How and why do travel patterns vary by gender and parenting status?

Mon 1 July, 2024

A lot of published transport analysis – including on this blog – has been gender-blind. Yet there are quite significant differences in travel patterns between men and women, and also between parents and non-parents. Advances in equality of opportunity have not eliminated these differences.

This post goes all-in with disaggregating a wide range of available data on transport behaviour on gender and parenting status in Melbourne, and explores some factors likely influencing these behaviours.

I will look at trip rates, trip chaining, time spent travelling, destination distance from home, distance travelled, travel to the central city, time of day, mode splits, use of different modes, trip purposes, and radial-ness of travel. I’ll also look at explanatory variables including main activity, occupation, employment industry, access to independent private mobility, and geographic distribution of home and work locations. Yeah that’s a lot, but don’t worry, there is a summary towards the end.

There’s also an interesting aside about dwelling bedroom counts around train stations.

This post is mostly focussed on working aged people (approximated by the age range 20-64), as children and seniors are likely to have different travel patterns again. And for the purposes of this analysis, I’m classifying people as “parents” or “parenting” if they live with their children – i.e. they are likely caring for their children (although some might have relatively independent adult children living with them). Parents whose adult children have all left home will be classified as other males/females.

About the data

I have access to very detailed household travel survey data for my home city of Melbourne for the pre-pandemic years 2012-2018, so that’s my primary source (officially VISTA – the Victorian Integrated Survey of Travel and Activity, get data here). It covers all types of non-commercial travel by residents, on all days of the year. Of course that data is pre-COVID and things will have changed somewhat since then but rich post-COVID data is not yet available.

I’m aggregating outputs to differentiate school weekdays, non-school weekdays, and weekends (I have excluded data for public holidays).

The VISTA data reports on binary gender, so unfortunately I can only cover males and females. That said, even if it did include more diverse gender categories, it would likely be very difficult to get statistically significant sample sizes for non-binary gender groupings.

There’s no special treatment required for same-sex parenting couples – they each count as mums or dads based on their reported gender.

Here’s how prevalent the different gender + parenting classifications are by age band in the weighted VISTA data for 2012-18:

The survey weightings don’t quite lead to a perfectly balance between genders across all age bands.

Parenting was most common amongst those aged 40-49 (almost three-quarters), and lower prevalence in younger and older age groups (under 8% for those aged 20-29).

Curiously there was a slight uptick in parents living with their children for ages 80+, which might be elderly parents living with – and being cared by – their adult children.

Across the approximate working aged population (20-64), parents accounted for 45% of the population.

In some sections I’ve also used ABS Census data from 2016 and 2021. This data is segmented slightly differently, with parenting being indicated by whether the person does unpaid work to care for their own children (so might exclude parents with relatively independent adult children living with them). Unless noted otherwise, it includes people aged 15+, and journey to work data only includes those who travelled to work and reported their travel modes.

Let’s get into it..

Trips per day

For this analysis a trip is travel between two places where a purposeful activity takes place, and may involve multiple trip legs (eg walk-bus-walk-train-walk).

Mums easily made the most number of trips on school weekdays, but dads made more trips on weekends than mums. Trip rates were higher on weekends for all person classifications except mums.

Trip chaining

I’ve heard much about women doing a lot more trip chaining – where a person leaves home and travels to one activity, then one or more other activities, before returning home. For example: home to school drop-off to work to school pickup to home.

As a simple measure of trip chaining, I’m counting the number of trips that don’t have an origin or destination at a place of accommodation (places of accommodation almost always being the survey home). I am aware of other definitions of trip chaining that only count where there is a short activity between trips but that would be require much more complex analysis.

As expected, mums were doing a lot of trip chaining on school weekdays, but curiously dads weren’t that far behind. And in the school holidays and on weekends dads were doing more train chaining than mums (perhaps to give mums a break?).

Trip chaining was much less common on weekends for all groups.

For mums the most common trip type not involving travel to or from home was between work and pick-up or drop-off of someone (most likely between a school and a workplace). A long way behind was travel between work and shopping, pick-up/drop-off someone and shopping, and between two pick-up/drop-off someone activities.

For dads the most common trip type not involving travel to/from home was between two work-related activities, closely followed by between work and pick-up / drop-off someone, and then between work and social activities.

So mums’ trip chaining was dominated by pick-ups and drop-offs of people, while dads’ was not.

Time spent travelling

There’s not a huge variation in median travel time per day between person groups, but dads had the highest on weekdays and mums generally had the lowest. Note that reported travel times were very often rounded to multiples of 5 minutes hence most of these medians are also multiples of 5.

Technical note: I have created a chart with average travel times and the numbers were higher but the shape of the chart was almost identical so I’m not including that here.

Travel distance from home

So were dads travelling further from home? I’ve calculated the straight distance between the home location and all travel destinations, and this chart shows the medians:

Dads sure did travel further from home on weekdays (particularly on school holidays when they might not be doing school drop-offs / pick-ups), with mums generally staying much closer to home.

Curiously, other males also travelled further from home than other females, so this pattern appears to be related to gender to some extent.

There was a lot less variation on weekends, with people generally travelling closer to home, as you might expect.

Daily distance travelled

Let’s broaden that out to median total distance travelled per day:

Dads generally travelled further on all day types, and mums the least. Everyone generally travelled less on weekends, and to some extent during school holidays (compared to school weekdays).

Travel distance to work

We can use ABS Census data to understand the on-road distance between home and workplaces, including for 2021. This data is for the working population aged 15+, and differentiates people based on whether they are caring for their own children (which is slightly different from living with their children).

