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

Sun 24 June, 2012

Part one of this analysis looked at how geography, motor vehicle ownership, driver’s licence ownership related to the use of public transport.

This second post will look at how other personal circumstances relate to public transport, including age, a person’s main activity (occupation), income, employment and household type. Much of this is purely for interest, but I have uncovered a few interesting factors that relate to levels of public transport use.

The analysis is of data from the 2007-08 and 2009-10 Victorian Integrated Survey of Travel and Activity (VISTA).

Make sure you read part one first, so you know how I have gone about this analysis and can decode the terms and acronyms used.

Age and gender

The following chart shows very clearly that public transport use (which includes school bus use) peaked for teenagers and fell away with age:

The chart debunks the myth that older people switch from cars to public transport as they give up driving. For males the trend in public transport use continued to decline with age, while females remained at around 7%.

Also of note is that young children had the lowest rates of public transport use of any age group. As you’ll see in a moment, they travelled a fair bit – just not on public transport.

Women aged 20-29 and over 60 were more likely to use public transport than men, while men aged 35-44 were more likely to use public transport than women of the same age. I’ll come to possible reasons for this soon.

As you might expect there were very similar patterns in driver’s licence ownership (see part one) and public transport use by age; although public transport use continued to be relatively high into the 30-34 age bracket and driver’s licence ownership is over 80% by age 30.

So why are there these discrepancies for people in their 30s and 40s? I’ll get to that soon.

But first, is public transport use related to the amount of travel people make?

People aged 40-44 were the busiest travellers with 3.7 trips per day on average, which then fell with age. Between the ages of 20 and 44 people made many more trips, but became less likely to use public transport with age.

Young children do travel a fair bit, but rarely on public transport.

The average number of public transport trips per day peaked for teenagers, who also had the lowest overall trip making average.

The average number of active transport trips (walking and/or cycling only) did not seem to vary considerably by age.

Main activity

The VISTA survey classifies people by their main activity in life (you might think of this as occupation). Here’s a look at average public transport use on school weekdays.

As we saw with age, public transport use peaked for secondary school children, with full time tertiary students not far behind. Children not yet at school were the least likely to use public transport, with those keeping house the next least likely.

Is that because of their driver’s licence and car ownership status? The following chart tests public transport use by main activity and groupings of licence and motor vehicle ownership (where I could get a cohort of 200 or more – missing values are not 0%).

This chart suggests that full time students, full time workers and part time workers were generally more likely to use public transport even if they had access to private transport. Those unemployed, “keeping house”, or retired were only somewhat likely to use public transport if they had limited access to private transport.

So, motor vehicle ownership does not explain the low rate of public transport use by those “keeping house”. I’ll come back to that.

I expect the general explanation for the above chart is that public transport is more likely to be competitive to places of full time work or study, particularly those in the inner city. We know from a previous post that public transport use to suburban employment destinations is very low.

Here’s the picture for journeys to education in VISTA, by the location of education activity (note: cohort sizes down to 120 – a margin of error of 9%).

Very few primary school children took public transport to school (except in the regional centres), while 25-40% of suburban secondary and tertiary students used public transport. Public transport had a very high mode share in journeys to tertiary education in the inner city of Melbourne (where public transport works well and students probably cannot afford to park, even if they can drive a car).

What about trip making rates by main occupation?

Part-time workers made the largest number of trips on average, while the unemployed and retired travelled the least. Those keeping house did a lot of travel, but very little of it on public transport.

And in case you are interested in the relationship between age and main activity…

No big surprises there when you think about it. Notice that part time work became much more common from the late 30s.

Income

What impact does income have on public transport use?

I have used equivalised weekly household income per person as my measure, as this takes into account household size and the number of adults/children in those households. It essentially brings all households to the equivalent of a solo adult.

The pattern shows those on lower (but not very low) incomes were the least likely to use public transport. Those with no income were just as likely to use public transport as those on $2500 per week equivalised. So that debunks the myth that public transport is only for poor people! In fact people on very high incomes were more likely to use public transport than those on $500-1000 per week (peaking with those on $2250-2500 per week).

What’s driving this pattern? Well, we know that people on higher incomes are more likely to live closer to the city and probably work in the city centre, so what if I take geography out of the equation? The following chart looks at patterns within each home sub region and excludes people who travelled to or from the City of Melbourne (cohorts of less than 300 people not shown).

