What sorts of people use public transport? (part one)

Fri 15 June, 2012

On this blog I’ve previously had a good look at public transport mode share by where people live and where they work, and I did some profiling for Melbourne CBD commuters by age, gender, income, profession.

This post will focus on what personal circumstances are associated with higher and lower public transport use, and possibly why (although of course correlation often doesn’t mean causation). There’s a lot that is as you might expect, but also a few hunches confirmed and possibly some surprises (particularly in part two).

This post (part one) looks at geography, motor vehicle ownership, and driver’s license ownership. The second part will look at other personal circumstances.

About the data

Most of the analysis in this post comes from the Victorian Integrated Survey of Transport and Activity (VISTA), using the 2007-08 and 2009-10 datasets combined. The survey covers Melbourne Statistical Division (MSD), Geelong, Bendigo, Ballarat, Shepparton and the Latrobe Valley (that is, the capital and major regional cities in Victoria, Australia). The combined dataset includes some 85,824 people in 33,526 households who recorded their travel for one calendar day each.

When I measure public transport use, I am measuring whether a person used any public transport on their nominated travel survey day (the survey covered every day of the year). See here for a map showing the geographic breakdown of Melbourne into city, inner, middle and outer.

I must say thanks to the Victorian Department of Transport for making this data available for analysis at no cost.

Public transport use by geography

Firstly as a reference, here is what public transport use looks like spatially across Melbourne and Geelong. Sample sizes with Statistical Local Areas (SLAs) range from around 200 to 1300 people in Melbourne, so the margin of error will be up to around +/-7% in some areas (including a few of the outer suburban areas).

Note: the Melbourne CBD and Southbank/Docklands SLAs unfortunately have very small sample sizes (9 and 31 respectively) so should be ignored (in the map below the 27 belongs to the CBD, the 28 belongs to Southbank/Docklands and the 21 above is “Melbourne (C) – Remainder”).

(click to enlarge)

It is little surprise that public transport use declines in areas further from the city centre as public transport supply decreases.

The following chart shows public transport use also had a lot to do with whether the person travels to/from the City of Melbourne on their survey day:

The green line indicates the proportion of all persons who travelled to/from the City of Melbourne on their survey day, which decreases with distance from the city.

And here is a scatter plot showing public transport use and the proportion of people travelling to/from the City of Melbourne (excluding those who live in the City of Melbourne) at the SLA level:

That’s a strong relationship. And the biggest outlier at {36% travel to/from Melbourne, 16% public transport use} is Port Phillip – West, which is just on the border of the City of Melbourne where walking would be a significant access mode.

So there is little surprise that public transport use had a lot to do with distance from the CBD (most probably as a proxy for public transport supply), and whether a person visited the City of Melbourne (where public transport is a highly competitive transport option).

Has it got anything to do with how close you live to a train station? I’ll just look at Melbourne and exclude the inner city area where trains are probably less important because of the plethora of trams and ease of active transport.

Proximity to train stations has an impact, but perhaps not by as much as you might expect.

Overall people living closer to train stations were slightly more likely to travel to/from the City of Melbourne, and if they did, they were a little more likely to use public transport.

But only those within 1km of a station were more likely to use public transport if they didn’t travel to/from the City of Melbourne (7% v 5%). Because most people living near to a train station didn’t travel to the City of Melbourne, their average rate of public transport use wasn’t much higher than those living further away.

This result is consistent with the 2006 journey to work patterns.

But what else might explain public transport use?

Motor vehicle ownership

If you don’t own a motor vehicle, you’re going to need to some help getting around, particularly for longer distance travel.

I’ve created a three level measure of motor vehicle ownership:

  • No MVs: No household motor vehicles at all (you’ll be reliant on lifts, taxis, public transport, or public car share schemes for motorised transport)
  • Limited MVs: A household where there are more licensed drivers than motor vehicles (some sharing of vehicles or use of other modes such as public transport will probably required from time to time)
  • MV saturated: A household where there are at least as many motor vehicles as licensed drivers (sharing vehicles between drivers is unlikely to be required)

The VISTA data shows that 25% of households had limited or no motor vehicle ownership, and as you might expect it varies by geography, with higher rates of motor vehicle ownership in the outer suburbs.

In fact here is a map showing the percentage of people living in households with saturated car ownership around Melbourne and Geelong according to VISTA (again, margin of error is up to around 7%). Click to enlarge.

You can see very high rates of saturation in the fringe areas of Melbourne, and much lower rates in the inner city.