The median distances to work were highest for dads at around 15.4 km for dads, followed by 11.9 km for mums, 11.7 km for other males, and 10.2 km for other females.

Travel to/from Central Melbourne

Public transport has its highest mode shares for travel to/from central Melbourne, so how did that vary by sex and parenting status? (for this analysis I’ve defined central Melbourne as the SA2s of Melbourne, Docklands, Southbank, and East Melbourne – on 2016 boundaries).

Before you get too excited about the differences, it’s worth pointing out all the proportions are small. The vast majority of people in Greater Melbourne don’t travel to central Melbourne on any given day. And of course people who lived in central Melbourne had many of their trips counted in this chart.

Sure enough, on weekdays dads were much more likely to travel to central Melbourne, and mums were least likely (although it was higher in the school holidays). On weekends, non-parents were much more likely to travel to the central city than parents (a fair bit of socialising by younger independent adults, no doubt).

Time of day of travel

The following chart shows the share of trip start times across the day for the different person types, and different day types:

Technical note: due to smaller sample sizes, weekend travel has been aggregated into 2-hour intervals. Weekdays have been aggregated into 1-hour intervals.

You can clearly see that on school weekdays, mums are doing a lot of travel between 8 and 9am, and between 3 and 4pm, which obviously relate to school start and finish times. In the school holidays, mums are doing a lot more travel through the interpeak period, probably reflecting parenting activities for kids not at school.

On school days, trips by dads started earlier and finished later than mums. But during school holidays dads made a smaller proportion of their trips between 8am and 9am, suggesting they also had a significant role in school drop offs in the morning.

During the weekday inter-peak period dads were less likely to travel than mums (except around lunchtime). Other females had a small peak in travel around 5-6pm, which is probably related to them being more likely to work full time.

On weekends it seems dads were slightly more likely to travel in the morning compared to mums who were slightly more likely to travel in the afternoon.

Did mum or dad take the kids to/from school?

We’re seeing some pretty strong themes related to the school peaks. It is possible to filter for trips to pick up or drop off someone from a place of education on school weekdays and then disaggregate between mums and dads. I’ve split this analysis into an AM peak, a PM school peak (2-4pm), and a PM commuter peak (4-6pm) – as there were significant numbers of pick ups later in the afternoon – presumably following after-school care.

Mums did the bulk of school drop offs and pick ups at all times of day, particularly in the PM school peak. In the PM commuter peak, dads share of pick ups rose to 35% – no doubt related to the ability to do these pick ups after a full-time day at work.

What types of adults are using modes at different times of day?

For this question I have limited analysis to school weekdays, aggregated all of public transport to one group, and aggregated vehicle drivers, passengers, and motorcyclists into “vehicle” to overcome issues with small sample sizes. I’ve included the proportion of the working aged population sample on the right-hand side for ready reference.

In general, parents were over-represented in vehicles in peak periods, mums were over-represented in the interpeak in vehicles, and parents were under-represented in public and active transport at most times of day.

The peak periods saw more public transport trips by dads than mums, while the roads (and footpaths) saw a lot more trips by mums than dads.

Early morning travel was predominately by males (76%), while females were slightly more prevalent in vehicles during the interpeak (60%). Reported walking trips skewed female at all times of day.

However if we look at travel time, rather trip counts, we get a slightly different picture:

Dads spent more time travelling than mums in peak periods on both public and private transport, but much less time than mums in the inter-peak.

Mode split

Here’s how it looks for travel in general:

Mums were the least likely to use public transport (especially on the weekend), closely followed by dads.

Non-parents had the lowest private transport mode share (although still a majority mode share), and were most likely to use active transport.

Here’s overall mode shares of journeys to work (Greater Melbourne 2016), which I’ve disaggregated for workplaces inside and outside the City of Melbourne area (as workplace location has a massive impact on mode shares):

Parents were much more likely to use private transport across the geographies and sexes. Of those working outside the City of Melbourne, parents also had about half the public transport mode share of non-parents.

Men were much more likely to cycle to work than women, and dads were more likely to cycle than other men.

Here is a look at private transport mode shares by distance between home and work, gender and parenting status:

The difference in private mode share between parents and non-parents was largest for journeys up to 10 km. Mums had the highest private mode share for journeys 1 to 20 kms. For journeys over 25 km, sex became more influential than parenting status with men more likely to use private transport.

Another curiosity here is the very short journeys (less than 0.5 km) where men were much more likely to use private transport than women (regardless of parenting status) – for what is probably a walkable distance for most people. Are men more lazy when it comes to short walks to work? And/or are men more likely to need their car at work?

I have previously also analysed public transport mode share by age and family position. I’ve reproduced that analysis here:

For ages 35 to 59, mums generally had lower public transport mode share than dads. Younger non-parenting women had higher public transport mode shares than younger non-parenting men.

Here’s how it looks for 2016 journeys to work (I’m not using 2021 data because of COVID lockdowns):

Female public transport mode share was signficantly higher than males for most ages – except for those typical parenting years between their late 30s to early 50s. Younger adults were much more likely to work in the inner city, and even more so for females. For more discussion on this, see Why are younger adults more likely to use public transport? (an exploration of mode shares by age – part 1)

I’ve also split this by sex and parenting status and analysed the changes between 2006 and 2016 (analysis lifted from: Why are young adults more likely to use public transport? (an exploration of mode shares by age – part 3))

Note there is a very different Y-axis scale for City of Melbourne and elsewhere.

