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.


Are Australian cities sprawling with low-density car-dependent suburbs?

Wed 30 January, 2019

Many people talk about urban growth in Australian cities being car-dependent low-density suburban sprawl. But how true is that in more recent times? Are new greenfield density targets making a difference? Are cities growing around their rapid public transport networks? And how do growth areas compare to established areas at a similar distance out from city centres?

This post takes a look at what census data can tell us about outer urban growth areas in terms of population density, motor vehicle ownership, distance from train/busway stations, and journey to work mode shares.

How much of city population growth is in outer areas?

Firstly a recap, here is the percentage of annual population growth in each city that has occurred in “outer” areas (defined by groupings of SA3s around the edges of cities – refer my previous post for maps showing outer areas) for Greater Capital City Statistical Areas.

Sydney has had less than a third of its population growth in outer areas since around 2003, while Perth has mostly had the highest outer growth percentage (since 1996), and more recently pretty much all population growth in Perth has been on the fringe. You can see how the other cities sit in between.

However, not all of this “outer” population growth was in urban growth on the fringe. For that we need to distinguish between urban growth and infill development, even in “outer” areas. So we really need a better definition of outer growth areas.

How to define outer urban growth areas

I have built groupings of SA1s (Statistical Area Level 1) that try to represent outer urban greenfield residential development. SA1s are the smallest census geographic areas (average population 400) for which all census data variables are available.

I’ve selected 2016 SA1s that meet all of the following criteria:

  • Brand new SA1 or significant population growth: The 2016 SA1 is new and cannot be matched to a 2011 SA1 (by location/size and/or ABS correspondences), or if it can be matched, the population at least doubled between 2011 and 2016. Brand new SA1s are very common in urban growth areas as new SA1s are created to avoid oversized SA1s on last census boundaries (except this doesn’t always happen – more on that shortly).
  • In an SA2 with significant population growth: The SA2 (Statistical Area Level 2 – roughly suburb sized with typically 3,000 to 25,000 residents) that contains the SA1 had population growth of at least 1000 people between 2011 and 2016 (based on 2016 boundaries). That is, the general area is seeing population growth, not just one or two SA1s.
  • Are on – or close to – the urban fringe. I’ve filtered out particular SA2s that I’ve judged to be contain all or mostly in-fill development rather than greenfield development, or that are largely surrounded by existing urban areas and are not close to the urban fringe. I’ll be the first to admit that some of the inclusions/exclusions are a little arbitrary.

The criteria aren’t perfect, but it seems to work pretty well when I inspect the data. I’m calling these “Growth SA1s” or outer urban growth in this post.

For urban centres, I’m using Significant Urban Area 2016 boundaries (rather than Greater Capital City boundaries), and I’ve bundled Yanchep with Perth, Melton with Melbourne, and the Sunshine Coast and Gold Coast with Brisbane to form South East Queensland (SEQ).

Where are these outer urban growth areas?

What follows are maps for each city with the density of these growth SA1s shown by colour.

Melbourne’s northern and western growth areas:

Technical note: The maps do not show non-growth SA1s with fewer than 5 people per hectare, or “growth SA1s” with fewer than 1/hectare, although these SA1s are including in later analysis.

And the south of Melbourne:

Note: not shown on these Melbourne maps are isolated tiny growth SA1s in Rosebud and Mooroolbark.

Here are Sydney’s growth SA1s – all in the western suburbs:

Next up South East Queensland, starting in the north with the Sunshine Coast:

Northern Brisbane:

Outer urban growth is scattered in southern Brisbane and northern Gold Coast:

Gold Coast – Tweed Heads:

Perth’s northern and eastern growth areas:

Perth’s southern growth areas:

Note: Canning Vale East is an inclusion you could debate – the previous land use of the growth SA1s appear to have been rural based on satellite imagery.

Northern Adelaide:

Southern Adelaide:

And finally Canberra:

So how much of each cities’ population growth has been in outer growth areas?

Here’s a breakdown of the population growth for my six urban areas:

Over the five-year period, outer urban growth areas accounted for 19% of Sydney’s population growth, 43% of Melbourne’s, 37% of SEQ’s, 60% of Perth’s, 27% of Adelaide’s and 69% of Canberra’s.

Technical note: These “outer urban growth” figures are different to the chart at the top of this post which had a coarser definition of “outer” and used Greater Capital City boundaries. Some of my “outer urban growth” areas actually don’t quality as “outer” in the coarser definition, and I’ve also excluded several “outer” SA2s from “outer urban growth” where I’ve deemed the growth to be mostly infill. Hence the differences.

In case you are wondering, it’s not easy to create a longer-term time-series analysis about the proportion of population growth in “outer urban growth” areas because the classification of SA2s would have to change on a year-by-year basis which would be messy and somewhat arbitrary.

A challenge for density analysis: some SA1s are over-sized

You might have noticed some SA1s in the maps above are very large and show a low average density of 1-5 persons per hectare (I’ve coloured them in a light cyan). Many of these SA1s had thousands of residents in 2016, which is way more than the ABS guideline of 200 to 800 residents. Unfortunately what seems to have happened for 2011 and 2016 in some cities is that the ABS did not create enough SA1s to account for new urban areas. Some Melbourne SA1s had a population over 4000 in 2016. Many of these SA1s contain a combination or urban and rural land use, so their calculated density is rather misleading.

I’m designating any SA1s with more than 1000 residents and larger than 100 hectares as “oversized”, and I’ve exclude these from some density analysis below. Here’s a chart showing the proportion of outer growth area populations that are in oversized SA1s:

You can see it is a substantial problem in Sydney, Melbourne, Perth and South East Queensland, but miraculously not a problem at all in Adelaide or Canberra (I’m sure someone in ABS could explain why this is so!).

If you are interested, in 2011 it was a bigger problem in Melbourne, and only Canberra was fully clean.

So how dense are outer urban growth areas?

Firstly, I am excluding over-sized SA1s from this analysis for the reasons just mentioned.

Secondly, all cities will also have growth areas that were partially developed at the time of the census (ie some lots with occupied houses and other lots empty) so the densities measured here may be understated of the likely fully built-out density of these SA1s. That said, those areas perhaps are more likely to be in over-sized SA1s, but it’s hard to be sure. So keep this in mind when looking at growth area densities.

You can see dramatic differences, with Sydney, Canberra, and Melbourne showing higher densities, and South East Queensland with much lower densities. As we saw on the maps above, South East Queensland’s outer growth areas are very dispersed, so perhaps more of them are growing slowly and more of them are partially built-out? It’s hard to be sure.

But perhaps what is most remarkable is that Canberra had the highest densities in outer urban growth areas of any city – nothing like what you might consider suburban sprawl. Here’s what was 144.5 people per hectare in 2016 in Wright on Canberra’s new western growth front looks like:

(pic from Google Streetview, dated December 2016)

The densest SA1 in Sydney’s growth areas was 101 persons/ha. Nothing like this was seen in other cities.