A more detailed view of car ownership is possible with census data. The following map shows the ratio of household motor vehicles to 100 people aged 20-74 in each census collector district in 2006 (note: I have had to assume “4+” cars averages to 4.2, and no response implies zero cars). The red areas have saturated car ownership as a district (probably closely correlated with households with saturated car ownership).

(click to enlarge)

The census figures show a slightly different distribution but should be more accurate (being a census not a survey). Suburban areas with lower car ownership were generally those that are less well-off (and the large green areas on the western fringe of Melbourne are actually mostly prisoners, who tend not to own cars).

It will come as no surprise that there was a pretty strong relationship between motor vehicle ownership and public transport use:

And here is a scatter plot of saturated car ownership and PT use by SLA (removing SLAs with less than 200 people surveyed):

That’s a fairly strong relationship (the r-squared is higher still (0.87) at the LGA level).

Trends in car ownership are examined in another post.

Driver’s license ownership

It’s not much good having a motor vehicle to yourself if you are not licensed to drive it. In the VISTA sample, the driver’s license ownership rate peaks for people aged 40-54, and drops off more considerably after 85 years of age. Interestingly, 3.5% of people in the sample had their learner permit.

So are there heaps of older people out there without a driver’s license?

Not especially. It almost looks as if many people die in possession of a driver’s license (hard to be sure though).

(note: the chart averages the population in each category over the two VISTA surveys)

In part two we will see the rates of public transport use by age.

Here’s a map showing the percentage of surveyed people aged 20-89 who owned a probationary or full license (click to enlarge).

Similar to motor vehicle ownership, driver’s license ownership was highest in the outer areas of Melbourne, but still quite high in the inner city (please ignore the CBD and Southbank/Docklands figures of 100% and 96% as the sample sizes are too small).

And it will be no surprise that people with a full driver’s license were least likely to use public transport:

Maybe they use public transport less because they have a driver’s license, or maybe they are forced to have a driver’s license because of low public transport supply. I would guess a bit of both.

What you might not have expected is for people with a learner permit to be the most likely to have used public transport, even more than people with no licence at all. They are mostly younger people and you will see their rates of public transport in part two of this series.

Here’s a scatter plot of driver’s license ownership and public transport use by SLA:

The relationship is much weaker than for saturated car ownership.

In fact, 55% of people who used public transport on their survey date had a full or probationary driver’s license. As driver’s license ownership is more saturated than motor vehicle ownership, it appears to be a weaker driver of public transport use.

Click here for some interesting research about why young people are driving less.

How do motor vehicle ownership and driver’s license ownership interplay?

In the following chart I have used “independent license” as shorthand for probationary or full license.

This again suggests that household motor vehicle ownership had more bearing on public transport use than driver’s license ownership (for driving aged adults at least).  In fact, those with a driver’s license but no household vehicles were MORE likely to use public transport than those without a license (I’m not entirely sure why, when I disaggregate the sample sizes get small). But for adults in households with motor vehicles, people without an independent driver’s license were more likely to use public transport than those with licenses.

Home location, City of Melbourne travel and motor vehicle ownership

These three factors seem to be the strongest indicators of public transport use. So what do they look like together?

From this chart we can see:

  • For people travelling to/from the City of Melbourne:
    • Public transport use was generally higher for people living further from the city.
    • Public transport use was lower for people from households with saturated motor vehicle ownership (compared to those with limited motor vehicle ownership).
  • For people not travelling to/from the City of Melbourne, public transport use seems largely related to distance from the city centre (a rough proxy for PT supply) and the level of motor vehicle ownership, with the exception of those in the inner city where non-motorised transport modes are likely to be more significant.

Is driver’s license ownership still a driver? Unfortunately I can only sensibly disaggregate further for people who didn’t travel to/from the City of Melbourne. The following chart looks at motor vehicle and license ownership groupings with a sample size of 200 or more for different geographies.

This suggests that license ownership was quite a strong driver of public transport use. Those without a license in otherwise saturated households were much more likely to use public transport (purple line) and the red dot indicates people with no license in a limited motor vehicle ownership household were quite strong users of public transport.

Okay, so those findings probably won’t shatter your understanding of the world, but I always find it interesting to test whether your hunches are true.

In part two of this series, I’ll look at patterns across age, gender, income, employment status and household type. There are perhaps a few more surprises in those results.

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What’s driving Melbourne public transport patronage?

Fri 11 May, 2012

[Updated June 2012 to include ratios over time, inner city parking, and other updated data. First posted January 2010]

In this post, I test out a number of possible explanations for the trend in Melbourne’s public transport (PT) patronage growth over recent years to try to find out what might be driving growth. Is it population growth, CBD employment, fuel prices, international students, or the widening of CityLink? You’ll have to read on.

