What might explain journey to work mode shifts in Australia’s largest cities?

Mon 28 May, 2018

[Updated 29 June 2018 with further analysis of parking levies and their impact]

Between 2011 and 2016, journey to work public transport mode shares went up significantly in Melbourne and Sydney but dropped significantly in Perth and Brisbane. Private transport mode shifts did the opposite. Can this be explained by the changing distribution of jobs within cities, or other factors such as changes in transport costs?

In a recent post focused on Brisbane I found that stronger growth in suburban jobs relative to central city jobs could explain around half of the city’s mode shift towards private transport, with other factors (mostly the changes in relative attractiveness of modes) explaining the rest.

So how is job distribution changing in other Australian cities? How much of the mode shifts can be attributed to changing job distribution and how much could be attributed to other factors like changes in transport costs, or increasing employment density?

(for details about how I define public, private and active transport, see the appendix in this post)

How is job distribution changing in Australian cities?

Here’s a view of the changing distribution of all jobs within each city by workplaces distance from the city centre.

(Unfortunately I only have 2006 data for Sydney and Melbourne)

The changes are relatively subtle, but if look at how the bands shift between years, you’ll see increasing centralisation in Sydney but a decentralisation in all other cities between 2011 and 2016.

The strongest decentralisation was in Brisbane and Perth, which also showed the biggest increases in private transport mode share.

However Melbourne saw both a slight decentralisation of jobs and a mode shift away from private transport between 2011 and 2016.

So we need to dig deeper to find out what’s going on here.

How does private mode share vary by distance from the city centre?

The following chart shows private transport mode shares by distance from the city centre for the last two or three censuses for each city. The darkest line for each city is for 2016, with lighter lines being previous years (I only have 2006 data for Melbourne and Sydney).

There’s a clear pattern in all cities that private mode shares are lower in areas closer to the city centre, with Sydney the lowest, followed by Melbourne, Brisbane, Perth, Adelaide, and Canberra (which is also the order of their population size).

Notably Sydney private mode share averaged lower than 90% out as far as 24km from the city centre, whereas Adelaide sees 90% mode shares as close as 2km from the city centre.

If you look carefully you can see that Brisbane increased private transport mode shares in the central city between 2011 and 2016, while private mode shares dropped or were stable in all other cities at most distances.

You can also see that the central city mode shifts away from private transport were largest in Melbourne, something I’ll come back to.

Here’s the same again but for public transport:

Sydney and Melbourne saw mode shifts to public transport at most distances from the city centre, unlike all other cities.

What mode shift can we attribute to changing job distributions?

A city’s mode share (measured by place of work) will be fundamentally impacted by two types of changes between censuses:

  • Changes in the volume of jobs in each SA2 – because different SA2s generally have different mode shares due to factors like proximity to the city centre and public transport access. If there is stronger jobs growth in areas that already had lower private mode shares, you would get a mode shift away from private transport, all other things being equal.
  • Changes in the mode share in each SA2 – because different modes became more or less attractive for commuters between census years. This might be due to changes in public transport service quality, transport infrastructure provision, and relative changes in the cost of public transport, private motoring, and commuter parking. It could also be influenced by broader demographic changes.

For each city I have calculated what the city-level private transport mode share would have been in 2016, had mode shares in each workplace SA2 remained exactly the same as 2011, but the job volumes in each SA2s had still changed. The city level mode shift due to SA2 volume changes is then the difference between this hypothetical 2016 mode share and the 2011 mode share. The remainder of the city-level mode shift between 2011 and 2016 results can then be attributed to mode shifts at the SA2 level.

Here’s a chart showing the mode shift impact of both volume changes at the SA2 level, and mode shifts at the SA2 level:

As we noted above, Sydney saw a slight trend to centralisation of jobs between 2011 and 2016, and it had the largest volume change attributed reduction in private mode share (-0.4%). However other factors were responsible for a further 2.5% of the mode shift away from private transport.

The story is similar in Melbourne but to a smaller magnitude in both aspects. Both of these cities also saw increasing inner city job density – which matters – and I’ll back come to that in a moment.

In Brisbane you can see that the total mode shift towards private transport was roughly equally attributable to SA2 volume changes and SA2 mode shifts (as I discussed in my earlier post).

Perth had an overall 1.3% mode shift to private transport, and the majority of this was due to significant jobs growth in the suburbs compared to the CBD (in fact, the SA2 with the largest jobs growth was Murdoch in the southern suburbs). But there were also other factors that led to a mode shift to private transport.

In Canberra – Queanbeyan, volume changes by themselves would have seen a mode shift to private transport, but other factors were larger and led to an overall mode shift away from private transport (although it is actually complicated because the 2011 census day was in a federal parliamentary sitting week, while 2016 was not).

Nothing much changed in Adelaide.

Next I’m going to explore what could be behind the mode shifts at SA2 level, in terms of job density and real transport costs.

Can increases in workplace density impact mode shares?

As discussed in my Brisbane analysis, if the relative attractiveness of modes hadn’t changed, you might still expect a mode shift to public transport in high density employment areas with increasing jobs numbers because you would expect the cost of parking provision to increase with increasing land use density (i.e. more competition for space).

Indeed, in Sydney and Melbourne a number of inner city SA2s became significantly more job dense between 2011 and 2016, and also saw mode shifts away from private transport:

(inspect this data in Tableau)

A similar thing happened in Civic (the main centre of Canberra).

But Adelaide and Perth saw both declining job density and declining private transport mode share, which suggests something else is at play.

Job density didn’t really go down in Brisbane – see my Brisbane post for an explanation (basically, ABS redrew the SA2 boundary along the Brisbane River).

Could changes in the real cost of transport be causing mode shifts?

The following chart shows the real change in urban transport fares in Australian cities since 2000, as measured by the ABS as part of the Consumer Price Index series (which unfortunately includes public transport, taxis, and “ride share” but is for a representative sample of journeys so hopefully mostly dominated by public transport fares):

The lines are somewhat saw-toothed because public transport fares generally only rise once a year, and become better value in real terms over the course of the following 12 months.

Many cities have seen above-CPI public transport fare increases at various times, most notably Brisbane in 2010-2014. Melbourne has had above CPI fare increases, but also reduced zone 1+2 fares in 2015 which lead to a reduction on the ABS measure (the fare reduction only really applied to people travelling across zones 1 and 2 – which roughly summarised means travel between the outer and inner suburbs). Brisbane fares peaked in 2014, which was followed by a freeze and then a large reduction in 2017.

By contrast, here is the (negative) growth in the cost of “private motoring” (which includes vehicles, fuel and maintenance):

Private motoring costs have declined in real terms since 2000, although they increased a little during the second half of 2017.

The next chart shows the change in ratio between the two costs. Urban transport fares have become less competitive than private motoring over time in all cities:

But if we are looking at changes between census figures, we should probably also look at cost changes between the times of each census. Here’s how prices changed in real terms between the September quarters of 2011 and 2016 (which cover the August census dates):

The real cost of private motoring dropped in all cities, but so did the real “average” cost of urban transport fares in Sydney and Melbourne (the Melbourne drop being mostly around large fare reductions for travel across zones 1 and 2).

The biggest differences in cost changes were in Brisbane and Perth (around 18%), which I think will go a fair way to explaining why these cities had the biggest shifts to private transport attributable to SA2 mode shifts.

Brisbane saw a rapid increase in public transport fares between 2011 and 2014 which is likely to have changed many commuting habits, but those habits may or may not have changed back when fares were subsequently reduced (e.g. if someone bought a car due to fare increases, they may not have subsequently sold their car when fares reduced). Perth certainly had less mode shift at the SA2 level compared to Brisbane, which might support this hypothesis.

