Why are younger adults more likely to use public transport? (an exploration of mode shares by age – part 2)

Sun 27 September, 2020

This is the second post in a series that explores why younger adults are more likely to use public transport (PT) than older adults, with a focus on the types of places where people live and work, including proximity to train stations, population density, job density, motor vehicle ownership and driver’s licence ownership.

In the first post, we found younger adults in Melbourne were more likely to live and work close to the CBD, but this didn’t fully explain why they were more likely to use public transport.

This analysis uses 2016 ABS census data for Melbourne, and data for the years 2012-18 from Melbourne’s household travel survey (VISTA) – all being pre-COVID19. See the first post for more background on the data.

Proximity to train stations

Melbourne’s train network is the core mass rapid transit network of the city offering relatively car-competitive travel times, particularly for radial travel. It’s not Melbourne’s only high quality public transport, but for the want of a better metric, I’m going to use distance from train stations as a proxy for public transport modal competitiveness, as it is simple and easy to calculate.

In 2016 younger adults (and curiously the elderly) were more likely to live near train stations:

Almost 40% of people in their 20s lived within one km of a station. Could this partly explain why they were more likely to use public transport?

Well, maybe partly, but public transport mode shares of journeys to work were quite different between younger and older adults at all distances from train stations:

Public transport mode shares fell away with distance from stations, and age above 20 (the 15-19 age band being an exception).

With VISTA data we can look at general travel mode share by home distance from a train station:

There’s clearly a relationship between PT mode share and proximity to stations, but there’s also a strong relationship between age and PT use, at all home distance bands from train stations.

Younger adults were also more likely to work close to a train station. Indeed 46% of them worked within about 1 km of a station:

And unsurprisingly people who work near train stations are also more likely to live near train stations:

The chart shows around 70% of people who worked within 1 km of a station lived within 2 km of a station. Also, 37% of people who worked more than 5 km from a station, also lived more than 5 km from a station.

But again, journey to work PT mode shares varied by both age and workplace distance from a train station:

For completeness, here is another matrix-of-worms chart looking at journey to work PT mode shares by age for both work and home distances from train stations:

PT mode share declined with age for most distance combinations, but this wasn’t true for the 15-19 age band, particularly where both home and work were within a couple of kms of a station. We know from part one that teenagers are much less likely to work in the city centre, so this might represent teenagers who happen to live near a station, but work locally and can easily walk or cycle to work.

If we take age out for a moment, here is the relationship between PT mode share of journeys to work and both home and work distance from train stations:

The relationship between PT mode share and work distance from a train station is much stronger than for home distance from a station.

So while home and work proximity to train stations influenced mode shares, it doesn’t fully explain the variations across ages. So what if we combine…

Work distance from the CBD, home distance from a train station

Work distance from a station is strongly related to work distance from the CBD, as the CBD and inner city has a higher density of train stations:

I expect workplace proximity to a train station to be a weaker predictor of mode share when compared workplace distance from CBD. That’s pretty evident when looking at journey to work PT mode share by place of work on a map:

And even more evident when you look at PT mode shares for both factors (regardless of age):

So perhaps work distance from the CBD, and home distance from a train station might be two strong factors for mode share? If we control for these factors, is there still a difference in PT mode shares across ages?

Time for another matrix of worms:

The chart shows that even when you control for both home distance from a station, and work distance from the CBD, there is still a relationship with age (generally declining PT mode share with age, with teenagers sometimes an exception). So there must be other factors at play.

Population density

Consistent with proximity to train stations and the CBD, younger adults are more likely to live in denser residential areas:

Higher residential density often comes with proximity to higher quality public transport. Indeed, here is the distribution of population densities for people living at different distances from train stations:

The next chart shows the relationship between residential density and mode shares – split between adults aged 20-39 and those aged 40-69:

The chart shows that both age and residential density are factors for journey to work mode shares. Younger adults had higher public transport mode shares for journeys to work at all residential density bands.

Similarly, VISTA data also shows PT mode shares vary significantly by both age and population density for general travel:

Technical note: data only shown where age band and density combination had at least 400 trips in the survey.

