What impact has the 2020 COVID-19 pandemic had on road traffic volumes in Victoria?

Sun 3 May, 2020

[Last updated 25 July 2020, not all charts]

Roads in Victoria were noticeably quieter during the depth of the pandemic shutdown, but just how much did traffic reduced? Has it varied by day of the week, time of day, and/or distance from the city centre? How have volumes increased as restrictions have been eased? What has been the impact naming identifying hot spots and postcode lock downs?

To answer these questions I’ve downloaded traffic signal loop vehicle count data from data.vic.gov.au. The data includes vehicle detection loops at 3,760 signalised intersections across Victoria (87% of which are in Greater Melbourne).

I should state that it is not a perfect measure of traffic volume:

  • It may under-count motorway-based and rural travel which may cross fewer loop detectors.
  • There are occasional faults with loops, and I’m only able to filter out some of the faulty data (supplied with negative count values), so there is a little noise but I will attempt to wash that out by using median counts rather than sums or averages (although charts of averages show very similar patterns to charts of medians).
  • Some vehicles moving through an intersection might get counted at multiple loops, but I would hope this has minimal impact on overall traffic volume trends.

How have traffic volumes reduced during the pandemic?

Firstly, median 24-hour loop volumes for each day:

Note: the actual numbers aren’t very meaningful, it is the relative numbers that matter.

There are regular variances by day type (eg Fridays generally having the most traffic), so here is a chart looking changes by day of the week, relative to the first two weeks of March 2020. I’ve annotated various significant announcements and changes in rules.

At their lowest, weekday volumes went down around 40%, while weekend volumes went down more like 50%.

In late-June volumes were down only 10-20%, with significant growth on Saturdays. However volumes declined again as a second wave of infections hit, and more restrictions were reintroduced. The key turning point was Saturday 20 June when the first warnings were raised about outbreaks, increasing cases, and a slow down in easing of restrictions.

In the early part of the second lock down, volumes were similar to April, the bottom of the first lock down, but then they settled at higher levels (more on that shortly).

Some curious outliers:

  • Thursday 9 April – the day before Good Friday: there may have been some travel to holiday homes, and/or other travel that happens normally on the last workday of the week.
  • Wednesday 8 July – the day before Melbourne and Mitchell Shire re-entered stage 3 restrictions (lock down), suggesting many people brought forward travel activity that was about to no longer be allowed.
  • Saturday 16 May & Sunday 17 May: there was a surge in traffic volumes on the first weekend after restrictions where eased.

Have traffic trends been different in different parts of the state?

There have been many more COVID-19 cases in Melbourne than regional Victoria. Here’s a chart showing daily volume changes in Greater Melbourne:

There is very little difference compared to the whole of Victoria chart, as most signals are located within Greater Melbourne.

Here is a chart of only signals outside Greater Melbourne, showing much less decline in late June / early July.

A notable exception here is Sundays where there has been a decline in July – perhaps Sundays normally involve a lot of travel to/from Melbourne.

How has traffic changed during the second wave?

From late June, there were increasing warnings about outbreaks in LGAs, suburbs, specific postcodes entered lock down before all of Melbourne plus the Shire of Mitchell also went into lock down. This section looks at the impact of some of the responses to what has become a second wave.

On 25 June, 10 suburbs were announced as outbreak concerns, with door-to-door testing campaigns to be conducted. These suburbs were within 6 LGAs identified on 20 June, so this may have refined people’s concern.

It is possible to filter to signal sites in the listed hot spot suburbs, although there are only around 100 signalised such sites (and none at all fall into the small suburb of Albanvale) which makes for some noisy data. Also, I would dare say that a lot of traffic in these suburbs is through traffic rather than local traffic.

To overcome daily noise, I’ve calculated the rolling 7 day average volume – excluding public holidays with with some normalisation (see below chart explanation). That does mean that sudden daily changes in traffic are smoothed out over the following 7 days.

Boring but necessary technical notes: Many traffic signals are on roads that are LGA boundaries – and which LGA an intersection falls into is almost random – it depends on the coordinates of the intersection point. To normalise volumes, I have calculated the ratio of the average volume for each day of the week in February to the overall February average, and then adjusted daily volumes using these ratios to produce a relatively smooth daily time series. The rolling 7 day average then omits any public holidays. It’s not perfect, as you can see around Easter, but it was necessary to avoid having large gaps or blips in the above chart. For this analysis I used February as the baseline, as there was a public holiday in the first two weeks of March, complicating the normalisation.