What about changes in car parking costs?

The ABS CPI’s private motoring cost index does not include car parking costs – which would be difficult as they vary considerably with geography.

However we do know about central city car parking levies that governments charge in a bid to reduce road congestion and fund inner city transport initiatives. Sydney, Melbourne, and Perth apply levies to central city non-residential car parking spaces, and ultimately these levies will need to be recovered through parking prices.

I’ve calculated these levies in 2017 dollars (adjusting for inflation as measured in June quarters), and here’s how they have changed since 2000:

Melbourne increased its central city parking levy by 40% per space in 2014 (category 1), and created a new lower-priced levy area in some neighbouring areas to the north and south in 2015 (category 2, see map). This is likely to have contributed to the larger mode shifts away from private transport in the central city area of Melbourne compared to most other cities (particularly considering there were similar changes in average private motoring and urban transport fares in Melbourne between 2011 and 2016).

Sydney’s category 1 fee applies in the Sydney CBD area, Milsons Points and North Sydney. It was $2390 in 2017, and has only risen with indexation since 2009 (when it was doubled). A lower category 2 levy applies in the business districts centres of Bondi Junction, Chatswood, Parramatta, and St Leonards.

Perth has an annual licence fee per bay which ranged from $1039 to $1169 in 2017.  The Perth fee was increased by around 167% in 2010, and there were also above-inflation increases from 2014. The fee increased 63% in real terms between 2011 and 2016 for “long stay” spaces, and 69% for “tenant” spaces.

I am not aware of any such fees or levies in place in Brisbane or Adelaide (a proposal for Adelaide was voted down).

So how are CBD parking prices changing?

Unfortunately good data is a little hard to find, but this Colliers Car Parking White Paper provides “average daily rates” for CBDs for 2009-2015, and early bird rates for 2015. I expect most commuters would pay early bird rates – which average between 28% and 62% of daily rates depending on the city (quite some variation!). I’ve adjusted the pre-2015 figures for inflation to be in 2015 dollars:

In real terms, “average daily” parking costs have declined in Melbourne, rocketed up in Brisbane and Canberra, and moved less in Sydney and Perth. I don’t know whether these reflect trends in early bird prices. And we don’t know how prices changed between 2015 and the census year of 2016.

So how much are parking levies contributing to parking prices?

I have to make some assumptions (guesstimates) here. Regular weekdays represent about 60% of the days of the year. If we assume say 80% of the levy is recovered from weekday commuter parking (there generally being less demand for parking on weekends), we can calculate the average weekday commuter cost of the levy to be 27% of the Sydney early bird price, 25% of the Melbourne early bird price, and 15% of the Perth early bird price. Certainly not insignificant.

Here’s a summary of the levy and “average daily” price changes and mode shifts in the central city parking levy areas:

Changes 2011 to 2016
Parking levy area or CBD SA2 Levy real increase Average daily real price change (2011 to 2015) Private mode shift New private trips Private share of new trips
Perth 63% -5% -0.8% -60 -3%
Melbourne – category 1 40% -11% -5.3% 3200 5%
Melbourne – category 2 (new) n/a -6.4% 5315 30%
Sydney CBD 0% +1% -2.6% 6204 9%
Brisbane City SA2 n/a +64% +1.7% 3135 68%
Adelaide SA2 n/a -11% -1.5% 2567 35%
Canberra Civic SA2 n/a +71% -3.2% 746 30%

Firstly, “average daily” parking prices don’t seem to be following the changes in parking levies in Perth and Melbourne (category 1 area). Other factors influencing parking prices will include supply (influenced by competition for real estate and planning rules) and demand (influenced by employment density) with the market ultimately determining prices.

Car park operators appear to be absorbing the increased cost of the levy (although we don’t know the trends in early bird prices so we cannot be entirely sure). But that’s not to say that the levy hasn’t had any impact on prices – for example, the price reductions might have been larger if the levies had not increased.

Secondly, price changes do not appear to be correlated with mode shifts as you might expect (except Canberra). Brisbane prices increased dramatically, but so did private mode share! Price reductions in Perth, Adelaide, and Melbourne did not result in increased private transport shares.

Maybe other factors are driving mode shift away from private transport in those cities. Maybe early bird prices are trending differently to “average daily” prices. Maybe increased traffic congestion persuaded people to shift modes. Maybe there were significant price changes between 2015 and 2016. Maybe most existing public transport users were not aware of reductions in parking prices.

I don’t know what happened to parking prices in the new category 2 areas of Melbourne but there was a large mode shift away from private transport (-6.4%), and they may well be linked. Indeed, Infrastructure Victoria has recently recommended the category 2 area be expanded to include the inner-eastern suburbs of Richmond, South Yarra, Windsor and Prahran. And the Grattan Institute has recommended increasing the levy to match Sydney’s rates.

Curiously, when I look at City of Melbourne Census of Land Use and Employment (CLUE) data, the category 1 area (approximated with CLUE areas) had an increase of only around 367 non-residential parking bays between 2011-12 and 2015-16 (a four year period), a lot less than the additional 3200 private trips, which might suggest increased average occupancy.

Also, it is likely that a significant portion of people who drive to city centres are not paying for their parking costs (eg employer provided car parking). Employers may simply be absorbing price increases.

For more interesting discussion and research about car parking in the City of Melbourne, see a recent discussion paper and background report prepared by Dr Elizabeth Taylor.

Did changes in population distribution impact mode shares?

While this post has been focused on changes by workplace location, it is possible to separate the overall mode shifts into the two components by home location. Here are the results:

In Sydney, Melbourne, and Canberra, stronger population growth in areas that already had low private mode shares in 2011 made a small contribution to overall mode shifts away from private transport. These cities have all seen densifying population in inner city areas better served by public transport.

The distribution of population growth in Perth and Brisbane had a small effect in the opposite direction.

And again, nothing much changed in Adelaide.

What about active transport?

Cycling-only mode share was pretty stable in most cities (except Canberra up 0.2%). Walking-only mode share declined in Sydney (-0.2%), Brisbane (-0.3%), Adelaide (-0.4%), Perth (-0.3%) but was steady in Melbourne and increased in Canberra (+0.2%). So Canberra has the biggest shift to active transport.

Can you summarise all that?

If your head is spinning with all that information, here’s a summary of what some of the major factors could be in each city between 2011 and 2016. I say “could be” because I’ve not looked at every possible factor influencing mode share.

Sydney: the 2.9% mode shift away from private transport was probably mostly to do with increasing job density in employment centres (more on that in my next post), but was also partly by a shift to more centralised jobs, and increasing population density in places well served by public transport.

Melbourne: The 1.8% mode shift away from private transport probably had a fair bit to do with increasing central city job density, the significant spatial expansion of the central city parking levy area and rates (although we don’t know if early bird prices also rose), a reduction in some public transport fares, and strong population growth in areas well served by public transport.

Brisbane: The 1.9% mode shift towards private transport appears roughly half about the decentralisation of jobs, and half the reduced attractiveness of public transport – particularly following significant fare rises between 2010 and 2014, and possibly/arguably declines in service quality.

Perth: The 1.2% mode shift towards private transport was probably mostly due to a decentralisation of jobs, and partly due to public transport becoming less cost competitive with private transport (despite an increase in the central city parking levy). Urban sprawl is probably also a factor.

Adelaide: The 0.2% mode shift to private transport is probably mostly due to public transport becoming less cost competitive with private transport. Changes in job and population distribution, and employment density do not appear to have had a significant impact.