Curiously, people in their 60s living in areas with densities of 50-80 persons/ha were more likely to use public transport to get to work than those in their 40s and 50s living in the same densities (maybe due the presence of children?). For lower densities, PT mode share generally declined with increasing age (from 20s onward).

Population density is also generally related to distance from the CBD:

And here is a chart showing how PT mode share of journeys to work varied across both:

The chart shows home distance from the CBD had a larger impact on mode shares than population density. Indeed population density only seemed to have a secondary impact for densities above 40 persons/ha. However, as we saw in the first post, people living closer to the CBD were more likely to work in the city centre, and therefore more likely to use public transport in their journey to work.

Job density

Young adults were more likely to work in higher density employment areas in 2016, where public transport is generally more competitive (with more expensive car parking):

But yet again, there is a difference in mode shares between age groups regardless of work location job density:

So job density doesn’t fully explain the difference in PT mode shares across age groups.

I should add that job density is also strongly related to workplace distance from the CBD:

and workplace distance from train stations:

And putting aside age, PT mode shares for journeys to work are related to both workplace distance from the CBD and job density:

PT mode shares are also related to both job density and workplace distance from stations:

You might be wondering about the dot of higher job density (200-300 workers/ha) that is between 3 and 4 km from a train station. It’s one destination zone that covers Doncaster Westfield shopping centre – a large shopping centre on a relatively small piece of land (almost all of the car parking is multistory – see Google Maps)

Motor vehicle ownership

Are younger adults more likely to use public transport because they are less likely to own motor vehicles?

With census data, it is possible to measure motor vehicle ownership on an SA1 area basis by adding up household motor vehicles and persons aged 18-84 (as an approximation of driving aged people) and calculating the ratio. Of course individual households within these areas will have different levels of motor vehicle ownership.

Using this metric, young adults were indeed more likely to live in areas which have lower levels of motor vehicle ownership (in 2016):

But yet again, the PT journey to work mode shares varied between younger and older adults regardless of the levels of motor vehicle ownership of the area (SA1) in which they live:

Using VISTA data, we can calculate motor vehicle ownership at a household level. I’ve classified households by the ratio of motor vehicles to adults.

VISTA data shows PT mode shares strongly related to both age and motor vehicle ownership (I’ve shown the most common ratios):

You might be wondering why I didn’t calculate motor vehicle ownership at the household level for census data. Unfortunately it’s not possible for me to calculate the ratio of household motor vehicles to number of adults because ABS TableBuilder doesn’t let me combine the relevant data fields (for some reason).

The best I can do is the ratio of household motor vehicles to the usual number of residents (of any age). The usual residents may or may not include children under driving age – we just don’t know.

Nevertheless the data is still interesting. Here is how public transport mode shares of journeys to work varied across different vehicle : occupant combinations for households in Greater Melbourne:

Yes that’s a lot of squiggly lines – but for most combinations (excluding those with zero motor vehicles) there was a peak of PT mode share in the early 20s, and then a decline with increasing age.

The lines with green and yellow shades – where the ratio is around 1:2 or 1:3 – show a sharp drop around the mid 20s. I expect these lines are actually a mix of working parents with younger children, and working adult children living with their (older) parents. The high mode shares for those in their early 20s could represent many adult children living with their parents (but without their own car), while those in their 30s and 40s are more likely to be parents of children under the driving age. So the sharp drop is probably more to do with a change in household age composition.

If we want to escape the issue of children, the highest pink line is for households with one motor vehicle and one person (so no issues about the age of children because there are none present) – and that line has a peak in PT mode share in the mid 30s and then declines with age, suggesting other age-related factors must be in play.

But motor vehicle ownership levels aren’t only related to age. They are strongly related to population density,

..home distance from the CBD,

..and home distance from train stations:

And public transport mode shares are related to both motor vehicle ownership rates and population density (with motor vehicle ownership probably being the stronger factor):

Technical note: for these charts I’ve excluded data points with fewer than 5 qualifying SA1s to remove anomalous exceptions.