The trend now looks the reverse – people on higher incomes used public transport less for trips outside the City of Melbourne. But is that because people on higher income were more likely to travel to the City of Melbourne?

Well they certainly were much more likely to travel to/from the City of Melbourne. The shape of this chart is very similar to the chart showing overall public transport use by income, but the variation is much greater.

In order to remove the impact of travel to/from the City of Melbourne, I’ve calculated the use of public transport by those who did and those who did not travel to/from the City of Melbourne (chart shows cohorts with 200 or more):

While the rate of public transport use went down by income for the two divisions (travel to/from City of Melbourne or not), the overall rate increased with income as a result of blending – at higher incomes more people were travelling to/from the City of Melbourne which lifts the overall average use of public transport.

We know from part one that people living closer to the centre of Melbourne are more likely to use public transport for trips not involving the City of Melbourne. So here is a chart showing the rates of public transport use by income for those people not travelling to/from the City of Melbourne:

This suggests there may be a relationship between income and public transport use, though it is much less significant a determinant than whether or not someone travelled to the City of Melbourne.

But what about the other factors – like motor vehicle and licence ownership? In the following chart I’ve again limited myself to groupings where I could get a cohort of 200 or more (margin of error up to 7%).

The pattern now looks like slightly increasing public transport use with income for some groups, when taking out motor vehicle/licence ownership (although the variation is within the margin of error so it might not be a significant pattern).

Might geography be at play here – that wealthier people live in areas with greater PT supply (ie closer to the city)? I cannot prove that because I cannot disaggregate this further.

But thinking about it, wouldn’t licence and motor vehicle ownership increase with income? And we saw in part one that public transport use declines with licence and motor vehicle ownership.

Well, here is licence ownership by income (for adults):

And here is motor vehicle ownership by income:

Licence ownership and motor vehicle ownership certainly increased with income, which you would expect to generally lead to lower public transport use.

Furthermore, people in higher income households travelled more often on average, which might increase their chance of using public transport:

This leads me to conclude that income is very likely a driver of public transport use, and that people on higher incomes are less likely to use public transport, all other things being equal (though I haven’t tested for every other thing!). But the fact that people on higher incomes were more likely to travel to travel to/from the City of Melbourne trumped this income effect.

Employment type

As we saw in a previous post, location of employment has the biggest bearing on public transport use. But here are a few breakdowns anyway (on weekday journey to work):

For comparison, here are the figures from the 2006 census for the whole of Victoria:

The margin of error on the VISTA data is around 4%, so they figures are reasonably similar.

And sure enough the jobs most prevalent in the inner city have the highest public transport mode share:

The two groups with highest public transport use are more likely to work in the inner city, so little surprise that they have the highest public transport use.

Managers are probably widely distributed across the sample area, and many would have packaged cars and/or parking as part of their salary packages.

Unfortunately the dataset is too small for me to disaggregate to people who don’t live or work in the City of Melbourne (in a previous post I found managers had lower rates of public transport use in the journey to work to the Melbourne CBD).

What about employment industry?

I suspect public transport use by employment industry will largely reflect employment location. Melbourne’s recent strong public transport growth could well relate to the changing mix of employment, with a move away from manufacturing and towards professional services. This might also be fuelling growth in CBD employment.

Household type

How does public transport use vary by household type? In some recent work I was looking at young families more closely, as they are a very common household type moving into growth areas on the fringes of our cities. I’ve defined a young family as being one or two parents with all children under 10 years of age.

Consistent with very low rates of public transport use by young children, young families were least likely to use public transport (taken as the average across all household members). Sole person and mixed household structures were most likely to use public transport.

The above chart is a blend of parents and children, so here’s public transport use by age and household type:

You can see between the ages of around 20 to 44 that parents (with children at home) had much lower rates of public transport use than other people. This suggests that becoming a parent is probably a major cause for people to abandon public transport. I suspect this may be because travelling with young children on public transport can be a challenge. But maybe they are also time poor (more on that shortly).

I note also that sole person households had higher rates of public transport use, particularly after 35 years of age. Perhaps the slow demographic shift towards smaller households might lead to increased public transport use? A topic for further research perhaps.

Anyway, investigating family households further, I have defined each person by their household family position: mum, dad, child, or other (everyone not in a simple family household structure).

