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.


Questioning assumptions about transport trends (presentation to Transport Economics Forum)

Wed 21 March, 2012

On Tuesday 20 March 2012 I gave this presentation to the Transport Economics Forum in Melbourne using material from this blog and some recently released data in BITRE’s Working Paper 127 on traffic growth in Australia. The presentation challenges some orthodox assumptions about transport trends in Australia and Melbourne.

When I get time, I hope to update existing posts to include the most recent data on (the lack of ) traffic growth.


Traffic volumes on Australian toll roads

Sat 3 March, 2012

[Last updated September 2016]

What are the trends in traffic volumes on major toll roads in Australian cities? How sensitive are motorists to toll prices? How accurate have forecasts been on some recent toll roads? This post aims to shed some light on these questions.

Average daily volumes

Firstly, here is a chart comparing the most recent available figures for average daily traffic volumes (at the time of last updating this post):

Citylink, which is effectively made up of two mostly radial motorways in Melbourne, has by far the greatest traffic volume of any of the roads. In comparing these values, be aware that some toll roads are very short (eg just one bridge or tunnel), and others are over 20 km in length with many exit and entry points along the way.

For reference, the proposed east-west link toll road between Melbourne’s Eastern Freeway and Citylink was forecast to carry 100-120 thousand vehicles per day.

Traffic growth on roads with regular data

The next chart shows the relative growth in traffic volumes on several toll roads in Melbourne, Sydney and Brisbane (where regular data is published) since 2006, or whenever data became available:

You might want to click to zoom in and see all the detail. Another way of looking at this data is to consider rolling year on year traffic growth (ie 12 months versus the previous 12 months):

(click here for an experimental interactive version of this chart in Tableau)

Some observations:

  • Most roads show a cyclical trend, with weak growth in 2008-09 (possibly GFC related) and 2012, and strong growth in 2013-14 and again in 2015.
  • Growth has been much faster on non-radial roads. This might reflect the creation of new demand corridors as these roads provided significantly better links than the established road networks. But it also might reflect the low base from which the traffic volumes grow on these road. The high growth roads are:
    • Melbourne’s Eastlink, which runs north-south in the outer Eastern suburbs.
    • Sydney’s Westlink M7, which mostly runs north-south in the western suburbs.
    • Brisbane’s Gateway Bridge, which provides a north-south link east of the city centre.
  • Melbourne’s CityLink has shown a fairly steady growth trend of around 3%, except for a decline in 2009 (probably related to roadworks, but traffic soon recovered to trend once these works were complete). The road upgrade appears to possibly have had an impact on train patronage – refer another post.
  • Traffic volumes on Sydney’s M2 declined between late 2011 and mid 2013 due to major road upgrade works, but have since rebounded quite spectacularly.

An important note on growth rate precision: Transurban report daily traffic volumes rounded to the nearest thousand. For roads with relatively small volumes (eg Clem7), the growth rates will be more impacted by rounding errors. For example, the traffic volumes on Clem7 went from 27+26+27+27=107 thousand in 2014/15 to 27+26+26+27=106 thousand in 2015/16, which is notional growth of -0.9%. But actual values for each quarter will be within +/-500, and the rounding errors will add up over the eight quarters making up the calculation. The actual growth could be anywhere between -4.6% and 2.9%, but is more likely to be in the middle of that range.

Unfortunately data isn’t always readily available:

  • The Brisbane Gateway Bridge and Logan/Gateway Motorway extension data is only available for financial years in annual reports up until 2010. Transurban took over these roads and have reported traffic volumes since 2013 but they do not appear to be the same measures so I have listed them separately.
  • In October 2011, Horizon Roads purchased Melbourne’s Eastlink, and they do not seem to be publishing traffic volumes.
  • I haven’t been able to source Clem7 data for 2012 and the first half of 2013.

Traffic growth on other toll roads

Sydney Harbour Bridge and Tunnel

Calum Hutcheson from Hyder Consulting has generously compiled and shared time-series data with me on traffic volumes going back to 1971 for these two toll roads. He has sourced data from several available sources but has had to estimate some figures where data is missing.