Public transport mode shares are also related to both motor vehicle ownership and home distance from the CBD:

And shares are also related to both motor vehicle ownership and home distance from a train station:

In all three cases, PT mode shares fell with increasing levels of motor vehicle ownership, but this effect mostly stopped once there were more motor vehicles than persons aged 18-84.

Drivers licence ownership

I’ve previously shown on this blog that people without a full car driver’s licence are much more likely to use public transport, which will surprise no one. So are younger adults less likely to have a driver’s licence?

VISTA data shows us that younger adults are indeed less likely to have a car driver’s licence, with licence ownership peaking around 97% for those in their late 40s and early 50s, and only dropping to 91% by age 75 (there is a little noise in the data):

So the lack of a driver’s licence by many young adults will no doubt partly explain why they are more likely to use public transport.

Consistent with VISTA, data from the BITRE yearbooks also shows that younger adults have become less likely to own a licence over time:

At the same time, those aged 60-79 have been more likely to own a licence over time.

But do public transport mode shares vary by age, even for those with a solo driver’s licence? (by solo, I mean full or probationary licence). The following chart shows public transport mode shares for age bands and licence ownership levels (data points only shown where 400+ trips exist in the survey data).

PT mode shares peaked for age band 23-29 for most licence ownership levels, including no licence ownership (there isn’t enough survey data for people older than 22 with red probationary licences – the licence you have for your first year of solo driving).

As an aside, there is a curious increase in public transport mode share for those aged over 60 without a drivers licence – this may be related to these people being eligible for concession fares and occasional free travel with a Seniors Card (if they work less than 35 hours per week).

So even younger adults who own a driver’s licence are more likely to use public transport.

But is this because they don’t necessarily have a car available to them? Let’s put the two together…

Motor vehicle and driver’s licence ownership

For the following chart I’ve classified households as:

  • “Limited MVs” if there were more licensed drivers than motor vehicles attached to the household,
  • “Saturated MVs” if there was at least as many motor vehicles as licensed drivers, and
  • “No MVs” if there were no motor vehicles associated with the household.

If there were any household motor vehicles I’ve further disaggregated by individuals with a solo licence and those without a solo licence (the latter may have a learner’s permit). I’ve only shown data points with at least 400 trip records in the category to avoid small sample noise (I am reliant on VISTA survey data).

Except for households with no motor vehicles, public transport mode share peaked for age band 18-22 or 23-29 and then declined with increasing age. So again there must be other age-related factors. However the impact of age is smaller than that of motor vehicle ownership and licence ownership.

Unfortunately driver’s licence ownership data is not collected by the census, so it is not possible to combine it with other demographic variables from the census.

Summary

So, what have we learnt in part two:

  • Younger adults are more likely to work and live near train stations, but that only partly explains why younger adults are more likely to use public transport.
  • Workplace distance from the CBD has a much bigger impact on public transport mode shares for journeys to work than home distance from a train station.
  • Younger adults are more likely to live in areas with higher residential density, but this only partly explains why they are more likely to use public transport.
  • Younger adults are more likely to work in areas with higher job density but this is highly correlated with workplace distance from the CBD, which is a stronger factor influencing mode shares.
  • Younger adults are more likely to live in areas with lower motor vehicle ownership (these areas are generally also have higher residential density and are closer to the city centre and to train stations), but this again only partly explains why they are more likely to use public transport. Motor vehicle ownership appears to be a stronger factor influencing mode shares than population density, distance from stations, or distance from the city.
  • Younger adults are less likely to have a driver’s licence, but again this only partly explains why they are more likely to use public transport.

While this analysis confirms younger adults tend to align with known factors correlating with higher public transport use, we are yet to uncover a factor or combination of factors that mostly explain the differences in public transport use between younger and older adults. That is, when we control for these factors we still see differences in public transport use between ages.

The next post in this series will explore the impacts on public transport use of parenting responsibilities, generational factors (birth years), and year of immigration to Australia.


Why are younger adults more likely to use public transport? (an exploration of mode shares by age – part 1)

Sat 19 September, 2020

Young adults are much more likely to use public transport (PT) than older adults.

