What impact does paid car parking have on travel mode choice in Melbourne?

Thu 3 October, 2019

Paid parking is often used when too many people want to park their car in the same place at the same time. Does it encourage people to cycle or use public transport instead of driving? Does that depend on the type of destination and/or availability of public transport? Are places with paid parking good targets for public transport upgrades?

In this post I’m going to try to answer the above questions. I’ll look at where there is paid parking in Melbourne, how transport mode shares vary for destinations across the city, and then the relationship between the two. I’ll take a deeper look at different destination types (particularly hospitals), explore the link between paid parking and employment density, and conclude with some implications for public transport planners. There’s a bit to get through so get comfortable.

This post uses data from around 158,000 surveyed trips around Greater Melbourne collected as part of a household travel survey (VISTA) between 2012 and 2018, as well as journey to work data from the 2016 ABS census.

Unfortunately the data available doesn’t allow for perfect analysis. The VISTA’s survey sample sizes are not large, I don’t have data about how much was paid for parking, nor whether other parking restrictions might impact mode choice (e.g. time limits), and I suspect some people interpreted survey questions differently. But I think there are still some fairly clear insights from the data.

Where is there paid parking in Melbourne?

I’m not aware of an available comprehensive car park pricing data set for Melbourne. Parkopedia tells you about formal car parks (not on street options) and doesn’t share data sets for free, while the City of Melbourne provides data on the location, fees, and time restrictions of on-street bays (only). So I’ve created my own – using the VISTA household travel survey.

For every surveyed trip involving parking a car, van, or truck, we know whether a parking fee was payable. However the challenge is that VISTA is a survey, so the trip volumes are small for any particular place. For my analysis I’ve used groups of ABS Destination Zones (2016 boundaries) that together have at least 40 parking trips (excluding trips where the purpose was “go home” as residential parking is unlikely to involve a parking fee). I’ve chosen 40 as a compromise between not wanting to have too small a sample, and not wanting to have to aggregate too many destination zones. In some cases a single destination zone has enough parking trips, but in most cases I have had to create groups.

I’ve tried to avoid merging different land uses where possible, and for some parts of Melbourne there are just not enough surveyed parking trips in an area (see appendix at the end of this post for more details). Whether I combine zones or use a single zone, I’m calling these “DZ groups” for short.

For each DZ group I’ve calculated the percentage of vehicle parking trips surveyed that involved someone paying a parking fee. The value will be low if only some circumstances require parking payment (eg all-day parking on weekdays), and higher if most people need to pay at most times of the week for both short and long stays (but curiously never 100%). The sample for each DZ group will be a small random sample of trips from different times of week, survey years, and durations. For DZ groups with paid parking rates above 20%, the margin of error for paid parking percentage is typically up to +/- 13% (at a 90% confidence interval).

Imperfect as the measure is, the following map shows DZ groups with at least 10% paid parking, along with my land use categorisations (where a DZ group has a specialised land use).

There are high percentages of paid parking in the central city, as you’d expect. Paid parking is more isolated in the suburbs – and mostly occurs at university campuses, hospitals, larger activity centres, and of course Melbourne Airport.

The next chart shows the DZ groups with the highest percentages of paid parking (together with the margin of error).

Technical note: the Y-axis shows the SA2 name, rather than the (unique but meaningless) DZ code(s), so you will see multiple DZ groups with the same SA2 name.

At the top of the chart are central city areas, major hospitals, several university campuses, and Melbourne Airport.

Further down the chart are:

  • larger activity centres – many inner suburban centres plus also Dandenong, Frankston, Box Hill, and curiously Springvale (where some controversial parking meters were switched off in 2017),
  • the area around Melbourne Zoo (Parkville SA2 – classified as “other”),
  • some inner city mixed-use areas,
  • two shopping centres – the inner suburban Victoria Gardens Shopping Centre in Richmond (which includes an IKEA store), and Doncaster (Westfield) – the only large middle suburban centre to show up with significant paid parking (many others now have time restrictions), and
  • some suburban industrial employment areas (towards the bottom of the chart) – in which I’ve not found commercial car parks.

These are mostly places of high activity density, where land values don’t support the provision of sufficient free parking to meet all demand.

While the data looks quite plausible, the calculated values not perfect, for several reasons:

  • Some people almost certainly forget that they paid for parking (or misinterpreted the survey question). For example, on the Monash University Clayton campus, 45% of vehicle driver trips (n = 126) said no parking fee was payable, 2% said their employer paid, and 12% said it was paid through a salary arrangement. However there is pretty much no free parking on campus (at least on weekdays), so I suspect many people forgot to mention that they had paid for parking in the form of a year or half-year permit (I’m told that very few staff get free parking permits).
  • Many people said they parked for free in an employee provided off-street car park. In this instance the employer is actually paying for parking (real estate, infrastructure, maintenance, etc). If this parking is rationed to senior employees only then other employees may be more likely to use non-car modes. But if employer provided is plentiful then car travel would be an attractive option. 22% of surveyed trips involving driving to the Melbourne CBD reported parking in an employer provided car park, about a quarter of those said no parking fee was required (most others said their employer paid for parking).
  • As already mentioned, the sample sizes are quite small, and different parking events will be at different times of the week, for different durations, and the applicability of parking fees may have changed over the survey period between 2012 and 2018.
  • The data doesn’t tell us how much was paid for parking. I would expect price to be a significant factor influencing mode choices.
  • Paid parking is not the only disincentive to travel by private car – there might be time restrictions or availability issues, but unfortunately VISTA does not collect such data (it would be tricky to collect).

How does private transport mode share vary across Melbourne?

The other part of this analysis is around private transport mode shares for destinations. As usual I define private transport as a trip that involved some motorised transport, but not any modes of public transport.

Rich data is available for journeys to work from the ABS census, but I’m also interested in general travel, and for that I have to use the VISTA survey data.

For much of my analysis I am going to exclude walking trips, on the basis that I’m primarily interested in trips where private transport is in competition with cycling and public transport. Yes there will be cases where people choose to walk instead of drive because of parking challenges, but I’m assuming not that many (indeed, around 93% of vehicle driver trips in the VISTA survey are more than 1 km). An alternative might be to exclude trips shorter than a certain distance, but then that presents difficult decisions around an appropriate distance threshold.

Here’s a map of private transport mode share of non-walking trips by SA2 destination:

Technical note: I have set the threshold at 40 trips per SA2, but most SA2s have hundreds of surveyed trips. The grey areas of the map are SA2s with fewer than 40 trips, and/or destination zones with no surveyed trips.

For all but the inner suburbs of Melbourne, private transport is by far the dominant mode for non-walking trips. Public transport and cycling only get a significant combined share in the central and inner city areas.

Where is private transport mode share unusually low? And could paid parking explain that?

The above chart showed a pretty strong pattern where private transport mode share is lower in the central city and very high in the suburbs. But are there places where private mode share in unusually low compared to surround land uses? These might be places where public transport can win a higher mode share because of paid parking, or other reasons.

Here’s a similar mode share map, but showing only DZ groups that have a private mode share below 90%:

If you look carefully you can see DZ groups with lower than 80% mode share, including some university/health campuses.

To better illustrate the impact of distance from the city centre, here’s a chart summarising the average private transport mode share of non-walking trips for selected types of places, by distance from the city centre:

Most destination place types are above 90% private transport mode share, except within the inner 5 km. The lowest mode shares are at tertiary education places, workplaces in the central city, secondary schools and parks/recreation. Up the top of the chart are childcare centres, supermarkets and kinders/preschool. Sorry it is hard to decode all the lines – but the point is that they are mostly right up the top.

The next chart brings together the presence of paid parking, distance from the CBD, destination place type, and private transport mode shares. I’ve greyed out DZ groups with less than 20% paid parking, and you can see they are mostly more than 3 km from the CBD. I’ve coloured and labelled the DZ groups with higher rates of paid parking. Also note I’ve used a log scale on the X-axis to spread out the paid DZ groups (distance from CBD).

Most of the DZ groups follow a general curve from bottom-left to top-right, which might reflect generally declining public transport service levels as you move away from the city centre.

The outliers below the main cloud are places with paid parking where private modes shares are lower than other destinations a similar distance from the CBD. Most of these non-private trips will be by public transport. The biggest outliers are university campuses, including Parkville, Clayton, Caulfield, Burwood, and Hawthorn. Some destinations at the bottom edge of the main cloud include university campuses in Kingsbury and Footscray, and parts of the large activity centres of Box Hill and Frankston.

Arguably the presence of paid parking could be acting as a disincentive to use private transport to these destinations.

Contrast these with other paid parking destinations such as hospitals, many activity centres, and Melbourne Airport. The presence of paid parking doesn’t seem to have dissuaded people from driving to these destinations.

Which raises a critical question: is this because of the nature of travel to these destinations means people choose to drive, or is this because of lower quality public transport to those centres? Something we need to unpack.

How strongly does paid car parking correlate with low private transport mode shares?

Here’s a chart showing DZ groups with their private transport mode share of (non-walking) trips and percent of vehicle parking trips involving payment.