The first chart shows estimated financial year public transport patronage in Melbourne. Note: The method of patronage estimation has changed over the years for all modes. I have assumed comparable measurement for trains and trams and applied my own informed adjustments to bus patronage history (although I am less confident about the early 1990s – officially patronage stayed much the same despite significant service cuts).

Patronage was bumpy in the 1990s, followed by modest growth for about 10 years and then a distinct uptick in growth around 2004/05.

I will attempt to find an explanation for this pattern in this analysis (particularly more recent years). Short of a fully comprehensive analysis, I will compare trends in possible drivers with the trend in public transport patronage.

Note due to the nature of available data sources, the years covered in chart will vary – you can spot each year by checking the year range in the chart titles.

Population growth

If this was a dominant factor then you’d expect to see a straight line on this chart. It does show that as population growth has increased, so has public transport patronage growth, but the overall relationship isn’t very linear. Here’s the ratio of patronage to population for all of Melbourne:

We know that public transport use is higher closer to the inner city of Melbourne. So is public transport better correlated with inner city population? The following charts compare PT patronage with “inner” population (LGAs of Melbourne, Port Phillip, Stonnington, Yarra, Hobsons Bay, Maribyrnong, Moonee Valley, Moreland, Darebin, Banyule, Boroondara, and Glen Eira).

The correlation appears to be slightly stronger, but still not very strong.

Employment

People often use PT to get to work. The next chart compares total employed people in Melbourne to public transport patronage (employment figures average monthly total employed people for each financial year, from ABS Labour Force surveys).

Again, the relationship isn’t very linear – despite a small growth in employed persons in 2008-09, public transport patronage still increased significantly. But then in 2009-10, employed persons grew but patronage didn’t. Likewise PT patronage increased more between 2000/01 and 2001/02, despite little growth in total employment, whereas in the previous year employment grew strongly, but PT patronage didn’t.

This chart also shows kinks in the trend around 2005 and in 2008-09 – so employment doesn’t seem to explain the kink. Note also that journeys to work only make up around 40% of public transport trips in Melbourne (according to VISTA data). And public transport has a very low mode share of journeys to work outside the city centre.

Here is the relationship shown as a ratio over time:

ABS publish figures monthly, and here’s the picture for total persons employed in Melbourne. There was virtually no growth between late 2010 and May 2012 (at least). There was also a flat patch between the start of 2008 and the middle of 2009 (2008-09 shows substantial patronage growth on public transport).

City population (including visitors)

Another hypothesis suggests that if PT is heavily focussed on the inner city (where it has the highest destination mode share), then if more people need to travel to the inner city, this would probably increase PT patronage. This sounds very plausible, especially for trains and trams. The City of Melbourne has estimated weekday daytime population for 2004 to 2010 calendar years. So I am mixing calendar year visitor data with financial year PT patronage – which is not ideal. Anyway, here is what that relationship looks like:

The year 2005/06 includes the 2006 Commonwealth Games that were held in March 2006 and boosted city visitors considerably. If you take out this anomaly, the other four data points look like they form a very linear pattern (as drawn), suggesting it is quite probably a strong driver. There was weak growth in both public transport patronage and city population in 2009-10, suggesting a strong relationship.

The next chart shows the same relationship as a ratio over time. The 2006 anomaly is much less noticeable (note not a huge variation in weekday daytime population the chart above). This suggests that City of Melbourne weekday daytime population is not directly proportional to public transport patronage (in other words: the y-intercept is not zero).

A longer time series of CBD data is available for  employment, thanks to the City of Melbourne’s Census of Land Use and Employment. As it hasn’t been an annual survey (red dots are census results), I have made linear interpolations between the years for CBD employment numbers.

Between 1997/98 and 2007/08, the trend was remarkably linear suggesting a strong relationship. When CBD employment grew very weakly between 2002 and 2004, so did PT patronage. Looking at census data for 2001 and 2006, we know that PT mode share to the Melbourne CBD for journeys to work (well, technically the inner Melbourne SLA which is much the same) grew only slightly from 59.1% to 60.8%. So it looks fairly safe to assume that the growth in people using PT to get to jobs in the CBD grew at much the same pace as CBD employment itself.

However between 2007/08 and 2009/10 the trend seems very different. Public transport patronage grew strongly even though the number of employees in the Melbourne CBD did not show much growth at all.

Here’s the same relationship expressed as a ratio over time. The ratio is remarkably flat over time.