Canberra’s outer growth areas are actually, on average, denser than the rest of Canberra (on a population weighted density measure):

The same was also true by a slim margin in both Perth and Adelaide, but they have relatively “suburban” densities for both growth and established areas. The growth areas of Sydney and Melbourne are more dense than Perth and Adelaide, but not compared to the rest of these cities as a whole. That’s probably got to do a lot with the large cities having dense inner suburbs.

So perhaps it is better to compare the urban growth areas with established areas a similar distance from city centres, which the following chart does (I’ve filtered out 5 km distance intervals without growth areas of at least 2000 population, and apologies for rather squashed Canberra label):

Technical note: for South East Queensland I’ve measured distances from the Brisbane CBD.

Outer growth areas were much more dense than the rest of each city at most distances from the city centre, except in Sydney.

One issue with the above chart is that different distance intervals have different populations – for example only 2,815 people were in growth SA1s at a distance of 45-50 km from the Perth CBD (just above my threshold of 2000), so the low population density of that interval is not hugely significant.

To get around that issue, I’ve calculated the overall population weighted density of non-growth SA1s that are within these 5 km distance intervals from the CBD (including all of SEQ beyond 15 km from the CBD). The following chart compares those calculations with the population weighted density of the growth areas overall:

This shows that urban growth areas are on average more dense than other parts of the city at similar distance from the CBD, except in South East Queensland. And remember, many of the growth SA1s will be partially built out, so their expected density is understated.

Are outer urban growth areas near rapid public transport?

The next chart shows the proportion of growth SA1 population by distance from the nearest train or busway station:

Technical notes: Distances are measured from the centroid of each SA1 to a point location defined for each station (sourced from August 2016 GTFS feeds). For oversized SA1s these distances might be a little longer than reality for the average resident. I haven’t excluded oversized SA1s because I want to see the population alignment of growth areas overall. Canberra excluded due to lack of separated rapid transit.

What sticks out clearly is that just over half the of the population in Perth’s outer growth areas was more than 5 km from a station in 2016. That is to say Perth has had the least alignment of outer urban growth areas and rapid public transport networks of all five cities. I’m not sure many urban planners would recommend such a strategy.

However, Perth’s MetroNet program appears to be trying to rectify this with new lines and stations proposed near urban growth areas such as Yanchep, Canning Vale East, Ellenbrook, Byford, and Karnup (Golden Bay). It will however take some time to get to them all built and open.

South East Queensland was second to Perth in terms of urban growth remote from stations, with a lot of the growth scattered rather than concentrated around rail corridors. I haven’t included the Gold Coast light rail in my proximity calculation – it runs at an average speed of 27 km/h (which is slower than most train networks) and doesn’t serve outer urban growth areas.

Sydney and Adelaide had the highest proximity of growth areas to stations.

Around half of Melbourne’s growth SA1s that were more than 5km from a train station were in Mernda and Doreen, a corridor in which a rail extension opened in 2018. Many of the rest are not in the current designated growth corridors, or are where future train stations are planned. Melbourne’s current designated urban growth corridors are fairly well aligned to its train network. From a transport perspective this is arguably a better kind of sprawl than what Perth has been experiencing.

Adelaide’s outer growth areas more than 5 km from a station were in Mount Barker (satellite town to the east) and Aldinga (on the far south coast of Adelaide).

Are the outer urban growth areas better aligned to rapid public transport stations than non-growth areas at the same distance from city centres? Here’s the chart as above but with an extra column for non-growth areas within the same distance intervals from the CBD (as before).

The populations of urban growth areas are less likely to be within a couple of kilometres of a station (most of that land probably has long-established urban development), but curiously in Adelaide and South East Queensland the urban growth areas are more likely to be within 5 kilometres of a station than the non-growth areas, suggesting better rapid public transport alignment than older urban growth areas. Older urban areas in other cities are more closely aligned to stations, particularly in Perth.

As an interesting aside, here’s a breakdown over the last three censuses of population by distance from train/busway stations (operational in 2016 – so it overstates 2006 and 2011 slightly):

You can really see how Perth has had much population growth remote from its rapid public transport network, which probably goes some way to explaining the overall 1.2% journey to work mode shift towards private transport between 2011 and 2016.

So how did people in these outer growth areas get to work?

Technical note: The figures here for “private transport” are for journeys involving only private transport modes – i.e. they exclude journeys involving both private and public transport (eg car+train).

While private transport (mostly car driver only journeys) dominated journeys to work from almost all growth areas, Melbourne and Sydney were the only cities to see significant numbers of residents in outer growth areas with private transport mode shares below 80%.

South East Queensland’s outer urban growth areas were the most reliant on private transport to get to work, with an overall private transport mode share of 93%, followed by Adelaide on 92%, Canberra on 91%, Perth on 90%, Melbourne on 86%, and Sydney on 81%.

Here’s how the growth area mode shares compare to other areas a similar distance from city centres (note: the Y-axis is not zero-based):

Significantly, the growth areas of Sydney and Melbourne had lower private transport mode shares of journeys to work than other parts of the city a similar distance out – even though they are generally further away from train or busway stations (as we saw above)! That’s not to say they didn’t drive themselves to a train station to get to work.

Similar to population density, here is a summary of growth areas compared to other areas in the same distance interval from the CBD:

There’s really not a huge amount of difference within cities. Sydney’s growth areas had a mode share 1.5% lower than non-growth areas, while Canberra’s growth areas had a mode share 2.5% higher.

What are motor vehicle ownership rates like in the outer growth areas?

My preferred measure is household motor vehicles per persons aged 18-84 (roughly people of driving age).

Motor vehicle ownership rates are generally very high across the growth areas – with the notable exceptions of Melbourne and Canberra where around a quarter of the growth area population had a motor vehicle ownership rate of less than 80 (although that is still pretty high!). (I explored this in more detail in an earlier post on Melbourne)

South East Queensland, Perth, and Adelaide outer urban growth areas had the highest motor vehicle ownership rates. Perth’s urban growth areas overall averaged 96.7 motor vehicles per persons aged 18-84 – pretty close to saturation.

How does motor vehicle ownership compare to established areas a similar distance from the city centre? The following chart compares motor vehicle ownership between urban growth and other areas at the same distance from the CBD (note: the Y-axis is not zero-based):

Motor vehicle ownership tends to increase with distance from the CBD, and in Sydney and South East Queensland the growth areas have higher ownership compared to non-growth areas. But the opposite is true in Melbourne, Perth and Canberra.

The population at each distance interval varies considerably, so here is a summary of the data across all distance intervals that have growth SA1s for each city:

The growth areas of Melbourne, Perth and Canberra had slightly lower motor vehicle ownership than other areas a similar distance from the city, while the opposite was true in other cities. That said, motor vehicle ownership rates are very high across all cities.

 

Finally, I’ll look at the relationships between these measures for growth areas (see another post for analysis for whole cities).