Technical note: A colour has been assigned to each SA2 to help associate labels to data points, although there are only 20 unique colours so they are re-used for multiple SA2s. I have endeavoured to make labels unambiguous. It’s obviously not possible to label all points on the chart.

In the top-left are many trip destinations with mostly free parking and very high private transport mode share, suggesting it is very hard for other modes to compete with free parking (although this says nothing about the level of public transport service provision or cycling infrastructure). In the bottom-right are central city DZ groups with paid parking and low private transport mode share.

There is a significant relationship between the two variables (p-value < 0.0001 on a linear regression as per line shown), and it appears that the relative use of paid parking explains a little over half of the pattern of private transport mode shares (R-squared = 0.61). But there is definitely a wide scattering of data points, suggesting many other factors are at play, which I want to understand.

In particular it’s notable that the data points close to the line in the bottom-right are in the central city, while most of the data points in the top-right are mostly in the suburbs (they are also the same land use types that were an exception in the last chart – Melbourne Airport, hospitals, some university campuses, and activity centres).

As always, it’s interesting to look at the outliers, which I am going to consider by land use category.

Melbourne Airport

The airport destination zone has around 62% paid parking and around 92% private transport mode share for general trips (noting the VISTA survey is only of travel by Melbourne and Geelong residents). The airport estimates 14% of non-transferring passengers use some form of public transport, and that 27% of weekday traffic demand is employee travel.

Some plausible explanations for high private mode share despite paid parking include:

  • shift workers travelling when public transport is infrequent or unavailable (I understand many airport workers commence at 4 am, before public transport has started for the day),
  • unreliable work finish times (for example, if planes are delayed),
  • longer travel distances making public transport journeys slower and requiring transfers for many origins,
  • travellers with luggage finding public transport less convenient,
  • highly time-sensitive air travellers who might feel more in control of a private transport trip,
  • active transport involving long travel distances with poor infrastructure, and
  • many travel costs being paid by businesses (not users).

It’s worth noting that the staff car park is remote from the terminal buildings, such that shuttle bus services operate – an added inconvenience of private transport. But by the same token, the public transport bus stops are a fairly long walk from terminals 1 and 2.

The destination zone that includes the airport terminals also includes industrial areas on the south side of the airport. If I aggregate only the surveyed trips with a destination around the airport terminals, that yields 69% paid parking, and 93% private mode share. Conversely, the industrial area south of the airport yields 6% paid parking, and 100% private mode share.

Hospitals

Almost all hospitals are above the line – i.e. high private mode share despite high rates of paid parking.

The biggest outliers are the Monash Medical Centre in Clayton, Austin/Mercy Hospitals in Heidelberg, and Sunshine Hospital in St Albans South.

The Heidelberg hospitals are adjacent to Heidelberg train station. The Monash Medical Centre at Clayton is within 10 minutes walk of Clayton train station where trains run every 10 minutes or better for much of the week, and there’s also a SmartBus route out the front. Sunshine Hospital is within 10 minutes walk of Ginifer train station (although off-peak services mostly run every 20 minutes).

It’s not like these hospitals are a long way from reasonably high quality public transport. But they are a fair way out from the CBD, and only have high quality public transport in some directions.

The DZ containing Royal Melbourne Hospital, Royal Women’s Hospital, and Victoria Comprehensive Cancer Centre in Parkville is the exception below the line. It is served by multiple high frequency public transport lines, and serves the inner suburbs of Melbourne (also well served by public transport) which might help explain its ~45% private transport mode share.

The Richmond hospital DZ group is close to the line – but this is actually a blend of the Epworth Hospital and many adjacent mixed land uses so it’s not a great data point to analyse unfortunately.

So what might explain high private transport mode shares? I think there are several plausible explanations:

  • shift workers find public transport infrequent, less safe, or unavailable at shift change times (similar to the airport),
  • visitors travel at off-peak times when public transport is less frequent,
  • longer average travel distances (hospitals serve large population catchments with patients and visitor origins widely dispersed),
  • specialist staff who work across multiple hospitals on the same day,
  • patients need travel assistance when being admitted/discharged, and
  • visitor households are time-poor when a family member is in hospital.

The Parkville hospital data point above the line is the Royal Children’s Hospital. Despite having paid parking and being on two frequent tram routes, there is around 80% private transport mode share. This result is consistent with the hypotheses around time-poor visitor households, patients needing assistance when travelling to/from hospitals, and longer average travel distances (being a specialised hospital).

We can also look at census journey to work data for hospitals (without worrying about small survey sample sizes). Here’s a map showing the relative size, mode split and location of hospitals around Melbourne (with at least 200 journeys reported with a work industry of “Hospital”):

It’s a bit congested in the central city so here is an enlargement:

The only hospitals with a minority private mode share of journeys to work are the Epworth (Richmond), St Vincent’s (Fitzroy), Eye & Ear (East Melbourne), and the Aboriginal Health Service (Fitzroy) (I’m not sure that this is a hospital but it’s the only thing resembling a hospital in the destination zone).

Here’s another chart of hospitals showing the number of journeys to work, private transport mode share, and distance from the Melbourne CBD:

Again, there’s a very strong relationship between distance from the CBD and private transport mode share.

Larger hospitals more than 10 km from the CBD (Austin/Mercy, Box Hill, Monash) seem to have slightly lower private mode shares than other hospitals at a similar distance, which might be related to higher parking prices, different employee parking arrangements, or it might be that they are slightly closer to train stations.

The (relatively small) Royal Talbot Hospital is an outlier on the curve. It is relatively close to the CBD but only served by ten bus trips per weekday (route 609).

To test the public transport quality issue, here’s a chart of journey to work private mode shares by distance from train stations:

While being close to a train station seems to enable lower private transport mode shares, it doesn’t guarantee low private transport mode shares. The hospitals with low private transport mode shares are all in the central city.

So perhaps the issue is as much to do with the public transport service quality of the trip origins. The hospitals in the suburbs largely serve people living in the suburbs which generally have lower public transport service levels, while the inner city hospitals probably more serve inner city residents who generally have higher public transport service levels and lower rates of motor vehicle ownership (see: What does the census tell us about motor vehicle ownership in Australian cities? (2006-2016)).

Indeed, here is a map showing private transport mode share of non-walking trips by origin SA2:

Technical notes: grey areas are SA1s (within SA2s) with no survey trips.

Finally for hospitals, here is private transport mode share of journeys to work (from the census) compared to paid parking % from VISTA (note: sufficient paid parking data is only available for some hospitals, and we don’t know whether staff have to pay for parking):

There doesn’t appear to be a strong relationship here, as many hospitals with high rates of paid parking also have high private transport mode shares.

In summary:

  • The distance of a hospital from the CBD seems to be the primary influence on mode share.
  • Specialised hospitals with larger catchments (eg Children’s Hospital) might have higher private transport mode shares.
  • The quality of public transport to the hospital seems to have a secondary impact on mode shares.

Activity centres

Suburban activity centres such as Frankston, Box Hill, Dandenong, and Springvale have high private mode shares, which might reflect lower public transport service levels than the inner city (particularly for off-rail origins).

Box Hill is the biggest outlier for activity centres in terms of high private mode share despite paid parking. But compared to other destinations that far from the Melbourne CBD, it has a relatively low private transport mode share. It is located on a major train line, and is served by several frequent bus routes.

In general, there are fewer reasons why increased public transport investment might not lead to higher public transport mode share compared to airports and hospitals. Travel distances are generally shorter, many people will be travelling in peak periods and during the day, there are probably few shift workers (certainly few around-the-clock shift workers).

University campuses

The biggest university outliers above the line (higher private mode shares and higher paid parking %) are Deakin University (Burwood) and La Trobe University (Kingsbury). Furthermore, private transport also has a majority mode share for Monash University Clayton, Victoria University Footscray Park, Monash University (Caulfield) and Swinburne University (Hawthorn).

As discussed earlier, I suspect the rates of paid parking may be understated for university campuses because people forget they have purchased long-term parking permits.

The following chart shows the full mode split of trips to the University DZ groups in various SA2s (this time including walking trips):

Of the campuses listed, only Hawthorn and Caulfield are adjacent to a train station. Of the off-rail campuses:

  • Parkville (Melbourne Uni, 43% public transport) is served by multiple frequent tram routes, plus a high frequency express shuttle bus to North Melbourne train station. In a few years it will also have a train station.
  • Clayton (Monash, 22% PT) is also served by a high frequency express shuttle bus service to Huntingdale train station.
  • Burwood (Deakin, 19% PT) is on a frequent tram route, but otherwise moderately frequent bus services (its express shuttle bus service to Box Hill train station – route 201 – currently runs every 20 minutes)
  • Footscray (Park) (Victoria Uni, 14% PT) has bus and tram services to Footscray train station but they operate at frequencies of around 15 minutes in peak periods, and 20 minutes inter-peak.
  • Kingsbury (La Trobe Uni, 13% PT) has an express shuttle bus service from Reservoir station operating every 10 minutes on weekdays (introduced in 2016).