Sydney Harbour Traffic 2

Traffic volumes levelled off on the bridge around 1988 and on the combined bridge and tunnel around 2005. It would appear the tunnel brought additional vehicle capacity good for around 17 years’ growth but that has now been exhausted (although I’m far from an expert in Sydney traffic).

In 1992 one lane was converted to a southbound bus lane (presumably related to the capacity freed up by the tunnel). The bridge’s vehicular traffic levels have not returned to 1988 levels, but I suspect the number of people moved in (road-based) vehicles has increased significantly (not to mention the train line across the bridge).

Sydney Cross City Tunnel

Transurban now own this asset and reported an average 33,057 daily transactions in the June quarter of 2014 – which is below the figure for late 2006.

I have not been able to source much data pre-2013, but a 2006 NSW Auditor General’s report contains some traffic volume data for 2005 and 2006, reproduced here (from page 32 of the report).

The tunnel is now carrying around 39,000 daily trips – not a large increase since 2006.

It would appear that motorists are highly sensitive to toll pricing, and the forecast volumes were not achieved even when tolls were removed.

Brisbane’s Clem7 cross-city tunnel

Brisbane’s first new road tunnel, the Clem7, opened in March 2010. During the first three weeks of toll-free operation, there was an average of 59,109 vehicles per day. During the first week of tolling, this fell to 20,602. The forecast was for initial traffic of around 60,000 vehicles per day, rising to 100,000 within 18 months. Owners at the time, Rivercity Motorways, went to the extraordinary step of publishing daily traffic data, as can be seen in the following chart showing traffic volumes since tolling commenced:

You can see an up-tick from the beginning of July 2010, when toll prices were cut. Tolls were raised in November 2010 and again in April 2011 and you can see corresponding drops in traffic volumes. Average daily traffic in calendar 2011 was 10% lower than for the first 12 months of operation (includes one overlapping quarter).

During the 2011 flood crisis tolls were waived for one week, and at the end of that period on Monday 17 January 2011, 40,566 vehicles were recorded, the highest since tolling commenced. This may or may not have also reflected closures to other roads making Clem7 more attractive. (footnote: actual weekend volumes have not been published for April 2010, so I have substituted the average non-workday figures, that have been published).

More recently, this road is carrying an average of 26,000 vehicles per day, around a quarter of the forecast.

Brisbane’s Airportlink

This toll road was forecast to attract 135,000 vehicles per day one month into operations, and have 165,000 vehicles per day after the ramp up period.

AirportLink traffic

The traffic volumes declined as tolls were progressively introduced to all traffic. BrisConnections, the owner of the road, went into voluntary administration in February 2013.

The Clem7 and Airportlink roads are the first two major tollways as part of the TransApex plan for adding major road capacity to Brisbane. The third piece of this puzzle is the Go Between Bridge, now owned by Transurban and they report 12,000 vehicles per day as of late 2015 (see some data on the Wikipedia page for what it’s worth). The forecast was for 17,500 by 2011 and 21,000 by 2021. Current patronage is around two-thirds of that forecast.

I’m guessing it may be a very long time before these TransApex roads reach capacity.

According to Wikipedia, this covers all major toll roads in Australia in operation at the time of writing. I’ll try to update these figures periodically.

Eastlink volumes compared to forecast

The following chart shows that Eastlink actual traffic volumes have been fairly consistently around 60-65% of original (2004) forecast since tolling began. It suggests the forecasts were good at estimating the ramp-up shape, but not so much the overall traffic volumes.

Note: ConnectEast issued revised forecasts in August 2009, including that (steady state annual) average daily trips in 2011 would be 209,900. That forecast doesn’t appear to have been realised either. Unfortunately data reporting stopped in October 2011 following the sale to Horizon Roads.