How does motor vehicle ownership relate to distance from stations?

Technical note: for scatter plots I’ve filtered out SA1s with less than 50 population as they are more likely to have outlier results (one person can change a measure by 2% or more).

Lower rates of motor vehicle ownership are generally only found close to train/busway stations (and are dominated by Melbourne examples), but close proximity to a station does not guarantee lower rates of motor vehicle ownership. Quite a few Adelaide SA1s are found the top middle part of the chart – these are all in Mount Barker which has frequent peak period express buses to the Adelaide CBD operating along the South East Freeway – which is similar to rapid transit although without a dedicated right of way.

How do journey to work mode shares relate to distance from stations?

Here’s a scatter plot of private transport mode shares of journeys to work and distance from train/busway station:

This shows that lower private transport mode shares are only generally seen within proximity of train or busway stations, and areas remote from stations are very likely to have high private transport mode shares. But also that proximity to a station does not guarantee lower private transport mode shares of journeys to work (particularly in SEQ).

Technical aside: You might have noticed that almost no SA1s report 99% private mode share. How can that be? The ABS make random adjustments to small figures to avoid identification of individuals which means you never see counts of 1 and 2 in their data. To get a mode share of 99% you’d need at least 300 journeys to work with “3” being non-private (or a similar but larger ratio). Very few SA1s have 300+ journeys to work, and even for over-sized SA1s, they are very unlikely to have only 3 or 4 non-private journeys to work. A mode share of 100% is much easier because you can get that no matter the total number of journeys.

How does population density relate to distances from train/busway stations?

Densities above 45 persons/ha were mostly only found within 5 km of stations, and almost entirely in Sydney and Melbourne. The highest densities were very close to train stations in Sydney. In the middle area of the chart you can see quite a few Perth SA1s that are around 30-40 persons/ha but remote from stations. These are all in the Ellenbrook area of Perth’s north-east, generating a lot of car traffic.

How does motor vehicle ownership relate to private transport mode shares of journeys work to work?

For interest, here is the relationship as a scatter plot:

There is certainly a relationship, but it’s not strong (r-squared = 0.22). Other factors are at play.

Conclusions

  • Perth and Canberra are seeing most of their population growth on the fringe, with Sydney, Adelaide, Melbourne, and South East Queensland seeing most of their population growth in established areas.
  • Growth areas in Sydney, Melbourne, and Canberra have higher than traditional urban densities, indeed Sydney and Canberra have a few very high density greenfield developments. Perth, Adelaide, and particularly South East Queensland have urban growth at relatively low densities. In fact, SEQ is the only major urban centre where growth areas are measured as less dense than non-growth areas at similar distances from the CBD.
  • Perth’s urban growth areas are largely remote from rapid transit stations, and this is likely contributing directly to very high and increasing rates of motor vehicle ownership and private transport mode shares. Melbourne’s current urban growth corridors are closely aligned to train stations (thanks to the opening of the Mernda line), and this is also largely true of Sydney and Adelaide.
  • Almost all outer urban growth areas had high rates of motor vehicle ownership. Overall, Melbourne, Perth, and Canberra’s outer urban growth areas had slightly lower rates of motor vehicle ownership compared to other areas at the same distance from the CBD. Only Sydney, Melbourne and Canberra have some growth areas with lower motor vehicle ownership and/or lower private transport mode shares of journeys to work – and these were all close to train or busway stations.

I hope you’ve found this at least half as interesting as I have.

For a similar and more detailed analysis around these topics, see this excellent 2013 BITRE research report on changes between 2001 and 2006.


Update on Australian transport trends (December 2018)

Fri 28 December, 2018

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

Vehicle kilometres travelled

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

Here’s the growth by vehicle type since 1971:

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

Car kilometre growth has slowed significantly since 2004.

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

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

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

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

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

Passenger kilometres travelled

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Car ownership

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

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

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

Driver’s licence ownership

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

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

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

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

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

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

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

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

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

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

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

Transport greenhouse gas emissions

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

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

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

And the growth in each sector since 1990:

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

Here are per capita transport emissions for each state:

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

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

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

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

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

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

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

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

Cost of transport

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

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

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

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

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

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

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

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

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


What explains variations in journey to work mode shares between and within Australian cities?

Thu 6 December, 2018

Private and public transport journey to work mode shares vary considerably both between Australian cities and within them. Are these differences related to factors such as population density, motor vehicle ownership, employment density, proximity to train stations, proximity to busway stations, jobs within walking distance of homes, and distance from the city centre?

This posts sheds some light on those relationships for Australia’s six largest cities. I’m afraid it isn’t a short post (so get comfortable) but it’s fairly comprehensive (over 30 charts).

I should stress up front that a strong relationship between a certain factor and high or low mode shares does not imply causation. There are complex relationships between many of these factors, for example motor vehicle ownership rates are generally lower in areas of higher residential density (which I will also explore), and more factors beyond what I will explore here.

If you are interested in seeing spatial mode share patterns, see previous posts for Melbourne, Brisbane, and Sydney. You might also be interested in my analysis explaining the mode shifts between 2011 and 2016.

Population density

Higher population densities are commonly associated with higher public transport use. This stands to reason, as high density areas have more potential users per unit of area, but also higher density is likely to mean high land prices, which in turn increases the cost of residential parking. But higher public transport mode share can only happen if government’s invest in higher service levels, and this isn’t guaranteed to happen (although it often does, through pressures of overcrowding).

My preferred measure is population weighted density, which is the weighted average density of land parcels in a city, weighted by their population (this gets around problems of including sparsely populated urban land). I’ve measured it at census district (CD) geography for 2006 and Statistical Area Level 1 (SA1) geography for 2011 and 2016, using 2011 Significant Urban Area boundaries to define cities. The 2006 density figures are not perfectly comparable with 2011 and 2016 because CDs are slightly larger than SA1s, so the density values will be calculated as slightly smaller.

Here is the relationships at city level (the thin end of each worm is 2006 and the thick end 2016, with 2011 in the middle):

The relationship is very strong for Melbourne and Sydney over time. Between 2011 and 2016, Perth and Brisbane saw increased population density but reduced public transport mode share (mostly because of changes in the distribution of jobs between the centre and the suburbs).

Brisbane was a bit of an outlier in 2006 and 2011 with high public transport mode share relative to its lower population density.

Canberra is also perhaps a bit of an outlier, with much lower public transport mode share compared to similarly low density cities. This might be explained by the smaller total population, lower jobs density, and lack of rapid public transport services segregated from traffic.

But Canberra does have higher active transport mode share, so it’s worth doing the same analysis with private transport mode shares:

Brisbane was still an outlier in the relationship in 2006 and 2011, but Canberra is more in line with other data points.

Another interesting note is that Canberra went from being the least dense city in 2006 to the third most dense in 2016.