Volumes immediately dropped more quickly in these suburbs compared to the rest of Melbourne, although they later settled at higher levels than the rest of Melbourne.

On 30 June there was an announcement that 10 postcodes would return to “lock down” (only four essential travel purposes allowed) from 2 July. Those postcodes mostly – but not entirely – lined up with the 10 warning suburbs. Here’s a similar chart that separates out those postcodes, from the rest of Melbourne (plus Mitchell Shire) that went into lock down on 9 July:

There was a step change from 2 July as the restrictions took hold (on top of a reduction from the school holidays), and the rest of Melbourne followed after 9 July.

During the first lock down, these 10 postcodes saw a slightly smaller traffic reduction compared to the rest of Melbourne, but in the second lock down other parts of Melbourne have not seen the same traffic reductions.

The 7 day averaging process hides a little of the behaviour change, so here is a daily volume chart for those 10 postcodes:

While volumes in these postcodes started declining from the first warning announcement on 20 June, if you look carefully you’ll see that on Wednesday 1 July there was little change in volume compared to the previous Wednesday. This was the last day before the lock down, and presumably some people made some extra travel that was about to become against the rules. Once the lock down had commenced, volumes were very similar to those experienced during the “stage 3” restrictions of early April. This is similar to the surge in traffic seen in Melbourne the day before the second lock down.

A more detailed look at Melbourne

The following animated map shows the change in weekday volume relative to the first two weeks of March, for each site, each week since the beginning of March. Note that there are anomalous sites for various reasons (eg faults, roadworks) – I’ve tried to filter out some sites with unusual data, but it’s difficult to get all of them.

If you ignore individual sites that look like outliers you can see some clear patterns:

  • Volumes haven’t reduced as much in industrial areas during lock downs, as freight and logistics largely keep operating, and factory workers continued to go to work.
  • Volumes didn’t recover in the central city as they have in the suburbs, which makes sense with so many office workers have continued to work from home.
  • Melbourne Airport volumes have been significantly below normal throughout, obviously due to national and international travel restrictions.
  • Volumes were slower to recover in the Clayton area – probably related to working from home, and Monash University not having on-campus teaching.
  • Volumes reduced from the week of 29 June, a mix of the school holiday impact, an increase in travel restrictions, and probably general fear about a second wave of infections.

I must apologise to the those with colour-blindness, it’s much more difficult to show the changes with only two-three colours.

This map doesn’t however explain the slightly smaller traffic reduction in Melbourne outside the initial 10 lock down postcodes.

The following map compares traffic volumes on Wednesday 22 July with those in the first two weeks of April (I’ve chosen a Wednesday to be clear of the Easter long weekend that happened in the second week of April). Note that the flip between orange and blue occurs at 110% (you might intuitively expect it to be at 100%).

This map pretty clearly shows that second lock down volumes were higher in the eastern and south-western suburbs, but much closer to April in the north-eastern suburbs. There have been fewer COVID-19 cases in the south-eastern suburbs, and this might reflect people’s self-regulation based on perceived local risk.

Indeed, here is a chart comparing active cases as at 19 July to traffic on 20 July relative to the first lock down:

Local government areas (LGAs) with higher numbers of active cases tend to have traffic levels closer to those in early April, while LGAs with fewer cases have seen higher traffic volumes in April. I might try to explore this relationship over time in future.

How does 2020 compare to 2019?

The above analysis hasn’t differentiated school days and school holidays, and any general seasonality across the year. Here is a chart comparing 2020 with 2019 for weekdays, Saturdays and Sundays (excluding public holidays):

I will emphasise that there will be week-to-week variations, particularly on weekends, due to short term factors such as weather and special events. Also, while school returned in week 16 of 2020, most students were not attending schools in person (ditto week 29).

The winter school holidays began in week 27, and traffic volumes in 2020 appeared to drop in proportion to the traffic reduction in the same week in 2019.

The following chart compares 2020 to 2019 on a daily basis (with 2019 days offset by -1 to align days of the week):

We can also look at the percentage difference between the years, but only for days that have the same day type in terms of school term or holidays, and public holidays where they fall on the equivalent day of the year. So there are some gaps in the following chart, plus some noise due to daily fluctuations:

This chart shows January to late July. There are gaps around the autumn school holidays and Easter as they didn’t perfect match days of the year perfectly.

You need to not get too excited about daily variations (the Tuesday in the second week of 2019 school holidays had unusually low volume in Melbourne for some reason, which shows up as a spike for 2020).