The success of high frequency express shuttle bus services to Parkville and Clayton may bode well for further public transport frequency upgrades to other campuses.

University campuses are also natural targets for public transport as university students on low incomes are likely to be more sensitive to private motoring and parking costs.

However university campuses also have longer average travel distances which might impact mode shares – more on that shortly.

Central city

Most central city DZ groups are in the bottom-right of the scatter plot, but there are some notable exceptions:

  • A Southbank DZ around Crown Casino has 65% paid parking and 70% private transport mode share. This was also an exception when I analysed journey to work (see: How is the journey to work changing in Melbourne? (2006-2016)) and might be explained be relatively cheap parking, casino shift workers, and possibly more off-peak travel (eg evenings, weekends).
  • Similarly, a Southbank DZ group around the Melbourne Convention and Exhibition Centre / South Wharf retail complex has 62% paid parking and around 74% private mode share. Many parts of this area are a long walk from public transport stops, and also there are around 2,200 car parks on site (with $17 early bird parking at the time of writing).
  • Albert Park – a destination zone centred around the park – has around 54% paid parking and 87% private transport mode share. Most of the VISTA survey trips were recreation or sport related, which may include many trips to the Melbourne Sports and Aquatic Centre. The park is surrounded by tram routes on most sides, but is relatively remote from the (rapid) train network.
  • Northern Docklands shows up with around 50% paid parking and around 88% private transport mode share, despite being very close to the Melbourne CBD. While this area is served by multiple frequent tram routes, it is a relatively long walk (or even tram ride) from a nearby a train station (from Leven Avenue it is 16 minutes by tram to Southern Cross Station and around 18 minutes to Flagstaff Station, according to Google). The closest train station is actually North Melbourne, but there is currently no direct public transport or pedestrian connection (the E-gate rail site and future Westgate Tunnel road link would need to be crossed).

Inner suburbs

Some places to the bottom-left of the cloud on the chart include inner suburban areas such as South Yarra, Fitzroy, Richmond, Abbotsford, Brunswick, and Collingwood. While paid parking doesn’t seem to be as common, private transport mode shares are relatively low (even when walking trips are excluded). These areas typically have dense mixed-use activity with higher public transport service levels, which might explain the lower private transport mode shares. These areas probably also have a lot of time-restricted (but free) parking.

What is the relationship between paid parking and journey to work mode shares?

For journeys to work we thankfully have rich census data, with no issues of small survey sample sizes.

The following chart combines VISTA data on paid parking, with 2016 census data on journey to work mode shares (note: the margin of error on the paid parking percentage is still up to +/-12%).

The pattern is very similar to that for general travel, and the relationship is of a similar strength (r-squared = 0.59).

There are more DZ groups below the line on the left side of the chart, meaning that the private transport mode share of journeys to work is often lower than for general travel.

Indeed, here is a chart comparing private transport mode share of general travel (VISTA survey excluding walking and trips to go home) with journeys to work (ABS census):

Note the margin of error for private transport mode shares is around +/-10% because of the small VISTA sample sizes.

For most DZ groups of all types, private transport mode shares are lower for journeys to work compared to general travel (ie below the diagonal line). This might reflect public transport being more competitive for commuters than for visitors – all-day parking might be harder to find and/or more expensive. This suggests investment in public transport might want to target journeys to work.

The DZ groups above the line include Flemington Racecourse (census day was almost certainly not a race day so there was probably ample parking for employees, while many VISTA survey trips will be from event days), Deakin Uni (Burwood), and a few others. Some of these DZ groups are dominated by schools, where workers (teachers) drive while students are more likely to cycle or catch public transport.

What about public transport mode shares?

The following chart shows VISTA public transport mode shares (for general travel) against paid parking percentages:

There are similar patterns to the earlier private transport chart, but flipped. The outliers are very similar (eg hospitals and Melbourne Airport in the bottom-right), although the top-left outliers include some destinations in socio-economically disadvantaged areas (eg Braybrook, Broadmeadows, Dandenong).

The DZ group in Blackburn South with no paid parking but 22% public transport mode share contains several schools but otherwise mostly residential areas, and the survey data includes many education related trips.

Are shift workers less likely to use public transport?

Shift workers at hospitals, Melbourne Airport, and the casino might be less likely to use public transport because of the inconvenience of travelling at off-peak shift change times, when service levels may be lower or non-existent.

Here’s a chart showing the mode split of VISTA journeys to work by destination type categories, and also type of working hours:

For hospitals, rostered shifts had a lower public transport mode share, compared to fixed and flexible hours workers, so this seems to support (but not prove) the hypothesis.

Public transport use is actually higher for rostered shift workers at other destination types, but I suspect these are mostly not around-the-clock shifts (eg retail work), and are more likely to be lower paid jobs, where price sensitivity might contribute more to mode choice.

Unfortunately there are not enough VISTA journey to work survey responses for Melbourne Airport to get sensible estimates of mode shares for different work types.

Do longer travel distances result in lower public transport mode shares?

Another earlier hypothesis was that destinations that attract longer distance trips (such as universities, hospitals, and airports) are more likely to result in private transport mode choice, as public transport journeys are more likely to require one or more transfers.

Trip distances to specialised places such as airports, suburban employment areas, universities and hospitals are indeed longer. But the central city also rates here and that has low private transport mode shares.

Digging deeper, here are median travel distances to DZ groups around Melbourne:

The central city has higher median trip distances but low private mode shares, while many suburban destinations (particularly employment/industrial areas, universities, and hospitals) have similar median travel distances but much higher public transport mode shares.

I think a likely explanation for this is that public transport to the central city is generally faster (often involving trains), more frequent, and involves fewer/easier transfers. Central city workers are also more likely to live near radial public transport lines. On the other hand, the trip origins for suburban destinations are more likely to be in the suburbs where public transport service levels are generally lower (compared to trip origins in the inner suburbs).

Cross-suburban public transport travel will often require transfers between lower frequency services, and will generally involve at least one bus leg. Very few Melbourne bus routes are currently separated from traffic, so such trips are unlikely to be as fast as private motoring (unless parking takes a long time to find), but they might be able to compete on marginal cost (if there is more expensive paid parking).

Of course this is not to suggest that cross-suburban public transport cannot be improved. More direct routes, higher frequencies, and separation from traffic can all make public transport more time-competitive.

How does parking pricing relate to employment density?

My previous research has confirmed a strong relationship between job density and lower journey to work private transport mode shares (see: What explains variations in journey to work mode shares between and within Australian cities?). Can this be explained by more paid parking in areas with higher job density?

The following chart compares weighted job density (from census 2016) and paid parking percentages (from VISTA):

Technical notes: Weighted job density is calculated as a weighted average of the job densities of individual destination zones in a DZ group, with the weighting being the number of jobs in each zone (the same principle as population weighted density). I have used a log-scale on the X-axis, and not shown DZ groups with less than 1 job/ha as they are not really interesting

There appears to be a relationship between job density and paid parking – as you would expect. The top right quadrant contains many university campuses, hospitals, and central city areas with high job density and high paid parking percentages.

In the bottom-right are many large job-dense shopping centres that offer “free” parking. Of course in reality the cost of parking is built into the price of goods and services at the centres (here’s a thought: what if people who arrive by non-car modes got a discount?). An earlier chart showed us that employees are less likely to commute by private transport than visitors.

The outliers to the top-left of the chart are actually mostly misleading. An example is Melbourne Airport where the density calculation is based on a destination zone that includes runways, taxiways, a low density business park, and much green space. The jobs are actually very concentrated in parts of that zone (e.g. passenger terminals) so the density is vastly understated (I’ve recommended to the ABS that they create smaller destination zones around airport terminal precincts in future census years).

Inclusion of significant green space and/or adjacent residential areas is also an issue at La Trobe University (Kingsbury data point with just under 50% mode share), RMIT Bundoora campus (Mill Park South), Royal Children’s Hospital (Parkville), Sunshine Hospital (St Albans South), Victoria University (Footscray (Park)), Albert Park (the actual park), and Melbourne Polytechnic Fairfield campus / Thomas Embling Hospital (Yarra – North).

I am at a loss to explain paid parking in Mooroolbark – the only major employer seems to be the private school Billanook College.

Can you summarise the relationship between paid parking and mode shares?

I know I’ve gone down quite a few rabbit holes, so here’s a summary of insights:

  • Distance from the Melbourne CBD seems to be the strongest single predictor of private transport mode share (as origin or destination). This probably reflects public transport service levels generally being higher in the central city and lower in the suburbs. Destinations further from the central city are likely to have trip origins that are also further from the central city, for which public transport journeys are often slower.
  • Paid parking seems to be particularly effective at reducing private transport mode shares at university campuses, and the impact is probably greater if there are higher quality public transport alternatives available.
  • There’s some evidence to suggest paid parking may reduce private transport mode shares at larger activity centres such as Box Hill and Frankston.
  • Most hospitals have very high private transport mode shares, despite also having paid parking. Hospitals with better public transport access have slightly lower private transport mode shares.
  • Destinations with around-the-clock shift workers (e.g. hospitals and airports) seem generally likely to have high private transport mode shares, as public transport services at shift change times might be infrequent or unavailable.
  • Suburban destinations that have longer median travel distances (such as hospitals, airports and industrial areas) mostly have higher private transport mode shares.
  • Even if there isn’t much paid parking, destinations well served by public transport tend to have lower private transport mode shares (although this could be related to time-restricted free parking).