Drilling down to SA2 geography (SA2s are roughly the size of a suburb), here’s a chart showing all SA2s in all cities across the three census years (filtered for CDs and SA1s with at least 5 persons per hectare). I’ve animated it to highlight one city at a time so you can compare the cities, and I’ve used a log scale on the X-axis to spread out the data points (only the Sydney and Melbourne CBDs go off the chart to the right).

(if these animated GIF charts are not clear on your screen, you can click to enlarge the image, then use “back” to come back to this page).

You can see a fairly strong relationship, although it is very much a “cloud” rather than a tight relationship – there are other factors at play.

What I find interesting is that Sydney has had a lot of SA2s with population weighted densities around 50-100 but private mode shares over 55% (toward the upper-right part of the cloud of data points) – which are rare in all other cities. That’s a lot of traffic generation density, which cannot be great for road congestion. In a future post I might focus in on the outlier SA2s that are in the top right of these charts (can public transport do better in those places?).

In case you are wondering about the Brisbane SA2 with low density and low private transport mode share (middle left of chart) it is the Redland Islands where car-carrying ferries are essential to get off an island, and are counted as public transport in my methodology. The Canberra outlier in the bottom left is Acton (which is dominated by the Australian National University).

Employment density

I’ve calculated a weighted job density in the same way I’ve calculated population weighted density, but using Destination Zones (which can actually be quite large so it certainly isn’t perfect). Weighted job density is a weighted average of job densities of all destination zones, weighted by the number of jobs in each zone. In a sense it is the density at which the average person works

(technical notes: I’ve actually only counted jobs as people who travelled on census day and reported their mode(s) of travel. Unfortunately I only have 2006 data for Sydney and Melbourne)

This chart suggests a very strong relationship at the city level, with all cities either moving up and left (Adelaide, Perth and Brisbane) or down and right (Sydney, Melbourne, Canberra).

So is the relationship as strong when you break it down to the Destination Zone level? The next chart shows jobs density and private mode share for all destination zones for 2016. Note that there is a log scale on the x-axis, and Adelaide dots are drawn on top of other cities in the top left which explains why that dense cloud of dots appears mostly green.

There’s clearly a strong relationship, although again the data points form a large cloud rather than tightly bunch around a line, so other factors will be at play.

It’s also interesting to see that the blue Sydney dots are generally lower than other cities at all job densities. That is, Sydney generally has lower private transport mode shares than other cities, regardless of employment density.

Which leads us to the next view: the private transport mode shares for jobs in different density ranges in each city for 2011 and 2016.

(click to enlarge if the chart appears blurry)

You can see a fairly consistent relationship between weighted job density and mode shares across all cities in both 2011 and 2016.

At almost all job density ranges, Sydney had the lowest average private transport mode share, while Adelaide and Perth were generally the highest (data points are not shown when there are fewer than 5 destination zones at a density range for a city). This shows that something other than jobs density is impacting private transport mode shares in Sydney. Is it walking catchment, public transport quality & quantity, or something else?

For more on the relationship between job density and mode share, see this previous post.

Proximity to public transport

Trains generally provide the fastest and most punctual public transport services (being largely separated from road traffic and having longer stop spacing), and are the most common form of rapid transit in Australian cities. So you would expect higher public transport mode shares around train stations.

Here is a chart showing average journey to work public transport mode shares by home distance from train stations. It’s animated over the three census years, with a longer pause on 2016.

Technical note: A limitation here is that I’ve measured all census years against train stations that were operational in 2016 – so the 2006 and 2011 mode shares will be under-stated for the operational stations of those years. For example, in Melbourne the following stations opened between 2011 and 2016: Williams Landing, South Morang, Lynbrook, and Cardinia Road.

You can see that public transport shares went up between 2006 and 2011 in most cities at all distances from train stations. In both Perth and Brisbane there were new train lines opened between 2006 and 2011, which will explain some of that growth.

But if you watch carefully you will see public transport mode shares near train stations fell in both Brisbane and Perth between 2011 and 2016. That is, there was a mode shift away from public transport, even for people living close to train stations. As discussed previously, this is most likely related to there being only small jobs growth in the CBDs of those cities between 2011 and 2016, compared to suburban locations.

You can also see that public transport mode shares aren’t that much higher for areas near train stations in Adelaide (I’ll come back to that).

We can do the same for train mode shares (any journey involving train):

Again, Sydney’s train stations seem to have the biggest pulling power, while Adelaide’s have the least.

Busways are the other major form of rapid transit in Australian cities, with major lines in Brisbane, Sydney and Adelaide. Here is a chart of public transport mode share by distance from busway stations, excluding areas also within 1.5 km of a train station:

Note for Adelaide this data only considers suburban stations on the O-bahn, and not bus stops in the CBD. For Sydney all “T-Way” station are included, plus the four busway stations on the M2 motorway for which buses run into the CBD (but not the relatively short busway along Anzac Parade in Moore Park). Sydney’s north west T-Ways opened in 2007

Proximity to a busway station appears to influence public transport mode share strongly in Brisbane and Adelaide, where busways are mostly located in the inner and middle suburbs and cater for trips to the CBD. Sydney’s busway stations are in the “outer” western suburbs, feeding Blacktown, Parramatta, but also relatively long distance services to the Sydney CBD via the M2.

Curiously, public transport mode shares were higher in places between 3 and 5 km from busway stations in Sydney, compared to immediately adjacent areas. I’m not sure that I can explain that easily, but it suggests equally attractive public transport options exist away from busway and train stations.

The station proximity influence appears to extend around 1 km, which possibly reflects the fact that few busway stations have park and ride facilities, and are therefore more dependent on walking as an access mode (although cycling may be another station access mode).

Over time Sydney public transport mode share lifted at all distances from busway stations, while in Brisbane it rose in 2011 and then fell again in 2016, in line with other city mode shares.

So are busway stations similar to train stations in their impact on public transport mode share? To answer this I’ve segmented cities into areas near train stations, near busway stations, near both, and near neither. I’ve used 1.5 km as a proximity threshold that might represent an extended walking catchment.

In Sydney, train stations appear to have a much stronger influence on public transport mode shares than busway stations, but the opposite is true in Brisbane and Adelaide. This possibly reflects the much higher service frequencies on Adelaide and Brisbane busways compared to their trains, and the fact Sydney’s busway stations are so far from the CBD (and thus have fewer workers travelling to the CBD where public transport dominates mode share).

Also of note in this chart is that for areas more than 1.5 km from a train or busway station, Sydney had a much higher public transport mode share compared to the other cities. These areas will be served mostly by on-road buses, but also some ferries and one light rail line. Adelaide has the least difference between mode share for areas near and not-near train or busway stations.

We can do the a similar analysis for workplaces:

The most curious pattern here is Adelaide – where public transport mode share was highest for jobs between 1.5-2.5 kms from train stations. This distance band is dominated by the centre of the Adelaide CBD (the station being on the edge, arguably a “corner”), for which bus was the dominant public transport access mode. Also, there was no destination zone small enough near Adelaide central train station to register as 0 – 0.5 km away, and only one that is 0.5 – 1 km away (I use distances between station data points and destination zone centroids). So the results might look slightly different if smaller destination zones were drawn in the Adelaide CBD.