You can see here that children’s public transport use peaked at ages 15-19 and then fell with age. My cut-off for this chart was 400 persons in the cohort, and yes there were over 400 children aged 35-39 living with their parents in the sample.

Mums used public transport a lot less than dads, particularly younger mums. Perhaps this is because they made a lot more trips per day?

This result is consistent with the data showing that mums were much less likely to be working full time than dad. In fact over half were “keeping house” or working part time. Be careful of the subtle colour differences in the following chart:

So does making more trips in a day reduce your chance of using public transport?

This chart excludes people who travel to/from the City of Melbourne (sorry about the mouthful of a chart title!). Having three or more trips in your day significantly reduced your chances of using public transport, but only really if you had limited household motor vehicle ownership. I’m guessing that the motor vehicles were more used by the people in the household who had to make more trips.

Curiously, a lot of single parents are retired. The data shows them to indeed be of retirement age – probably with adult children caring for them. They are probably not what you generally think of as single parent households, but technically that’s how they get classified.

So what are the strongest determinates of public transport use?

In my first post on this topic, the likely determinants of public transport use were:

  • Much higher for people travelling to/from the City of Melbourne (possibly increasing with home distance from the central Melbourne)
  • Decreases with distance from central Melbourne (probably a proxy for PT supply)
  • Higher for people with no or limited household motor vehicle ownership.
  • Higher for people without a probationary/full driver’s licence.

From this post we can probably add:

  • Very low usage by young children (primary school and below);
  • Very low for those for keeping house or working part time (often mums);
  • Lower for parents (in family households with non-adult children);
  • Lower for people on higher incomes (all other things being equal, which they usually are not!); and,
  • Lower for people making more trips per day.

Ideally I should run a logistic regression model to the data to analyse the drivers more systematically. I might see if I can do that in a part three.


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.


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!


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

Sun 19 June, 2011

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

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

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

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

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

How do travel distances vary by geography?

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

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

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

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

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

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

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

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

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

You can see differences between distributions are not straight forward:

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

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

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

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

How does mode share vary by geography?

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

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

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

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

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

What about mode share of very short trips?

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

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

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

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

Does the number of trips made vary by geography?

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

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

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

How do trip lengths vary?

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

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

What about trip distances for different purposes?

First up, median trip distances by purpose:

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

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

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

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

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

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

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

How does travel time vary by trip purpose?

You can see:

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

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

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

How does travel speed vary by trip purpose?

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

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

How does mode share vary by trip purpose?

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

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

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

Here is another view, private transport mode share:

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

What other factors influence travel distance and mode split?

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

And median per person travel distance varied by household type:

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

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

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

Equally household incomes were not consistent across Melbourne:

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

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

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

Do rich people shun public transport?


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

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

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

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

What about age:

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

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

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

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

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

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

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

However, there are several points worth considering:

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

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

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

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

Notes about the data:

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

Where do the employees come from? (Melbourne 2006)

Sat 15 January, 2011

If we want to improve transport options into employment areas, it helps to know where the employees are coming from.The answers are in the gold mine that is census journey to work data.

This post maps where employees come from for major employment destinations around Melbourne, and looks at whether public transport is servicing these areas well. The CBD will be the subject of a separate post.

About the maps

Skip this section at your own peril – there’s some important things to keep in mind:

  • For each employment centre I have generated two or three maps:
    • the number of commuter trips originating in each SLA – ie where do the workers live?
    • the private transport mode share for commuter trips from each SLA. While I often look at public transport mode share, that doesn’t account for walking/cycling for shorter trips, and car trips are really what we need to reduce. Car mode share is only shown for an SLA where there are more than 100 originating trips, Note that given census data never reports values of 1 or 2 (for privacy reasons), we need to be careful about reported car mode shares that are 97%+.
    • for inner city areas only: the number of private transport based trips originating from each SLA (a high car mode share might not be such an issue if there aren’t many car trips being made).
  • I have used an SLA-to-destination zone journey to work dataset from the 2006 census (with thanks to the Victorian Department of Transport).
  • I have defined each employment area as a collection of destination zones, and then looked at the SLA origins for people working in those areas. These employment areas are shown in black shading on the maps.
  • On the maps showing quantities, shading represents relative density while the numbers are absolutes for each SLA. Some SLAs have larger areas and/or populations than others. For outer metro SLAs (which are often only partially urbanised) the density figures will always be low, but the concentration within the urban are might be higher. In an ideal world I would use density of residential areas only, but that takes a fair bit of work and I do this blogging in my own time. So you need to interpret those carefully.
  • The private transport mode share maps use the same colour scale for mode share values (hence some are very green and some very red).
  • As usual, you will need to click to enlarge maps.