If you’d like more on factors influencing mode shares, I’ve also explored this more broadly elsewhere on this blog, with employment density (related to parking prices), cycling infrastructure quality, proximity to rapid public transport, and walking catchment density found to be significant factors (see: What explains variations in journey to work mode shares between and within Australian cities?).

Are places with paid parking good targets for public transport investments?

Many of my recent conversations with transport professionals around this topic have suggested an hypothesis that public transport wins mode share in places that have paid parking. While that’s clearly the case in the centre of Melbourne and at many university campuses, this research has found it’s more of a mixed story for other destinations.

While this post hasn’t directly examined the impact of public transport investments on mode shares in specific places, I think it can inform the types of destinations where public transport investments might be more likely to deliver significant mode shifts.

Here’s my assessment of different destination types (most of which have paid parking):

  • Suburban hospitals may be challenging due to the presence of shift workers, patients needing assistance, visitors from time-poor households, and long average travel distances making public transport more difficult for cross-suburban travel. There’s no doubt many people use public transport to travel to hospitals, but it might not include many travellers who have a private transport option.
  • Larger activity centres with paid parking show lower private transport mode shares. Trips to these centres involve shorter travel distances that probably don’t require public transport transfers, and don’t suffer the challenges of around-the-clock shift workers, so they are likely to be good targets for public transport investment.
  • Universities are natural targets for public transport, particularly as many students would find the cost of maintaining, operating and parking a car more challenging, or don’t have access to private transport at all (around 35% of full time university/TAFE students do not have a full or probationary licence according to the VISTA sample). Universities do attract relatively higher public transport mode shares (even in the suburbs) and recent investments in express shuttle services from nearby train stations appear to have been successful at growing public transport patronage.
  • Melbourne Airport has high rates of paid parking and private transport mode share. It is probably a challenging public transport destination for employees who work rostered shifts. However already public transport does well for travel from the CBD, and this will soon be upgraded to heavy rail. Stations along the way may attract new employees in these areas, but span of operating hours may be an issue.
  • Job dense central city areas that are not currently well connected to the rapid public transport network could be public transport growth opportunity. In a previous post I found the largest journey to work mode shifts to public transport between 2011 and 2016 were in SA2s around the CBD (see: How is the journey to work changing in Melbourne? (2006-2016)). The most obvious target to me is northern Docklands which is not (yet) conveniently connected its nearby train station. Public transport is also gaining patronage in the densifying Fishermans Bend employment area (buses now operate as often as every 8 minutes in peak periods following an upgrade in October 2018).
  • Lower density suburban employment/industrial areas tend to have free parking, longer travel distances, and very high private transport mode shares. These are very challenging places for public transport to win significant mode share, although there will be some demand from people with limited transport options.

An emerging target for public transport might be large shopping centres that are starting to introduce paid or time-restricted car parking (particularly those located adjacent to train stations, e.g. Southland). That said, Westfield Doncaster, which has some paid parking (around 19%), has achieved only 6% public transport mode share in the VISTA survey (n=365), athough this may be growing over time. Meanwhile, Dandenong Plaza has around 16% public transport mode share despite only 6% paid parking.

Upgraded public transport to shopping centres might be particularly attractive for workers who are generally on lower incomes (we’ve already seen staff having lower private transport mode shares than visitors). Also, customer parking may be time-consuming to find on busy shopping days, which might make public transport a more attractive option, particularly if buses are not delayed by congested car park traffic.

There’s a lot going on in this space, so if you have further observations or suggestions please comment below.

Appendix: About destination group zones

Here is a map showing my destination zone groups in the central city area which have 15% or higher paid parking. Each group is given a different colour (although there are only 20 unique colours used so there is some reuse). The numbers indicate the number of surveyed parking trips in each group:

Some of the DZ groups have slightly less than 40 parking trips, which means they are excluded from much of my analysis. In many cases I’ve decided that merging these with neighbouring zones would be mixing disparate land uses, or would significantly dilute paid parking rates to not be meaningful (examples include northern Abbotsford, and parts of Kew and Fairfield). Unfortunately that’s the limitation of the using survey data, but there are still plenty of qualifying DZ groups to inform the analysis.

I have created destination zone groups for most destination zones with 10%+ paid parking, and most of the inner city area to facilitate the DZ group private transport mode share chart. I haven’t gone to the effort of creating DZ groups across the entire of Melbourne, as most areas have little paid parking and are not a focus for my analysis.


What might explain journey to work mode shifts in Australia’s largest cities?

Mon 28 May, 2018

[Updated 29 June 2018 with further analysis of parking levies and their impact]

Between 2011 and 2016, journey to work public transport mode shares went up significantly in Melbourne and Sydney but dropped significantly in Perth and Brisbane. Private transport mode shifts did the opposite. Can this be explained by the changing distribution of jobs within cities, or other factors such as changes in transport costs?

In a recent post focused on Brisbane I found that stronger growth in suburban jobs relative to central city jobs could explain around half of the city’s mode shift towards private transport, with other factors (mostly the changes in relative attractiveness of modes) explaining the rest.

So how is job distribution changing in other Australian cities? How much of the mode shifts can be attributed to changing job distribution and how much could be attributed to other factors like changes in transport costs, or increasing employment density?

(for details about how I define public, private and active transport, see the appendix in this post)

How is job distribution changing in Australian cities?

Here’s a view of the changing distribution of all jobs within each city by workplaces distance from the city centre.

(Unfortunately I only have 2006 data for Sydney and Melbourne)

The changes are relatively subtle, but if look at how the bands shift between years, you’ll see increasing centralisation in Sydney but a decentralisation in all other cities between 2011 and 2016.

The strongest decentralisation was in Brisbane and Perth, which also showed the biggest increases in private transport mode share.

However Melbourne saw both a slight decentralisation of jobs and a mode shift away from private transport between 2011 and 2016.

So we need to dig deeper to find out what’s going on here.

How does private mode share vary by distance from the city centre?

The following chart shows private transport mode shares by distance from the city centre for the last two or three censuses for each city. The darkest line for each city is for 2016, with lighter lines being previous years (I only have 2006 data for Melbourne and Sydney).

There’s a clear pattern in all cities that private mode shares are lower in areas closer to the city centre, with Sydney the lowest, followed by Melbourne, Brisbane, Perth, Adelaide, and Canberra (which is also the order of their population size).

Notably Sydney private mode share averaged lower than 90% out as far as 24km from the city centre, whereas Adelaide sees 90% mode shares as close as 2km from the city centre.

If you look carefully you can see that Brisbane increased private transport mode shares in the central city between 2011 and 2016, while private mode shares dropped or were stable in all other cities at most distances.

You can also see that the central city mode shifts away from private transport were largest in Melbourne, something I’ll come back to.

Here’s the same again but for public transport:

Sydney and Melbourne saw mode shifts to public transport at most distances from the city centre, unlike all other cities.

What mode shift can we attribute to changing job distributions?

A city’s mode share (measured by place of work) will be fundamentally impacted by two types of changes between censuses:

  • Changes in the volume of jobs in each SA2 – because different SA2s generally have different mode shares due to factors like proximity to the city centre and public transport access. If there is stronger jobs growth in areas that already had lower private mode shares, you would get a mode shift away from private transport, all other things being equal.
  • Changes in the mode share in each SA2 – because different modes became more or less attractive for commuters between census years. This might be due to changes in public transport service quality, transport infrastructure provision, and relative changes in the cost of public transport, private motoring, and commuter parking. It could also be influenced by broader demographic changes.

For each city I have calculated what the city-level private transport mode share would have been in 2016, had mode shares in each workplace SA2 remained exactly the same as 2011, but the job volumes in each SA2s had still changed. The city level mode shift due to SA2 volume changes is then the difference between this hypothetical 2016 mode share and the 2011 mode share. The remainder of the city-level mode shift between 2011 and 2016 results can then be attributed to mode shifts at the SA2 level.

Here’s a chart showing the mode shift impact of both volume changes at the SA2 level, and mode shifts at the SA2 level:

As we noted above, Sydney saw a slight trend to centralisation of jobs between 2011 and 2016, and it had the largest volume change attributed reduction in private mode share (-0.4%). However other factors were responsible for a further 2.5% of the mode shift away from private transport.

The story is similar in Melbourne but to a smaller magnitude in both aspects. Both of these cities also saw increasing inner city job density – which matters – and I’ll back come to that in a moment.

In Brisbane you can see that the total mode shift towards private transport was roughly equally attributable to SA2 volume changes and SA2 mode shifts (as I discussed in my earlier post).

Perth had an overall 1.3% mode shift to private transport, and the majority of this was due to significant jobs growth in the suburbs compared to the CBD (in fact, the SA2 with the largest jobs growth was Murdoch in the southern suburbs). But there were also other factors that led to a mode shift to private transport.