In all other cities there was a very strong relationship between train station proximity and public transport mode share, as you would expect. And Sydney again stands out with high public transport mode shares for workplaces more distant from train stations.

If you are wondering, the bump in Sydney at 2.5 to 3 km includes the Kensington / Randwick area which has high employment density and a strong bus connection to the central city (partly assisted by the Anzac Parade busway). And the relatively high figure for Melbourne at 1 – 1.5 km includes parts of Docklands, Parkville, Southbank, and St Kilda Road, which all have high tram service levels.

Unfortunately destination zones around busway stations are generally too large to provide meaningful insights so I’m not presenting such data.

Motor vehicle ownership

It will come as little surprise that there is a relationship between household motor vehicle ownership and journey to work mode shares.

Here’s a summary chart for each city for the 2006, 2011 and 2016 censuses:

There appears to be a fairly strong relationship between the two factors at city level.

Sydney and Melbourne have seen the largest mode shift away from private transport, but only Melbourne has also seen declining motor vehicle ownership rates.

Canberra saw only weak growth in motor vehicle ownership between 2011 and 2016, and at the same time there was a shift away from private transport (and a large increase in population weighted density).

Perth and Brisbane saw increasing private transport mode share and increasing motor vehicle ownership between 2011 and 2016.

Here’s a more detailed look at the relationship over time for Melbourne at SA2 geography:

The outliers on the upper left are generally less-wealthy middle-outer suburban areas (lower motor vehicle ownership but high private mode share), while the outliers to the lower-right are wealthy inner suburbs where people can afford to own plenty of motor vehicles, but they didn’t use them all to get to work.

In the bottom left of the chart are inner city SA2s with declining private mode share and declining motor vehicle ownership. For motor vehicle ownership rates around 70-80 (motor vehicles per persons aged 18-84), there are many SA2s with private mode shares that declined 2006 to 2016, but not significantly lowering motor vehicle ownership rates. That suggests that just because people own many motor vehicles, they don’t necessarily use them to drive them to work.

Here is the same data for Sydney:

There are many SA2s with motor vehicle ownership rates around 50 to 70 where the private mode shares are dropping faster than motor vehicle ownership. But there are also many areas with high private mode shares and increasing rates of motor vehicle ownership.

How do the other cities compare? Here are all the SA2s for all cities on the same chart, with alternating highlighted cities:

You can see big differences between the cities, but also that Brisbane and Perth have many SA2s with very high private mode share and rapidly increasing motor vehicle ownership (ie moving up and right, although it’s a little difficult to see with so many lines overlapping). Melbourne and Sydney have plenty of SA2s moving down and left – reducing motor vehicle ownership and declining private transport mode share (which may make some planners proud).

Of course there will be a relationship between motor vehicle ownership and where people choose to live and work. People working in the central city may prefer to live near train stations so they can avoid driving in congested traffic to expensive car parks. People who prefer not to drive might choose to live close to work and/or a frequent public transport line. People who are happy to drive to work in the suburbs might avoid higher priced real estate near train stations or the inner city.

As an aside, we can compare total household motor vehicles to the number of people driving to work, to estimate the proportion of household motor vehicles actually used in the journey to work. Here is Melbourne:

SA2s with a lower estimate are generally nearer the CBD, are wealthier areas, have reasonable public transport accessibility, and/or might be areas with a higher proportion of people not in the workforce (for whatever reason). The areas where the highest proportion of motor vehicles are required for the journey to work are relatively new outer suburbs on the fringe (perhaps suggesting forced car ownership), where adult workforce participation is probably high and public transport accessibility is lower.

The proportion of cars used in the journey to work declined on average in many parts of Melbourne. Given that motor vehicle ownership rates in Melbourne barely changed between 2011 and 2016, this probably represents people mode shifting, rather than people acquiring more motor vehicles even though they don’t need them to drive to work.

Jobs within walking distance of home

It stands to reason that people would be more likely to walk to work if there were more work opportunities within walking distance of their home.

For every SA1 I’ve measured how many jobs are approximately within 1 km as a notional walking catchment (measured as the sum of jobs in destination zones whose centroid are within 1 km of the centroid of each home SA1, so it is not perfect). Here’s the relationship with walking mode share:

(there are a lot of dots overlapping in the bottom left-corner and Adelaide dots have been drawn on top so try not to get thrown by that).

You don’t have to have a lot of nearby jobs to get a higher walking mode share, but if you do, you are very likely to get a high walking more share. The exceptions (many jobs, but low walking share) include many parts of Parramatta (Sydney), and areas separated from nearby jobs by water bodies or other topographical barriers (eg Kangaroo Point in Brisbane).

Workplace distance from the city centre

As was seen in a previous post, workplaces closer to city centres had much lower private transport mode shares, which is unsurprising as these are locations with generally the best public transport accessibility, high land values that can lead to higher car parking prices (which impact commuters who pay them), and often higher traffic congestion.

Here is a chart showing private transport mode share by workplace distance from the city centre. I’ve used faded lines to show 2011 and 2006 results (2006 only available for Sydney and Melbourne).

Here’s a chart that shows the mode shifts between 2011 and 2016:

Inner Melbourne had the biggest mode shifts away from private transport (particularly in Docklands that falls into the 1-2 km range, which saw significant employment and tram service growth), but Sydney had more consistent mode shifts across most distances from the city centre. Adelaide and Canberra saw mode shifts away from private transport in the inner city but towards private transport further out.

Brisbane and Perth saw – on average – a mode shift to private transport across almost all distances from the city centre, with the highest mode shift to private transport in Brisbane actually for the CBD itself(!).

Home distance from the city centre

There’s unquestionably a relationship here too, and it’s probably mostly driven by public transport service levels being roughly proportional to distance from the CBD, but also the proportion of the population who work in the CBD being much higher for homes nearer the CBD.

Sydney had the lowest average private transport mode share at all distances up to 20 km from the CBD, followed by Melbourne and Brisbane, in line with overall mode shares.

The trends over time are also interesting. Brisbane saw mode shifts towards private transport at all distances more than 2 km from the city centre between 2011 and 2016. However there were not significant shifts for Perth outside the city centre – that is: modes shares by geography didn’t change very much. The mode shift away from public transport in Perth is best explained by the shift in jobs balance away from the city centre.

Here are public transport mode shares by home distance from city centres:

In most cities, public transport mode share peaked at a few kilometres from the city (as active transport has a higher mode in the central city).

Here are public transport mode shifts by distance from the city centre between 2011 and 2016:

The significant shift in central Melbourne is likely to be largely explained by the Free Tram Zone introduced in 2015. Outside of the city centre the mode shifts are surprisingly uniform across each city.