Employment has grown around the Melbourne CBD in neighbouring Docklands, Southbank and there are also a number of office buildings in East Melbourne. In fact between 2008 and 2010 there were around 3,300 new jobs in the CBD, and 11,400 new jobs in Docklands.

These areas are also well serviced by public transport. Unfortunately data for these surrounding precincts only goes back to 2002. Here’s a chart comparing PT patronage to total employment in the CBD, Southbank, Docklands and East Melbourne for 2002 to 2010:

Suddenly the trend looks a lot more linear, with a deviation only for the interpolated result in 2008-09 (which might be a product of the GFC in that timeframe). CBD employment alone is no longer a strong driver of public transport patronage. Although bear in mind that public transport mode share in these CBD fringe areas was much lower than the CBD in 2006 (see previous post).

Here’s the same relationship as a ratio over time, which is a little flatter:

While the CLUE data series only runs until 2010 at present, a more timely and regular dataset that might be related to CBD employment is occupied office floor space, calculated from the Property Council of Australia’s Office Market Reports. While I do not have access to the reports themselves, much of the data is available on the internet in various forms, and I have used that data to reconstruct the data series (there is chance of errors creeping in, particularly for earlier years).

Here is the trend in occupied Melbourne CBD office space:

Slow growth until about 2005, then very strong growth. Does that trend sound familiar?

This charts shows very strong correlation (r-squared = 0.99). Although there are still a few small kinks such as 2009-10.

Here it is as a ratio over time, which is not entirely flat:

But the overall strong relationship this confirms the high likelihood of CBD employment being a very significant driver of public transport patronage. Ideally Southbank, Docklands and East Melbourne should be added to the mix, but the data is not readily available.

Inner city parking

A commenter on this blog suggested I look at parking in the inner city. The following chart looks at public transport patronage and total commercial parking spaces the CBD, Southbank, Docklands and East Melbourne.

Between 2004 and 2006, commercial parking spaces grew strongly, while public transport patronage did not. Then public transport patronage grew strongly and there was actually a decline in the number of commercial parking spaces.

I would expect the price of parking to be a stronger driver of public transport use than the capacity available. Unfortunately I do not have a long enough time series of parking prices to test this hypothesis. See also my post on the Melbourne CBD.

Fuel prices

I have taken the monthly average unleaded fuel prices for Melbourne, adjusted for CPI, and then averaged the months for each financial year, to produce the following chart:

Fuel prices are highly volatile, even on an annual basis. Again, even though fuel prices dropped in 2008-09, PT patronage still increased. There seems to be a lot more at work than fuel prices. That said, since 2004-05, real fuel prices jumped from around 115 cents to over 130 cents and have remained higher since. So fuel might be an explanation for the kick up in PT patronage since 2005, perhaps more as the breaking of a psychological price barrier. Or perhaps people’s responses to fuel prices have longer lag times that wash out short-term fluctuations – as people make major decisions – such as the decision to purchase a new car or not. More on that later.

International students

Another hypothesis is that the recent boom in international student numbers drove public transport patronage, as many international students come from countries where public transport is the “default” mode. And while their finances might stretch to studying in Australia, it might not stretch to owning a car (certainly in the car ownership maps we see low car ownership around many universities).

Unfortunately I’ve only found complete data for financial year 2002/03 onwards, and only at the state level (more detailed data is not freely available).

The boom in international students looks like it really took off in 2007, but fell away sharply in 2009-10 and has been lower since. In 2009-10 patronage grew more slowly, perhaps reflecting the drop in international student numbers. But 2010-11 patronage growth was strong again, despite little growth in international student numbers.

The international student numbers are very small in comparison to the total patronage. However if half of those students averaged 10 trips per week for say 40 weeks a year (purely a guess), that’s 38 million trips. I’ve not got data on what their PT use is actually like (I suspect many live close to their school or university and actually walk). And their boom doesn’t coincide with the boom in public transport patronage that started around 2005. So they might be having an impact – hard to conclude much.

Road congestion

Until 2006-07 there was a fairly linear correlation, but then speeds only slowed slight while public transport patronage increased. In 2009-10 speeds increased and public transport patronage grew slowly. Perhaps congestion wasn’t a driver for patronage growth in 2009-10?

Another point to note is the scale on the X axis – the average speed hasn’t changed by very much. Although the variations in AM peak speeds for particular road segments are likely to have changed more significantly, I somewhat doubt whether the average driver would notice the difference between 35.8 km/h and 34.8 km/h (the change between 2005/06 and 2007/08).