Canberra:  The 1.0% mode shift away from private transport was probably the result of competing forces of higher jobs growth in car-dominated workplace areas with increasing job density in dense employment centres, increasing central city parking prices, higher population growth in areas better served by public transport (and possibly cycling facilities), and also the fact census 2016 was not a parliamentary sitting week while 2011 was (so really, it’s hard to be too sure!).

You might want to add your own views about changes in the service quality of public transport and cycling infrastructure in each city. I also haven’t looked at the impact of major new public transport infrastructure and service initiatives (such as the opening of new train stations), which we know does impact mode shares at a local level (maybe that’s for a future post).

I hope you found this interesting. My next post will look at suburban employment centres, and their role in changing mode shares in cities.

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.

Trends in car ownership

Sun 7 August, 2011

[post updated in April 2016 with 2015 data. For some more recent data see this post published in December 2018]

Is the rate of car ownership still growing in Australia?

Firstly, by car ownership rate I mean the ratio of the number of registered “passenger vehicles” (from the ABS Motor Vehicle Census) to population (also from ABS). So while some of the measures in the post are not strictly for cars only, I’ve not worried too much about the distinction because I’m most interested in the trends.

The oldest motor vehicle census data is from 1955, and it is no surprise to see car ownership rates in Australia have risen considerably since then:

What is interesting in this chart is the relative rate of car ownership between states and territories. The Northern Territory is consistently the lowest – I’m guessing related to remote indigenous populations with low car ownership. New South Wales may reflect the relatively dense Sydney where car ownership is less important for many. I’m not sure of the reasons for other differences. It might be slight differences in reporting from the state agencies (see ABS’s explanatory notes).

But what about the most recent trends? Here is the same data from 2000 onwards (NT off the chart): 

You can see growth across all states, although there are several periods where some states flat-lined, particularly around 2008.

So while we have reached peak car use, we haven’t reached peak car ownership as a nation.

What about car ownership in cities?

Motor vehicle ownership data is also available from the census, with data provided on the number of households with different numbers of vehicles. The 2006 census reported the number of households with every number of motor vehicles 0 to 99, and here is the frequency distribution:

household car frequency 2006

In 2011 census data ABS only report the number of households with “4 or more” motor vehicles. I’ve calculated the average number of cars for this category for 2006 for each city and applied that to the 2011 data to get total motor vehicle estimates for 2011.

The following chart shows household motor vehicle ownership rates for major city areas for 2006 and 2011 (boundaries changing slightly to include more peripheral areas that are likely to have higher car ownership):

City car ownership 2006 and 2011

Sydney has the lowest rate of motor vehicle ownership, and Perth the highest, with Melbourne showing the least growth.

Here is the relationship between car ownership and journey to work by car-only:

car ownership v car JTW

While all cities had an increase in car ownership between 2006 and 2011, all but two had a reduction in car-only mode share of journeys to work. They were Adelaide and Canberra which also had the largest increases in car ownership rates.

While cities overall show increasing ownership rates, there were reductions in motor vehicles per capita in many municipalities between 2006 and 2011, including the City of Perth, the City of Melbourne, the City of Adelaide, the City of Willoughby, and the City of North Sydney. This suggests car ownership is in decline in some inner city areas of Australian cities (more spatial detail for Melbourne is available in another post). These areas generally have good public transport and many local services within walking distance, and I’d guess many new residents are not bothering with car ownership.

The following chart compares motor vehicle ownership rates between capital city areas and the rest of each state or territory for 2011 census data:

car ownership capital v rest of state 2011

Car ownership is certainly higher outside most capital cities – except in the Northern Territory as I suspected (curiously Darwin has around the same car ownership rate as Melbourne).

How does car ownership vary by demographics?

The Victorian Integrated Survey of Travel and Activity (VISTA) provides detailed data about households in Melbourne and regional Victorian cities for the years 2007-2009. So while I cannot extract trends, we can look at the patterns of car ownership rates.

I have classified all households in the VISTA dataset into one of three categories:

  • household with no motor vehicles
  • limited motor vehicle ownership: less motor vehicles than people of driving age (arbitrarily defined as 18-80), or
  • saturated motor vehicle ownership: motor vehicle count equals or exceeds the number of people of driving age (“MV saturated” in the chart).

mv ownership by age draft

You can see that people aged 35 to 59 are least likely to live in households without motor vehicles, while younger adults are most likely to live in a household with limited car ownership. There are curiously two peaks in saturated car ownership – aged 35-39 and 60-64. The saddle in between might be explained by family households with driving age children.

The following chart looks at household car ownership by household type, with “young families” classed as households where all children are under 10 years of age.

mv ownership by hh status

Some very clear patterns emerge, with households incorporating parents and children very likely to own at least one motor vehicle. Sole person households were most likely to not own a motor vehicle. Limited motor vehicle ownership was most common in “other” household structures, parent+children households with older children, and couple households with no kids.

It seems Australians find car ownership a high priority if they have young children. Other analysis on this blog found that such households also have the lowest rates of public transport use, and a very strong inverse relationship between motor vehicle ownership and public transport use.

What about usage of each car?

Using data from the BITRE 2015 yearbook, it is possible to calculate estimated annual kms per passenger car. For this I’m comparing the number of vehicles at the motor vehicle census date with an estimate of total car kms in the previous 12 months (straight line interpolation of BITRE year ending June figures). This isn’t a perfect measure as the number of cars grows throughout the 12 month period where kilometres are taken, but it is still a guide to the trend.

The steeper downwards trend since 2005 is similar to the downwards trend in car passenger kms per capita in Australian cities:

Since around 2005, car ownership has continued to rise while car passenger kilometres per capita has fallen. This suggests we are driving cars shorter distances and/or less often.

What about motorcycles?

Are more people owning motorcycles instead of cars? Here’s the long-term trend:

You can see motorcycle ownership rates peaked around 1980, dipped in the mid 1990s and have grown significantly since around 2004 (although still very small). Does it explain the slowdown in the car ownership rate from 2008?

This chart still shows a slow-down after 2008, so it doesn’t look like rising motorcycle ownership fully explains the slow-down in car ownership. Motorcycle ownership took off in 2004, but car ownership slowed in 2008.

What about the ageing population?

Could the data be impacted by a changing age profile? We know that older aged people are less likely to have their driver’s license and are more likely to live in a household with lower car ownership (refer above), so maybe this would lead to a declining car ownership rate per head of population as a greater portion of the population is older.

Suppose most car owners are aged 18 to 80 years. Here’s the percentage of Australia’s population within that age band:

Population aged 18-80

The share has been very steady at around 73 to 74% for all of the last 21 years, which suggests little impact on overall car ownership rates. Then again, those aged 80 today are more likely to have a driver’s license that those aged 80 in 1994. So the rate of car ownership of younger people has probably grown less. We know their rate of driver’s license ownership has declined over time, but I’m not aware of any readily available data that would confirm a lower rate of car ownership by younger people over time (it’s probably available from the Sydney Household Travel Survey datasets).

Notes on the data:

  • The ABS Motor Vehicle Census has been taken in different months in different years. State population estimates are only available on a quarterly basis. I have used the nearest quarterly population figure for each motor vehicle census where they do not align (never more than one month out).

A look at Melbourne CBD transport

Sun 23 January, 2011

My last post looked at suburban employment areas, but what about the CBD? With the review of the City of Melbourne’s Transport Strategy, I’ve taken on a detailed analysis of transport to and from the CBD.

In this post I’ll look at questions like:

  • Do CBD commuters come from the inner or outer suburbs?
  • Do wealthy executive types snub public transport?
  • How does mode share vary between the sexes and young and old?
  • What impact are employer parking and driving subsidies having on mode choice?

I’m mostly focussing on the inner Melbourne CBD – using the ABS definition of “Melbourne – inner” SLA, which is essentially the Hoddle grid. However I’ve included Southbank or Docklands a couple of times, and there are also some comparisons with Sydney, Brisbane and Perth CBDs.