This chart gives a feel for variations from expected patterns. Traffic in the Melbourne was down a similar percentage in the first week of the winter school holidays compared to the previous week of school.

Melbourne traffic volumes began falling in the second week of winter school holidays with the rise in cases and commencement of some postcode lock downs, and then fell further with the Melbourne + Mitchell lock down from 9 July.

However in regional Victoria volumes were relatively higher in the winter school holidays – perhaps as Melbourne people were more likely to travel intrastate for holidays (interstate travel being heavily restricted, and travel not having been an option in the previous autumn school holidays). Regional Victoria travel volumes have been tracking around 10% below 2019 since early June.

The next chart compares each 2020 week with the same week 2019 for Melbourne LGAs plus Mitchell. However it is important to note that there was quite a bit of week to week variation in 2019, and the autumn school holidays started a couple of weeks earlier in 2020.

On this measure, weekdays bottomed out around 38% below 2019, but recovered to be ~10% down in week 24 (on weekdays and Saturdays). Weekends were down around 50%, but recovered to around 10-15% down before the second wave. However pre-pandemic volumes were around 5% higher than 2019, so you could perhaps add another 5% to the reduction figures.

How has traffic reduced by time of day?

The traffic signal data is available in 15 minute intervals, so it is possible to examine patterns in more detail.

Here’s a look at the traffic volumes by time of day for Wednesdays:

You can see a significant flattening of the traditional peaks from late March, although curiously the PM peak still commences around 3 pm, even during the school holidays. From late May there was a significant jump in peak period traffic, coinciding with the return to school of grades Prep, 1, 2, 11 and 12.

1 July was the first week of the winter school holidays and you can see substantial traffic reductions at school times, most notably in the AM peak. Meanwhile the PM commuter peak (around 5 pm) was very similar to late June.

There was a spike in traffic on 8 July – the last day before the second Melbourne full lock down.

Evening traffic was down considerably but it’s a little hard to gauge this reduction the chart. So here is a chart showing traffic volume changes relative to the first two weeks of March (with apologies to anyone with colour-blindness):

Volumes went down the most in the evenings (particularly around 9 pm) which might reflect the closure of hospitality venues, cessation of sports and reduced social activity. The AM and PM peak periods were down around 50% at the bottom, while the inter-peak period has held up the most – being only down around 30%.

Volumes recovered considerably over May and June, with volumes around 3pm back near pre-COVID levels (prior to the winter school holidays). The AM peak is interesting – at 7am, traffic on 17 June was still down around 28%, but at 8:45am is was only around 9% down – possibly reflecting the school peak, and/or a narrowing of the commuter peak (as lower congestion provides less incentive for peak spreading). As at mid-June, evening traffic was still down around 40%.

Again 8 July is an outlier – evening traffic was a lot busier, in fact traffic leading up to midnight was busier than early March, suggesting people cramming in travel activity that was about to become restricted.

I should point out that this analysis compares to a baseline of a two days in early March, and there may be some associated noise (eg weather or event impacts on particular days).

Here is the same for Fridays (excluding the Good Friday public holiday):

Late evening traffic was down even more than for Wednesdays, which probably reflects higher volumes of hospitality-related travel on Friday nights. Friday evening traffic jumped on 15 May when small social gatherings were allowed, and again on 5 June when restaurants and cafes were allowed to have dine-in patrons.

Here is Saturdays (excluding Anzac Day):

The Saturday profile shape hasn’t changed as much as weekdays, but the evenings were down most significantly.

Curiously there are several spikes in the curve in the morning – and they are the 15 minute intervals leading up to the hours of 7am, 8am, 9am, and 10am. Initially I wondered if it was a data quality issue, but I suspect it reflects a surge in travel just before work shifts and other activities that start on the hour.

For some reason traffic volumes were relatively low around 6 am on Saturday 7 March, which has resulted in other days showing less reduction.

Saturday night travel was down considerably – by over 70% by midnight at the depths of the shutdown, but jumped with restrictions easing, similar to Friday evenings. As of mid-June it was down around 25-30%.

You can also see early Saturday morning (Friday night) travel down around 60-70% at worst (discounting 11 April which was the Saturday morning following Good Friday).

Here is Sundays:

Sunday 8 March was on the Labour Day long weekend (including the Moomba festival), which probably explains the much busier traffic that Sunday night (not being a “school night”). You can more clearly see that on the following chart:

Another anomaly here is Sunday 7 June – which was another public holiday eve.