The opening of CityLink in 2001 may have led to a slight increase in AM peak speeds, but this seems to have been quickly eroded the following year (so do new freeways ease congestion?). I’m not sure why traffic sped up in 2003/04, but then dropped again significantly the next year.

Road congestion impacts the majority of the tram network, and essentially all of the bus network. So perhaps only trains are attractive as an alternative to driving in congested traffic. Here’s same chart again but plotted only against train patronage:

The chart looks much the same. So congestion might be a driver of PT patronage growth, but it probably doesn’t explain the growth in tram and bus patronage, and the relationship isn’t nearly as linear as other factors.

Perhaps also at play here is congestion being relieved for non-radial commuting, where PT had a low market share beforehand anyway. Further research might look at congestion on CBD-radial roads only, though even then, many will also cater for some cross-city trips.

Two of the radial freeways that feed inner Melbourne are operated as the CityLink toll roads, and quarterly data is available on average daily transactions. If the CityLink toll roads compete with public transport it is probably mostly with trains for longer distance travel to the inner city. Here is a chart showing growth in CityLink transactions and train patronage:

There was very little train growth in the first few years of CityLink (which started in 2001). But then train patronage grew strongly from 2005 while CityLink transaction growth went flat until 2010. A major upgrade project on the eastern leg of CityLink (M1 upgrade) caused delays between 2007 and early 2010, and there was little traffic growth. After the project was largely completed and the fourth lane opened, traffic growth accelerated over 2010. This happened at much the same time that trains recorded weak patronage growth. Then in 2011, train patronage grew again, while traffic seems to have flattened again.

To take a closer look at the two growth rates, here are financial year growth rates on CityLink and trains:

After most of the works were completed, CityLink transaction growth exceeded train patronage growth in 2009-10 and 2010-11 (note that the flattening evident in the previous chart doesn’t show with annual data). The evidence suggests there could well be a relationship between freeway capacity and train patronage, and that the M1 widening project may have reduced patronage growth on the train network. It has certainly enabled a return to strong growth on CityLink.

Car ownership

People who don’t own cars are probably much more likely to use public transport. The following chart uses cars per 100 persons aged 20-74 (as a proxy for people of car driving age).

This chart shows in the early 2000s that car ownership rose quickly, while public transport patronage growth was slow. Then from 2006-07, car ownership levels peaked and public transport patronage grew quickly. Car ownership dropped in 2008-09 just as public transport patronage surged, but recovered in 2009-10, as public transport stalled. This suggests there may be some relationship between PT patronage and car ownership, but the annual change rates aren’t always consistent.

Service kms

Another potential driver of PT patronage is the amount of service provided. Thankfully, this data is available in Victorian State Budget papers (hidden away in budget paper 3) on the number of timetabled service kms for each mode. As the modes are quite different, I’ve plotted modal charts:

Train patronage doesn’t seem to be very strongly related to timetabled kms. Perhaps this is because the service levels at peak times on most lines are already attractive from a frequency point of view at least. Many of the extra train kms are providing capacity without a substantial jump in frequency (although some of the additional kms have been in off-peak periods).  That’s not to suggest there isn’t a relationship, just that it doesn’t look likely to be the dominant driver. In the early 2000s it seems that there wasn’t a strong response to increased timetable kms (including Sydenham electrification in 2002), while in the mid 2000s patronage grew despite kms staying much the same (other factors must be at work).

Again, not a strong relationship between tram kms and patronage, despite strong growth in timetabled kms in the early 2000s (partly from tram extensions into lower density suburbs in 2003 (Box Hill) and 2005 (Vermont South) – see here for more history). It also looks like some cuts in 2000 (when some city routes had to be joined due to the loss of W class trams) were done in a way that didn’t result in a loss of patronage. Perhaps because service frequencies were still fairly good after the cuts.

There does seem to be a stronger relationship between bus kms and patronage. This is perhaps to be expected as bus service levels are on average very low in Melbourne, so improved service levels are likely to result in existing users travelling more, and better attract new users.

What is unexpected is that patronage grew at much the same rate as kms between 2005-06 and 2009-10 – an average elasticity of around 1, which is much higher than you would normally expect. In 2010-11, the annual elasticity fell to 0.42. One possible explanation for the slightly steeper rate in recent years is that more of the new kms have come in the form of SmartBus kms (with higher frequencies). We know that long run implied service elasticities for SmartBus can be around 2 – which is higher than the textbook expectation of service elasticities of up to 1 in the long run. Bus upgrades in the early 2000s were a little more focussed on providing new low-frequency services to the urban fringe, which would be unlikely to lead to as much patronage growth.