In Canberra – Queanbeyan, volume changes by themselves would have seen a mode shift to private transport, but other factors were larger and led to an overall mode shift away from private transport (although it is actually complicated because the 2011 census day was in a federal parliamentary sitting week, while 2016 was not).

Nothing much changed in Adelaide.

Next I’m going to explore what could be behind the mode shifts at SA2 level, in terms of job density and real transport costs.

Can increases in workplace density impact mode shares?

As discussed in my Brisbane analysis, if the relative attractiveness of modes hadn’t changed, you might still expect a mode shift to public transport in high density employment areas with increasing jobs numbers because you would expect the cost of parking provision to increase with increasing land use density (i.e. more competition for space).

Indeed, in Sydney and Melbourne a number of inner city SA2s became significantly more job dense between 2011 and 2016, and also saw mode shifts away from private transport:

(inspect this data in Tableau)

A similar thing happened in Civic (the main centre of Canberra).

But Adelaide and Perth saw both declining job density and declining private transport mode share, which suggests something else is at play.

Job density didn’t really go down in Brisbane – see my Brisbane post for an explanation (basically, ABS redrew the SA2 boundary along the Brisbane River).

Could changes in the real cost of transport be causing mode shifts?

The following chart shows the real change in urban transport fares in Australian cities since 2000, as measured by the ABS as part of the Consumer Price Index series (which unfortunately includes public transport, taxis, and “ride share” but is for a representative sample of journeys so hopefully mostly dominated by public transport fares):

The lines are somewhat saw-toothed because public transport fares generally only rise once a year, and become better value in real terms over the course of the following 12 months.

Many cities have seen above-CPI public transport fare increases at various times, most notably Brisbane in 2010-2014. Melbourne has had above CPI fare increases, but also reduced zone 1+2 fares in 2015 which lead to a reduction on the ABS measure (the fare reduction only really applied to people travelling across zones 1 and 2 – which roughly summarised means travel between the outer and inner suburbs). Brisbane fares peaked in 2014, which was followed by a freeze and then a large reduction in 2017.

By contrast, here is the (negative) growth in the cost of “private motoring” (which includes vehicles, fuel and maintenance):

Private motoring costs have declined in real terms since 2000, although they increased a little during the second half of 2017.

The next chart shows the change in ratio between the two costs. Urban transport fares have become less competitive than private motoring over time in all cities:

But if we are looking at changes between census figures, we should probably also look at cost changes between the times of each census. Here’s how prices changed in real terms between the September quarters of 2011 and 2016 (which cover the August census dates):

The real cost of private motoring dropped in all cities, but so did the real “average” cost of urban transport fares in Sydney and Melbourne (the Melbourne drop being mostly around large fare reductions for travel across zones 1 and 2).

The biggest differences in cost changes were in Brisbane and Perth (around 18%), which I think will go a fair way to explaining why these cities had the biggest shifts to private transport attributable to SA2 mode shifts.

Brisbane saw a rapid increase in public transport fares between 2011 and 2014 which is likely to have changed many commuting habits, but those habits may or may not have changed back when fares were subsequently reduced (e.g. if someone bought a car due to fare increases, they may not have subsequently sold their car when fares reduced). Perth certainly had less mode shift at the SA2 level compared to Brisbane, which might support this hypothesis.

What about changes in car parking costs?

The ABS CPI’s private motoring cost index does not include car parking costs – which would be difficult as they vary considerably with geography.

However we do know about central city car parking levies that governments charge in a bid to reduce road congestion and fund inner city transport initiatives. Sydney, Melbourne, and Perth apply levies to central city non-residential car parking spaces, and ultimately these levies will need to be recovered through parking prices.

I’ve calculated these levies in 2017 dollars (adjusting for inflation as measured in June quarters), and here’s how they have changed since 2000:

Melbourne increased its central city parking levy by 40% per space in 2014 (category 1), and created a new lower-priced levy area in some neighbouring areas to the north and south in 2015 (category 2, see map). This is likely to have contributed to the larger mode shifts away from private transport in the central city area of Melbourne compared to most other cities (particularly considering there were similar changes in average private motoring and urban transport fares in Melbourne between 2011 and 2016).

Sydney’s category 1 fee applies in the Sydney CBD area, Milsons Points and North Sydney. It was $2390 in 2017, and has only risen with indexation since 2009 (when it was doubled). A lower category 2 levy applies in the business districts centres of Bondi Junction, Chatswood, Parramatta, and St Leonards.

Perth has an annual licence fee per bay which ranged from $1039 to $1169 in 2017.  The Perth fee was increased by around 167% in 2010, and there were also above-inflation increases from 2014. The fee increased 63% in real terms between 2011 and 2016 for “long stay” spaces, and 69% for “tenant” spaces.

I am not aware of any such fees or levies in place in Brisbane or Adelaide (a proposal for Adelaide was voted down).

So how are CBD parking prices changing?

Unfortunately good data is a little hard to find, but this Colliers Car Parking White Paper provides “average daily rates” for CBDs for 2009-2015, and early bird rates for 2015. I expect most commuters would pay early bird rates – which average between 28% and 62% of daily rates depending on the city (quite some variation!). I’ve adjusted the pre-2015 figures for inflation to be in 2015 dollars:

In real terms, “average daily” parking costs have declined in Melbourne, rocketed up in Brisbane and Canberra, and moved less in Sydney and Perth. I don’t know whether these reflect trends in early bird prices. And we don’t know how prices changed between 2015 and the census year of 2016.

So how much are parking levies contributing to parking prices?

I have to make some assumptions (guesstimates) here. Regular weekdays represent about 60% of the days of the year. If we assume say 80% of the levy is recovered from weekday commuter parking (there generally being less demand for parking on weekends), we can calculate the average weekday commuter cost of the levy to be 27% of the Sydney early bird price, 25% of the Melbourne early bird price, and 15% of the Perth early bird price. Certainly not insignificant.

Here’s a summary of the levy and “average daily” price changes and mode shifts in the central city parking levy areas:

Changes 2011 to 2016
Parking levy area or CBD SA2 Levy real increase Average daily real price change (2011 to 2015) Private mode shift New private trips Private share of new trips
Perth 63% -5% -0.8% -60 -3%
Melbourne – category 1 40% -11% -5.3% 3200 5%
Melbourne – category 2 (new) n/a -6.4% 5315 30%
Sydney CBD 0% +1% -2.6% 6204 9%
Brisbane City SA2 n/a +64% +1.7% 3135 68%
Adelaide SA2 n/a -11% -1.5% 2567 35%
Canberra Civic SA2 n/a +71% -3.2% 746 30%

Firstly, “average daily” parking prices don’t seem to be following the changes in parking levies in Perth and Melbourne (category 1 area). Other factors influencing parking prices will include supply (influenced by competition for real estate and planning rules) and demand (influenced by employment density) with the market ultimately determining prices.

Car park operators appear to be absorbing the increased cost of the levy (although we don’t know the trends in early bird prices so we cannot be entirely sure). But that’s not to say that the levy hasn’t had any impact on prices – for example, the price reductions might have been larger if the levies had not increased.

Secondly, price changes do not appear to be correlated with mode shifts as you might expect (except Canberra). Brisbane prices increased dramatically, but so did private mode share! Price reductions in Perth, Adelaide, and Melbourne did not result in increased private transport shares.

Maybe other factors are driving mode shift away from private transport in those cities. Maybe early bird prices are trending differently to “average daily” prices. Maybe increased traffic congestion persuaded people to shift modes. Maybe there were significant price changes between 2015 and 2016. Maybe most existing public transport users were not aware of reductions in parking prices.

I don’t know what happened to parking prices in the new category 2 areas of Melbourne but there was a large mode shift away from private transport (-6.4%), and they may well be linked. Indeed, Infrastructure Victoria has recently recommended the category 2 area be expanded to include the inner-eastern suburbs of Richmond, South Yarra, Windsor and Prahran. And the Grattan Institute has recommended increasing the levy to match Sydney’s rates.

Curiously, when I look at City of Melbourne Census of Land Use and Employment (CLUE) data, the category 1 area (approximated with CLUE areas) had an increase of only around 367 non-residential parking bays between 2011-12 and 2015-16 (a four year period), a lot less than the additional 3200 private trips, which might suggest increased average occupancy.

Also, it is likely that a significant portion of people who drive to city centres are not paying for their parking costs (eg employer provided car parking). Employers may simply be absorbing price increases.

For more interesting discussion and research about car parking in the City of Melbourne, see a recent discussion paper and background report prepared by Dr Elizabeth Taylor.

Did changes in population distribution impact mode shares?

While this post has been focused on changes by workplace location, it is possible to separate the overall mode shifts into the two components by home location. Here are the results:

In Sydney, Melbourne, and Canberra, stronger population growth in areas that already had low private mode shares in 2011 made a small contribution to overall mode shifts away from private transport. These cities have all seen densifying population in inner city areas better served by public transport.