Maps of Australian Toll Roads

Here are some rough Google maps: Melbourne Sydney Brisbane
Maps and more information about many of the roads is also available on the Transurban website.

Other sources of traffic volume data

See another post on Melbourne traffic volumes. Some interesting recent data on Brisbane traffic volumes is in this report prepared for RiverCity Motorways (who operated the Clem7). It shows many major roads in Brisbane with stable or declining traffic volumes (possibly because they are at capacity, or possibly because of a mode shift to public transport).


Are congestion costs going to double? An analysis of vehicle kms in Australian cities

Tue 25 October, 2011

A frequently cited forecast is that the avoidable costs of congestion in Australia will double in most Australian cities between 2005 and 2020. These BITRE forecasts were published in 2007 (Working Paper 71), assuming continued strong growth in vehicle kms in our cities (“business-as-usual” conditions). But as this blog has demonstrated several times, transport trends have not been business-as-usual in recent years.

In August 2011, BITRE published revised estimates of vehicle kms in Australia (Report 124), derived from fuel sales data (using with fleet/fuel mix and fuel intensities etc).

How are we tracking with forecast traffic volumes?

I don’t have access to the complex model BITRE used to forecast congestion costs, but vehicle kilometres is an obvious major driver of congestion costs, and it is easy to compare the 2007 forecast (Working Paper 71) of vehicle kms in major cities with the most recent estimates of actuals (Report 124):

Consistent with other evidence, the growth in vehicle kilometres appears to be significantly below forecast. In 2007, BITRE assumed that city travel growth would fall to population growth rates, and that mode shares of travel would remain static. They also assumed world oil prices would peak at around US$65 in 2008 and drop to the low US$50s by 2011 (in 2004 dollars). None of these assumptions have played out in reality.

When looking at the components of the vehicle km estimates, the estimated actuals (in Report 124) for 2009-10 appear to be 15% lower than forecasts for cars and light commercial vehicles. For trucks, the 2009-10 estimated actual is around 8% lower than forecast.

To be fair, there was little evidence of the emerging mode shifts available at the time. That said, a BITRE forecast presented at ATRF in September 2011 showed a return to business as usual upwards growth, despite the last 6 years showing little growth.

What cost of congestion might we have avoided?

The relationship between travel volume and congestion costs is not linear. It is usually conceptually represented as an exponential curve. That is, a small reduction in traffic volumes will have a large impact on congestion costs (as evidenced each school holiday period where a claimed 5% reduction in traffic volumes has a significant impact on congestion levels).

While I am not equipped to do a robust calculation, the recent shift away from private car motoring is probably having a significant impact on the avoidable costs of congestion. Estimated actual capital city vehicle kms in 2010 (117.9 billion km) were just under the forecast for 2004 (118.2 billion km). The estimated cost of congestion for forecast 2004 vehicle km levels was $9.1b, while it 2010 it was forecast to be $12.9b. Road capacity has been increased in most cities between 2004 and 2010, which would reduce congestion costs for the same traffic volume, so the difference in 2010 between actual and forecast avoidable congestion costs might be in the order of around $3 billion.

So what is happening with vehicle kms per capita?

In another post, I used BITRE yearbook data on motorised passenger kms per capita. BITRE Report 124 only includes figures on vehicle (not passenger) kms, but they are still interesting figures.

And in response to requests from across the Tasman, I’ve added New Zealand’s one “big” city Auckland (data for ‘Auckland Region’ from their Transport Indicator Monitoring Framework, accessed October 2011).

Total vehicle kms per capita appear to be trending down in all Australian cities since around 2004/2005, with the sharpest drop in Melbourne in 2008-09. Auckland appears to be showing no such trend, with perhaps a flattening at best since 2005-06 (the vehicle km data is marked as under review, as is the public transport data which shows patronage growth of 25% in the four years to 2009-10).

Comparing values for different cities requires caution. The physical size of the urbanised area, and the administrative boundaries used to define cities will have an impact. For example, Adelaide shows up with lower vehicle kms per capita than Melbourne, even though it has much lower public transport mode share. The Adelaide urban area has a smaller footprint and is more constrained than Melbourne, which might explain this difference.