Here’s the profile by day of the week for each week since February (public holidays excluded):

This data suggests a roughly a one hour lag on Sunday mornings compared to Saturday mornings – ie travel volumes hold up an hour later on Saturday nights and ramp up an hour later on Sunday mornings. This pattern holds up for other weeks. It also shows the middle of the day on Saturdays to mostly be busier than the same time on weekdays.

Here’s another look at relative time of day traffic volumes for March through to July:

If you look closely (no, your eyes are not losing focus!) you can see:

  • Significant volume reductions after schools finished on 23 March
  • A surge in traffic on 9 April – the Thursday before Good Friday
  • Extremely quiet traffic on Good Friday (10 April)
  • Higher traffic volumes on 8 July (the day before the second lock down), particularly into the evening.
  • Generally higher traffic on the last weekday of the week, particularly in the afternoon and evening (including during the shut down period)

Have traffic impacts been different by distance from the CBD?

Here’s a chart showing year-on-year reduction in median traffic volumes at intersections by distance from the Melbourne CBD for weeks 14 and 15 (the lowest two weeks of the lock-down):

What is clear is that the central city experienced much larger traffic volume reductions than other parts of Melbourne, which makes sense as office workers stayed home, universities, cafes, restaurants and night-life closed, and (non-essential) retail activity slowed considerably.

There is some noise in the variations by distance from the CBD but I suggest not too much should be read into that as there will be various local factors at play.

The following animated chart shows median weekday volumes per week, by distance from the CBD, since the start of March 2020:

You can see the traffic decline has remained the largest in the central city. The reduction in traffic in the week of 28 June was mostly in the suburbs more than 3 km from the CBD.

Traffic signal data comes out daily, and so I will try to update this analysis at least once a week during the recovery period. There may be more frequent updates on Twitter.


Update on Australian transport trends (December 2019)

Mon 30 December, 2019

Each year, just in time for Christmas, the good folks at the Australian Bureau of Infrastructure, Transport, and Regional Economics (BITRE) publish a mountain of data in their Yearbook. This post aims to turn those numbers (and some other data sources) into useful knowledge – with a focus on vehicle kilometres travelled, passenger kilometres travelled, mode shares, car ownership, driver’s licence ownership, greenhouse gas emissions, and transport costs.

There are some interesting new patterns emerging – read on.

Vehicle kilometres travelled

According to the latest data, road transport volumes actually fell in 2018-19:

Here’s the growth by vehicle type since 1971:

Light commercial vehicle kilometres have grown the fastest, curiously followed by buses (although much of that growth was in the 1980s).

Car kilometre growth has slowed significantly since 2004, and actually went down in 2018-19 according to BITRE estimates (enough to result in a reduction in total vehicle kilometres travelled).

On a per capita basis car use peaked in 2004, with a general decline since then. Here’s the Australian trend (in grey) as well as city level estimates to 2015 (from BITRE Information Sheet 74):

Technical note: “Australia” lines in these charts represent data points for the entire country (including areas outside capital cities).

Darwin has the lowest average which might reflect the small size of the city. The blip in 1975 is related to a significant population exodus after Cyclone Tracey caused significant destruction in late 1974 (the vehicle km estimate might be on the high side).

Canberra, the most car dependent capital city, has had the highest average car kilometres per person (but it might also reflect kilometres driven by people from across the NSW border in Queanbeyan).

The Australia-wide average is higher than most cities, with areas outside capital cities probably involving longer average car journeys and certainly a higher car mode share.

Passenger kilometres travelled

Overall, here are passenger kms per capital for various modes for Australia as a whole (note the log-scale on the Y axis):

Air travel took off (pardon the pun) in the late 1980s (with a lull in 1990), car travel peaked in 2004, bus travel peaked in 1990 and has been relatively flat since, while rail has been increasing in recent years.

It’s possible to look at car passenger kilometres per capita, which takes into account car occupancy – and also includes more recent estimates up until 2018/19.

Here’s a chart showing total car passenger kms in each city:

The data shows that Melbourne has now overtaken Sydney as having the most car travel in total.

Another interesting observation is that total car travel declined in Perth, Adelaide, and Sydney in 2018-19. The Sydney result may reflect a mode shift to public transport (more on that shortly), while Perth might be impacted by economic downturn.

While car passenger kilometres per capita peaked in 2004, there were some increases until 2018 in some cities, but most cities declined in 2019. Darwin is looking like an outlier with an increase between 2015 and 2018.

BITRE also produce estimates of passenger kilometres for other modes (data available up to 2017-18 at the time of writing).