This is a long post, so grab a cuppa and get comfortable.

Where do the commuters come from?

According to the 2006 census, there were 137,853 commuter journeys into the CBD.

The first map shows the number of commuters from each SLA in Melbourne. The shading represents simple density of CBD commuters by area, which is not ideal because outer metro SLAs can be impacted by low average population density. At the same time, not all SLAs have the same population so some will always have large numbers (eg Manningham west). As always, click to zoom in.

The CBD attracted workers from all over Melbourne, but certainly with a high concentration from the inner suburbs.

To get around the density issue, I’ve drawn a map showing the percentage of workers from each SLA who work in the CBD, Southbank or Docklands:

You can see the percentage drops off fairly uniformly by distance. The CBD is not a major destination for most middle and outer suburban areas.

What modes of transport do commuters use? (by area)

Firstly a map showing the public transport mode share from each SLA (green = higher):

Public transport mode share was largely above 70% for much of Melbourne and indeed most surrounding areas.

A few low spots stick out:

  • Manningham west and east, serviced only by buses (that have recently been signficantly upgraded)
  • Northern parts of Boroondara and 52% and 55%. These wealthy areas are serviced by frequent trams and buses, although with a relatively slow trip in.
  • Rowville (Knox south) is at 57%, but bear in mind there were only 800 commuters from Rowville to the CBD (and I expect most of these would be park and ride train commuters). In fact, the catchment of the proposed Rowville rail line passes through three SLAs, with a total CBD commuter population of 4138. Allowing for catchments of other radial public transport lines in the SLAs, the CBD commuter catchment of the proposed Rowville line might be 2000-3000, or about 3 full trains. But of course a line would also be used for trips to other destinations (particularly Monash), and it would probably cause changes in travel patterns over time once built. I might look at this more in a future post. In the meantime you might want to read Alan Davies take, and a 2004 pre-feasibility study (here is a summary presentation).
  • Wealthy Brighton is well serviced by the Sandringham line, but only half used public transport to get to the CBD. There is no easy freeway connecting Brighton and the CBD, so why are they driving? I’ll come back to that.
  • The inner SLAs in Melbourne, Yarra and Port Phillip are slightly lower, probably due to a high rate of walking and cycling. More on that later too.

You can see a high PT mode share for the relatively small numbers of commuters from Geelong (around 800 in total). $4.3b is being spent on a regional rail link, that will separate regional trains from suburban trains. Regional trains from outside Melbourne seat less than 500 people, but because they run express through much of Melbourne they each consume probably around two all-stopping suburban train paths (which have a capacity of around 1000 each). I haven’t seen any debate about whether encouraging regional commuting by train into central Melbourne is worthwhile, though I’m sure people living in those areas appreciate the trains.

Next a map showing private transport mode share (red = higher):

Private transport mode share was highest for Manningham, northern Boroondara, Wyndham South (including Point Cook), Bayside, Rowville, and the outer northern fringes.

But a high car mode share may not be a huge issue if the number of car commuters is low. The next map shows the number of private transport commuter trips from each SLA, shaded by relative density:


  • Like we saw in my last post for South Melbourne, there were large numbers of car commuters coming from the inner suburbs, particularly to the south-east. These areas are well connected to the CBD by public transport, and also quite wealthy. Is wealth a driver of higher car mode share? Read on.
  • Manningham west had a large number of car commuters (with a reasonable density). This area is entirely reliant on bus services, which have been upgraded considerably since 2006, with strong patronage growth resulting. In 2006, the last bus from the CBD on the Eastern Freeway – Doncaster Road route (307) was around 6:45pm. It’s now around midnight (on route 907 that replaced 307).
  • There were also a large number from Wyndham north-east (Werribee – Hoppers Crossing area) which is not shaded dark on the map due to low average population density. In 2006, peak train services on the Werribee line were often 20 minutes apart, and bus services only ran every 40 minutes. The train frequency has since increased to 6/hour but the (feeder) bus frequencies are still 40 minutes in peak periods.
  • Moonee Valley (Moonee Ponds-Essendon area) was a large contributor of cars, despite frequent trains and trams to the CBD. Not sure why that is, although Essendon is a relatively wealthy area.

Here is a another map of private transport commuters, except it is shaded by numbers rather than density. Manningham west stands out, but bear in mind it is one of the largest SLAs in Melbourne by population. You can see the outer western SLAs show up on this map also.

And for a flip side, here is where the public transport passengers were coming from (shaded by density):

There are large concentrations coming from the inner suburbs, but also the middle eastern suburbs which are well connected by trains. The Manningham west area had over 2000 public transport commuters to the CBD, many of which would have been on buses only.

Again, to get around the low population density problem, I’ve also drawn a similar map shaded by total numbers:

We saw low PT and car mode shares for the inner city. I haven’t drawn a map of walking mode share for the CBD but you can see public and private transport mode shares are low in the inner city, with walking likely to fill the gap. A map of walking mode share to any work destination is in another post.

The cycling figures are quite interesting. Next map shows the bicycle mode share to the CBD (any trip involving bicycle) (green=higher):

The figures are for Yarra north, Brunswick and Northcote are surprisingly high at 8-10%. Remember that the census is taken in winter (August). As I recall it wasn’t a rainy day. Bicycle mode share is also lower for commuters from the City of Melbourne itself. SLAs in grey lacked sufficient data.

Here are the total number of CBD bicycle commuters per SLA (shading by numbers, not density):

According to the data, people also rode from as far out as Frankston, Croydon, Ringwood and Sunbury! Census data is like that (as I recall, someone in Banyule claimed to have gone to work by ferry).

What modes did people use overall?

Here is a chart showing the overall mode split for all CBD workers:

Trains accounted for almost half of all CBD arrivals.

While buses accounted for only 2% of all CBD commuters, they were the only mode used by 32% in Manningham west, 11% in Kew, 9% in Camberwell north, 7% in Maribyrnong, and 5% in Altona.

Next chart shows mode split in a more simplified form:

Public Transport dominates, but still over a quarter came by car – including over 32,000 car drivers.

Public transport took 67% of motorised commuter trips into the CBD.

Active transport is at 8%, which probably represents those who live within walking distance of the CBD.

So how does Melbourne compare to other large Australian cities? The following chart compares Sydney, Brisbane, Perth and Melbourne CBDs. I’ve used the SLA that represents the inner core of business activity in each city to try to make in a reasonably fair comparison. Unfortunately Adelaide does not have a true inner CBD SLA to compare against (the central SLA includes all of North Adelaide, including lower density residential areas).

Sydney has the highest public transport mode share, with Melbourne and Brisbane very close (to my surprise). Perth is very much a car CBD, although mode shares are likely to have changed following the opening of the Mandurah rail line since 2006. The 2011 figures will be very interesting.

Perth walking more share was 3.0%, lower than 5.3-5.8% in the other cities – probably because of a lack of inner city residents.

And for the record, cycling was highest in Melbourne at 2.3%, followed by Perth at 2.0%, Brisbane at 1.5%, and Sydney at 0.8%.

The number of car driver journeys to work in the Melbourne CBD actually decreased from 34,289 in 2001 to 30,570 in 2006, a mode share drop from 27% to 23% (ref). This happened despite a 20% increase in the number of parking spaces in the CBD between 2000 and 2006 (ref):

I’ve included Southbank and Docklands in this chart for interest – Southbank parking supply actually went down between 2006 and 2008.

[parking stats updated June 2012 with 2010 CLUE data:]

Looking at commercial parking spaces only:

The number of commercial parking spaces has actually declined in the CBD and there has been very little growth in Docklands (despite an increase in employment).