Is it because younger adults are more likely to live and/or work near the city centre? Is it because they are more likely to live near train stations? Is it because they tend to live in higher density areas with better public transport? Is it because they are less likely to own a car? Is it because they are less likely to own a driver’s licence? Is it because they are less likely to be parents? Is it to do with their income? Is it related to how many of them are recent immigrants? Or is it a generational thing?

The answers are not as simple as you might expect. This is the first post in a series that aims to understand what influences mode choice across different ages. I’ll focus on (pre-COVID19) data about general travel and journeys to work in my home city of Melbourne, but I suspect the patterns will be similar in comparable cities.

About the data (boring but important)

My largest data source is the 2016 ABS census of population and housing which provides detailed demographic data about residents, captures the modes used for journeys to work, but doesn’t record travel for any other purposes and only covers a single day in August. There’s data on the travel choices of millions of people, and so it is possible to disaggregate data by several dimensions before you run into problems with small counts.

For general travel mode shares my data source is the Victoria Integrated Survey of Travel and Activity (VISTA) which is Victoria’s household travel survey, recording the transport and activity of a representative sample of Melbourne and Geelong residents across the whole year. The data set is smaller than the census (being a survey), but also contains rich demographic information and covers all travel purposes by people of all ages. I have used aggregated data over the survey years 2012-2018 to form a larger sample, so any underlying trends in behaviour over that period will be averaged out.

For this analysis I am filtering my data to either Greater Melbourne (for 2011 and 2016 census data) or otherwise the 31 local government areas (LGAs) that make up metropolitan Melbourne (all are entirely inside Greater Melbourne except Yarra Ranges).

All of this data was collected before the COVID19 pandemic, and of course travel patterns may well not return to similar patterns once the pandemic is over.

Consistent with elsewhere on this blog, I attribute

  • any journey involving a train, tram, bus, or ferry as a public transport journey (even if other modes were also used, including private transport),
  • a journey only involving walking and cycling as an active transport journey, and
  • any other journey as a private transport journey (mostly being car journeys).

This post mostly focuses on public transport – which I will often abbreviate to PT.

While the data sets I am using only identify sex as male or female, I want to acknowledge that not all people fit into binary classifications.

How does PT mode share vary by age and sex for general travel?

Here’s a chart showing public transport mode shares by age and gender using VISTA data for all travel purposes:

Public transport mode share peaked in the 15-19 age group – essentially around the later years of secondary school and early years of tertiary education or working life where people have more independence, may need to travel longer distances to get to school or university, and are too young and/or cannot afford private transport.

Public transport mode share then fell away with age, though the profile by gender is slightly different (some of this may be noise in the survey). Women under the age of 30 were more likely to use PT, but then they became less likely to use PT after age 30 – perhaps after the arrival of children.

Children under 10 years were least likely to use public transport, and there was only a small increase in public transport use amongst women aged over 65. Public transport use dropped considerably for those aged 85-89, and there wasn’t sufficient sample to confidently calculate mode shares for any older age groups.

How does PT mode share vary by age and sex for journeys to work?

Here is a chart of public transport mode share of journeys to work in Greater Melbourne by age and sex (using census data):

The chart shows women were much more likely to use public transport to get to work than men, particularly for young adults but also those in their 60s. Overall PT mode share was 17.7% for males and 20.3% for females. PT mode share peaked for females at age 26, and for males at age 24.

So what might explain the variations across age and gender? In this first post I’m going to explore the how mode share varies by home and work distance from the CBD.

Travelling to the city centre

We know that travel to/from the central city is much more likely to involve public transport, so here are general travel PT mode shares dis-aggregated by whether or not the trip was to/from the City of Melbourne (local government area):

For travel to/from the City of Melbourne, PT mode shares peaked around 50% for workers in their early 20s, and generally fell away with age, with females showing a higher PT mode share in all age groups (mode shares are only shown for ages 20-64 due to small samples of trips in other age groups).

For travel not to/from the City of Melbourne, PT mode shares peaked for teenagers and was very low for all other age groups, with only slightly higher mode shares for those in their 20s, early 30s, and early 80s.