There were a few really interesting take-aways:

  • Public transport (PT) mode shares increased over time for almost all age bands, work locations, and for parenting and non-parenting workers.
  • Parenting workers mostly had lower public transport mode shares than non-parenting workers of the same age, except for:
    • dads over 30 who worked in the City of Melbourne,
    • mums in their early 30s who worked in the City of Melbourne in 2016, and
    • mums and dads in their 50s who worked outside the City of Melbourne (who had low PT mode shares around 4-5%, similar to non-parenting workers of the same age)
  • Public transport mode shares for journeys to work in the City of Melbourne mostly declined with increasing age between 20 and 50, regardless of parenting responsibilities.
  • For people who worked outside the City of Melbourne, the mode share profile across age changed significantly over time for young adults. In 2006 there was a steady decline with age, but in 2011 PT mode shares were generally flat for those in their 20s, and in 2016 PT mode shares peaked for women in their late 20s (and also had a quite new pattern for dads in their 20s).
  • For parenting workers who worked outside the City of Melbourne there was actually a slightly higher PT mode share for those over the age of 50. Parents over 50 might have older children who are more independent and therefore less reliant on their parents for transport. This might make it easier for the parents to use public transport. However this trend did not hold for dads in 2016.
  • PT mode shares for non-parenting women increased slightly beyond age 55 for all work locations. This will include women who were never parents, as well mums with non-dependent children so might again reflect a small return to public transport once children become independent. It may also be influenced by discounted PT “Seniors” fares available to people over 60 who are not working 35+ hours per week.

Mode split of public transport use

Which modes of public transport were the different person classifications using in Melbourne? Sufficient survey sample is only available for school weekdays, and it’s important to keep in mind that trams dominate inner city radial on-street public transport in Melbourne (unlike most comparable cities where buses dominate this function). This chart adds up all trip legs so there is no data loss with multi-modal public trips:

Unfortunately this data doesn’t line up with reported public transport patronage for the same time period (below), suggesting that tram travel may be under-reported in VISTA (although the above chart is filtered for persons aged 20-64):

Biased as the VISTA data might be towards certain modes, it still suggests dads were more likely to be using trains and least likely to be using buses.

I’ve also looked at use of public transport in journeys to work for 2016. Workers can report up to three modes of travel, and I’ve extracted counts of workers who used each of the three main modes of public transport in Greater Melbourne (note: people who used multiple public transport modes will be counted in multiple columns).

Parents (who travelled to work) were much less likely use bus or tram to get to work than non parents. But the story is bit different for trains: Dads were slightly more likely to commute by train than other males, while mums were less likely to commute by train than other females. This might be related to where mums work – more on that soon.

Mode use by sex and parenting

We can flip the mode-split charts around to look at the composition of adult users of different travel modes:

Technical Note: there’s insufficient sample of tram, bus, and bicycle travel on non-school weekdays and weekends so those are not on the chart.

Trams, buses, private vehicles, and walking generally skewed female, while trains and particularly bicycles skewed male (except weekend trains).

Mums were under-represented on all modes except private vehicles where they were significantly over-represented. Mums were least represented on bicycles.

Dads were under-represented on trams and buses, and over-represented in vehicles, and on bicycles.

Non-parents were over-represented on trains and trams, and walking on weekends.

There were many more dads than mums on trains on weekdays, and many more mums than dads travelling in (private) vehicles on school weekdays (but not so much on weekends and school holidays).

Trip purposes

We want to know the purposes of people’s travel, but actually purpose can only really be attributed to the activity before and after a trip. For this analysis I’ve used the trip destination purpose as the trip purpose, and I’ve excluded trips where the destination was home (as that would be close to half of trips and not very interesting). Also keep in mind that trips can also vary considerably in length and duration.

On weekdays, significantly more trips by males were work-related. Mums had a standout different pattern on school weekdays with many more trips being about someone else’s travel (particularly school children) and much less often being work-related (or should we say “paid work”-related).

During school holidays, about 1 in 5 trips by mums were about other people’s travel. But on weekends dads were doing slightly more trips that are about other people’s travels (perhaps to make up for them doing less of such trips on weekdays?).

On weekends social and shopping trips were much more common than work trips, as you’d expect.

Radial-ness of travel

A while ago I looked at the radial-ness of travel – that is the difference in bearing (angle) between a trip aligned directly to/from the Melbourne CBD and the actual alignment of the trip. Trips generally skew towards being radial, reflecting the importance of the central city, and just generally the shape of the city. Previously I’ve disaggregated by age, sex, and many other variables.

So how does radial-ness vary across sex and parenting status?

On weekdays mums were the clear outlier, with substantially fewer radial trips and more non-radial trips, likely including many trips to/from schools and other caring destinations.

Weekend travel was a fair bit less radial in general, and again mums had the least radial travel of all person groups.

Okay so that’s a lot of ways we can compare travel patterns by sex and parenting (let me know if you think I’ve missed any other useful breakdowns). Now…

What can explain these differences?

A lot of the above data is probably unsurprising, because males and females, and particularly mums and dads, generally have different levels of workforce participation and caring responsibility, amongst other differences. What follows is an examination of potential explanatory variables for the different travel behaviour observed.

Main activity

First up, main activity as captured by VISTA:

Dads were most likely to be working full-time, and mums least likely to be working full-time. Mums were much more likely to be working part-time or “keeping house”.

As an aside: I actually find “keeping house” to be a bit devaluing of parents (usually mums) who dedicate much of their time doing the critically important work of raising children. And I know from personal experience it’s pretty hard to actually “keep house” when you have young children who need active engagement across most of their waking hours. No doubt others falling in the “keeping house” category might be caring for other adults or the elderly. Is it time for a caring-related category?