Car vehicle kms per capita appear to have peaked in either 2003-04 or 2004-05 in the five big cities, with Melbourne showing the biggest decline (a 14% decline since 2004-05).

The last two charts showed financial year estimates, but data is actually available at a quarterly level. I’ve created the following chart using simple interpolation of June estimates of residential population for each of the large Australian cities:

The underlying fuel data was actually seasonally adjusted, but there still appears to be some noise in the data (or the world may just be that variable, but I doubt it).

Vehicle use outside the big cities

What about traffic volumes in the rest of Australia? I’ve extracted the five big cities (Sydney, Melbourne, Brisbane, Perth and Adelaide) from the remainder:

The reduction in vehicle use does not appear to be limited to the big cities (most of which have seen strong growth in public transport). The trends for car km per capita outside the five cities are no different to overall vehicle use.

I should note: the report does not actually specify how vehicle kms for each state were split between capital city and other areas (section 8.2, citing unpublished data), but the fractions used were published.

What about total vehicle kms in cities?

While I like to look at per capita transport usage (everything is relative), it is instructive to look at trends in total volume as well. They provide some input into whether increased road capacity might be required, for example.

This charts shows that total vehicle kms in Melbourne, Sydney and Adelaide have been relatively flat since around 2004, while Auckland, Perth and Brisbane have shown continued growth. Perth and Brisbane show a downturn only in more recent times, but have had several years of declining vehicle kms per capita, the difference probably explained by stronger population growth.

How do BITRE Melbourne figures compare with VicRoads’ data?

Here is a chart comparing vehicle km index values for Melbourne from BITRE report 124, and an index created from annual growth figures reported in VicRoads Traffic Systems Performance Monitoring reports (with fully revised history):

A significant gap opens around 2003-04, but this substantially closes from 2008-09. Both datasets show a stabilisation of total traffic volumes, with BITRE data stabilising one year later than for VicRoads. BITRE aimed to estimate total metropolitan traffic, while the VicRoads figures are based on a defined set of monitored roads that might not reflect total traffic, particularly in growth areas on the fringe.

(Note: I did a similar comparison of VicRoads data to BITRE Working Paper 71 estimates of actuals in an earlier post).

In conclusion

  • There is strong evidence that “business-as-usual” growth in vehicle kms is just not happening in Australian cities, and thus the 2007 forecast doubling of congestion costs by 2020 is very unlikely to play out.
  • The dampened growth in travel demand is probably saving the economy a few billion in avoidable congestion costs, and has implications on the need for multi-billion dollar expansions of road capacity (though changes in demand will not be uniform across road networks).
  • I’d also suggest it is important that planners and policy makers understand why travel demand trends have changed so significantly, and apply this understanding to forecasts of future demand.
I’d like to acknowledge BITRE for conducting the excellent work that went into Report 124 and making the data publicly available, without which this analysis would not have been possible.

What’s happening with car occupancy?

Sat 20 August, 2011

[updated April 2016]

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

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

What are the trends in car occupancy? 

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

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

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

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

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

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

Trends in car occupancy by time of day

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

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

Firstly, all day (weekday) average occupancy:

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

Data is available for three sub-periods:

Again lots of noise, and no clear trends.

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

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

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

 

Notes on the AustRoads/VicRoads data:

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

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

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

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

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

There are five quite different periods:

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

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

What about different road types?

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

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

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

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

How do Australian cities compare?

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

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

Factors influencing car/vehicle occupancy (in Melbourne)

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

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

Vehicle type

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

Are they used for different purposes?

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

More likely explanations (from the data) are:

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

Day of the week

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

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

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

Household types and sizes

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

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

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

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

Trip purpose

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

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

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

Time of day

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

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

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

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

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

Geography

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

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

Age

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

Income

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

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

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

Trip distance

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

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

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

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

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

Main Activity

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

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


A simple look at passenger transport trends in Australian cities

Sat 25 June, 2011

While I’ve covered passenger transport trends in detail in another post, here are a couple of simple views of the data that provide a pretty stark summary of the recent mode shifts:

Or per capita growth:

I think those charts mostly speak for themselves.