The distribution of population growth in Perth and Brisbane had a small effect in the opposite direction.

And again, nothing much changed in Adelaide.

What about active transport?

Cycling-only mode share was pretty stable in most cities (except Canberra up 0.2%). Walking-only mode share declined in Sydney (-0.2%), Brisbane (-0.3%), Adelaide (-0.4%), Perth (-0.3%) but was steady in Melbourne and increased in Canberra (+0.2%). So Canberra has the biggest shift to active transport.

Can you summarise all that?

If your head is spinning with all that information, here’s a summary of what some of the major factors could be in each city between 2011 and 2016. I say “could be” because I’ve not looked at every possible factor influencing mode share.

Sydney: the 2.9% mode shift away from private transport was probably mostly to do with increasing job density in employment centres (more on that in my next post), but was also partly by a shift to more centralised jobs, and increasing population density in places well served by public transport.

Melbourne: The 1.8% mode shift away from private transport probably had a fair bit to do with increasing central city job density, the significant spatial expansion of the central city parking levy area and rates (although we don’t know if early bird prices also rose), a reduction in some public transport fares, and strong population growth in areas well served by public transport.

Brisbane: The 1.9% mode shift towards private transport appears roughly half about the decentralisation of jobs, and half the reduced attractiveness of public transport – particularly following significant fare rises between 2010 and 2014, and possibly/arguably declines in service quality.

Perth: The 1.2% mode shift towards private transport was probably mostly due to a decentralisation of jobs, and partly due to public transport becoming less cost competitive with private transport (despite an increase in the central city parking levy). Urban sprawl is probably also a factor.

Adelaide: The 0.2% mode shift to private transport is probably mostly due to public transport becoming less cost competitive with private transport. Changes in job and population distribution, and employment density do not appear to have had a significant impact.

Canberra:  The 1.0% mode shift away from private transport was probably the result of competing forces of higher jobs growth in car-dominated workplace areas with increasing job density in dense employment centres, increasing central city parking prices, higher population growth in areas better served by public transport (and possibly cycling facilities), and also the fact census 2016 was not a parliamentary sitting week while 2011 was (so really, it’s hard to be too sure!).

You might want to add your own views about changes in the service quality of public transport and cycling infrastructure in each city. I also haven’t looked at the impact of major new public transport infrastructure and service initiatives (such as the opening of new train stations), which we know does impact mode shares at a local level (maybe that’s for a future post).

I hope you found this interesting. My next post will look at suburban employment centres, and their role in changing mode shares in cities.


How did the journey to work change in Brisbane between 2011 and 2016?

Wed 25 April, 2018

Between 2011 and 2016, Greater Brisbane saw a 2% mode shift towards private motorised transport for journeys to work, the largest such shift of all large Australian cities. Was it to do with where jobs growth happened, or because public transport became less attractive over that time?

This post takes a more detailed look at the spatial changes in private transport mode shares, and then examines the relative impact on spatial variations in jobs growth compared to other factors.

Greater Brisbane main mode shares

Firstly for reference, here are the Brisbane Greater Capital City Statistical Area main mode shares and shifts for 2011 and 2016, measured by place of enumeration and place of work:

2011 2016 Change
Private Place of enumeration 80.0% 81.9% +1.9%
Place of work 79.1% 81.1% +2.0%
Public Place of enumeration 15.1% 13.5% -1.6%
Place of work 15.9% 14.2% -1.7%
Active Place of enumeration 4.9% 4.6% -0.3%
Place of work 5.0% 4.7% -0.3%

More information about main mode definitions and data in general is available at the appendix at the end of this post.

Mode shares and shifts by home location

Here are private transport mode shares by home location for 2006, 2011, and 2016:

(you might need to click on these charts to see them larger and more clearly)

You can see lower private mode shares around the central city and to some extent along the rail lines. In case you are wondering, the Redcliffe Peninsula railway opened in October 2016 – after the August 2016 census.

The changes between years are a little difficult to make out on the map above, so here are the mode shifts to private transport by home location at SA2 level:

Mode shifts to private transport can be seen over most parts of Brisbane, with the biggest being Auchenflower (+6%), Lawnton (+6%), Toowong (+5%), Norman Park (+5%), Strathpine – Brendale (+5%), Keperra (+5%), and Sandgate – Shorncliffe (+5%). Many of the large mode shifts to private transport were actually seen around the train network.

The Redland Islands area had a larger shift to public transport – but keep in mind this will include use of car ferries.

Here’s a map showing the mode split of net new trips by home SA2:

There were a lot of new trips from outer growth areas in the north, west and south, and the vast majority of these trips were by private transport (although the southern growth area of Springfield Lakes, where a rail line opened in 2010, had a relatively high 15% of new trips by public transport). Private transport mode shares of new new trips were also high in middle and most inner suburbs (unlike inner Melbourne).

To sum all that up, here are the changes in trip volumes by main mode and home distance from the CBD:

Private transport dominated most new trips, and there were net declines in public transport trips beyond 2 km from the CBD.

Here’s a look at the main mode split over time, by distance from the CBD:

Brisbane achieved significant mode shift away from private transport between 2006 and 2011, but that was pretty much reversed between 2011 and 2016.

Private transport mode shares dropped in 2011 but pretty much returned to 2006 values in 2016. On average, only the city centre saw a mode shift away from private transport between 2011 and 2016, and that’s only a tiny fraction of the Brisbane’s population.

Mode shares and shifts by work location

Here are workplace private transport mode shares for 2011 and 2016:

(more areas are coloured in 2016 because they reached my minimum density threshold of 4 jobs per hectare at destination zone level for inclusion on the map)

Low private mode share is only really seen around the city centre. Some lower mode share areas further out include St Lucia (UQ campus, 52% in 2016) and Nundah (74%), but most of the suburban jobs are dominated by private transport.

Here are the mode shifts by workplace location:

The biggest mode shifts to private transport were to workplaces in Wooloowin – Lutwyche (+7%), Spring Hill (just north of the CBD, +5%) and Jindalee – Mount Ommaney (+5%). The biggest shifts away from private transport were in Newstead – Bowen Hills (-6%), St Lucia (-4%, which includes the University of Queensland main campus), and West End (-3%).

Notably, the job rich Brisbane CBD had a 2% shift to private transport (with 3,135 more private transport trips in 2016).

Here’s a map of the net new jobs and their main mode splits:

And a zoom in on the inner city to separate the overlapping pie charts:

The SA2 with the biggest jobs growth was “Brisbane City” (covering the CBD) with 4584 new jobs – with 68% of this net increase attributable to private transport. North Lanes – Mango Hill in the northern suburbs was not far behind (4472 new jobs at 96% by private transport), followed by Newstead – Bowen Hills (4266 new jobs at 49% private transport) and Brisbane Airport (4197 new jobs at 95% private transport).

The distribution of jobs growth was not heavily concentrated in central Brisbane – in stark contrast to Melbourne where the central city jobs growth was much more signficant.

Here’s a clearer view of new jobs by workplace distance from the city centre and main mode:

At all distances from the CBD, private transport new trips outnumbered active and public transport new trips (and there was a decline in public transport trips to the very city centre). The vast majority of net new trips were to workplaces more than 4 km from the city centre, and by private transport.

So why was there an overall 2% mode shift to private transport?

The relative lack of jobs growth in the public transport rich city centre is very likely to have contributed to the mode shift to private transport. The vast majority of new jobs were in the suburbs where public transport is significantly less competitive (relative to the CBD).

Others will point to factors that have made public transport less attractive relative to private transport, including problems on the train network, extensive new motorway infrastructure, and public transport fares growing around twice the rate of inflation after 2010.

There was very rapid growth in fares between 2010 and 2015, but then fares were frozen in 2016 and substantially reduced in 2017:

Looking at people working in Greater Brisbane (Greater Capital City Statistical Area), there were 94,055 new private transport commutes, just 246 new public transport commutes, and 2,506 new active transport commutes. So around 97% of net new trips in 2016 were by private transport, much higher than the 2011 baseline private transport mode share of 79% of trips (measured for workplaces in Greater Brisbane), hence the overall 2% mode shift.

Looking at people living in Greater Brisbane, there were 61,557 new private transport commutes, a net reduction of 6,069 public transport commutes, and a net reduction of 54 active transport commutes. Thus every new commute was accounted for by private transport, and further to this there was mode shift away from active and public transport.

So how much of the mode shift can be explained by spatial changes in jobs distribution? If mode shares in each workplace SA2 had not changed between 2011 and 2016 then city level mode shares would be influenced only by spatial variations in jobs growth.

I’ve done the calculations at SA2 geography: if place of work mode shares in Brisbane had not changed between 2011 and 2016 (but volumes had), then the overall private transport mode share would have increased only 1.0% in 2016 (essentially because of higher jobs growth in the suburbs compared to the centre).

Actual private mode share increased by 2.0% (measured by place of work).

So this suggests only half of the mode shift can be explained the spatial variations in jobs growth. The other half will be explained by other factors, particularly changes in the relative attractiveness of modes.