Curiously non-parenting females were much less likely to be working full time than non-parenting males. Perhaps non-parenting females were more likely to be doing some caring for others not living with them? Perhaps some mums decide to stay working part-time after their children move out? Or it might be something else?

We can break the analysis down further by age:

Technical note: Data isn’t presented for mums and dads aged 20-29 due to insufficient survey sample.

Curiously, dads were less likely to be working full-time with increasing age, while mums became slightly more likely to be working full-time at older ages (as children get older and require less supervision?).

Occupation (employment)

We call drill down further by looking at employment occupations:

Mums were much less likely to be in the workforce than dads, but curiously had almost the same proportion of professionals (perhaps reflecting women’s slightly higher levels of education, on average).

Men were more likely to work in occupations where public transport is probably less competitive, including technicians, trades workers, labourers, and machinery operators and drivers (with likely exceptions for central city work sites).

Employment Industry

There are also notable differences in employment industries by sex and parenting:

There are probably no great surprises in the above chart, with men much more likely to work in construction, information media and telecommunications, manufacturing, transport, postal, and warehousing, and women much more likely to work in education, training, health care, and social assistance.

Access to independent private mobility

Does the ability of people to drive themselves around in private vehicles differ by gender and parenting status? And could this explain their different travel patterns?

For this analysis, I’ve re-used the following household classifications from a previous post:

  • No MVs – no motor vehicles,
  • Limited MVs – fewer motor vehicles than licenced drivers, or
  • Saturated MVs – at least as many motor vehicles as licenced drivers.

I’ve also classified individuals as to whether or not they have a “solo” driving licence (i.e. probationary or full licence, but not learner’s permit).

I’ve then combined these two dimensions (except for people in households with no motor vehicles as driver’s licence ownership is largely immaterial for this analysis).

There were small differences between mums and dads, with mums slightly less likely to have a solo driver’s licence than dads (95% v 98%), mums slightly less likely to have independent private mobility (75.5% v 78.6%), and mums slightly more likely to live in a household without any motor vehicles (1.7% v 1.0%). These slight differences might suggest mums would have lower private transport mode shares than dads, but we’ve actually seen above that the opposite is true. Therefore access to independent private mobility is unlikely to explain much of the differences in travel between mums and dads.

There weren’t substantial differences between non-parenting men and women, other than non-parenting men having slightly high solo licence ownership (91% v 88%).

Parents were more likely to have a solo driver’s licence than non-parents, and over three-quarters lived in a household with saturated motor vehicle ownership. Access to independent private mobility aligns strongly with parents’ much higher private transport mode shares, and is probably considered essential for parents in most parts of Melbourne.

Indeed, we can also break this down by geography – using a simple inner/middle/outer disaggregation of Melbourne:

For all person categories there’s a strong relationship with distance from the city centre, with significantly lower levels of motor vehicle ownership in the inner areas. However solo licence ownership was very high for parents even in the inner suburbs (94% of mums and 98% of dads).

86% of dads and 87% of mums in outer Melbourne lived in households with saturation motor vehicle ownership. However, 5% of mums in the outer suburbs didn’t have a solo licence, which could make getting around quite challenging, and highlights the importance of quality public transport services in these areas.

Around 14% of non-parents in the inner suburbs lived in households without motor vehicles.

Where do parents tend to live?

It probably won’t surprise many readers to hear that parents made up a much larger share of the residential population in the outer suburbs, particularly urban growth areas:

But if you look closely, you’ll also see quite low proportions of parents along train lines, tram lines, and the public transport rich inner suburbs.

In fact, it’s possible to examine the type of households per dwelling by distance from train stations (I’m excluding areas within 3 km of the CBD).

Technical notes: I’ve calculated straight distance between SA1s centroids and their nearest train station points as per GTFS data in May 2024. The only significant change in train stations between August 2021 and May 2024 was the merger of Surrey Hills and Mont Albert into Union Station in 2023. So it’s not perfect analysis but I’m also not interested in precision below 1% resolution. I’ve also excluded unoccupied and non-private dwellings.

Dwellings close to train stations are significantly less likely to contain parents.

Is this because parents cannot afford family-friendly dwellings near train stations? Is it because dwellings near train stations are less family-friendly? Or is it because many parents like to build their own home on the urban fringe? Or some combination of these?

Well, the census tells us how many bedrooms there are in most occupied private dwellings, and the following chart shows the relationship between number of bedrooms and distance from train stations (again, excluding areas within 3 km of the CBD):

Sure enough, dwellings near train stations generally had fewer bedrooms.

And we can also use census data to show the relationship between number of bedrooms in a dwelling, and whether the household includes parents + children:

Over 90% of parenting households had three or more bedrooms, and half had four or more bedrooms. But almost half of all dwellings within 1 km of a train station had two or fewer bedrooms rendering them not very family-friendly.

Just to take it slightly further, I’ve put all three dimensions on one chart and this shows that dwellings close to stations with three or more bedrooms were slightly less likely to house parenting families:

I think the lower availability of family-friendly housing near rapid public transport is quite likely to be contributing to lower public transport mode shares for parents, particularly as there is a clear relationship between public transport use and proximity to rapid transit stations (see: Are Australian cities growing around their rapid transit networks?)

That said, there may also be an issue around whether many families can afford three-bedroom homes close to train stations as they often have less than two full-time incomes supporting three or more people. Might young professional couples with no kids and/or share houses of young professionals be better placed to compete for this housing?