(For the record, the five biggest cities are Sydney, Melbourne, Brisbane, Perth, and Adelaide)

By popular demand, here are charts for each city (plus Canberra):

Note:

  • These charts have very different scales on the Y axis. Compare with caution.
  • Canberra public transport passenger km (actually just bus passenger kms) is reported as “0.25” billion passenger kms for five straight years, hence the straight green line.
  • While I haven’t drawn the second set of charts for each city, in all cities, car passenger kms per capita have reduced (red lines below blue lines). Public tranpsort passenger kms per capita have increased in all cities except Canberra.

A look at Melbourne CBD transport

Sun 23 January, 2011

My last post looked at suburban employment areas, but what about the CBD? With the review of the City of Melbourne’s Transport Strategy, I’ve taken on a detailed analysis of transport to and from the CBD.

In this post I’ll look at questions like:

  • Do CBD commuters come from the inner or outer suburbs?
  • Do wealthy executive types snub public transport?
  • How does mode share vary between the sexes and young and old?
  • What impact are employer parking and driving subsidies having on mode choice?

I’m mostly focussing on the inner Melbourne CBD – using the ABS definition of “Melbourne – inner” SLA, which is essentially the Hoddle grid. However I’ve included Southbank or Docklands a couple of times, and there are also some comparisons with Sydney, Brisbane and Perth CBDs.

This is a long post, so grab a cuppa and get comfortable.

Where do the commuters come from?

According to the 2006 census, there were 137,853 commuter journeys into the CBD.

The first map shows the number of commuters from each SLA in Melbourne. The shading represents simple density of CBD commuters by area, which is not ideal because outer metro SLAs can be impacted by low average population density. At the same time, not all SLAs have the same population so some will always have large numbers (eg Manningham west). As always, click to zoom in.

The CBD attracted workers from all over Melbourne, but certainly with a high concentration from the inner suburbs.

To get around the density issue, I’ve drawn a map showing the percentage of workers from each SLA who work in the CBD, Southbank or Docklands:

You can see the percentage drops off fairly uniformly by distance. The CBD is not a major destination for most middle and outer suburban areas.

What modes of transport do commuters use? (by area)

Firstly a map showing the public transport mode share from each SLA (green = higher):

Public transport mode share was largely above 70% for much of Melbourne and indeed most surrounding areas.

A few low spots stick out:

  • Manningham west and east, serviced only by buses (that have recently been signficantly upgraded)
  • Northern parts of Boroondara and 52% and 55%. These wealthy areas are serviced by frequent trams and buses, although with a relatively slow trip in.
  • Rowville (Knox south) is at 57%, but bear in mind there were only 800 commuters from Rowville to the CBD (and I expect most of these would be park and ride train commuters). In fact, the catchment of the proposed Rowville rail line passes through three SLAs, with a total CBD commuter population of 4138. Allowing for catchments of other radial public transport lines in the SLAs, the CBD commuter catchment of the proposed Rowville line might be 2000-3000, or about 3 full trains. But of course a line would also be used for trips to other destinations (particularly Monash), and it would probably cause changes in travel patterns over time once built. I might look at this more in a future post. In the meantime you might want to read Alan Davies take, and a 2004 pre-feasibility study (here is a summary presentation).
  • Wealthy Brighton is well serviced by the Sandringham line, but only half used public transport to get to the CBD. There is no easy freeway connecting Brighton and the CBD, so why are they driving? I’ll come back to that.
  • The inner SLAs in Melbourne, Yarra and Port Phillip are slightly lower, probably due to a high rate of walking and cycling. More on that later too.