Here is the ratio of employees to commercial parking spaces:

While the ratios are flat in three of the areas, Docklands has seen strong growth in employment without equivalent growth in commercial car parking.

Colliers International have recently begun surveying CBD parking costs. Here are the results for Australia (adjusted to AUD using 1 July exchange rates):

I don’t pretend to be an expert in CBD parking markets, but the differences between daily and monthly rates suggest some complexity. In Melbourne at least, it is quite common to find “early bird” parking for $13-17 (and “early bird” usually means parking your car before 10am).

I’m perhaps more inclined to go on the monthly rates, as they are probably more competitive. Melbourne prices collapsed in 2010, at the same time that public transport patronage growth stalled. Prices also went down in all other cities except Perth (which had the strongest public transport growth of the major cities in 2009-10).

So is CBD parking price a driver of public transport patronage? Probably too early to tell because of a lack of much time series on parking cost data (including 2006 data), but worth looking at in future.

What modes are different commuters using?

Firstly, mode share of motorised journeys by age and gender:

As you might expect, public transport mode share is higher amongst younger people and females. But for females it is also high for older women, with a curious dip at 35-44 years (typical kids at primary school years?). For men, private transport mode share was higher for older men. I’ve not shown 65-74 because the total number of such commuters was very small.

I’ve put non-motorised modes on a separate chart as they are much lower shares:

Walking was much higher for younger people. Is this because of lower car ownership, less willingness/ability to pay for transport, higher residential proximity to the CBD, and/or higher health and fitness focus? Unfortunately I don’t have the datasets to answer those questions.

Cycling mode share peaked with men aged 35-44, with men much more likely to cycle than women.

For reference, here is a demographic breakdown of CBD workers – it peaks at 25-34, with women slightly younger on average:

And here are the same charts for Brisbane:


and Perth:

You can see:

  • cycling mode share peaked for men aged 35-44 in all cities
  • walking tended to peak for people aged 25-34
  • public transport mode share dipped for women aged 35-44 in all cities
  • In Perth, men aged over 35 had a higher private transport mode share than public transport, the only city where this occurred.

So, do executives (presumably many from wealthy inner city suburbs) shy away from using public transport?

Indeed they do. They represented 16% of Melbourne CBD workers, but 24% of car commuters (9538 car trips in total). Maybe because many of them get company cars/parking as parts of their packages? More on that coming up.

Lower paid clerical and administrative workers were most likely to use public transport (and probably least able to afford driving and parking costs).

Note that Machinery operators & drivers also had a higher private transport mode share – I expect many are professional drivers coming in their work car (there were only around 1000 in this occupational category).

Back to managers – the next chart shows they are also more likely to snub public transport in Sydney, Brisbane and Perth:

What about other trip purposes?

The following charts show data from the VISTA 2007 household travel survey, that includes all trip types and all of Melbourne.VISTA is a survey, not a census, so there is a margin or error involved, and unfortunately the sample sizes are not large (provided in charts as “n=”). The total VISTA 2007 dataset has 2955 surveyed trips into the Melbourne CBD (across all days of the week), of which 1973 were motorised.

First chart shows mode split for trip legs into and out of the CBD, by time of day on weekdays:

Weekday AM peak is 7-9am, and PM peak is 3-6pm, anything else is classed as off-peak. Unfortunately there are only 190 trips in/out of the CBD on weekends in the sample, which has too large a margin of error to be too meaningful (7%).

Active transport (walking and cycling) and public transport were clearly dominant. When looking only at motorised trips, Public transport took 74% of inbound AM peak and outbound PM peak trips, and 67%/62% of off-peak in/out bound trips.

Recall above that motorised journeys to work in 2006 were 67% by public transport, suggesting people travelling for reasons other than work in peak periods were slightly more likely to use public transport.

What about wealth? I’ve used average household income per occupant, to remove the impacts of household size, and grouped this by $500 amounts. Note: the sample sizes are quite small for larger income groups.

Sure enough, there appears to be a trend that people from higher income households were more likely to use private transport for travel into the CBD.

What about age?

While the sample sizes are relatively small, there certainly appears to have been a higher propensity to use private transport for travel to the CBD by middle-aged people.

There may be a trend back to public transport for older people, but the margin of error is around 10% for the last two age groups so this is not certain. However it would fit with Seniors being able to access cheaper public transport fares.

In terms of gender, 73% of females who used motorised transport came by public transport, compared to 67% of males – a similar difference to commuters.

Who’s paying for the private transport?

While for many people driving to the CBD for work everyday is something of a non-option, there are still tens of thousands who do. Is employer sponsored driving and parking costs influencing their mode choice?

VISTA lets us take a look at that also, although there is only a sample of 183 AM peak private transport trips (margin of error around 7%).

According to the data, around 29% of cars driven into the CBD in the AM peak had their running costs paid by a company, and 36% had parking paid for by employers. Remarkably, 34% reported no parking costs for off-street parking (these trips mostly for work purposes) – which doesn’t sound right for the CBD in the AM peak! I’m not aware of any publicly available free off-street parking spaces. Perhaps the respondents overlooked the fact that someone else was at least paying for the land on which they parked? If that is the case, then it would appear that less than a third of cars driven into the CBD in the AM peak were not employer subsidised in parking or running costs.

Employer subsidies appear to be an incentive to drive to the CBD. By contrast, only around 2% of general Melbourne AM peak car drivers had employee paid parking, and only around 13% had car running costs paid by an employer (VISTA 2007).

One of the most effective ways to reduce car mode share for journeys to work in the Melbourne CBD would appear to be reducing employer subsidies for parking and driving costs. Schemes such as parking cash out help employees see how much their parking and driving costs are being subsidised. If they have the option of receiving that money directly as salary they might make different choices (depending on tax treatment of course!).

That said, with current capacity issues on Melbourne’s trains and trams, trying to shift more CBD commuter trips from car to public transport in the short-term might not be a government priority just at the moment.

And lastly, for the record, 6 and 8 cylinder cars parked in the CBD did not appear to be over-represented. Cars of well-known luxury brands were over-represented (15% v 6% metro average).

I think that’s enough now! 🙂

Active transport is at 8%, which probably represents those who live within walking distance of the CBD. In order to take out the walking component, I’ve also taken a sample that excludes an “inner ring” around the CBD, as shown in the following map:

If you take out the inner ring, the mode split is 69% PT, 28% car, 1.9% cycling and 1.4% walking longer distances.

Illustrating the perverse Fringe Benefits Tax statutory formula for employer-provided cars

Sun 9 May, 2010

There was a chart on page 10 of the recently released Henry Tax Review Overview report that really caught my attention. It shows the kms travelled by employer-provided cars in Australia.

The massive spikes are a result of rate thresholds in the much-despised Fringe Benefits Tax (FBT) statutory formula for employer-provided cars, where travelling more kilometres often reduces your total costs.

In this method, the taxable value of the fringe benefit is essentially a percentage of the value of the car, and the percentage used depends on the total kms travelled each year:

  • Less than 15,000km – 26%
  • 15,000 – 24,999km – 20%
  • 25,000 – 40,000km – 11%
  • Over 40,000km  – 7%

These are not marginal rates, because the kms travelled is not directly used in the calculation of taxable value. Hence getting into the next bracket reduces your rate and your total tax bill, despite the marginal increase in direct running costs. This is utterly perverse as it provides an incentive for people to drive more kms, increasing congestion and greenhouse emissions!

There is also an operating costs method where the taxable value is essentially the proportion of running costs that were for private use (which is actually quite logical!). People are currently free to choose whichever method involves paying less tax. If there is a lot of personal use, then the statutory formula is often the way to go (travelling to and from work is generally classed as private travel).