Inner City Destinations

South Melbourne employees by SLA:

You can see large sources from the inner southern suburbs, particularly to the east and south.

South Melbourne private transport commuter mode share:

In general only about half commuters come by car, but this includes the nearby inner southern suburbs, despite being directly connected by tram routes. Perhaps the high car mode share reflects relative ease of parking and some awkwardness in getting to all parts of South Melbourne via public transport (for many it would require changing trams at Domain Interchange). A previous post showed that car mode share varied from 35% near Flinders Street Station up to 58% along St Kilda Road, and 67% in the south-west.

Car mode share is 87% from Point Cook/Werribee South (227 commuters) and Rowville (188 commuters), both of which lacked good radial public transport connections (Rowville is now served by a SmartBus direct to Huntingdale Station). Another high car share area is the affluent suburbs in Bayside, particularly south of the Sandringham train line (refer previous post).

South Melbourne employees commuting by private transport, by SLA:

This third picture is interesting:

  • The highest car origin density is still from the inner southern and south-eastern suburbs, on relatively short trips where public transport is generally frequent and direct (although not always fast).
  • There are many cars coming from the inner north and Williamstown, which requires crossing the city or Westgate Bridge.

Perhaps the number of cars coming from the inner south-eastern suburbs could be reduced if it were possible to travel by public transport directly to more parts of South Melbourne? Tram 8 does provide a link from South Yarra station to St Kilda Road (you can then change again to tram 55), but perhaps better access is required to other parts of South Melbourne?

Or perhaps it is more to do with ease of parking arrangements?

I’ve had a look at VISTA 2007 data for the Docklands/Southbank SLA – although the sample is very small so need to treat this with caution!

Only 41% involved self-paid parking of a personal car. Another 21% was free parking of personal cars, while 38% involved employer parking and/or company cars. While this is a very small sample, it suggests ease of parking is quite probably a strong determinant of car mode share. I’ll do a similar analysis for the CBD in an upcoming post where there is a larger sample (n=250).

Parkville employees by SLA:

The major sources for Parkville are the inner northern suburbs, which is not surprising. People tend to work and live on the same side of the city because transport is usually easier. Parkville is not directly served by the rail city loop for those coming from the south.

The proposed metro rail tunnel would connect the Sunbury/Sydenham line (north-western suburbs) to Parkville direct. However, the maps shows that the north-western suburbs are not currently a major source of Parkville employees (there will be students as well of course). This may of course change once the line is open, but it does present a patronage challenge to the project.

The relatively high catchment along the Epping and Hurstbridge lines suggests a more direct link from there to Parkville might have some potential for public transport mode shift. Already there is a high frequency bus service along Johnston Street which connects to Victoria Park Station on these lines. However, only half of peak period trains stop at Victoria Park station at present.

Parkville private transport commuter mode share:

(17/1 – this map has now been corrected since original posting)

A standout is Moonee Valley West at 87%. Not quite sure what is going on there – although most residents would have had to catch a bus, train and tram to get to Parkville by public transport in 2006. Such a trip would still involve two transfers now, but to a more direct and high frequency route 401 bus at North Melbourne station.

Car mode share is also high to the east in Boroondara and Manningham and in the outer western and northern suburbs. Being effectively on the same side of the city would make car commuting relatively easier.

Those coming from the south-eastern suburbs would appear to be largely content with public transport, although wealthier Brighton had a higher car mode share.

Parkville employees commuting by private transport, by SLA:

Car mode share and volume is also relatively high from Moonee Valley. While the 59 tram connects these two areas, it is relatively slow. The recently introduced high frequency 401 shuttle bus from North Melbourne to Parkville may have since increase public transport mode share from Moonee Valley workers in the catchment of the Craigieburn rail line.

There are also quite a few cars making the awkward trip from the St Kilda and South Yarra areas. But otherwise they tend to come from nearby suburbs – many of which are directly connected to Parkville (at least from the north).