Back to cities, here is growth in rail passenger kms since 2010:

Sydney trains have seen rapid growth in the last few years, probably reflecting significant service level upgrades to provide more stations with “turn up and go” frequencies at more times of the week.

Adelaide’s rail patronage dipped in 2012, but then rebounded following completion of the first round of electrification in 2014.

Here’s a longer-term series looking at per-capita train use:

Sydney has the highest train use of all cities. You can see two big jumps in Perth following the opening of the Joondalup line in 1992 and the Mandurah line in 2007. Melbourne, Brisbane and Perth have shown declines over recent years.

Here is recent growth in (public and private) bus use:

Darwin saw a massive increase in bus use in 2014 thanks to a new nearby LNG project running staff services.

In more recent years Sydney, Canberra, and Hobart are showing rapid growth in bus patronage.

Here’s bus passenger kms per capita:

Investments in increased bus services in Melbourne and Brisbane between around 2005 and 2012 led to significant patronage growth.

Bus passenger kms per capita have been declining in most cities in recent years.

Australia-wide bus usage is surprisingly high. While public transport bus service levels and patronage would certainly be on average low outside capital cities, buses do play a large role in carrying children to school – particularly over longer distances in rural areas. The peak for bus usage in 1990 may be related to deregulation of domestic aviation, which reduced air fares by around 20%.

Melbourne has the lowest bus use of all the cities, but this likely reflects the extensive train and tram networks carrying the bulk of the public transport passenger task. Melbourne is different to every other Australian city in that trams provide most of the on-road public transport access to the CBD (with buses performing most of this function in other cities).

For completeness, here’s growth in light rail patronage:

Sydney light rail patronage increased following the Dulwich Hill extension that opened in 2014, while Adelaide patronage increased following an extension to the Adelaide Entertainment Centre in 2010.

We can sum all of the mass transit modes (I use the term “mass transit” to account for both public and private bus services):

Sydney is leading the country in mass transport use per capita and is growing strongly, while Melbourne, Brisbane, Perth have declined in recent years.

Mass transit mode share

We can also calculate mass transit mode share of motorised passenger kilometres (walking and cycling kilometres are unfortunately not estimated by BITRE):

Sydney has maintained the highest mass transit mode share, and in recent years has grown rapidly with a 3% mode shift in the three years 2016 to 2019, mostly attributable to trains. The Sydney north west Metro line opened in May 2019, so would only have a small impact on these figures.

Melbourne made significant gains between 2005 and 2009, and Perth also grew strongly 2007 to 2013.

Here’s how car and mass transit passenger kilometres have grown since car used peaked in 2004:

Mass transit use has grown much faster than car use in Australia’s three largest cities. In Sydney and Melbourne it has exceeded population growth, while in Brisbane it is more recently tracking with population growth.

Mass transit has also outpaced car use in Perth, Adelaide, and Hobart:

In Canberra, both car and mass transit use has grown much slower than population, and it is the only city where car growth has exceeded public transport growth.

Car ownership

The ABS regularly conduct a Motor Vehicle Census, and the following chart includes data up until January 2019.

Technical note: Motor Vehicle Census data (currently conducted in January each year, but previously conducted in March or October) has been interpolated to produce June estimates for each year, with the latest estimate being for June 2018.

In 2017-18 car ownership declined slightly in New South Wales, Victoria, and Western Australia, but there was a significant increase in the Northern Territory. Tasmania has just overtaken South Australia as the state with the highest car ownership at 63.1 cars per 100 residents.

Victorian car ownership has been in decline since 2011, which is consistent with a finding of declining motor vehicle ownership in Melbourne from census data (see also an older post on car ownership).

Driver’s licence ownership

Thanks to BITRE Information Sheet 84, the BITRE Yearbook 2019, and some useful state government websites (NSW, SA, Qld), here is motor vehicle licence ownership per 100 persons (of any age) from June 1971 to June 2018 or 2019 (depending on data availability):

Technical note: the ownership rate is calculated as the sum of car, motorbike and truck licenses – including learner and probationary licences, divided by population. Some people have more than one driver’s licence so it’s likely to be an over-estimate of the proportion of the population with any licence.

There’s been slowing growth over time, but Victoria has seen slow decline since 2011, and the ACT peaked in 2014.

Here’s a breakdown by age bands for Australia as a whole (note each chart has a different Y-axis scale):

There was a notable uptick in licence ownership for 16-19 year-olds in 2018. Otherwise licencing rates have increased for those over 40, and declined for those aged 20-39.