So I’ve looked at the total cost of car ownership for a $35,000 car that has per-km costs of 17 cents per km, and standing costs of $167 per week (roughly the running costs of a Holden Commodore according to the RACV), using the fringe benefits tax rate of 46.5%. I’ve also assumed 70% private use when using the operating costs method.

The following chart shows the net running cost of such a vehicle, depending on the annual kms travelled, by both the statutory formula and operating cost methods:

You can see the step reductions in total costs when reaching each threshold in the statutory formula. In this example:

  • Driving 15,000 kms instead of 14,999 kms saves you $976
  • Driving 25,000 kms instead of 24,999 kms saves you $1465
  • Driving 40,000 kms instead of 39,999 kms saves you $488

These are big incentives to drive more kms!

You can also see that the operating costs method is not particularly attractive if most of the car’s use is private.

The FBT year ends March 31st each year. So is there evidence of big driving holidays in March each year as people try to get their kms over the next threshold?

The following chart shows the average monthly automotive gasoline sales for Australia for the period 2005-2009 (Source: Australian Petroleum Statistics). March stands out as the second highest month of the year for automotive fuel sales.

The FBT statutory formula might be responsible for the high average March figure, but I am a little reluctant to make that conclusion because I just don’t know enough about the other influences on monthly fuel sale volumes in Australia. Perhaps someone more knowledgeable than me could comment.

Peak oil

Sun 14 March, 2010

[updated September 2011]

While there is a plethora of content on the web about peak oil, I can’t seem to find many charts that track the current medium-term trends in global oil supply and price (a lot of the reports go into great detail about explaining short-term trends, or show long-term trends without the most recent data).

So here is an attempt at some medium term analysis of the data (if anyone can suggest other/better sources of such analysis I’d be pleased to hear about them).

Current medium-term trends

The first chart uses International Energy Agency (IEA) quarterly data on world oil supply until 2011Q2, projected demand for the last two quarters of 2011 (as at September 2011), together with the average WTI oil price in $US for each quarter.

The second chart simplistically looks at the relationship between price and demand/supply for each quarter 2000 to 2011Q1. I’ve also added forecast 2011 demand for remaining 2011 (assuming a $91 price). Note: there are small differences between supply and demand each quarter due to stockpiling etc.


Yes this is a very simplistic representation of world oil markets (it doesn’t adjust for inflation or global exchange rates), but I think it is still interesting. You can see that there are differences between quarterly demand and supply as the red and blue lines don’t always overlap. For example, according to the data, supply exceeded demand in the quarters where prices peaked, but then demand exceeded supply for the two previous quarters.


I’m certainly not a qualified oil market analyst, and this isn’t rigorous analysis, but a few things do stick out:

  • World oil supply grew strongly between 2002 and 2005 and then was stuck around 84-87 million barrels per day (mb/d) until 2010. Supply grew to a new high of 88.5 mb/d in 2011 Q1, but then dropped in 2011 Q2. The September 2011 Oil Market Report suggests supply is now 89.1 mb/d.
  • During 2007 and the first half of 2008, oil prices grew strongly while supply was relatively unchanged, suggesting a demand-supply crunch.
  • After mid 2008, both prices and supply collapsed, around the same time as the global financial crisis hit.
  • During 2009, prices almost doubled, while supply only grew a small amount (although a devaluing of the US dollar explains some of this).
  • During the first three-quarters of 2010 supply grew but prices stabilised. But since 2010Q4, prices increased, even though supply reduced in 2011Q2. At the time of writing WTI oil was around US$91, and supply had increased again.
  • Looking at the second chart, it appears we have broken through the 88 mb/d threshold without the same prices seen in 2008.

The future?

It’s very hard to speculate, and I’m not particularly qualified. At present it appears oil prices have fallen with global economic conditions, supply has increased to a new high, and demand is also higher than ever.

Will it be possible to continue to ramp up supply to meet the further increases in demand?

The peak oil theory essentially suggests that new oil sources are harder to find and extract, that many existing fields are in unavoidable production decline, and so it is getting harder just to maintain current supply levels with new fields, let alone grow production overall. And harder still to grow production without increasing prices.

We’ve seen some supporting evidence in 2008, but conflicting evidence in 2010. That might have something to do with exchange rates. Oil prices fell in 2010Q3, but the US dollar was strong in mid 2010. Probably need more thorough analysis than what I have done. But the data is what it is. And all will be revealed in time.

But what will transport investors assume? Increasing global supply without significant price growth over the next 30 years does not look like a particularly safe assumption! But I fear it is the default assumption.

What’s a good PT fare system? And what does that mean for smartcards?

Sun 14 February, 2010

[This post has been fully revised in December 2011, including removal of Brisbane periodical paper tickets, the introduction of smartcard ticketing to Canberra, and the addition of Newcastle, Auckland, Wellington and Christchurch]

With smartcard public transport ticketing coming to more cities, and the recent adoption of the MyZone fare system in Sydney, I thought it might be worthwhile to compare the current fare systems, assess them against “desirable” design criteria, and then look at the complexity of implementing each fare system in a smartcard ticketing solution.

The analysis offers some potential explanations as to why some cities are probably doing better than others with smartcards, and I’ve made some suggestions how fare systems might be tweaked in some cities.

This post has come out longer than I intended, so get yourself a cuppa and get comfortable.

Fare products in Australasian Cities

Firstly, what types of fare products are available in each city? This table is a rough summary of the main ticket types available, depending on the ticketing systems in each major city:

City System single return all day 10 trip week month/
28 day
quarter year
Melbourne Metcard Yes Yes Yes Yes Yes
Myki Yes Yes** Yes Yes Yes
Sydney MyTrain Yes Yes* (Yes) Yes (Yes) Yes Yes
MyBus Yes Yes
MyFerry Yes Yes
MyMulti (Yes) Yes Yes Yes
Perth Paper Yes Yes*
SmartRider Yes
Adelaide paper Yes Yes Yes
Brisbane paper Yes
go card Yes
Canberra cash Yes Yes
MyWay Yes Yes** Yes**
Hobart Paper Yes Yes*
Greencard Yes Yes**
Newcastle Bus Yes Yes Yes Yes Yes Yes
Auckland Bus – paper Yes Yes# Yes# Yes# Yes# Yes#
Bus – HOP Yes
Train Yes Yes* Yes
Wellington Bus – paper Yes Yes#* Yes#
Bus – Snapper Yes Yes
Train Yes Yes* Yes Yes
Train+bus Yes#* Yes#
Ferry Yes Yes# Yes# Yes#
Christchurch paper Yes
metrocard Yes Yes** Yes**

Yes* = the product is not useable in peak periods and is only of value for certain numbers of trips/zones.
Yes** = a cap applies
(Yes) = available but no discount ahead of smaller tickets for peak travel.
Yes# = only available for some operators/services

A few things to note:

  • All cities have bulk ticket products and/or smart card systems that in effect provide for bulk purchase discounts.
  • At the time of writing Melbourne was in transition from metcard to myki, with both metcard and myki fare systems active.
  • I’ve only really looked at products available to regular full fare passengers in the above (eg it does not include things like all day Seniors tickets).
  • Translink in Brisbane has removed most paper ticket options other than single fares.
  • I’ve not included Auckland ferries as different operators have different tickets, although most have 10-trip and monthly tickets (some have week and 40 trip tickets)

For the following analysis I have used fares current as at November 2011 for each system, and compared the costs of full fare travel of about 9-10 km. There may be some variations for different distances, so this analysis isn’t going to be perfect.

Why bulk purchase discounts?