With census journey to work data, we can increase the resolution to 2 year age-bands and dis-aggregate work destinations by distance bands from the CBD. The darker line of each colour is for females, the lighter for males.

Public transport mode share was much higher for workplaces near the CBD, and then declined with workplace distance from the CBD.

But within each workplace distance band from the CBD there was also a generally declining PT mode share with age, flattening out somewhat for ages above 45. While there was a difference between genders at all workplace distance bands, it was generally smaller than the overall mode share difference between genders for journeys to work.

How can these lines have quite a different curve shape to overall PT mode share by age/sex? Well, here is a chart showing the proportion of Greater Melbourne workers at every age who worked within 4 kms – and within 10 kms – of the Melbourne CBD:

Young adults were much more likely to work closer to the CBD than older adults, and women even more so (although they are not actually a majority of workers close to the CBD).

Teenagers were least likely to work in the City of Melbourne, which likely reflects their lack of qualifications for high-skill jobs that tend to locate in the central city.

The curves for men and women peaked at different ages, with younger adult women more likely to work in the City of Melbourne than younger adult men, which then flipped for ages 38+. This isn’t because of stay-at-home mums because the data only counts people who travelled to work.

Here’s another look at that data – showing the distribution of work locations from the CBD for age bands. Around 40% of young adult workers worked within 6 km of the CBD:

And flipping that, workplaces closer to the CBD have a higher proportion of younger adults:

Public transport mode shares for general travel (in the VISTA survey) were related to both age and trip destination distance from the CBD, with those in their 50s least likely to use PT for destinations within 5 km of the CBD:

So a major explanation why younger adults were more likely to use public transport in their journey to work is that they were more likely to work in the central city. However, when you control for travel proximity to the CBD there is still a significant relationship between PT mode share and age – other factors must be at play.

I’m curious – is the fact that younger adults (particularly women) were more likely to work in the city centre related to their…

Educational qualifications

Well, younger adults turn out to have the highest educational qualifications of any age group, with those in their early 30s generally being the most qualified (as at 2016):

Note: Supplementary codes includes people with no educational attainment.

Furthermore, younger women are generally more qualified than younger men, which could explain why a higher proportion of younger women work in the City of Melbourne, and therefore have a higher public transport mode share overall.

[As an aside: I find that chart fascinating – there’s been a generational shift in educational attainment which will continue to work it’s way up the age brackets in the decades ahead, resulting in an increasingly skilled workforce over time. Part of this will be skilled migration, part may be temporary migrants (eg international postgraduate students), and another part presumably reflects greater access to higher education in recent decades.]

Looking to the future perhaps this cohort of highly educated young adults will continue to work in the inner city as they age, along with younger skilled graduates, leading to more centralisation of employment in Melbourne as we become more of a “knowledge economy”? Or maybe the recent mass working-from-home experience of highly skilled workers during the COVID-19 pandemic will see more workers based in the central city but travelling to their workplace less often.

But back to the how education levels impact work location and mode choice…

People with higher educational attainment are more likely to work closer to the CBD:

The chart shows around half of workers with postgraduate degrees worked within 4 km of the CBD, whereas those who didn’t complete secondary education were much more likely to work in the suburbs.

We know that workplace distance from the CBD impacts PT mode shares, but does varying educational qualifications explain the differences in mode share between ages?

The following animated chart shows how PT mode shares for journeys to work vary by age for people with the same level of educational qualifications and working the same distance from the Melbourne CBD. I have animated the chart across workplace distance from the CBD bands.

If you watch and study the chart, you’ll see that there is a relationship between age and PT mode share for most combinations of educational attainment and workplace distance from the CBD. That is, age is significant in itself, or there is some other explanation for mode share difference by ages.

You’ll also see that public transport mode shares were generally higher for higher levels of educational attainment, with postgraduate degrees mostly showing the highest public transport mode share.

Here’s an alternative, non-animated view of that data. It’s a matrix of mini line charts showing PT mode share by age, for each combination of workplace distance from the CBD and highest level of educational attainment. You could call it a matrix of worms. The light horizontal line within each matrix box represents a 50% PT mode share, and the colours give you a rough sense of age (refer legend). I don’t expect you to be able read the mode share values for any age band on any line, but it does show PT mode shares falls with rising age for all education levels and workplace distances from the CBD (except some further out where PT mode share is just very low for all ages).