Licencing rates for teenagers (refer next chart) had been trending down in South Australia and Victoria until 2017, but all states saw an increase in 2018 (particularly Western Australia). The most recent 2019 data from NSW and Queensland shows a decline. The differences between states partly reflects different minimum ages for licensing.

The trends are mixed for 20-24 year-olds: the largest states of Victoria and New South Wales have seen continuing declines in licence ownership, but all other states and territories are up (except Queensland in 2019).

New South Wales, Victoria, and – more recently – Queensland are seeing downward trends in the 25-29 age bracket:

Licencing rates for people in their 70s are rising in all states (I suspect a data error for South Australia in 2016):

A similar trend is clear for people aged 80+ (Victoria was an anomaly before 2015):

See also an older post on driver’s licence ownership for more detailed analysis.

Transport greenhouse gas emissions

[this emissions section updated on 8 January 2020 with BITRE estimates for 1975-2019]

According to the latest adjusted quarterly figures, Australia’s domestic non-electric transport emissions peaked in 2018 and have been slightly declining in 2019, which reflects reduced consumption of petrol and diesel. However it is too early to know whether this is another temporary peak or long-term peak.

Non-electric transport emissions made up 18.8% of Australia’s total emissions as at September 2019.

Here’s a breakdown of transport emissions:

A more detailed breakdown of road transport emissions is available back to 1990:

Here’s growth in transport sectors since 1975:

Road emissions have grown steadily, while aviation emissions took off around 1991. You can see that 1990 was a lull in aviation emissions, probably due to the pilots strike around that time.

In more recent years non-electric rail emissions have grown strongly. This will include a mix of freight transport and diesel passenger rail services – the most significant of which will be V/Line in Victoria, which have grown strongly in recent years (140% scheduled service kms growth between 2005 and 2019). Adelaide’s metropolitan passenger train network has run on diesel, but more recently has been transitioning to electric.

Here is the growth in each sector since 1990 (including a breakdown of road emissions):

Here are average emissions per capita for various transport modes in Australia, noting that I have used a log-scale on the Y-axis:

Per capita emissions are increasing for most modes, except cars. Total road transport emissions per capita peaked in 2004 (along with vehicle kms per capita, as above).

It’s possible to combine data sets to estimate average emissions per vehicle kilometre for different vehicle types (note I have again used a log-scale on the Y-axis):

Note: I suspect the kinks for buses and trucks in 2015, and motor cycles in 2011 are issues to do with assumptions made by BITRE, rather than actual changes.

The only mode showing significant change is cars – which have reduced from 281 g/km in 1990 to 243 g/km in 2019.

However, the above figures don’t take into account the average passenger occupancy of vehicles. To get around that we can calculate average emissions per passenger kilometre for the passenger-orientated modes:

Domestic aviation estimates go back to 1975, and you can see a dramatic decline between then and around 2004 – followed little change (even a rise in recent years). However I should mention that some of the domestic aviation emissions will be freight related, so the per passenger estimates might be a little high.

Car emissions per passenger km in 2018-19 were 154.5g/pkm, while bus was 79.4g/pkm and aviation 127.2g/pkm.

Of course the emissions per passenger kilometres of a bus or plane will depend on occupancy – a full aeroplane or bus will have likely have significantly lower emissions per passenger km. Indeed, the BITRE figures imply an average bus occupancy of around 9 people (typical bus capacity is around 60) – so a well loaded bus should have much lower emissions per passenger km. The operating environment (city v country) might also impact car and bus emissions. On the aviation side, BITRE report a domestic aviation average load factor of 78% in 2016-17.

Cost of transport

The final topic for this post is the real cost of transport. Here are headline real costs (relative to CPI) for Australia:

Technical note: Private motoring is a combination of factors, including motor vehicle retail prices and automotive fuel. Urban transport fares include public transport as well as taxi/ride-share.

The cost of private motoring has tracked relatively close to CPI, although it trended down between 2008 and 2016. The real cost of motor vehicles has plummeted since 1996. Urban transport fares have been increasing faster than CPI since the late 1970s, although they have grown slower than CPI (on aggregate) since 2013.

Here’s a breakdown of the real cost of private motoring and urban transport fares by city (note different Y-axis scales):

Note: I suspect there is some issue with the urban transport fares figure for Canberra in June 2019. The index values for March, June, and September 2019 were 116.3, 102.0, and 118.4 respectively.

Urban transport fares have grown the most in Brisbane, Perth and Canberra – relative to 1973.