Where do men and women work in Melbourne?

Could differences in journey to work mode splits be explained by differences in workplace location?

Here’s a map of gender balance by workplace location across Melbourne for 2021 at destination zone geography (DZs) (sorry not all outer suburbs included on the map as I didn’t want to lose the inner area detail). Blue areas skew male, orange areas skew female.

Anyone with knowledge of Melbourne’s urban geography will instantly see large industrial areas shaded blue, and plenty of orange in most other places.

These skews follow industries with male and female dominant workforces. In fact, I’ve manually done some rough grouping of destination zones where there is a clear dominant land uses (not exhaustive but results should be fairly indicative), and here is the sex breakdown by land use type:

Industrial areas and Melbourne Airport skewed heavily male, while hospitals and large shopping centres skewed female. Universities skewed female, and the CBD and surrounding areas slightly skewed male.

What about parenting? Something to keep in mind is that 43% of the working population were living with their children.

Parenting workers were seen more in the middle and outer suburbs, which is also where parents skewed as a home location, so there’s undoubtedly a relationship there.

Here’s the parenting breakdown by dominant land use classification:

Parents were under-represented in major shopping centres (I’m guessing a skew to younger employees), but also to a small extent universities and the central city. Parents were slightly over-represented in hospitals, Melbourne Airport, industrial areas, and the rest of Melbourne.

Another way to represent this data is looking at the distribution of workplace locations by distance from the Melbourne CBD:

Probably the biggest stand-out is that mums skewed towards suburban employment locations, while non-parenting females were more likely to be working closer to the city centre.

The distribution of workplace distance from the CBD for males only differed slightly between those parenting and non-parenting. Dads were less likely to be work between 2-10 km from the Melbourne CBD than non-parenting males.

Employment density

I’ve previously shown that private transport mode shares are generally much lower in areas with higher job density (likely due to higher car parking costs and increased public transport accessibility). So do mums/dads/others typically work in areas of lower or higher job density, and could this explain differences in their mode splits?

To answer this I’ve calculated an aggregate weighted job density of the areas in which each category of person tends to work. How does that work? Well to start with I’ve calculated the job density of every destination zone in Greater Melbourne. I’ve then calculated a weighted average of these densities, where the density of each destination zone is weighted by the number of dads/mums/other males/other females working in that zone.

For females, those non-parenting generally worked in more jobs dense areas, compared to mums. This probably partly explains the lower public transport mode shares of mums.

For males it was the reverse – dads generally worked in more jobs-dense locations.

Overall was only a tiny difference between men and women in aggregated weighted job density:

That was a lot of charts, can you summarise that?

The following table attempts to highlight key variations from the overall average for different types of adults:

Type of adultTravel patternsDestination patternsMode split Explanatory factors
ParentsMore trips per person on weekdays.
More trip chaining.
Higher private mode share.Live further from public transport.
Lack of family-friendly dwellings near public transport.
Live in outer suburbs.
Higher car ownership.
MumsMore travel during weekday interpeak.
Highest trip chaining.
Travel closer to home.
Work closer to home.
Less radial travel.
Least likely to work in CBD.
Very high private transport mode share.Do most school drop offs / pick ups.
Least likely to work full time.
Less likely to work in job-dense areas.
DadsTravel longer distances.
Travel further from home.
More time spent travelling.
Travel further from home.
Work further from home.
More likely to work in CBD.
More likely to use trains.
More likely to use bicycles.
Most likely to work full time.
More likely to work in job-dense areas.
Non-parenting womenTravel closer to home.
Work closer to home.
Higher public transport use.More likely to work in job-dense areas.
Most likely to work in central city.

The explanatory factors in the right hand column will not be independent. For example, many parents probably find it infeasible to live near public transport, so they live further away and are more car-dependent.

What does all this mean for transport planning interventions?

I won’t say a lot on this topic (I tend to avoid policy prescriptions on this blog) but I will say I think some caution is required here.

One perspective might be that the proportion of males and females travelling on a mode at a particular time of the week will not change, and therefore interventions might predominantly benefit the existing user base (eg higher inter-peak public transport service frequencies might benefit women more than men).

However another perspective might be that interventions remove the barriers for one gender to utilise a mode of transport and might have significant benefits for the minority gender in the current user base. For example, significantly safer cycling infrastructure might encourage more women to cycle and lead to a more even balance between genders – indeed I’ve uncovered evidence about that on this blog.

So many mums driving kids to school!

One thing that really stands out to me is that mums do the vast majority of school drop offs and pick ups, and most of this travel is (now) happening by private vehicle. This is potentially impacting women’s workforce participation, and the traffic volumes are certainly contributing to road congestion. It might also be impacting women’s mode choices as school trips are generally more difficult on public transport, and mums do a lot of trip chaining. They might be using private transport for some trips mostly because those trips are chained with school drop-off/pick-ups.

What could you do to reduce private transport trips for school drop off / pick ups, and potentially also increase women’s workforce participation and public transport mode share?

  • Make interventions that increase the share of school students who travel to/from school independently by active or public transport
  • For school trips that are accompanied by a parent, encourage a mode shift towards active transport (realistically, public transport is less likely to be an attractive mode for many accompanied trips to school, unless it is on the way to another destination)
  • Provide at-school before-school and after-school care to enable both parents the opportunity to work full time (indeed government subsidies are provided in Victoria at least)

How might things have have changed post-COVID?

Unfortunately at the time of writing rich data is only really available for pre-COVID times.