Fishermans Bend is perhaps one of the most interesting areas in this analysis. It is on the eastern side of the Yarra River and relatively close to the CBD, but take a look at where the employees come from:

Fishermans Bend employees by SLA:

While the densities are highest from the inner southern suburbs, there are actually large numbers from the western suburbs (this is where low average density SLAs impact the results). There are around 3000 Fishermans Bend employees who live on the western side of the Maribyrnong River.

In fact, here is a similar map, except shaded by total number of employees (rather than density):

Most of them come from the western suburbs. And they come by car…

Fishermans Bend private transport commuter mode share:

No wonder the Westgate Bridge is heavily congested. As shown in the figure below (from a report to the City of Melbourne by Paul Mees citing a 2005 VicRoads survey), 34% of cars on the Westgate Bridge exit at Todd Road (the main Fishermans Bend exit) (for trucks the figure was 31%). That’s a third of the traffic on the Westgate Bridge . We seem to have a signficant public transport gap here!

Current public transport access to Fishermans Bend is primarily by bus from the CBD (routes 235/237/238), although one bus (route 232) runs from a small catchment in North Altona over the Westgate Bridge and along the southern edge of the industrial area (Williamstown Road). Driving over the Westgate Bridge would appear to be a more attractive option than the current train-bus option (here is a map of what such a trip can look like). Perhaps a stronger public transport link from the western suburbs is needed, maybe one that connects with the train network in the west. The current route 232 commences from the site of the now closed Paisley railway station, through which about half of Werribee peak period trains pass.

That said, the major bus service from the CBD have been realigned recently to operate from directly outside Southern Cross Station, and Werribee and Williamstown line trains run direct to Southern Cross in the AM peak. However there are still headway gaps of 20-30 minutes during some parts of the AM peak on these routes. It might be possible to slightly increase the frequency of these routes with existing resources if the routes terminated at Southern Cross station instead of Flinders & Market Streets (although I am sure a minority of existing users would not like this). Travelling via Southern Cross might indeed be the fastest way to reach Fishermans Bend by public transport, unless bus services could get priority over the Westgate Bridge (which is currently congested by single occupant cars).

Fishermans Bend employees commuting by private transport, by SLA:

The cars also come from the inner suburbs, including some density from Port Phillip west and St Kilda (which has a direct although infrequent bus service to Fishermans Bend).

Docklands is a fast growing area directly to the west of the CBD. Because of this growth, patterns may well have changed significantly since 2006.

Docklands employees by SLA:

For an employment area on the western side of the CBD, employees tended to live on the eastern side of the city – which is not what you would expect. But if they are coming via public transport, then trains from most parts of Melbourne provide relatively good access to Southern Cross Station, on the eastern edge of Docklands.

Docklands private transport commuter mode share:

Car mode share is significantly higher from the western suburbs, from which it is easier to drive to Docklands, and using public transport requires an indirect journey via Southern Cross.

However North Melbourne Station is only a short distance from Docklands. When the “E-Gate” site is redeveloped, it seems like a perfect opportunity to link the two with quality public transport (maybe a short tram extension, although we’d need a larger tram fleet). This might significantly increase public transport mode share and take pressure off the city loop railway.

Docklands employees commuting by private transport, by SLA:

The car numbers in 2006 were quite low, so we cannot read too much into them.

Suburban Employment Centres

(For subsequent centres, I will only show two maps, as car mode share is very high in most places)

While not a household name, Notting Hill is a relatively job dense area just north of the Clayton campus of Monash University.

Notting Hill employees by SLA:

You can see many workers live to the west, east and nearby north. But note that the two SLAs to the south (Kingston and Greater Dandenong) have a low average population density, so it is likely that the actual density of employees is higher to the south.

However there are also many employees from the Hallam and Berwick parts of Casey (which show as low density given the SLAs have a low urban density). There are fewer local jobs in Casey, meaning workers need to travel longer distances to work.

I would guess that nearby Clayton and Mulgrave industrial areas would show similar patterns (alas, I haven’t had time to analyse these areas).

Notting Hill private transport commuter mode share:

Notting Hill is full of cars (see the car parks on NearMap for yourself).

In 2006 there was a SmartBus route along Blackburn road (the eastern edge) and the 733 bus ran every 15 minutes in the peak on the western edge. East-west bus routes (693 and 742) run more like every 30 minutes in the peak. (All of the timetables hare hardly changed since 2006).