However if you choose a different base year you get a different chart:

What’s most relevant is the relative change between years – eg. you can see Brisbane’s experiment with high urban transport fare growth between 2009 and 2017 in both charts.

Hopefully this post has provided some useful insights into transport trends in Australia.


Update on Australian transport trends (December 2018)

Fri 28 December, 2018

For more recent trend information – skip to the December 2019 update on Australian transport trends.

Each year, just in time for Christmas, the good folks at the Australian Bureau of Infrastructure, Transport, and Regional Economics (BITRE) publish a mountain of data in their Yearbook. This post aims to turn those numbers (and some other data sources) into useful knowledge – with a focus on vehicle kilometres travelled, passenger kilometres travelled, mode shares, car ownership, driver’s licence ownership, greenhouse gas emissions, and transport costs.

Vehicle kilometres travelled

Road transport volumes are rising, and most of the traffic is of course cars:

Here’s the growth by vehicle type since 1971:

Light commercial vehicle kilometres have grown the fastest, curiously followed by buses (although much of that growth was in the 1980s).

Car kilometre growth has slowed significantly since 2004.

In fact, on a per capita basis car use peaked in 2004 and then declined until 2014, with a little growth since. Here’s the Australian trend (in grey) as well as city level estimates to 2015 (from BITRE Information Sheet 74):

Technical note: “Australia” lines in these charts represent data points for the entire country (including areas outside capital cities).

Darwin has the lowest average which might reflect the small size of the city. The blip in 1975 is related to a significant population exodus after Cyclone Tracey caused significant destruction in late 2014 (the vehicle km estimate might be on the high side).

Canberra, the most car dependent capital city, has had the highest average car kilometres per person (but it might also reflect kilometres driven by people from across the NSW border in Queanbeyan).

The Australia-wide average is higher than most cities, with areas outside capital cities probably involving longer average car journeys and certainly a higher car mode share.

Passenger kilometres travelled

It’s also possible to look at car passenger kilometres per capita, which takes into account car occupancy – and also includes more recent estimates up until 2017:

While car passenger kilometres per capita also peaked in 2004, they have increased slightly in recent years in Perth, Adelaide, Brisbane, and Sydney.

BITRE also produce estimates of passenger kilometres for other modes (data available up to 2017 at the time of writing).

Rail use is highest in Sydney followed by Melbourne. You can see two big jumps in Perth following the opening of the Joondalup line in 1992 and the Mandurah line in 2007.

(note: this includes both public and private bus travel)

Australia-wide bus usage is surprisingly high. While public transport bus service levels and patronage would certainly be on average low outside capital cities, buses do play a large role in carrying children to school – particularly over longer distances in rural areas. The peak for bus usage in 1990 may be related to deregulation of domestic aviation, which reduced air fares by around 20%.

Darwin saw a massive increase in bus use in 2014 thanks to a new nearby LNG project running staff services, while investments in increased bus services in Melbourne and Brisbane in the first decade of this century led to significant patronage growth.

We can sum all of the mass transit modes (I use the term “mass transit” to account for both public and private bus services):

We can also calculate mass transit mode share of motorised passenger kilometres (walking and cycling kilometres are unfortunately not estimated):

Sydney has maintained the highest mass transit mode share, while Melbourne made significant gains between 2005 and 2009, and Brisbane also grew strongly 2007 to 2013.

Here’s how car and mass transit passenger kilometres have grown since car used peaked in 2004:

Mass transit use has grown much faster than car use in Australia’s three largest cities. In Sydney and Melbourne it has exceeded population growth also.

Mass transit has also outpaced car use in Perth, Adelaide, and Hobart:

In Canberra, both car and mass transit use has grown much slower than population, and it is the only city where car growth exceeded public transport growth between 2004 and 2017.

Car ownership

The ABS regularly conduct a Motor Vehicle Census, and the following chart includes data up until January 2018.

Technical note: Motor Vehicle Census data (currently conducted in January each year) has been interpolated to produce June estimates for each year.

Car ownership has continued to rise slowly in all states – except Victoria, which is consistent with a finding of declining motor vehicle ownership in Melbourne from census data (see also an older post on car ownership).

Driver’s licence ownership

Thanks to BITRE Information Sheet 84, here is motor vehicle licence ownership per 100 persons (of any age) going back to 1971:

Technical note: the ownership rate is calculated as the sum of car, motorbike and truck licenses – including learner and probationary licences, divided by population. Some people have more than one driver’s licence so it’s likely to be an over-estimate of the proportion of the population with a licence.