Here’s the same chart for 2006-2011, and you can clearly see the impact of the opening of the Mandurah railway line in Perth with significant mode shift beyond 30 km:

Curiously there was a massive shift to public transport for CBD residents in Melbourne (and this is before the free tram zone was introduced).

So which factors best explain the patterns in mode shares across cities?

What we’ve clearly seen is that higher public transport mode shares are seen for journeys to work…

  • to higher density workplaces
  • from areas of lower motor vehicle ownership
  • to workplaces closer to train stations
  • from higher density residential areas
  • from areas around train and busway stations
  • to and from areas closer to city centres (except from the central city where walking takes over)
  • from less wealthy areas (while I haven’t tested this directly, wealth seems to explain a lot of the outliers in the scatter plots)

I’ve listed these roughly in order of the strength of the relationships seen in the data, but I haven’t put them all in a regression model (yet, sorry).

Of course most of these factors are inter-related, so we cannot isolate causation factors. I’m going to run through many of them, because they are often interesting: (note I have sometimes used log scales)

Population density is roughly related to distance from the city centre:

Motor vehicle ownership has a strong relationship with population density (see this post for more analysis):

Motor vehicle ownership has a weaker relationships with distance from the city centre:

Motor vehicle ownership is related to home distance from train stations, except in Adelaide:

Technical note: For this chart (and some below) I’ve calculated average quantities for the variable on the Y axis, as there would otherwise there are too many data points on the chart and it becomes very hard to see the relationship (I would need to show all SA1s because SA2s are too large in terms of distance from stations). The downside is that these style of charts don’t indicate the strength of relationships.

Population weighted density is related to distance from train stations, especially in Melbourne and Sydney, but not at all in Adelaide:

There is a relationship – although not strong – between weighted job density and distance from city centres:

There’s some relationship between average weighted jobs density and distance from train stations, except in Adelaide:

Here’s the same data, but as a scatter plot with a point for each destination zone, scaled by the number of journeys to each destination zone, and a linear Y-axis:

Technical note: the X-axis appears green mostly because Adelaide data points are drawn on top of other cities, but those data points aren’t of much interest.

In most cities, destination zones with high jobs density (over 700 jobs/ha) were only found within 1 km of a train station – with the notable major exception of Adelaide (again!).

(If you are curious, the large Melbourne zone at 1.4 km from a train station and 659 jobs/ha is the Parkville hospital precinct – where incidentally a train station is currently under construction).

There is a relationship between motor vehicle ownership and proximity to busway stations, but it varies between cities:

But there’s not much relationship between population density and proximity to busway stations (except in the immediate vicinity of busway stations in Brisbane):

Final remarks: there’s something about Adelaide’s train network

A few key observations come through clearly about the catchments around Adelaide’s train stations:

  • In aggregate they do not have higher population density, unlike other cities.
  • In aggregate they do not have particularly high public transport mode shares, unlike other cities.
  • In aggregate they do not have lower rates of motor vehicle ownership, unlike other cities.
  • They do not include the area of highest job density in the CBD (a longer walk or transfer to tram or bus is required), unlike other cities.

Few cities have spare land corridors available for new at-grade rapid public transport lines, and so transport planners generally want to make maximum use of the ones they’ve got, before opting for expensive and/or disruptive tunnelling or viaducts solutions. It looks like Adelaide’s rail corridors are not reaching their people-moving potential.

By contrast, Adelaide’s “O-Bahn” busway does go into the job dense heart of the CBD and the busway station catchments do have higher public transport mode share and lower motor vehicle ownership. However they do not have higher population density, possibly because the stations are surrounded by car parks, green space, and one large shopping centre (Tea Tree Plaza).

Mode shares, population densities, and motor vehicle ownership rates would quite probably change significantly if Adelaide could address the fourth issue by building a train station near the centre of the CBD.

In fact, Auckland had a very similar problem with its previous main city station being located away from the centre of the CBD. They fixed that with Britomart station opening in 2003 and train patronage soon rose quite dramatically (off a very low base, and also helped by service upgrades, subsequent electrification, and many other investments).

Should Adelaide do the same? It would certainly not be cheap and you would have to weigh up the costs and benefits.


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

Wed 17 October, 2018

Over a quarter of Sydney commuters (26.3%) went to work by public transport in 2016, the highest rate of Australian cities, and an increase of 3.0% on 2011. This post provides an overview of mode shares and mode shifts across Sydney from 2006 to 2016 (following on from my previous analysis of Melbourne and Brisbane).

I’m going to mostly look at trends in private motorised transport mode shares, as it is generally the least space-efficient and most polluting method of travel on a per person basis, and many cities aim to shift people away from private transport to active or public transport.

Firstly, here are private transport mode shares by home location (click to enlarge or explore in Tableau Public but be patient):

You can see lower private mode shares in the inner city and around train lines, as you might expect. In many places private transport accounts for a minority of commuters.

Here are the private transport mode shifts by home location (also in Tableau):

There were significant mode shifts away from private transport almost all over Sydney, but particularly strong in the inner south, inner west, north shore and hills area, including many areas served only by buses for public transport.

You can see the mode split of net new commuter origins on the next chart, with public transport dominating new trips from many areas on the north shore, eastern suburbs, and inner west and south-west (also in Tableau):

Private transport dominated new commuters in the outer western suburbs. Compared to other cities, a smaller proportion of new commutes came from the outer fringe, which may partly explain why Sydney had the strongest mode shift to public transport.

Here’s another look at that data, with the private transport mode share of net new journeys to work:

In many parts of Sydney there was an absolute reduction in the number of private transport journeys to  work (pink areas), and many where it represented a small minority. Private transport did however dominate new commutes from most outer western suburbs and the northern beaches.

Summarising the above, Sydney saw public transport journeys grow faster than private transport journeys across all but the outer suburbs:

Here are the private transport mode shares by work location (also in Tableau):

Sydney is distinctly different to the other cities in that there are many major employment centres outside the CBD with quite low private mode shares. The lowest 2016 private transport mode share destination zone in Macquarie Park was 59%, in Strathfield was 53%, in Manly was 55%, in Parramatta was 40%, in Chatswood was 40%, in St Leonards was 43%, in Bondi Beach was 43%, in Burwood was 46%, in Kensington was 45%, in Bondi Junction was 35%, and in North Sydney was 22%. Refer to my recent post about suburban employment clusters for more on this.

The Sydney CBD itself has a destination zone with only 6% private mode share in 2016. Sub-50% private mode shares stretch out from the CBD as far as Newtown south-west of the CBD.

Here are private transport mode shifts by work location:

There were significant mode shifts away from private transport across much of Sydney, with the largest in Mascot (-9%, noting that train fares were reduced at stations in Mascot in March 2011), and 7% declines in Sydney Airport, Kogarah, Waterloo – Beaconsfield, Newtown – Camperdown – Darlington, Redfern – Chippendale, Chatswood (East) – Artarmon. There was a 6% mode shift away from private transport in both North Ryde and Macquarie Park, where new train stations opened in 2009.