You can see on the map slightly lower car mode shares from the local area, but also up towards Blackburn (89%) – perhaps reflecting the attraction of the SmartBus service.

The south-eastern suburbs present a challenge: how does public transport service people who live in the suburbs of Casey and work in Notting Hill and surrounds? The current public transport system requires transferring from a low-frequency bus to a train, and then another (slightly more frequent) bus. Two transfers will struggle to compete with the car. Indeed, looking at VISTA 2007 data, it seems only 4.8% of public transport journeys from home to work in Melbourne involve two transfers.

There is a school of through that says bus routes should not be designed to connect everywhere to everywhere with a direct route – because you end up with a complex network of infrequent routes (than you would otherwise have with the same resources). However, if there are clear concentration of origins and destinations, might it make sense to run a direct service between the two?

Would a bus route that performs a collection function in Casey, runs express along the Monash freeway, and then performs a distribution function in Clayton/Notting Hill be viable? It may not need to run at a very high frequency, because transfers are not required. Perhaps the only way to find out is to do a trial (at a not insignificant cost). Such a service might not need operate extended hours, particularly if a guaranteed ride home was provided by employers who can otherwise save on parking costs. It would also still be possible to travel home using other public transport routes in off-peak times.

And now for the other major destination of Casey workers…

Dandenong South employees by SLA:

A majority of Dandenong South employees come from Casey to the east, although there are also quite a few from the Frankston area.

I think the lack of an east-west bus route connecting Casey to South Dandenong is probably one of the largely missing links in the Melbourne’s public transport network. Recent focus in the area has been on upgrading north-south bus routes (901 and 857), which will certainly help the a couple of thousand commuters from those corridors. But the 6000 odd employees from the east gained no better access following the December 2010 local bus upgrade.

Dandenong South private transport commuter mode share:

You can see almost universal car dependence for South Dandenong employees. It only drops to 91% in central Dandenong (sorry that number has been obscured by the shaded areas on the map).

Moonee Ponds employees by SLA:

While not recognised as a central activities district (CAD), Moonee Ponds actually has quite a bit of activity, includes some multi-storey office blocks (something other centres could only hope to achieve). Most workers come from the local area, or the nearby north-west.

Moonee Ponds private transport commuter mode share:

The reason I have included Moonee Ponds is the low car mode share from the Brunswick area (to the east). Perhaps this reflects the high frequency east-west bus routes that feed into Moonee Ponds from Brunswick? That said, the number of employees coming from Brunswick is low. Maribyrnong is connected to Moonee Ponds by one tram and two bus routes, but still 79% of people drive.

Central Activities Districts

The Melbourne @ 5 Million urban plan for Melbourne places greater emphasis on six suburban regional centres, to act like CBDs in the suburbs. I’ve taken a look at a couple of these that are perhaps better served by public transport. My earlier post showed many of these centres already have very low public transport mode shares.

Box Hill employees by SLA:

People working in Box Hill largely come from the local area, but from the north (Manningham west) and towards the east along the Lilydale and Belgrave rail lines. This is good – in theory – for the rail system in that it frees up some capacity on citybound trains in the morning.

But do they use public transport to get there?

Box Hill private transport commuter mode share:

Unfortunately not – car mode share is 85%+ for most of the eastern catchment. It drops to 59% in the Box Hill SLA itself – probably a combination of walking and bus access.

In 2006, 90% of commuters from Manningham west drove. This might have been reduced slightly by the introduction of the 903 SmartBus which runs every 7.5 minutes in the peak (although the previous 291 service was around every 10 mins in the peak). Some local routes connecting Manningham to Box Hill do run frequently in peak periods (eg 279, 286).

Dandenong CAD employees by SLA:

Central Dandenong attracts workers from the local area, particularly to the south and east. No surprises there.

Dandenong CAD private transport commuter mode share:

Car mode share is very high, with slight dips in the central Dandenong SLA (probably a fair amount of walking), the awkwardly U-shaped “Dandenong – remainder” SLA, and slightly lower in Pakenham. Overall Public transport mode share to central Dandenong is most around 4-7% (refer earlier post). It will be interesting to look at 2011 mode share from Frankston and Knox, which are now connected to Dandenong via the 901 SmartBus service.