There’s been slowing growth over time, but Victoria has seen slow decline since 2011.

Here’s a breakdown by age bands (note each chart has a different Y-axis scale):

Motor vehicle licence ownership rates have increased for people over 70 (presumably due to a healthier ageing population), and declined for people under 30.

Licencing rates for teenagers have been trending down in South Australia and Victoria recently, but not in other states:

The trends are mixed for 20-24 year-olds:

New South Wales and Victoria are seeing downward trends in the 25-29 age bracket:

Licencing rates for people in their 70s are rising in all states (I suspect a data error for South Australia in 2016):

A similar trend is clear for people aged 80-89 (Victoria was an anomaly before 2015):

(see also an older post on driver’s licence ownership for more detailed analysis)

Transport greenhouse gas emissions

Australia’s domestic non-electric transport emissions have increased steadily since 1990 and show no signs of slowing down, let alone declining (latest data at the time of writing is up to June 2018):

Depending on how you disaggregate total emissions, transport is the second largest sector and the fastest growing.

Here’s breakdown of transport emissions (detailed data only available to 2016 at time of writing):

And the growth in each sector since 1990:

Domestic aviation has had the fastest growth, followed by buses. In more recent years rail emissions have grown strongly (note: most of this is rail freight as the vast majority of passenger train movements are electric). Car emissions have grown 27%, but make up the largest share of transport emissions.

Here are per capita transport emissions for each state:

The data is a bit noisy (largely due to fluctuations in aviation emissions). Here are road emissions per capita:

In 2016 there were sharp increases in Western Australia, Queensland and the Northern Territory, while most other states appear to be on a downward trend.

Car emissions per capita have been generally trending downwards in most states, again except Queensland, Western Australia, and the Northern Territory:

Of course if we are to avoid dangerous climate change, total emissions need to reduce substantially, not just per capita emissions!

It’s possible to combine data sets to estimate average emissions per vehicle kilometre for different vehicle types:

It’s difficult to see any significant reductions in emissions intensity, while average bus emissions intensity has increased recently (not sure why). Average car emissions have fallen slightly from 281 g/km in 1990 to 244 g/km in 2016.

However, the above figures don’t take into account the average passenger occupancy of vehicles. To get around that we can calculate average emissions per passenger kilometre for the high person-capacity modes:

Of course the emissions per passenger kilometres of a bus or plane will depend on occupancy – a full aeroplane or bus will have likely have significantly lower emissions per passenger km. Indeed, the BITRE figures imply an average bus occupancy of around 9 people (typical bus capacity is around 60) – so a well loaded bus should have much lower emissions per passenger km. The operating environment (city v country) might also impact car and bus emissions. On the aviation side, BITRE report a domestic aviation average load factor of 78% in 2016-17.

Cost of transport

The final topic for this post is the real cost of transport. Here are headline real costs (relative to CPI) for Australia:

Technical note: Private motoring is a combination of factors, including motor vehicle retail prices and automotive fuel. Urban transport fares include public transport as well as taxi/ride-share.

The cost of private motoring has tracked relatively close to CPI, although has been trending down since around 2008. The real cost of motor vehicles has plummeted since 1996. Urban transport fares have been increasing faster than CPI since the late 1970s.

Here’s a breakdown of the real cost of private motoring and urban transport fares by city (note different Y-axis scales):

Urban transport fares have grown the most in Brisbane, Perth and Canberra – relative to 1973.

However if you choose a different base year you get a different chart:

What’s most relevant is the relative change between years – eg. you can see Brisbane’s experiment with high urban transport fare growth between 2009 and 2017 in both charts.

To illustrate the data visualisation problem of choosing a base year – here is the same data for every base year between 1973 and 2018:

Hopefully this post has provided some useful insights into transport trends in Australia. A future post might examine the relationships between the data sets further.


What’s driving Melbourne public transport patronage?

Fri 11 May, 2012

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

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

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

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

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

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

Population growth

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

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

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

Employment

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

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

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

Here is the relationship shown as a ratio over time:

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

City population (including visitors)

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

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

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

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

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

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

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

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

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

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

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

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

Here is the trend in occupied Melbourne CBD office space:

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

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

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

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

Inner city parking

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

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

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

Fuel prices

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

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

International students

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

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

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

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

Road congestion

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

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

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

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

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

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

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

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

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

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

Car ownership

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

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

Service kms

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

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

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

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

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

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

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

Comparing annual growth/change rates

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

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

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

Conclusions

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

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

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

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