Here is yet another view: the relationship between PT mode share, workplace distance from the CBD, and highest level of educational attainment. I have roughly sorted the education levels by PT mode share, rather than ordering by level of qualification.

PT mode shares were not directly proportional to education levels, but I suspect this will be partly related to occupations – eg Certificate III and IV qualifications often related to trades where driving to non-centralised work sites is a more convenient option.

Those with postgraduate degrees generally showed the highest public transport mode share at each distance interval.

So we’ve explored work distance from the CBD, but what about…

Home distance from CBD

Younger adults were more likely to live closer to the Melbourne CBD compared to other age groups:

Public transport service quality is generally higher closer to the CBD, so does the fact that younger adults were more likely to live closer to the city explain their higher PT mode shares?

The following chart shows how public and active transport journey to work mode shares vary by home distance from the Melbourne CBD:

Public transport mode shares show a relationship with both home distance from the CBD and age – with mode shares peaking for ages 20-39, and dropping with older age bands. I’ve plotted active transport mode shares as well (for interest), which shows teenage workers much more likely to get to work by active transport – which makes sense as many of them will be below driving age and/or unable to afford private transport. Curiously those aged 70-79 who live in the suburbs are slightly more likely to walk to work.

Okay, but people who live closer to the CBD are more likely to work closer to the CBD, as the following chart shows:

Or another way of looking at it:

While the distance bands vary on each axis (more intervals for work distances from the CBD), you can see a very common scenario is that people’s work is a very similar distance from the CBD as their home. That is, they work relatively locally (for more on this, see Introducing a census journey to work origin-destination explorer, with Melbourne examples)

The following chart looks at mode shares for those who worked within 2 km of the CBD:

PT mode shares for these commuters were relatively high and flat for workers who live more 5 km from the CBD (those closer are more likely to use active transport – as per the bottom half of the chart). PT mode shares rose slightly with distance from the CBD for home distances 25-40 km from the CBD. But there was still a difference between age bands, with younger adults more likely to have used PT to get to work, regardless of how far from the CBD they lived.

So are there still PT mode share differences by age if you control for both home and work distance from the CBD?

Home AND work distance from the CBD

Firstly, here are PT mode shares for journeys to work by home and work distance from the CBD, animated over age bands:

Technical notes: the chart only shows mode shares where at least 200 people fell within the age and distance bands – which is quite a low threshold so there is a little noise – so please try not to get distracted by small differences in numbers. For teenagers and those aged 60-69, many combinations failed this threshold so are left blank.

The chart shows that work distance from the CBD is a very strong driver of mode shares at all age bands. Home distance from the CBD is much weaker driver of PT mode share – and only really significant for those living within 5 km of the CBD, and those under 40 years within 15 km of the CBD.

If the animation is hard to follow, here’s another matrix-of-worms chart. It shows PT mode share by age band – for every combination of home and work distance from the CBD.

The thin horizontal lines within each square of the matrix represent 50% PT mode share. While you cannot read off the PT mode shares for any age and distance combination, you can see that within each pane PT mode share either generally fell with increasing age, or were very low for all ages. That is to say, that home and workplace distance from the CBD doesn’t fully explain the relationship between age and PT mode shares for journeys to work. Other factors must be at play.

The above chart makes it hard to compare mode shares for the different work distances from the CBD, so here is a transposed version with work distances as rows and home distances as columns:

There’s not a lot of difference between home distance bands for each work distance band, except for younger adults living closer to the CBD and working in the city centre. This confirms the earlier finding that work distance from the CBD is a much stronger determinate in PT mode shares.

So in summary, younger adults are more likely to live and work closer to the CBD, and that is likely related to them generally having higher educational qualifications. While these factors generally lead to higher public transport use, we’ve found they don’t fully explain why younger adults have higher public transport mode shares.

Further posts in this series will look at other demographic factors that may explain these differences. Read on to part two.


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.


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.