A major change post-COVID is that many white collar professionals are now working from home some days per week, which has reduced travel to major office precincts.

I would not be surprised to see dads taking a slightly higher share of the school drop-off pick-up task as this can be easier to do on a work-from-home day. Might this have enabled women to work longer hours? There have also been higher child-care subsidies implemented recently that might also lift women’s workforce participation.

Indeed here’s a chart summarising female labour force status since 2012 (not seasonally-adjusted):

Technical note: I would have preferred to use seasonally adjusted or trend series numbers to remove the noise, but these data sets do not include counts for “not in labour force”

Following the major COVID disruption period around 2020-2021, women have been more likely to be working full time and more likely to be in the labour force. This might be partly related to new working-from-home patterns.

Hopefully more post-COVID travel data will be released before too long and I can investigate if there are any substantial shifts in the patterns between men and women, parents and non-parents.

Do let me know if you think there is more that should explored regarding the differences in travel patterns and explanatory variables for men and women, parents and non-parents.


How radial is general travel in Melbourne? (part 2)

Wed 11 September, 2019

In part 1 of this series, I looked at the radialness of general travel around Melbourne based on the VISTA household travel survey. This part 2 digs deeper into radialness by time of the day and week, and maps radialness and mode share for general travel around Melbourne.

A brief recap on measuring radialness: I’ve been measuring the difference in angle between the bearing of a trip, and a straight line to the CBD from the trip endpoint that is furthest from the CBD (origin or destination). An angle of 0° means the trip is perfectly radial (directly towards or away from the CBD) while 90° means the trip is entirely orbital relative to the CBD. An average angle in the low 40s means that there isn’t really any bias towards radial travel. I’ve been calling this two-way off-radial angle. Refer to part 1 if you need more of a refresher.

How does trip radialness vary by time of week?

The first chart shows the average two-way off-radial angle for trips within Greater Melbourne by time and type of day, for private transport, public transport, and walking.

Technical notes: I’ve had to aggregate weekend data into two hour blocks to avoid issues with small sample sizes. I’m only showing data where there are at least 100 trips for a mode and time (that’s still not a huge sample size so there is some “noise”). Trips times are assigned by the clock hour of the middle of the trip duration. For example, a trip starting at 7:50 am and finishing at 9:30 am has a mid-trip time of 8:40 am and therefore is counted in 8 – 9 am for one hour intervals, and 8 – 10 am for two hour intervals.

You can see:

  • Public transport trips are much more radial at all times of the week, but most particularly in the early AM peak and in the PM commuter peak. They are least radial in the period 3-4 pm on weekdays (PM school peak), which no doubt reflects school student travel, which is generally less radial.
  • Private transport trips are more radial before 8 am on weekdays, and in the early morning and late evening on weekends. Curiously private transport trips in the PM peak don’t show up as particularly radial, possibly because there is more of a mix of commuter and other trips at that time.
  • Walking trips show very little radial bias, except perhaps in the commuter peak times on weekdays.

When I drill down into specific modes, the sample sizes get smaller, so I have used 2 hour intervals on weekdays, and 3 hour intervals on weekends. Also to note is that VISTA assigns a “link mode” to each trip, being the most important mode used in the journey (generally train is highest, followed by tram, bus, vehicle driver, vehicle passenger, bicycle, walking only). I am using this “link mode” in the following charts.

Some observations:

  • Train trips are the most radial, followed by tram trips (no surprise as these networks are highly radial).
  • Bicycle trips are generally the third most radial mode, except at school times.
  • Public bus trips are more radial in the commuter peak periods, and much less radial in the middle of the day on weekdays. The greater radialness in commuter peaks will likely reflect people using buses in non-rail corridors to travel to the city centre (particularly along the Eastern Freeway corridor). Most of Melbourne’s bus routes run across suburbs, rather than towards the city centre, which will likely explain bus-only trips being less radial than train and tram, particularly off-peak.

How does radialness vary by trip purpose and time of week?

The following chart shows the average two-way off-radial angle of trips by trip purpose (at destination) and time of day:

Some observations:

  • Work related trips are generally the most radial, particularly in the AM peak (as you might expect), but less so on weekdays afternoons.
  • Weekday education trips are the next most radial (excluding trips to go home in the afternoon and evening), except at school times (school travel being less radially biased than tertiary education travel).
  • Social trips become much more radial late at night on weekends, probably reflecting inner city destinations.
  • Recreational trips are the least radial on weekends.
  • Otherwise most other trip purposes average around 35-40° – which is only slightly weighted towards radial travel.

What is the distribution of off-radial angles by time of day?

So far my analysis has been looking at radialness, without regard to whether trips are towards or away from the CBD. I’ve also used average off-radial angles which hides the underlying distribution of trip radialness.

I’m curious as to whether modes are dominated by inbound or outbound trips at any times of the week (particularly private transport), and the distribution of trips across various off-radial angles.

So to add the inbound/outbound component of radialness, I am going to use a slightly different measure, which I call the “one-way off-radial angle”. For this I am using a scale of 0° to 180°, with 0° being directly towards the CBD, and 180° being directly away from the CBD, and 90° being a perfectly orbital trip with regard to the CBD. For inbound trips, the one-way off-radial angle will be the same as the two-way off-radial angle, but outbound trips will instead fall in the 90° to 180° range.

One-way off-radial angles are still calculated relative to the trip end point (origin or destination) that is furthest from the CBD. I explained this in part 1.

Here is the distribution of one-way off-radial angles by time of day for trips where train was the main mode:

A reminder: only time intervals with a sample of at least 100 trips are shown.