Here is a map showing the volume and mode split of new commuter destinations in Sydney:

The Sydney CBD is such a big pie chart it swamps all others with 63,732 new commuters, 86% of which were accounted for by public transport. Public transport also dominated in North Sydney – Lavender Bay (which actually had a net reduction in private transport trips), Surrey Hills (88% by public transport) and Pyrmont – Ultimo (84% by public transport).

It’s also notable that Sydney’s major regional centres had a significant share of their jobs growth accounted for by public transport trips, as explored in my earlier post on employment clusters.

Here’s a map of private transport mode share of net new trips by workplace:

There was a net reduction in private transport journeys to many SA2s, including North Sydney, Homebush, Epping – North Epping, and Mascot – Eastlakes (note: some others might be artifacts of boundary changes between 2011 and 2016). Private transport again dominated new journeys to the outer west and northern beaches.

You can see on the following chart that the central city accounted for a significant portion of the jobs growth and public transport accounted for almost all of those new trips, which helps explain the overall shift to public transport. Private transport only significantly dominated new jobs more than 10 km from the city centre.

For more on the journey to work, you might like another post about likely factors explaining city-wide mode shifts across Australia’s larger cities.

About the data

The mode share maps are filtered for residential areas (CD or SA1) with at least 5 persons/hectare or destination zones (DZs) with at least 4 jobs/hectare (as appropriate). Mode shifts, mode splits, and mode shares of net new commutes are calculated and shown on 2016 SA2 boundaries, with 2006 and 2011 CDs, SA1s and DZs mapped to 2016 SA2 boundaries on a majority overlap basis (mostly a perfect alignment, but sometimes not). I’ve only counted people who travelled on census day and stated what mode(s) they used, and – for work destinations – where the work SA2 is known. See my Brisbane post for a longer explanation.


Suburban employment clusters and the journey to work in Australian cities

Sun 8 July, 2018

Relatively dense suburban employment clusters can deliver more knowledge-based jobs closer to people living in the outer suburbs. Sydney has many such clusters, and Melbourne is now aiming to develop “National Employment and Innovation Clusters” as part of the city’s land use strategy, Plan Melbourne.

So what can we learn about existing employment clusters in Australian cities, particularly in regards to journeys to work? Can relatively dense suburban employment clusters contribute to more sustainable transport outcomes? Do such clusters have lower private transport mode shares than other parts of cities? How are mode shares changing for these clusters? How far do people travel to work in these clusters? Is there a relationship between job density, parking prices, and mode shares? How well served are these clusters by public transport? How do these clusters compare between cities?

This post investigates 46 existing clusters in Australia’s six largest cities. This is a longer post (there is a summary at the end), but I hope you find at least half as interesting as I do.

What’s a dense suburban employment cluster?

That’s always going to be an arbitrary matter. For my analysis, I’ve created clusters based on destination zones that had at least 40 employees per hectare in 2011 or 2016, were more than 4km from the city’s main CBD, and where collectively at least around 6,000 employees travelled on census day in 2016.

Unfortunately I can only work with the destination zone boundaries which may or may not tightly wrap around dense employment areas. Also, in order to ensure reasonable comparisons between census years, I’ve had to add in some otherwise non-qualifying zones to keep the footprints fairly similar. To mitigate potential issues with low density zones being included, I’ve used weighted employment density for each cluster in my analysis. But still, please don’t get too excited by differences in weighted job density as it’s far from a perfect representation of reality.

In particular, the following clusters include destination zones comprising both dense employment and non-employment land and so will potentially have understated weighted job density:

  • Nedlands
  • Fremantle
  • Bedford Park
  • Tooronga
  • Camberwell Junction
  • Hawthorn
  • Belconnen
  • Campbelltown
  • Hurstville
  • Kogarah
  • Randwick
  • North Ryde (quite significant – actual density is probably double)
  • Macquarie Park (a destination zone for the university includes large green areas)
  • Rhodes (significant residential area)
  • Parramatta (includes parkland)
  • Penrith (residential areas)
  • Bella Vista – Norwest – Castle Hill (includes a golf course)

Some of these clusters are a little long and thin and so are literally stretching things a little (eg Bella Vista – Norwest – Castle Hill, and Alexandria – Mascot), but it’s hard to cleanly break up these areas.

I think my criteria is a fairly low threshold for suburban employment clusters, but raising the criteria too much would knock out a lot of clusters. I should note that some potential clusters might be excluded simply because they did not contain small destination zones concentrated on more dense areas.

Belmont in Perth was the lowest density cluster to qualify (weighted jobs density of 42 jobs / ha). Here’s what it looks like (in 3D Google Maps in 2018):

Chatswood in Sydney was the highest density cluster – with a weighted job density of 433 jobs / ha. Here’s what it looks like (in 3D Apple Maps in 2018):

Apologies if your favourite cluster didn’t make the criteria, or you don’t like my boundaries. You can look up the 2016 boundaries for each cluster here, or view them all through Google maps.

Where are these clusters?

On the following maps I’ve scaled the clusters by employment size and used pie charts to show the modal split for journeys to work in 2016. All pie charts are to the same scale across the maps (the size of the pie charts is proportional to the number of journeys to the cluster in 2016).

Note that North Sydney is excluded because it is within 4 km of the CBD.

All of Melbourne’s clusters are east of the CBD, with Clayton the largest. Places just missing out on the cluster criteria include parts of the Tullamarine industrial area (5271 jobs at 55 jobs/ha), Doncaster (around 5000 jobs at 40+ jobs/ha), Chadstone Shopping Centre (5375 jobs at 105 jobs/ha), and La Trobe (around 7700 jobs but low density – and even if there was a destination zone tightly surrounding the university campus I suspect it would still not qualify on density ground).

Only three suburban clusters qualified in Brisbane.

Note: the Nedlands and Murdoch clusters are essentially the hospital precincts only and do not include the adjacent university campuses.

Adelaide only has one suburban cluster that qualifies – Bedford Park – which includes the Flinders University campus and Flinders Medical Centre.

The Canberra clusters cover the three largest town centres, each containing at least one major federal government department head office.

What proportion of jobs are in these dense suburban employment clusters?

The following chart shows that Sydney and Canberra have been most successful at locating jobs in suburban employment clusters (well, clusters that meet my arbitrary criteria anyway!):

The proportion of jobs not in the inner 4km or a suburban employment cluster increased between 2011 and 2016 in all cities except Sydney (although the shift was very small in Melbourne).

Here’s a summary of private transport mode shares for the clusters, versus the inner city versus everywhere else:

Inner city mode shares vary considerably between cities, in order of population size. Total job cluster private mode shares are only 4-7% lower than elsewhere in most cities, except for Sydney where they are 17% lower.

Sydney’s clusters combined also have a significantly lower private mode share of 68% – compared to 84-89% in other cities.

