakefield (DC) SA 13.5%
Nannup (S) WA 12.5%
Broome (S) WA 12.1%
Alice Springs (T) NT 11.8%
Narembeen (S) WA 11.5%
Blackall Tambo (R) Qld 11.3%
Kowanyama (S) Qld 11.2%
Exmouth (S) WA 11.1%
Yarra (C) Vic 10.4%
Glamorgan/Spring Bay (M) Tas 8.7%
Torres (S) Tas 8.6%
Yarriambiack (S) Qld 8.3%
Mallala (DC) Vic 8.0%
Richmond Valley (A) NSW 7.2%
McKinlay (S) Qld 6.7%

Most of the top 20 are non-metropolitan councils. Melbourne’s City of Yarra is the top metropolitan city council (within Greater Melbourne the next highest councils are Moreland 6.1%, Port Phillip 5.6%, Melbourne 5.6% and then Stonnington 4.9%).

National government employees had the highest bicycle mode share of all of Australia. I suspect this relates to university staff, as many of the earlier maps showed university campuses often had relatively high rates of employees cycling (85% of “higher education” employees count as “national government” employees).

The census data can also be disaggregated by income:

cycling mode share by income

Cycling mode shares were highest for people on high incomes. Initially I thought this might reflect the fact that high income jobs are often in city centres were cycling is relatively competitive with private and public transport. However, even within central Melbourne workers, cycling rates are higher for those on high incomes (curiously with a second peak for those on incomes between $300 and $399 per week).

Does cycling to work make you healthier and therefore more likely to get promoted and earn a higher income? Or are employers offering workplace cycling facilities to attract highly paid staff? I haven’t got data that answer those questions.

Consistent with higher rates of cycling for higher income earners, those in more highly skilled occupations were more likely to cycle to work:

cycling mode share by profession

I suspect this might reflect the presence/absence of workplace cycling facilities (perhaps office workplaces are more likely to provide cycling facilities than retailers for example) and/or the ability to afford to live close to work (which makes cycling easier).

Are recent immigrants more likely to ride to work?

This one really surprised me and I only investigated it because it was possible to do. The census asks what year people migrated to Australia (if not born here), and it turns out that recent immigrants were much more likely to cycle to work:

cycling mode share by migration year

This might be explained by the demographics of recent immigrants (eg car ownership, home location, income levels, occupation and age).

I’d welcome comments on any other trends people might spot in the data.