Encouraging bulk purchasing has a couple of major benefits for transport authorities:

  • Fewer ticketing machines and staffed ticketing windows are needed as fewer tickets are purchased for the same amount of travel.
  • In the case of disposable paper tickets, fewer physical tickets are required.
  • On buses, minimising ticket purchases from drivers can reduce a sometimes significant cause of delays, particularly as patronage increases.

Financial incentives for bulk purchase are often provided, but there is always a convenience incentive to buy fewer tickets (up to the limits of people’s available cash flow).

Bulk purchase discounts for the weekday peak commuter

I have taken the case of a straight forward weekday commuter, who makes a trip in the morning and afternoon peaks on weekdays only. Thus they don’t qualify for any off-peak discounts. Not all cities allow for free transfers on single and return tickets (see table further below), and I’ve assumed a transfer-less journey for the purposes of this analysis.

This first charts show the maximum discount possible given the purchasing frequency for people with this travel pattern. Because Sydney is so complicated I’ve put it on a separate chart.

A note on Sydney’s MyMulti: There is no “single” MyMulti ticket in Sydney. As a substitute I’ve used a 5km train trip ($3.20) followed by a 5 section bus trip ($3.30) = $6.50, which is relatively expensive compared to single mode 10km journeys. This is in line with the 10km base I am using as bus sections in Sydney seem to be roughly 1km apart, but you’ll see the implied bulk discounts are very high.  Also, I have used MyMulti1 fares where they are better value for bus and ferry users.

The lines show whether more discounts are available when purchasing less frequently. The last point where the line increments is the maximum purchasing frequency that gives you the most discount.


  • Most smartcard systems have a discount price for single trips, but most don’t appear on the chart above because the price of a single trips is less than the minimum top-up amount of $5 or $10. Melbourne is the exception where the minimum top up amount is $1 – less than the price of almost any fare.
  • Discounts kick in at the daily level for Adelaide and Canberra mostly because they have a daily or return ticket that is less than the price of two singles.
  • Melbourne, Sydney and Newcastle are the only cities where discounts continue until a yearly purchasing frequency.
  • Most cities have a 20-35% discount at a weekly purchasing interval. They outliers are:
    • Canberra which has a very strong incentive for Smartcard based travel over cash fares
    • Wellington trains that have a high value monthly ticket
    • Most Auckland buses have a monthly ticket, however the zones available vary between operators. In the above chart, the “all zones” monthly pass for NZ Bus operators was used – $200, which is not better value for simple weekday commuters travelling 3 stages. Other operators have monthly tickets priced as low as $97. The complexity of the ticket offerings makes it difficult to accurate represent  available discounts to Auckland bus travellers.

Oddities (skip these if you don’t care about the detail)

  • Canberra :
    • There is a higher discount for weekly travel because the additional 5% discount for auto-load means the price of two trips (on one day) is less than the minimum top up ($5).
    • There is a monthly discount because travel is free after 36 trips.
    • I’m not sure whether the $5 minimum top-up applies if topping up with BPay.
  • Sydney:
    • For 6+ section bus travel, a quarterly MyMulti1 is cheaper than using MyBus TravelTen tickets
    • For <9km ferry travel, a weekly or longer MyMulti1 is cheaper than using MyFerry TravelTen tickets.
    • Yearly train tickets offer no discount over quarterly train tickets
  • Newcastle:
    • The quarterly ticket (Orange TravelPass) is more expensive than travelling with weekly tickets if you are just making two trips per weekday.
  • Wellington bus
    • Wellington does have three monthly bus passes, with cheaper passes limited to fewer operators. Only the Go Wellington 30 day pass is of value to 5 day a week commuters, and this ticket is limited to one operator (GO Wellington, with a 36% saving over single cash fares). For the charts, the all-operator Platinum Pass ($210) has been used.

I’ll look at the discounts for lower purchase frequencies later in this post.

Bulk purchase discounts for the everyday traveller

Many systems have weekly or monthly caps, or offer discounts after a certain number of trips in a week or month. This provides an incentive for a regular weekday travellers to also travel on weekends. The discounts over single tickets can be quite high!

As you might expect, the discounts are greatest for those cities with periodical tickets. Those systems without periodical tickets (Perth, Hobart, Wellington bus, Adelaide and Brisbane) have the least discount for everyday travellers.

Bulk purchase incentives for the irregular traveller

All cities (except Sydney trains and multi-modal) have either a 10 trip ticket, or discounts over cash prices when using stored value on a smartcard:

Canberra and Brisbane offer strong incentives for people to use smartcards, while Adelaide provides a high discount for ten-trip travel (but has no higher value tickets such as periodicals). Bulk purchase discounts for irregular travel are lowest in Auckland (around 10%).

Why is Hobart Greencard and Melbourne myki money showing a 0% discount? For myki the minimum top up is $1, it is possible to pay for a single trip and get the same 21% discount over a metcard 2-hour (single) ticket that you get when you load value for 10 trips worth. There is no financial incentive for irregular travellers to top up less often than every trip. In Hobart, smartcard fares are the same as cash fares, but there is a 25% bonus value when topping up an amount of $20 or more (which equates to a 20% discount).

Sydney’s MyTrain and MyMulti do not offer 10 trip tickets. It would seem there might be much advantage in introducing these products (or a stored value smartcard equivalent) for irregular travellers to be rewarded and reduce congestion at ticket machines and windows.

Incentives for off-peak travel

From a policy perspective, there are benefits in getting peak period PT commuters to also use PT in off-peak periods (such as reducing emissions and off-peak traffic congestion). Usually there is ample spare capacity in off-peak times, so the costs of carrying extra people at these times are minimal and the resulting benefit/cost ratio is very high.

That said, Melbourne has to provide extra lunchtime peak trams in the CBD to meet demand, but arguably this adds to the liveability and productivity of the Melbourne CBD so is worthwhile anyway.

In cities with daily tickets, daily/weekly/monthly caps or weekly, monthly, quarterly and/or yearly periodicals, additional off-peak trips are have a zero marginal cost. That’s a 100% discount!

In cities with weekly tickets (and Brisbane through the frequent user scheme), regular weekday commuters often pay a lot less – or nothing at all – to also travel on weekends.

The table further below will show these discounts.

So what might an ideal fare system look like?

The following list builds on the above, and adds a few other criteria I think are worthwhile for a PT fare system:

  1. There are several points where the bulk purchase discount increases. Ideally a system would at least have discounts applying at the weekly and monthly purchasing frequencies. I’d argue the monthly discount is particularly important as it reduces the number of purchase transactions by a factor of 4 compared to weekly tickets (from 52 to 12 per year). Quarterly and yearly tickets are probably still worthwhile, but only for people with enough cash flow available to make a larger up-front purchase.
  2. For Smartcard systems, the minimum top-up amount is at least $10 (usually covering three trips), to reduce transaction costs.
  3. For Smartcard systems, there is an incentive to make larger top-ups, to reduce the number top-up transactions (and associated overhead costs).
  4. There is an incentive for people to set up automatic top-up of smartcards (via a linked bank account). This avoids any physical interaction with the ticketing system, reducing congestion, staffing and maintenance costs at ticket machines and windows. It also reduces the risk of inadvertent fare evasion when a customer forgets to top-up. Ad hoc online top ups also reduce load on ticketing machines and windows, but can require complex distribution of the top up transaction to every device on the network (such that the smartcard will be topped up when it next touches any piece of ticketing equipment). This can be problematic and delays in top-ups coming through are not unknown. The Hopper card in Auckland and Wellington only allows top ups online if the user has a device that can interact with the smartcard, or if the user has a smartcard that has a USB attachment built-in.
  5. There are strong incentives for regular weekday commuters to also use PT in off-peak periods/weekends. Ideally no marginal cost of additional off-peak trips.
  6. Passengers who have to transfer between services do not have to pay more to travel the same distance just because there is no single service connecting their origin and destination. Already, the need to transfer introduces inconvenience, usually a travel time penalty, a wait time penalty and missed connection risk into the journey, so why add a cost penalty on top? A desire for passengers to avoid transfers can put pressure on bus networks to connect many origins and destinations directly, at lower average frequencies. Also, incentives for train users to get to the station by bus – at no extra cost to the train fare – reduces the pressure on station car parks. Many train stations are in “activity centres” – where inactive land uses such as all day commuter parking are not desirable. Sydney, Auckland, Hobart and Wellington currently have financial penalties for most transfers (unless using periodicals).
  7. Fares are simple to understand in terms of which ticket type is best value depending on your purchasing frequency. A customer should not have to do complex calculations to determine the best fare product (a problem when offering both multi-modal and mode-specific fare options).
  8. Reduce production of physical materials to cut environmental impacts through waste and litter. Long life smartcards are obviously one solution to this issue, but so are periodical paper tickets.