You can see a high PT mode share for the relatively small numbers of commuters from Geelong (around 800 in total). $4.3b is being spent on a regional rail link, that will separate regional trains from suburban trains. Regional trains from outside Melbourne seat less than 500 people, but because they run express through much of Melbourne they each consume probably around two all-stopping suburban train paths (which have a capacity of around 1000 each). I haven’t seen any debate about whether encouraging regional commuting by train into central Melbourne is worthwhile, though I’m sure people living in those areas appreciate the trains.

Next a map showing private transport mode share (red = higher):

Private transport mode share was highest for Manningham, northern Boroondara, Wyndham South (including Point Cook), Bayside, Rowville, and the outer northern fringes.

But a high car mode share may not be a huge issue if the number of car commuters is low. The next map shows the number of private transport commuter trips from each SLA, shaded by relative density:

Observations:

  • Like we saw in my last post for South Melbourne, there were large numbers of car commuters coming from the inner suburbs, particularly to the south-east. These areas are well connected to the CBD by public transport, and also quite wealthy. Is wealth a driver of higher car mode share? Read on.
  • Manningham west had a large number of car commuters (with a reasonable density). This area is entirely reliant on bus services, which have been upgraded considerably since 2006, with strong patronage growth resulting. In 2006, the last bus from the CBD on the Eastern Freeway – Doncaster Road route (307) was around 6:45pm. It’s now around midnight (on route 907 that replaced 307).
  • There were also a large number from Wyndham north-east (Werribee – Hoppers Crossing area) which is not shaded dark on the map due to low average population density. In 2006, peak train services on the Werribee line were often 20 minutes apart, and bus services only ran every 40 minutes. The train frequency has since increased to 6/hour but the (feeder) bus frequencies are still 40 minutes in peak periods.
  • Moonee Valley (Moonee Ponds-Essendon area) was a large contributor of cars, despite frequent trains and trams to the CBD. Not sure why that is, although Essendon is a relatively wealthy area.

Here is a another map of private transport commuters, except it is shaded by numbers rather than density. Manningham west stands out, but bear in mind it is one of the largest SLAs in Melbourne by population. You can see the outer western SLAs show up on this map also.

And for a flip side, here is where the public transport passengers were coming from (shaded by density):

There are large concentrations coming from the inner suburbs, but also the middle eastern suburbs which are well connected by trains. The Manningham west area had over 2000 public transport commuters to the CBD, many of which would have been on buses only.

Again, to get around the low population density problem, I’ve also drawn a similar map shaded by total numbers:

We saw low PT and car mode shares for the inner city. I haven’t drawn a map of walking mode share for the CBD but you can see public and private transport mode shares are low in the inner city, with walking likely to fill the gap. A map of walking mode share to any work destination is in another post.

The cycling figures are quite interesting. Next map shows the bicycle mode share to the CBD (any trip involving bicycle) (green=higher):

The figures are for Yarra north, Brunswick and Northcote are surprisingly high at 8-10%. Remember that the census is taken in winter (August). As I recall it wasn’t a rainy day. Bicycle mode share is also lower for commuters from the City of Melbourne itself. SLAs in grey lacked sufficient data.

Here are the total number of CBD bicycle commuters per SLA (shading by numbers, not density):

According to the data, people also rode from as far out as Frankston, Croydon, Ringwood and Sunbury! Census data is like that (as I recall, someone in Banyule claimed to have gone to work by ferry).

What modes did people use overall?

Here is a chart showing the overall mode split for all CBD workers:

Trains accounted for almost half of all CBD arrivals.

While buses accounted for only 2% of all CBD commuters, they were the only mode used by 32% in Manningham west, 11% in Kew, 9% in Camberwell north, 7% in Maribyrnong, and 5% in Altona.

Next chart shows mode split in a more simplified form:

Public Transport dominates, but still over a quarter came by car – including over 32,000 car drivers.

Public transport took 67% of motorised commuter trips into the CBD.

Active transport is at 8%, which probably represents those who live within walking distance of the CBD.