I have looked at many other employment areas, but for reasons of space and time, I have not included them in this post. Many of those have quite predictable patterns (eg most Footscray employees come from the western suburbs). I’ve attempted to pick out centres with more interesting patterns.

Concluding remarks

I’ve not seen this sort of analysis done elsewhere, and I think it is important evidence to support planning transport systems – particularly public transport.

In the above analysis I’ve identified some “missing links” in Melbourne’s public transport network, including:

  • Casey to South Dandenong
  • Western suburbs to Fishermans Bend (via Westgate Bridge)
  • Casey to Monash industrial area

It might also be worth investigating new links to short-circuit trips to near-CBD locations:

  • North Melbourne station to Docklands
  • South Yarra station to South Melbourne (other than where route 8 runs)
  • More peak trains stopping at Victoria Park station to allow for convenient bus connections to Parkville

But it also looks like ease of car parking is having an impact on public transport mode share. South Melbourne sees many car commuters from nearby suburbs that are well connected by tram. Many areas outside the inner city would offer free employee parking, and driving is likely to be faster than public transport in most cases, particularly where on-road public transport is not insulated from traffic congestion. Unfortunately the census does not include data on who pays for parking and vehicles, so analysis of this issue is limited by the data available.

My next post will focus on the Melbourne CBD, and following that I hope to look at employment destinations of various SLAs (where do the workers go, rather than where they come from).


Spatial analysis of Melbourne household travel data

Sun 17 January, 2010

Thanks to the Department of Transport providing public access to VISTA (Victorian Integrated Survey of Travel and Activity) 2007-2008 travel data, it is fairly easy to plot some results geographically for Melbourne (I’m planning to plot some other patterns, but this is a first installment with some general patterns).

The gallery below contains maps showing mode shares and trip distances for each LGA (Local Government Area) in Melbourne.

If you don’t know Melbourne’s LGAs by name, a reference map is included above (also showing train lines).

Notes:

  • All data is by LGA of residence, which is different to LGA of trip origin.
  • Total weekday travel distance sums the kms of all trips (as opposed to median trip length)
  • Mode shares are for all trips, not just motorised trips.
  • The colour scale for each map is different. I have a 14 colour scale (using “equal count” ranges) and I have deliberately not included a legend. So refer to the numbers for each LGA for relative values. Note the colour band jumps are not always ideal, do refer to the numbers as well as colours when comparing.

Observations:

  • Average trip distances don’t actually vary a huge deal if you look at the numbers (except the inner city, Brimbank and the outer north-east) – probably because most trips are local trips. Longer median trip distances in the outer north-east might be due to a larger rural population (smaller urban areas in these LGAs).
  • Trips per capita seem to be lowest in the growth interface councils. This might be related to more babies being present in these areas(?).
  • Total weekday travel distance seems to be highest in the outer west and east (less so the outer north)
  • Walking and cycling rapidly declines with distance from the CBD, with Greater Dandenong something of an outlier.
  • Private transport mode share tends to increase with distance from CBD, with Dandenong again something of an outlier (probably due to low socio-economic status).
  • Public transport mode share is higher in the inner city and inner northern areas. Lowest in the outer south east and south west (Dandenong again perhaps a bit of a local outlier – even though many parts have relatively poor bus service levels).

Of course there are lots of factors at play in all this (income, household types, demographics, PT supply, employment distribution, etc) and I’ve mostly speculated on some potential causes – certainly not attempted to explain all the causes in this post!

How reliable is this data?

VISTA is a very comprehensive household travel survey, and it includes over 17,000 households, almost 44,000 people who made over 145,000 trips – all in 12 months. The median trips per LGA is around 3500 – by around 1000 people (although some more than others – the smallest is Cardinia with 578 trips measured).

So in most cases the sample sizes are very large, giving a small margin of error. The data has been weighted to match the demographics of Melbourne in the 2006 census, which will control against under or over sampling of particular demographic groups.

Travel distances aren’t perfect – straight line distances between points are scaled up to take account of indirect routes being used for all modes (except trains where exact distances are known).

Household travel surveys are never perfect, but I think VISTA is a well developed and comprehensive survey and the outputs will be quite reliable as long as you are not disaggregating data into small segments. The weekend data in the maps above has the smallest sample sizes the (median sample of trips for the weekend is around 700 per LGA).