Here’s a chart showing the ratio of patronage to service kms for all modes:

This chart shows increasing intensity of use of trains and trams between 2004 and 2009, while buses have remained around 1.0-1.1 boardings per service km for at least 12 years running. The significant difference between trams and buses is best explained by the territory covered: trams mostly the CBD, buses mostly not the CBD.

Comparing annual growth/change rates

The following table shows the annual change in Melbourne public transport patronage and a number of potential explanatory factors. I’ve used conditional formatting such that darker green cells indicate values you might expect to contribute to strong PT patronage growth. Rows that have dark green in the same years as PT patronage are potentially stronger at explaining the trends in public transport patronage. I’ve also included the r-squared value for a correlation for each factor compared to PT patronage (based on annual growth rates, not actual values). You might need to click to enlarge and make it easier to read.

The table confirms a strong correlation with CBD+fringe employment, City of Melbourne visitors (2006 removed due to Commonwealth Games anomaly), international student enrolments, population (particularly inner city), and CityLink volumes.

Fuel prices don’t show a strong relationship, although it is hard to believe that they would have no impact. If you offset the fuel price changes by one year the correlation rises to 0.3 so there might be some lag involved.

Conclusions

Based on these simple charts, I surmise that City of Melbourne (LGA) visitations is likely to be one of the strongest drivers of overall PT patronage in Melbourne (but certainly not the only driver). And it certainly stands to reason, given PT’s dominant mode share of travel to the inner city.

But international students, radial motorway traffic volumes, population are probably also having an impact. The impact of fuel prices appears to be more complex.

Buses probably show less response to growth the inner city travel market (as most do not serve the city centre), so service kms are likely to be the strongest driver of bus patronage.

The PCA’s Office Market Report provides the most timely and frequent data relating to CBD employment growth and reveals much slower growth over calendar 2011 (1.4% in occupied office floor space). We might find this trend reflected in slower patronage growth on the train network as  figures are published.


What’s happening with car occupancy?

Sat 20 August, 2011

[updated April 2016]

Is car occupancy trending down as car ownership goes up? What factors influence car occupancy? What is the impact of parents driving kids to school?

Following a suggestion in the comments on my last post about car ownership, this post takes a detailed look at car/vehicle occupancy.

What are the trends in car occupancy? 

This first chart shows average vehicle occupancy from a number of different measures that are more recently updated:

  • Australian passenger vehicles – measured as the ratio of person-kms in passenger vehicles, to total passenger vehicle kms (both estimates, and unfortunately this can only be calculated for all of Australia, using BITRE data).
  • Sydney weekday vehicle occupancy, both per trip and per km, from the Sydney Household Travel Survey (SHHTS). These figures include all private vehicles (not just cars).
  • Melbourne weekday vehicle occupancy per km, from the Victoria Integrated Survey of Travel and Activity (2012/13 data wasn’t available at the unlinked trip level at the time of updating this post). Again, these figures include all private vehicles (not just cars).

The BITRE figures show a fairly smooth and slow downwards trend from 1.62 in 1990 to 1.57 in 2014. The Sydney figures are a little more noisy, but surprisingly quite flat around 1.37 (on a distance measure), and increasing on a trip basis (suggesting occupancy is rising on shorter trips and/or declining on longer trips). Only the BITRE figures are confined to passenger vehicles, which probably explains the differences between the series (the SHHTS and VISTA data will include private vehicles such as motorbikes, trucks and light commuter vehicles).

The census journey to work question gathers data on how people travelled to work, including car drivers and car passengers. While not a clean measure, it is possible to calculate an implied car occupancy as (car drivers + car passengers) / (car drivers). For the purposes of this calculation, I have only taken “car driver only” and “car passenger only” trips (which excludes park-and-ride and kiss-and-ride public transport trips). I do not have data on trip lengths, and average car passenger trips might be different on average to car driver trips.

There’s a pretty clear downwards trend as relatively fewer people travel to work as car passengers. In fact, the data suggests extremely low levels of car pooling, and that over 90% of car journeys to work have no passengers in most cities. But keep in mind that car-only mode share of journeys to work peaked in 1996, so the net change is proportionally less people travelling as car passengers and proportionally more people travelling on non-car modes.

So in summary, there is some evidence of very gradual declines in car occupancy for all travel purposes, and strong evidence of a decline in vehicle occupancy on the journey to work.

Trends in car occupancy by time of day

Many state road agencies make direct and regular measurements of vehicle occupancy in capital cities and their data is collated by AustRoads.