Changes in the relative attractiveness of modes will include public transport service quality, public transport fares, fuel prices, toll prices, and infrastructure provision for private and active transport. Car ownership will undoubtedly be a factor, but I suspect many ownership decisions will be influenced by workplace locations and relative modal attractiveness. Other factors might include changes in real incomes, demographic changes, changes in employment density, and the locations of population growth. I’ll explore the last two in more detail.

What about the relationship between job density and mode share?

You could argue that if general public transport “attractiveness” had not changed, you could still expect a mode shift towards public transport in areas with both high and increasing job density, as car parking might struggle to grow at the same rate as jobs growth (as the land becomes increasingly valuable/scarce). This might particularly be the case in the city centre.

I’ve calculated weighted job density for each SA2 – that is, the average density of destination zones in the SA2, weighted by the number of jobs in each zone (similar to population weighted density, so that large areas within SA2s that house few jobs make little contribution to such scores).

Here’s how weighted job density and workplace private mode share changed in Brisbane for higher density SA2s:

While there is some relationship between job density and private mode share overall, there wasn’t a consistent negative correlation between changes in those values. If there was, you would expect all lines on the chart to be on a similar diagonal orientation (upper left – lower right).

South Brisbane and Upper Mount Gravatt saw increased density but little change in private mode share. Chermside, Auchenflower, and Woolloongabba (which incidentally is at the southern end of the Clem 7 motorway) saw increased job density but also increased private transport mode share (the opposite effect of what you might expect). Spring Hill had only a small drop in job density but a large increase in private mode share.

Newstead – Bowen Hills had the largest shift away from private transport, and also one of the largest increases in job density

You might be wondering how the Brisbane City SA2 (which includes the CBD) can have had an increase in total jobs, but a slight decline in weighted jobs density. It turns out that the 2016 SA2 boundary goes further into the Brisbane River than the 2011 boundary. Here’s a map generated on the ABS website, where blue lines are the 2011 boundaries and red the 2016 boundaries:

If you discounted the increase in area, you might expect a slight increase in job density (about 4% in unweighted average density) to result in a small mode shift away from private transport, quite the opposite of what actually happened. If increasing job density by itself might have pushed a mode shift away from private transport, it appears it was overpowered by factors working in the opposite direction.

The Brisbane City SA2 accounted for 12.5% of Brisbane’s jobs so its mode split impacts more than most on overall city mode shares.

So what might be the stand-alone impact of increased job density in the city centre on private mode share? It’s very hard to quantify. I can certainly look at other city centres, but there will be so many factors at play in those cities that it would be almost impossible to isolate the impact.

But as a rough stab, had Brisbane City SA2’s private mode share increased from 29.0% to 29.5% (instead of 30.6%), and all other things were the same, then the overall Brisbane private mode share would have been 0.14% lower.

While the actual impact is uncertain, it would only increase the influence of the “other factors” that are responsible for at least half of the 2% mode share towards private transport.

And what about the spatial distribution of population growth?

All other things being equal, if population growth had disproportionately occurred in places with high private transport mode share (eg the middle and outer suburbs), you might expect a mode shift to private transport. However I don’t think this was significant in Brisbane as there has also been inner city population growth.

Indeed, if the home-based private transport mode share of each SA2 had not changed between 2011 and 2016 (but population numbers had), then the overall Brisbane private mode share (by place of enumeration) would have increased only 0.1% (rather than 1.9%). So the overall mode shift doesn’t seem to have a lot to do with where population growth happened.

So what are these effects other cities? I’ll cover that in an upcoming post.

Appendix: about the data

Here’s how I have defined “main mode”:

Private (motorised) transport any journey to work involving car, motorcycle, taxi, truck and/or “other”, but not involving any mode of public transport (train, tram, bus, or ferry)
Public transport any journey involving train, bus, tram, or ferry (journeys could also involve private or active transport modes)
Active tranport journeys by walking or cycling only

I have extracted data from the ABS census for 2006, 2011, and 2016 for areas within the 2011 boundary of the Brisbane Significant Urban Area. The detailed maps are at the smallest available geography – Census Collector Districts (CD) for 2006 and Statistical Area Level 1 (SA1) for 2011 and 2016 for home locations, and Destination Zones (DZ) for workplaces in 2011 and 2016 (detailed workplace data is not readily available for 2006 for most cities). I’ve aggregated this data for distance from city centre calculations (filtered by 2011 Significant Urban Area boundaries), which means the small randomisations will have amplified slightly.

In 2011, a significant number of jobs were not assigned to a destination zone:

  • 3.8% of jobs were assigned to an SA2 but not a DZ – I’ve imputed these proportionately to the DZs in their SA2 based on modal volumes reported for each DZ (for want of something better).
  • 18,540 Queensland jobs (0.9%) were only known to be somewhere in Greater Brisbane.
  • 115,011 jobs (5.8%) were only known to be somewhere in Queensland (hopefully mostly outside Greater Brisbane!).

These special purpose codes are not present in the 2016 data – presumably the ABS did a much better job of coding jobs to DZs. It means that the volumes in 2011 may be slightly understated, and so growth between 2011 and 2016 might be slightly overstated.

I’ve also extracted the data at SA2 (Statistical Area Level 2) based on 2016 boundaries for the purposes of calculating mode shifts and changes in trip volumes at SA2 level (to avoid aggregating small random adjustments ABS applies). However this wasn’t possible for jobs where 2011 SA2s were split into smaller SA2s in 2016 – because some 2011 jobs were assigned an SA2 but not a DZ, so we cannot map those to a specific 2016 SA2 (I aggregated imputed DZ numbers to 2016 SA2 boundaries instead).

I also extracted data at the Brisbane Greater Capital City Statistical Area level, as noted (the boundary did not change between 2011 and 2016).

I have not counted jobs that were reported to have no fixed address in my place of work analysis. I’ve also excluded people who worked at home, did not go to work on census day, or did not provide information about their mode(s) of travel. These workers are also excluded from job density calculations.


Trends in car ownership

Sun 7 August, 2011

[post updated in April 2016 with 2015 data. For some more recent data see this post published in December 2018]

Is the rate of car ownership still growing in Australia?

Firstly, by car ownership rate I mean the ratio of the number of registered “passenger vehicles” (from the ABS Motor Vehicle Census) to population (also from ABS). So while some of the measures in the post are not strictly for cars only, I’ve not worried too much about the distinction because I’m most interested in the trends.

The oldest motor vehicle census data is from 1955, and it is no surprise to see car ownership rates in Australia have risen considerably since then:

What is interesting in this chart is the relative rate of car ownership between states and territories. The Northern Territory is consistently the lowest – I’m guessing related to remote indigenous populations with low car ownership. New South Wales may reflect the relatively dense Sydney where car ownership is less important for many. I’m not sure of the reasons for other differences. It might be slight differences in reporting from the state agencies (see ABS’s explanatory notes).

But what about the most recent trends? Here is the same data from 2000 onwards (NT off the chart): 

You can see growth across all states, although there are several periods where some states flat-lined, particularly around 2008.

So while we have reached peak car use, we haven’t reached peak car ownership as a nation.

What about car ownership in cities?

Motor vehicle ownership data is also available from the census, with data provided on the number of households with different numbers of vehicles. The 2006 census reported the number of households with every number of motor vehicles 0 to 99, and here is the frequency distribution:

household car frequency 2006

In 2011 census data ABS only report the number of households with “4 or more” motor vehicles. I’ve calculated the average number of cars for this category for 2006 for each city and applied that to the 2011 data to get total motor vehicle estimates for 2011.

The following chart shows household motor vehicle ownership rates for major city areas for 2006 and 2011 (boundaries changing slightly to include more peripheral areas that are likely to have higher car ownership):

City car ownership 2006 and 2011

Sydney has the lowest rate of motor vehicle ownership, and Perth the highest, with Melbourne showing the least growth.

Here is the relationship between car ownership and journey to work by car-only:

car ownership v car JTW

While all cities had an increase in car ownership between 2006 and 2011, all but two had a reduction in car-only mode share of journeys to work. They were Adelaide and Canberra which also had the largest increases in car ownership rates.

While cities overall show increasing ownership rates, there were reductions in motor vehicles per capita in many municipalities between 2006 and 2011, including the City of Perth, the City of Melbourne, the City of Adelaide, the City of Willoughby, and the City of North Sydney. This suggests car ownership is in decline in some inner city areas of Australian cities (more spatial detail for Melbourne is available in another post). These areas generally have good public transport and many local services within walking distance, and I’d guess many new residents are not bothering with car ownership.

The following chart compares motor vehicle ownership rates between capital city areas and the rest of each state or territory for 2011 census data:

car ownership capital v rest of state 2011

Car ownership is certainly higher outside most capital cities – except in the Northern Territory as I suspected (curiously Darwin has around the same car ownership rate as Melbourne).

How does car ownership vary by demographics?

The Victorian Integrated Survey of Travel and Activity (VISTA) provides detailed data about households in Melbourne and regional Victorian cities for the years 2007-2009. So while I cannot extract trends, we can look at the patterns of car ownership rates.