In the morning, trips are very much inbound radial, with around three-quarters being angles of 0°-10°. Likewise in the PM peak, almost three-quarters of train trips are very outbound radial with angles 170°-180°.

As per the second chart in this post, train trips remain very radial throughout the day. But there is slightly more diversity in off-radial angles 3-4 pm on weekdays, when many school students use trains for journeys home from school that are less radially biased. Less radial trips could be a result of using two train lines, using bus in combination with train, or using a short section of the train network that isn’t as radial (eg Eltham to Greensborough, Williamstown to Newport, or a section of the Alamein line).

On weekends it’s interesting to see that there are many more inbound than outbound journeys between 12 pm and 2 pm on weekends. The “flip time” when outbound journeys outnumber inbound journeys is probably around 2 pm. This is consistent with CBD pedestrian counters that show peak activity in the early afternoon.

One problem with the chart above is that volumes of train travel vary considerably across the day. So here’s the same data, but as (estimated) average daily trips:

You can see the intense peak periods on weekdays, and a gradual switch from inbound trips to outbound trips around 1 pm on weekdays. There’s also a mini-peak in the “contra-peak” directions (outbound trips in the AM peak and inbound trips in the PM peak).

The weekend volumes are for two hour intervals so not directly comparable to weekdays (which are calculated for one hour intervals), but you can see higher volumes of inbound trips until around 2 pm, and then outbound trip volumes are higher.

Those results for trains were probably not surprising, but what about private vehicle driver trips?

There is much more diversity in off-radial angles at all times of the day, and a less severe change between inbound and outbound trips across the day.

On both weekday and weekend mornings there is a definite bias towards inbound travel. Afternoons and evenings are biased towards outbound travel, but not nearly as much (it’s much stronger late at night). This is consistent with the higher average two-way off-radial angle seen for private transport in the PM peak compared to the AM peak.

Here is the same data again but in volumes:

This shows the weekday AM peak spread concentrated between 8 and 9 am, while the PM peak is more spread over three hours (beginning with the end of school).

Here are the same two charts for tram trips (the survey sample is smaller, so we can only see results for weekdays):

Again there is a strong bias to inbound trips in the morning and outbound in the afternoon, with slightly more diversity in the PM school peak, and early evening.

Next up public bus (a separate category to school buses, however many school students do travel on public buses):

There is a lot more diversity in off-radial angles (particularly 2-4 pm covering the end of school), but also the same trend of more inbound trips in the morning and outbound trips in the afternoon.

Next up, bicycle:

There’s a fair amount of diversity, across the day, with inbound trips dominating the AM peak and outbound trips in the PM commuter peak (but not as strongly in the PM school peak). Weekend late afternoon trips show a little more diversity than morning and early afternoon trips, but the volumes are relatively small.

Next is walking trips:

There is considerable diversity in off-radial angles across most of the week, although outbound trips have a larger share in the late evening.

Walking volumes on weekdays peak at school times. On weekends walking seems to peak between 10 am and 12 pm and again 4 pm to 6 pm, but not considerably compared to the rest of the day time.

Mapping mode shares and radialness

So far I’ve been looking at radialness for modes by time of day. This section next section looks at radialness and mode shares by origins and destinations within Melbourne.

In recent posts I’ve had fun mapping journeys to work from census data (see: Mapping Melbourne’s journeys to work), so I’ve been keep to explore what’s possible for general travel.

VISTA is only a survey of travel (rather than a census), so if you want to map mode shares of trips around the city, you unfortunately need to lose a lot of geographic resolution to get reasonable sample sizes.

The following map shows private transport mode shares for journeys between SA3s (which are roughly the size of municipalities), where there were at least 80 surveyed trips (yes, that is a small sample size so confidence intervals are wider, but I’m also showing mode shares in 10% ranges). Dots indicate trips within an SA3, and lines indicate trips between SA3s. I’ve animated the map to make try to make it slightly easier to call out the high and low private mode shares.

You can see lower private transport mode shares for radial travel involving the central city (Melbourne City SA3), particularly from inner and middle suburbs (less so from outer suburbs). Radial travel that doesn’t go to the city centre generally has high private transport mode shares.

I also have origin and destination SA1s for surveyed trips. Here is a map showing all SA1-SA1 survey trip combinations by main mode, animated to show intervals of two-way off-radial angles:

It’s certainly not a perfect representation because of the all the overlapping lines (I have used a high degree of transparency). You can generally see more blue lines (public transport) in the highly radial angles, and almost entirely red (private transport) and short green lines (active transport) for larger angle ranges. This is consistent with charts in my last post (see: How radial is general travel in Melbourne? (Part 1)).

You can also see that few trips fall into the 80-90° interval, which is because I’m measuring radialness relative to the trip endpoint furthest from the CBD. An angle of 80-90° requires the origin and destination to be about the same distance from the CBD and for the trip to be relatively short.

So there you go, almost certainly more than you ever wanted or needed to know about the radialness of travel in Melbourne. I suspect many of the patterns would also be found in other cities, although some aspects – such the as the geography of Port Phillip Bay – will be unique to Melbourne.

Again, I want to the thank the Department of Transport for sharing the full VISTA data set with me to enable this analysis.


How radial are journeys to work in Australian cities?

Fri 14 June, 2019

In almost every city, hordes of people commute towards the city centre in the morning and back away from the city in the evening. This largely radial travel causes plenty of congestion on road and public transport networks.

But only a fraction of commuters in each city actually work in the CBD. So just how radial are journeys to work? How does it vary between cities? And how does it vary by mode of transport?