Unfortunately only four cities report such data to AustRoads. Brisbane data has several missing years – and the three most recent years’ figures reported are all identical, so I’m inclined not to plot them. That leaves Melbourne, Sydney and Adelaide. Unfortunately the AustRoads website hosting these statistics appears to no longer work, but VicRoads separately publish Melbourne data (but much less for more recent years). What follows is all the data I have been able to collect.

Firstly, all day (weekday) average occupancy:

There doesn’t appear to be much in the way of clear trends as the data seems quite noisy (I’m not sure anyone could explain the year by year variations). Perhaps Melbourne average all day occupancy was trending down.

Data is available for three sub-periods:

Again lots of noise, and no clear trends.

Noisy again. It’s looks like Melbourne is no longer trending down.

This data is remarkably flat for Sydney, while Melbourne appears to still be trending down.

It’s little surprise that AM peak has the lowest occupancy, as it is dominated by journeys to work. More on that soon.

 

Notes on the AustRoads/VicRoads data:

Along with the noise in the data, there is some ambiguity in the methodology. The AustRoads website reports “car” occupancy, but the methodology doesn’t seem to filter for cars. Are buses included or not? It says the survey should be undertaken in March/April to avoid school and public holidays. But March and April have heaps of holidays (Easter, Anzac Day, and Labour Day in many states).

But the AustRoads data is certainly collected on representative arterial roads, where you might expect lower occupancy because of longer trips that are more likely to be work-related.

What’s the relationship between car ownership and car occupancy?

You might expect car occupancy to go down as car ownership goes up. In other words: we have more cars and need to share them less.

Here’s what the relationship looks like for Australia as a whole (using car occupancy derived from BITRE data):

There are five quite different periods:

  • From 1993 to 1999 (bottom right) car occupancy declined as car ownership increased. As you might expect.
  • From 1999 to 2001 car ownership stalled, but car occupancy continued to decline.
  • From 2001 to 2005 car ownership rose again, but car occupancy declined more slowly.
  • From 2005 to 2010 car occupancy increased slightly, while car ownership had slow growth. This is the period when public transport mode shift took hold in most Australian cities.
  • From 2010 to 2014 car occupancy dropped more quickly, while car ownership had slow growth. In this period there was much less mode shift to public transport in most Australian cities.

The relationship is changing, probably influenced by other factors. BUT it could also be that I’m reading too much into the precision of the car occupancy figures – we are talking about variations in the fourth significant figure only for the last few years. The BITRE figures are estimates themselves. Maybe someone from BITRE would care to comment on the precision?

What about different road types?

Looking at Melbourne data in more detail, car occupancy appears to have declined most on freeways and divided arterials:

On freeways, the decline is most evident during business hours:

Here is a chart comparing car occupancy figures for arterial roads in Melbourne (2009/10):

You can see car occupancy lowest on freeways, and highest on undivided arterials with trams (all in the inner suburbs). Otherwise very little difference (in 2009/10 at least).

How do Australian cities compare?

To try to take out some of the noise, I’ll take the average of the last four years for the AustRoads data and Sydney and Melbourne household travel survey data:

Melbourne appears to have the lowest occupancy, and Sydney the highest – except when it comes to household travel survey data where Melbourne is much higher. But this might just be differences in methodologies between states.

Factors influencing car/vehicle occupancy (in Melbourne)

Having access to the 2007-08 VISTA data, it’s possible to disaggregate vehicle occupancy on almost any dimension you can imagine. I’ll try to restrict myself to the more interesting dimensions!

For most charts I have used vehicle occupancy rather than car occupancy. Cars and 4WD/SUVs combined accounted for 88% of vehicle kms in the dataset so there shouldn’t be a lot of difference. But I’ll start with looking at..

Vehicle type

Now that’s a surprise: 4WD/SUVs have a much higher average occupancy than cars. Why is that?

Are they used for different purposes?

Not a great deal of difference between cars and 4WD/SUVs, although 4WD/SUVs are slightly more commonly used to pick up or drop off someone.

More likely explanations (from the data) are:

  • 4WD/SUV come from larger households on average (3.5 people v 3.1 for cars).
  • 4WD/SUVs are also more likely than cars to belong to households that are couples with kids.
More on both of these point soon.

Day of the week

Probably not a huge surprise that cars have less occupants on weekdays than weekends. Male drivers are much more likely to have no passengers on weekdays, but an average of one passenger on weekends. Whereas there is much less variation for females.

Is this traditional gender roles in the family? (There is a chart to answer almost any question you know..)

There you go: dads much more likely to drive the family around on weekends, and mums more likely to drive them around on weekdays. And while on the subject…

Household types and sizes

Little surprise that car occupancy increases with household size. It is easier to car pool when you have the same origin.