I have classified all households in the VISTA dataset into one of three categories:

  • household with no motor vehicles
  • limited motor vehicle ownership: less motor vehicles than people of driving age (arbitrarily defined as 18-80), or
  • saturated motor vehicle ownership: motor vehicle count equals or exceeds the number of people of driving age (“MV saturated” in the chart).

mv ownership by age draft

You can see that people aged 35 to 59 are least likely to live in households without motor vehicles, while younger adults are most likely to live in a household with limited car ownership. There are curiously two peaks in saturated car ownership – aged 35-39 and 60-64. The saddle in between might be explained by family households with driving age children.

The following chart looks at household car ownership by household type, with “young families” classed as households where all children are under 10 years of age.

mv ownership by hh status

Some very clear patterns emerge, with households incorporating parents and children very likely to own at least one motor vehicle. Sole person households were most likely to not own a motor vehicle. Limited motor vehicle ownership was most common in “other” household structures, parent+children households with older children, and couple households with no kids.

It seems Australians find car ownership a high priority if they have young children. Other analysis on this blog found that such households also have the lowest rates of public transport use, and a very strong inverse relationship between motor vehicle ownership and public transport use.

What about usage of each car?

Using data from the BITRE 2015 yearbook, it is possible to calculate estimated annual kms per passenger car. For this I’m comparing the number of vehicles at the motor vehicle census date with an estimate of total car kms in the previous 12 months (straight line interpolation of BITRE year ending June figures). This isn’t a perfect measure as the number of cars grows throughout the 12 month period where kilometres are taken, but it is still a guide to the trend.

The steeper downwards trend since 2005 is similar to the downwards trend in car passenger kms per capita in Australian cities:

Since around 2005, car ownership has continued to rise while car passenger kilometres per capita has fallen. This suggests we are driving cars shorter distances and/or less often.

What about motorcycles?

Are more people owning motorcycles instead of cars? Here’s the long-term trend:

You can see motorcycle ownership rates peaked around 1980, dipped in the mid 1990s and have grown significantly since around 2004 (although still very small). Does it explain the slowdown in the car ownership rate from 2008?

This chart still shows a slow-down after 2008, so it doesn’t look like rising motorcycle ownership fully explains the slow-down in car ownership. Motorcycle ownership took off in 2004, but car ownership slowed in 2008.

What about the ageing population?

Could the data be impacted by a changing age profile? We know that older aged people are less likely to have their driver’s license and are more likely to live in a household with lower car ownership (refer above), so maybe this would lead to a declining car ownership rate per head of population as a greater portion of the population is older.

Suppose most car owners are aged 18 to 80 years. Here’s the percentage of Australia’s population within that age band:

Population aged 18-80

The share has been very steady at around 73 to 74% for all of the last 21 years, which suggests little impact on overall car ownership rates. Then again, those aged 80 today are more likely to have a driver’s license that those aged 80 in 1994. So the rate of car ownership of younger people has probably grown less. We know their rate of driver’s license ownership has declined over time, but I’m not aware of any readily available data that would confirm a lower rate of car ownership by younger people over time (it’s probably available from the Sydney Household Travel Survey datasets).

Notes on the data:

  • The ABS Motor Vehicle Census has been taken in different months in different years. State population estimates are only available on a quarterly basis. I have used the nearest quarterly population figure for each motor vehicle census where they do not align (never more than one month out).

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.


Illustrating the perverse Fringe Benefits Tax statutory formula for employer-provided cars

Sun 9 May, 2010

There was a chart on page 10 of the recently released Henry Tax Review Overview report that really caught my attention. It shows the kms travelled by employer-provided cars in Australia.

The massive spikes are a result of rate thresholds in the much-despised Fringe Benefits Tax (FBT) statutory formula for employer-provided cars, where travelling more kilometres often reduces your total costs.

In this method, the taxable value of the fringe benefit is essentially a percentage of the value of the car, and the percentage used depends on the total kms travelled each year:

  • Less than 15,000km – 26%
  • 15,000 – 24,999km – 20%
  • 25,000 – 40,000km – 11%
  • Over 40,000km  – 7%

These are not marginal rates, because the kms travelled is not directly used in the calculation of taxable value. Hence getting into the next bracket reduces your rate and your total tax bill, despite the marginal increase in direct running costs. This is utterly perverse as it provides an incentive for people to drive more kms, increasing congestion and greenhouse emissions!

There is also an operating costs method where the taxable value is essentially the proportion of running costs that were for private use (which is actually quite logical!). People are currently free to choose whichever method involves paying less tax. If there is a lot of personal use, then the statutory formula is often the way to go (travelling to and from work is generally classed as private travel).

So I’ve looked at the total cost of car ownership for a $35,000 car that has per-km costs of 17 cents per km, and standing costs of $167 per week (roughly the running costs of a Holden Commodore according to the RACV), using the fringe benefits tax rate of 46.5%. I’ve also assumed 70% private use when using the operating costs method.

The following chart shows the net running cost of such a vehicle, depending on the annual kms travelled, by both the statutory formula and operating cost methods:

You can see the step reductions in total costs when reaching each threshold in the statutory formula. In this example:

  • Driving 15,000 kms instead of 14,999 kms saves you $976
  • Driving 25,000 kms instead of 24,999 kms saves you $1465
  • Driving 40,000 kms instead of 39,999 kms saves you $488

These are big incentives to drive more kms!

You can also see that the operating costs method is not particularly attractive if most of the car’s use is private.

The FBT year ends March 31st each year. So is there evidence of big driving holidays in March each year as people try to get their kms over the next threshold?

The following chart shows the average monthly automotive gasoline sales for Australia for the period 2005-2009 (Source: Australian Petroleum Statistics). March stands out as the second highest month of the year for automotive fuel sales.

The FBT statutory formula might be responsible for the high average March figure, but I am a little reluctant to make that conclusion because I just don’t know enough about the other influences on monthly fuel sale volumes in Australia. Perhaps someone more knowledgeable than me could comment.


Peak oil

Sun 14 March, 2010

[updated September 2011]

While there is a plethora of content on the web about peak oil, I can’t seem to find many charts that track the current medium-term trends in global oil supply and price (a lot of the reports go into great detail about explaining short-term trends, or show long-term trends without the most recent data).

So here is an attempt at some medium term analysis of the data (if anyone can suggest other/better sources of such analysis I’d be pleased to hear about them).

Current medium-term trends

The first chart uses International Energy Agency (IEA) quarterly data on world oil supply until 2011Q2, projected demand for the last two quarters of 2011 (as at September 2011), together with the average WTI oil price in $US for each quarter.

The second chart simplistically looks at the relationship between price and demand/supply for each quarter 2000 to 2011Q1. I’ve also added forecast 2011 demand for remaining 2011 (assuming a $91 price). Note: there are small differences between supply and demand each quarter due to stockpiling etc.

 

Yes this is a very simplistic representation of world oil markets (it doesn’t adjust for inflation or global exchange rates), but I think it is still interesting. You can see that there are differences between quarterly demand and supply as the red and blue lines don’t always overlap. For example, according to the data, supply exceeded demand in the quarters where prices peaked, but then demand exceeded supply for the two previous quarters.

Observations

I’m certainly not a qualified oil market analyst, and this isn’t rigorous analysis, but a few things do stick out:

  • World oil supply grew strongly between 2002 and 2005 and then was stuck around 84-87 million barrels per day (mb/d) until 2010. Supply grew to a new high of 88.5 mb/d in 2011 Q1, but then dropped in 2011 Q2. The September 2011 Oil Market Report suggests supply is now 89.1 mb/d.
  • During 2007 and the first half of 2008, oil prices grew strongly while supply was relatively unchanged, suggesting a demand-supply crunch.
  • After mid 2008, both prices and supply collapsed, around the same time as the global financial crisis hit.
  • During 2009, prices almost doubled, while supply only grew a small amount (although a devaluing of the US dollar explains some of this).
  • During the first three-quarters of 2010 supply grew but prices stabilised. But since 2010Q4, prices increased, even though supply reduced in 2011Q2. At the time of writing WTI oil was around US$91, and supply had increased again.
  • Looking at the second chart, it appears we have broken through the 88 mb/d threshold without the same prices seen in 2008.

The future?

It’s very hard to speculate, and I’m not particularly qualified. At present it appears oil prices have fallen with global economic conditions, supply has increased to a new high, and demand is also higher than ever.

Will it be possible to continue to ramp up supply to meet the further increases in demand?

The peak oil theory essentially suggests that new oil sources are harder to find and extract, that many existing fields are in unavoidable production decline, and so it is getting harder just to maintain current supply levels with new fields, let alone grow production overall. And harder still to grow production without increasing prices.

We’ve seen some supporting evidence in 2008, but conflicting evidence in 2010. That might have something to do with exchange rates. Oil prices fell in 2010Q3, but the US dollar was strong in mid 2010. Probably need more thorough analysis than what I have done. But the data is what it is. And all will be revealed in time.

But what will transport investors assume? Increasing global supply without significant price growth over the next 30 years does not look like a particularly safe assumption! But I fear it is the default assumption.