This post takes a detailed look at journey to work data from the ABS 2016 Census for Melbourne, Sydney, and to a less extent Brisbane, Perth, Adelaide and Canberra. I’ve included some visualisations for Melbourne and Sydney that I hope you will find interesting.

How to measure radialness?

I’m measuring radialness by the difference in degrees between the bearing of the journey to work, and a direct line from the home to the CBD of the city. I’m calling this the “off-radial angle”.

So an off-radial angle of 0° means the journey to work headed directly towards the CBD. However that doesn’t mean the workplace was the CBD, it might be have been short of the CBD or even on the opposite side of the CBD.

Similarly, an off-radial angle of 180° means the journey to work headed directly away from the CBD. And a value of 90° means that the trip was “orbital” relative to the CBD (a Melbourne example would be a journey from Box Hill that headed either north or south). And then there are all the angles in between.

To deal with data on literally millions of journeys to work, I’ve grouped journeys by home and work SA2 (SA2s are roughly the size of a suburb), and my bearing calculations are based on the residential centroid of the home SA2 and the employment centroid of the work SA2.

So it is certainly not precise analysis, but I’ve also grouped off-radial angles into 10 degree intervals, and I’m mostly looking for general trends and patterns.

So how radial are trips in Melbourne and Sydney?

Here’s a chart showing the proportion of 2016 journeys to work at different off-radial angle intervals:

Technical note: As per all my posts, I’ve designated a main mode for journeys to work: any journey involving public transport is classed as “Public”, any journey not involving motorised transport is classed as “Active”, and any other journey is classed as “Private”.

In both cities over 30% of journeys to work were what you might call “very radial” – within 10 degrees of perfectly radial. It was slightly higher in Melbourne.

You can also see that public transport trips are even more radial, particularly in Melbourne. In fact, around two-thirds of public transport journeys to work in 2016 had a destination within 2 km of the CBD.

Melbourne’s “mass transit” system (mostly trains and trams) is very radial, so you might be wondering why public transport accounts for less than half of those very radial journeys (41% in fact).

Here are Melbourne’s “very radial” journeys broken down by workplace distance from the Melbourne CBD:

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

Public transport dominates very radial journeys to workplaces within 2 km of the centre of the CBD, but is a minority mode for workplaces at all other distances. Many of these highly radial journeys might not line up with a transit line towards the city, and/or there could well be free parking at those suburban workplaces that make driving all too easy. I will explore this more shortly.

Sydney however had higher public transport mode shares for less radial journeys to work. I think this can be explained by Sydney’s large and dense suburban employment clusters that achieve relatively high public transport mode shares (see: Suburban employment clusters and the journey to work in Australian cities), the less radial nature of Sydney’s train network, and generally higher levels of public transport service provision, particularly in inner and middle suburbs.

Visualising radialness on maps

To visualise journeys to work it is necessary to simplify things a little so maps don’t get completely cluttered. On the following maps I show journey to work volumes between SA2s where there are at least 50 journeys for which the mode is known. The lines between home and work SA2s get thicker at the work end, and the thickness is proportional to the volume (although it’s hard to get a scale that works for all scenarios).

First up is an animated map that shows journeys to work coloured by private transport mode share, with each frame showing a different interval of off-radial angle (plus one very cluttered view with all trips):

(click/tap to enlarge maps)

I’ve had to use a lot of transparency so you have a chance at making out overlapping lines, but unfortunately that makes individual lines a little harder to see, particularly for the larger off-radial angles.

You can see a large number of near-radial journeys, and then a smattering of journeys at other off-radial angles, with some large volumes across the middle suburbs at particular angles.

The frame showing very radial trips was rather cluttered, so here is an map showing only those trips, animated to strip out workplaces in the CBD and surrounds so you can see the other journeys:

Private transport mode shares of very radial trips are only very low for trips to the central city. When the central city jobs are stripped out, you see mostly high private transport mode shares. Some relative exceptions to this include journeys to places like Box Hill, Hawthorn, and Footscray. More on that in a future post.

Here are the same maps for Sydney:

Across both of these maps you can find Sydney’s suburban employment clusters which have relatively low private transport mode shares. I explore this, and many other interesting ways to visualise journeys to work on maps in another post.

What about other Australian cities?

To compare several cities on one chart, I need some summary statistics. I’ve settled on two measures that are relatively easy to calculate – namely the average off-radial angle, and the percent of journeys that are very radial (up to 10°).

The ACT (Canberra) actually has the most radial journeys to work of these six cities, despite it being something of a polycentric city. Adelaide has the next most radial journeys to work, but there’s not a lot of difference in the largest four cities, despite Sydney being much more a polycentric city than the others. Note the two metrics do not correlate strongly – summary statistics are always problematic!

Here are those radialness measures again, but broken down by main mode:

Sydney now looks the least radial of the cities on most measures and modes, particularly by public transport.

The Australian Capital Territory (Canberra) has highly radial private and active journeys to work, but much less-radial public transport journeys than most other cities. This probably reflects Canberra’s relatively low cost parking (easy to drive to the inner city), but also that the public transport bus network is orientated around the suburban town centres that contain decent quantities of jobs.

Adelaide has the most radial journeys to work when it comes to active and public transport.

What about other types of travel?

In a future post, I’ll look at the radialness of general travel around Melbourne using household travel survey data (VISTA), and answer some questions I’ve been pondering for a while. Is general travel around cities significantly less radial than journeys to work? Is weekend travel less radial than weekday travel?

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