Note that the sample size of one parent households of size 5 are small (especially for male drivers). But curiously single mothers have much higher occupancies than single fathers.

There is also a small sample of other household structures with 5 people.

Unsurprisingly, people living alone are likely to have the lowest car occupancies. With increasingly prevalence of sole person households, you might expect continuing declines in average car occupancy.

Trip purpose

Again work trips are the least likely to involve passengers, particularly on weekdays (average occupancy 1.07). Driven trips to education are not far behind. Little surprise that accompanying someone, or picking up or dropping off someone averages around 2 or more. Occupancies for personal business, shopping, recreational and social trips are in the middle, but much higher on weekends when householders are probably more likely to travel together to common destinations.

Many people would argue that demand for public transport is lower on the weekend. These figures would support that argument, but lower weekend patronage would also reflect lower service levels.

Note: the sample sizes of weekend education and accompanying someone trips were too small to be meaningful so I left them off.

Time of day

There you go, car occupancy peaks between 8 and 9am and between 3 and 4 pm on school days: parents driving kids to/from school.

But vehicle occupancy is highest on Saturday nights when people are socialising, and interestingly Sundays are well above Saturdays (less personal business on Sundays perhaps?). Non-school weekdays have higher occupancies than school weekdays, possibly with parents also taking time off work and spending time with kids.

Just looking at the school peak more closely, here is a chart showing car driver trip purposes by hour of the day on school weekdays. You’ll almost certainly have to click on this one to read the detail.

The most frightening statistics are in the school peaks. A staggering 40% of car trips between 8 and 9am, and 42% of car trips between 3 and 4pm are to pick up or drop off someone (suggesting a fault in the reported vehicle occupancy for trips picking up somebody). This will almost certainly be dominated by school children. No wonder traffic congestion eases so much in school holidays.

That said, car trips to/from school are shorter than other trip types (as we saw in an earlier post). The data suggests 19% of car kilometres of trips starting between 8-9am are to pick-up/drop-off someone, and for 3-4pm the figure is 24%. That’s still a sizeable chunk of total road traffic. It suggests there are huge congestion relief benefits to be had in getting kids to walk, ride or use public transport to/from school.

Geography

There’s not a lot of difference other than for the inner city, where school day occupancies are lower. For someone in the inner city to drive a car, they are probably heading out of the city and any other members of their household might be less likely to have the same destination and/or would have good public transport options for their travel.

The non-school weekday figures show some variation, and while the sample sizes are all over 250, there are some vehicles with an occupancy of 14 recorded. unfortunately because the underlying data is discrete, medians aren’t an easy way around this issue.

Age

This would suggest traditional gender roles are in play: Average car occupancy is highest for drivers aged 30-45, the most common age groups for parents of pre-driving aged children. And women seem to be doing more ferrying of the kids than men.  In the older age groups men are more likely to be driving with passengers.

Income

Vehicle occupancy seems to go down as we have higher incomes (moreso for females), but there seems to be some noise in the data (eg the spike at 3000 is due to one vehicle with 12 occupants). Females with lower household incomes have higher vehicle occupancies (maybe those without an income but looking after a family).

This trend reflects the fact that car/vehicle ownership goes up as wealth goes up:

The threshold for car ownership is around $1250 per week (equivalised to a single occupant household). As Australians have become increasingly wealthy in real terms, we can afford to own more cars.

Trip distance

While there is probably a little noise in this data, there is a fairly clear pattern. Very short trips and very long trips are likely to have higher occupancies. The median trip distance for non-work trips is around 4kms, while work trips are much longer, which fits with the average occupancies for different trip purposes.

In fact, here is a mode share breakdown by trip distance (for trip legs):

You can see car passenger becomes more common for very long trips (note the X axis scale is not uniform). (Don’t ask me why driving is so popular for distances of 16-16.9 kms! It’s probably a bit of noise)

And if you look at the trip purposes of these very long trips, you’ll longer trips are more likely to be social or personal business:

(note: this chart is by trips, and not trip legs)

Main Activity

Probably little surprise that those “keeping house” have the highest occupancy in general, but that full-time workers have very low occupancy on weekdays, but very high occupancy on weekends.

There you go, possibly more than you ever wanted or needed to know about vehicle occupancy!


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).

Car ownership and public transport

Sun 17 January, 2010

 

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

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

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

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

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

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

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

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

 

 

Observations and Analysis

Pockets of low car ownership rates are generally found:

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

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

Conclusions and Commentary

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

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