So how does Melbourne compare to other large Australian cities? The following chart compares Sydney, Brisbane, Perth and Melbourne CBDs. I’ve used the SLA that represents the inner core of business activity in each city to try to make in a reasonably fair comparison. Unfortunately Adelaide does not have a true inner CBD SLA to compare against (the central SLA includes all of North Adelaide, including lower density residential areas).

Sydney has the highest public transport mode share, with Melbourne and Brisbane very close (to my surprise). Perth is very much a car CBD, although mode shares are likely to have changed following the opening of the Mandurah rail line since 2006. The 2011 figures will be very interesting.

Perth walking more share was 3.0%, lower than 5.3-5.8% in the other cities – probably because of a lack of inner city residents.

And for the record, cycling was highest in Melbourne at 2.3%, followed by Perth at 2.0%, Brisbane at 1.5%, and Sydney at 0.8%.

The number of car driver journeys to work in the Melbourne CBD actually decreased from 34,289 in 2001 to 30,570 in 2006, a mode share drop from 27% to 23% (ref). This happened despite a 20% increase in the number of parking spaces in the CBD between 2000 and 2006 (ref):

I’ve included Southbank and Docklands in this chart for interest – Southbank parking supply actually went down between 2006 and 2008.

[parking stats updated June 2012 with 2010 CLUE data:]

Looking at commercial parking spaces only:

The number of commercial parking spaces has actually declined in the CBD and there has been very little growth in Docklands (despite an increase in employment).

Here is the ratio of employees to commercial parking spaces:

While the ratios are flat in three of the areas, Docklands has seen strong growth in employment without equivalent growth in commercial car parking.

Colliers International have recently begun surveying CBD parking costs. Here are the results for Australia (adjusted to AUD using 1 July exchange rates):

I don’t pretend to be an expert in CBD parking markets, but the differences between daily and monthly rates suggest some complexity. In Melbourne at least, it is quite common to find “early bird” parking for $13-17 (and “early bird” usually means parking your car before 10am).

I’m perhaps more inclined to go on the monthly rates, as they are probably more competitive. Melbourne prices collapsed in 2010, at the same time that public transport patronage growth stalled. Prices also went down in all other cities except Perth (which had the strongest public transport growth of the major cities in 2009-10).

So is CBD parking price a driver of public transport patronage? Probably too early to tell because of a lack of much time series on parking cost data (including 2006 data), but worth looking at in future.

What modes are different commuters using?

Firstly, mode share of motorised journeys by age and gender:

As you might expect, public transport mode share is higher amongst younger people and females. But for females it is also high for older women, with a curious dip at 35-44 years (typical kids at primary school years?). For men, private transport mode share was higher for older men. I’ve not shown 65-74 because the total number of such commuters was very small.

I’ve put non-motorised modes on a separate chart as they are much lower shares:

Walking was much higher for younger people. Is this because of lower car ownership, less willingness/ability to pay for transport, higher residential proximity to the CBD, and/or higher health and fitness focus? Unfortunately I don’t have the datasets to answer those questions.

Cycling mode share peaked with men aged 35-44, with men much more likely to cycle than women.

For reference, here is a demographic breakdown of CBD workers – it peaks at 25-34, with women slightly younger on average:

And here are the same charts for Brisbane:

Sydney:

and Perth:

You can see:

  • cycling mode share peaked for men aged 35-44 in all cities
  • walking tended to peak for people aged 25-34
  • public transport mode share dipped for women aged 35-44 in all cities
  • In Perth, men aged over 35 had a higher private transport mode share than public transport, the only city where this occurred.

So, do executives (presumably many from wealthy inner city suburbs) shy away from using public transport?

Indeed they do. They represented 16% of Melbourne CBD workers, but 24% of car commuters (9538 car trips in total). Maybe because many of them get company cars/parking as parts of their packages? More on that coming up.

Lower paid clerical and administrative workers were most likely to use public transport (and probably least able to afford driving and parking costs).