Note: I’m not going to enter the debate about zonal v distance based fares in this post.

How do Australasian cities’ fare systems measure up?

The following table summarises how Australasian cities’ fare systems measure up to the above criteria, with colour coding giving a rough compliance rating (you will probably need to click on this and then zoom in to read it – sorry about that – nice formatted tables are difficult with wordpress).

Note: I have used the best value fare for each purchasing frequency for a roughly 10km trip to the CBD (for Sydney buses and ferries, a MyMulti ticket is sometimes better value than a single mode ticket). That took a lot of deduction which I haven’t documented here.

How do the fare systems compare?

Compared to the above criteria:

  • No fare system is perfect.
  • Melbourne (myki and Metcard) meets most criteria, except for financial incentives for automatic top-up, and higher minimum top-up amount.
  • Sydney buses and ferries and Wellington buses meet the fewest criteria.
  • Sydney MyMulti meets most criteria, but lacks a strong incentive to use a daily MyMulti ticket, and has the overlaying complexity where customers need to do complex calculations to establish whether or not MyMulti is good value.
  • Brisbane go card does not provide significant bulk purchase discount incentives, and additional off peak trips are not free (although they are around half price or less).
  • Adelaide lacks any periodical tickets, and hence any weekend travel incentives (indeed discounts applicable to inter-peak weekday travel do not apply on weekends!). This probably reflects the older ticketing technology in that city.
  • Perth fails to provide off-peak travel incentives for regular commuters. But it is the only city with an automatic top-up discount, which offsets the impact of the smaller bulk purchase incentive.

Reducing ticket waste

Since the first edition of this post, more cities now have either a SmartCard ticketing system, or have strong discounts for quarterly or yearly tickets.

This means to get the best discount, you’ll only buy one physical ticket (or less) per year. The notable exceptions are:

  • Adelaide – you need to buy a ticket every 10 trips (until their smartcard system comes online)
  • Auckland and Wellington trains – the best value option may be to buy monthly tickets.
  • Auckland buses – depending on your travel patterns and operator, you might be best off buying monthly paper tickets.

Is a good fare system easy to implement with smartcards?

There is obviously a link between the complexity of the fare system and the cost of a smartcard ticketing system. The Sydney T-Card project was abandoned (many blamed the complexity of the fare system), and Victoria’s myki system is costing considerably more than other cities (I have tried to get comparable system cost data but this isn’t easy).

The following table attempts to compare the network and fare complexity between cities (or states for systems that extend beyond the main metro area). Green denotes simple, and red denotes complex.

Note that many of the positive attributes of fare systems introduce complexity into smartcard systems. There’s clearly a trade-off involved!


  • Victoria’s myki system has the most complexity on all but two dimensions in the table. Perhaps this partly explains the implementation issues?
  • Perth, Hobart, Brisbane and Christchurch smartcard systems have much less complexity, but meet fewer of the desirable fare system features above (particularly discounts for purchasing less frequently than weekly).
  • Adelaide looks set to follow these cities with a simple stored value smartcard system.
  • Canberra has a relatively simple system, although daily and monthly caps, and large top-up incentives have been implemented. The monthly cap is a simple maximum paid trips per calendar month, which probably simplifies implementation.
  • Auckland and Wellington have very similar systems, but neither has been fully extended onto rail which involves monthly tickets. Wellington has implemented monthly bus passes on Snapper, but none of these are zone-based. Auckland’s HOP system uses the same technology and Wellington’s Snapper.
  • Sydney has the periodical challenge made more complex by having both zonal mode-specific and multi-modal tickets.
  • Adelaide probably has the easiest fare system to translate onto smartcards.

So there seems to be a clear choice in smartcard ticketing:

  • Include the complexity of zonal periodical fare products to create incentives for larger bulk purchasing and off-peak PT use,
  • Have a simpler trip-based system without periodicals, but maybe daily/weekly/monthly caps and/or incentives for larger top ups.

Victoria and Sydney have adopted the first option, whilst most other cities have gone the second option (Wellington trains may prove an issue when people use Snapper and over-run their monthly zones).

The use of daily, weekly or monthly caps combined with incentives for larger top-ups, can offset the downside of the second approach. Ideally this would involve caps that reward people for making more than two trips in a day, and more than 10 trips in a week. Canberra’s MyWay has managed to introduce relatively simple caps (although this is assisted by having only one fare zone).

Ideas for improving fare systems in each city

How might you try to improve the fare systems for the current smartcard systems to meet the criteria above? I’m not pretending I know how complex or expensive these would be, but here are some ideas:

  • Perth SmartRider
    • introduce a daily cap to encourage off-peak use by peak commuters (probably different for each number of zones).
    • potentially introduce weekend discounts or a weekly cap as a reward for travelling Monday-Friday.
  • Victoria myki
    • increase the minimum top up amount to $5 or $10.
    • Consider introducing an incentive for larger and/or automatic top ups.
  • Tasmania Greencard
    • introduce an incentive for automatic top ups.
  • Brisbane gocard:
    • introduce an incentive for larger and/or automatic top ups.
    • Consider introducing a daily and/or monthly cap.
  • Adelaide:
    • Consider introducing daily, and weekly or monthly caps based on number of trips (similar to Canberra MyWay, given the single fare zone).
    • Include an incentive for larger and/or automatic top ups.
  • Auckland and Sydney:
    • Adopt a fare system that removes financial penalties for passengers who have to transfer (ie time-based rather than single-boarding tickets)
    • Introduce consistent fares and fare bands for all modes and operators
    • Consider introducing daily and weekly or monthly caps (unless periodicals are retained)
    • (note that monthly MyMulti tickets have been introduced since the first edition of this post)
  • Wellington:
    • Adopt a fare system that removes financial penalties for passengers who have to transfer (ie time-based rather than single-boarding tickets)
    • introduce an incentive for larger and/or automatic top ups.
    • Reduce complexity by eliminating special fare products where possible.
    • Consider introducing daily and weekly or monthly caps

I must stress here that suggestions in this blog are my personal ideas only. I’m sure there are good reasons why many of these things haven’t happened. Apart from anything, there would need to be an assessment as to whether the benefits would offset the implementation cost and any loss in fare revenue.


Metro Tasmania fares

Translink South East Queensland (inc Brisbane) fares

Transperth fares

Melbourne metcard fares

Melbourne myki money fares

Melbourne myki pass fares

Sydney MyZone fares

Newcastle fares

Adelaide Metro fares

Canberra ACTION fares

Canberra MyWay fares

Auckland MAXX fares

Auckland HOP fares

Wellington Metlink fares

Christchurch fares