Note that Machinery operators & drivers also had a higher private transport mode share – I expect many are professional drivers coming in their work car (there were only around 1000 in this occupational category).

Back to managers – the next chart shows they are also more likely to snub public transport in Sydney, Brisbane and Perth:

What about other trip purposes?

The following charts show data from the VISTA 2007 household travel survey, that includes all trip types and all of Melbourne.VISTA is a survey, not a census, so there is a margin or error involved, and unfortunately the sample sizes are not large (provided in charts as “n=”). The total VISTA 2007 dataset has 2955 surveyed trips into the Melbourne CBD (across all days of the week), of which 1973 were motorised.

First chart shows mode split for trip legs into and out of the CBD, by time of day on weekdays:

Weekday AM peak is 7-9am, and PM peak is 3-6pm, anything else is classed as off-peak. Unfortunately there are only 190 trips in/out of the CBD on weekends in the sample, which has too large a margin of error to be too meaningful (7%).

Active transport (walking and cycling) and public transport were clearly dominant. When looking only at motorised trips, Public transport took 74% of inbound AM peak and outbound PM peak trips, and 67%/62% of off-peak in/out bound trips.

Recall above that motorised journeys to work in 2006 were 67% by public transport, suggesting people travelling for reasons other than work in peak periods were slightly more likely to use public transport.

What about wealth? I’ve used average household income per occupant, to remove the impacts of household size, and grouped this by $500 amounts. Note: the sample sizes are quite small for larger income groups.

Sure enough, there appears to be a trend that people from higher income households were more likely to use private transport for travel into the CBD.

What about age?

While the sample sizes are relatively small, there certainly appears to have been a higher propensity to use private transport for travel to the CBD by middle-aged people.

There may be a trend back to public transport for older people, but the margin of error is around 10% for the last two age groups so this is not certain. However it would fit with Seniors being able to access cheaper public transport fares.

In terms of gender, 73% of females who used motorised transport came by public transport, compared to 67% of males – a similar difference to commuters.

Who’s paying for the private transport?

While for many people driving to the CBD for work everyday is something of a non-option, there are still tens of thousands who do. Is employer sponsored driving and parking costs influencing their mode choice?

VISTA lets us take a look at that also, although there is only a sample of 183 AM peak private transport trips (margin of error around 7%).

According to the data, around 29% of cars driven into the CBD in the AM peak had their running costs paid by a company, and 36% had parking paid for by employers. Remarkably, 34% reported no parking costs for off-street parking (these trips mostly for work purposes) – which doesn’t sound right for the CBD in the AM peak! I’m not aware of any publicly available free off-street parking spaces. Perhaps the respondents overlooked the fact that someone else was at least paying for the land on which they parked? If that is the case, then it would appear that less than a third of cars driven into the CBD in the AM peak were not employer subsidised in parking or running costs.

Employer subsidies appear to be an incentive to drive to the CBD. By contrast, only around 2% of general Melbourne AM peak car drivers had employee paid parking, and only around 13% had car running costs paid by an employer (VISTA 2007).

One of the most effective ways to reduce car mode share for journeys to work in the Melbourne CBD would appear to be reducing employer subsidies for parking and driving costs. Schemes such as parking cash out help employees see how much their parking and driving costs are being subsidised. If they have the option of receiving that money directly as salary they might make different choices (depending on tax treatment of course!).

That said, with current capacity issues on Melbourne’s trains and trams, trying to shift more CBD commuter trips from car to public transport in the short-term might not be a government priority just at the moment.

And lastly, for the record, 6 and 8 cylinder cars parked in the CBD did not appear to be over-represented. Cars of well-known luxury brands were over-represented (15% v 6% metro average).

I think that’s enough now! 🙂

Active transport is at 8%, which probably represents those who live within walking distance of the CBD. In order to take out the walking component, I’ve also taken a sample that excludes an “inner ring” around the CBD, as shown in the following map:

If you take out the inner ring, the mode split is 69% PT, 28% car, 1.9% cycling and 1.4% walking longer distances.