How do the clusters compare?

Here is a chart showing their size, distance from CBD, and private transport mode share for journeys to work in 2016:

Next is a chart that looks at weighted job density, size, and private mode share for 2016. Note I’ve used a log scale on the X axis.

(Unfortunately the smaller Kogarah dot is entirely obscured by the larger Alexandria – Mascot dot – sorry that’s just how the data falls)

There is certainly a strong relationship between weighted job density and private mode shares (in fact this is the strongest of all relationships I’ve tested).

Sydney has many more clusters than the other cities (even Melbourne which has a similar population), it has much larger clusters, it has more dense clusters, and accounts for most of the clusters in the bottom-right of the chart.

And there’s just nothing like Parramatta in any other city. It’s large (~41,000 jobs in 2016), has relatively low private transport mode share (51%), is about 20 km from the Sydney CBD, and has a high jobs density.

Melbourne’s Clayton has about three-quarters the jobs of Parramatta, is around the same distance from its CBD, but is much less dense and has 90% private mode share for journeys to work.

Curiously Sydney’s Macquarie Park – which on my boundaries has about the same number of jobs as Parramatta – is closer to the Sydney CBD and has a much higher private transport mode share and a lower job density. However it’s rail service is relatively new, opening in 2009.

Perth’s Joondalup and Murdoch are relatively young transit oriented developments with relatively new train stations (opening 1992 and 2007 respectively), however they also have very high private transport mode shares, which I think highlights the challenge of creating suburban transit-adjacent employments clusters surrounded by low density suburbia.

Also, many of Sydney’s suburban clusters have a lower private mode share than that of the overall city (67.6%). That’s only true of Hawthorn and Camberwell Junction in Melbourne, Fremantle in Perth, and Woden and Belconnen in Canberra.

Some outliers to the top-right of the second chart include Heidelberg (in Melbourne), Liverpool (in Sydney), and Nedlands (in Perth). The Heidelberg and Nedlands clusters are relatively small and are dominated by hospitals, while 37% of jobs in Liverpool are in “health care and social assistance”. Hospitals employ many shift workers, who need to travel at times when public transport is less frequent or non-existent which probably explains their relatively high private transport mode shares. Heidelberg is located on a train line, and is also served by several relatively frequent bus routes, including one “SmartBus” route, but still has a very high private transport mode share of 85%.

Outliers to the bottom-left of the second chart include Randwick, Burwood, and Marrickville (all in Sydney). While these are less dense clusters, I suspect their relatively low private transport mode shares are because they are relatively inner city locations well served by public transport.

As an aside, if you were wondering about the relationship between job density and private mode shares for inner city areas, I think this chart is fairly convincing:

Of course this is not to say if you simply increase job density you’ll magically grow public transport patronage – there has to be capacity and service quality, and you probably won’t get the density increase without better public transport anyway.

How well connected are these job clusters to public transport?

Arguably the presence of rapid public transport is critical to enabling high public transport mode shares, as only rapid services can be time competitive with private transport. By “rapid” I consider services that are mostly separated from traffic, have long stop spacing, and therefore faster average speeds. For Australian cities this is mostly trains, but also some busways and light rail lines (but none of the clusters are served by what I would call “rapid” light rail). Of course there is a spectrum of speeds, including many partly separated tram and bus routes, and limited stops or express bus routes, but these often aren’t time competitive with private cars (they can however compete with parking costs).

I have classified each cluster by their access to rapid transit stations, with trains trumping busways (note Parramatta, Blacktown, Westmead, and Liverpool have both), and some clusters sub-classified as “edge” where only some edge areas of the cluster are within walking distance of a rapid transit station (although that’s not clear cut, eg Murdoch). Here are the public transport mode shares, split by whether journeys involved trains or not:

It’s probably of little surprise that all of the high public transport mode share centres are on train lines (except Randwick), and that most public transport journeys to these clusters involve trains. However the presence of a train station certainly does not guarantee higher public transport mode share.

Only four clusters have some degree of busway access (Chermside and Randwick are not actually on a busway but have a major line to them that uses a busway). Only Upper Mount Gravatt has a central busway stations, and it has the third highest non-train (read: bus) access share of 12%.

Randwick is an interesting exception – the University of New South Wales campus in this cluster is connected to Central (train) Station by high frequency express bus services which seem to win considerable mode share. A light rail connection is being constructed between Randwick and the Sydney CBD.

Non-rail (essentially bus) public transport mode shares are also relatively high in Bondi Junction (15%), Parramatta (11%), Belconnen (10%), Brookvale (10%), Woden (10%), Fremantle (9%), Macquarie Park (9%). These are all relatively strong bus nodes in their city’s networks.

Clayton and Nedlands are not on rapid transit lines, but both have high frequency bus services to nearby train stations which results in slightly higher train mode shares (4% and 5%). For Clayton, only the Monash University campus is connected by a high frequency express bus and it had a 17% public transport mode share, whereas the rest of the cluster had public transport mode shares varying between 3 and 7%.

The Bedford Park cluster is frustratingly just beyond reasonable walking distance of Tonsley Railway Station (12 minutes walk to the hospitals and almost half an hour’s walk to the university campus) – so only about 10 people got to work in the cluster by train in 2016. However that’s going to change with an extension of the train line to the Flinders Medical Centre.

The train-centred clusters with low public transport mode shares are mostly not in Sydney, and/or towards outer extremities of the train network (except Box Hill and Heidelberg in Melbourne). So what is it about Sydney’s trains that makes such a difference?

Sydney’s train network is distinctly different to all other Australian cities in that there are many more points where lines intersect (outside the central city), creating many “loops” on the network (for want of a better expression). In all other cities, lines only generally intersect in the central city and where radial lines split into branches, and cross city trips by public transport generally only possible by buses (in mixed traffic). In Sydney lines do branch out then but then often bend around to intersect other neighbouring lines. This provides significantly more connectivity between stations. For example, you can get to Parramatta from most lines directly or with a single transfer somewhere outside central Sydney. Indeed, Sydney is the only city with a regular non-radial train service (T5 Leppington – Richmond, although it only runs every half-hour).

I’ve roughly overlaid Sydney’s dense suburban job clusters (in red) on its rail network map, and then marked the train mode shares:

While some clusters can only be accessed by a radial train line (or are off-rail), many are at intersection points, and most can be accessed by multiple paths along the network. The 29%+ train mode shares for Chatswood, Parramatta, St Leonards, Burwood, and Rhodes might be partly explained by these being highly accessible on the train network.

Here are Melbourne’s dense suburban employment clusters and train mode shares overlaid on Victoria’s rail network map:

The clusters connected to more train lines (Hawthorn and Camberwell) have higher train mode shares, although they are also closer to the city.

The Spatial Network Analysis for Multimodal Urban Transport Systems (SNAMUTS) methodology (led by Professor Carey Curtis and Dr Jan Scheurer) uses graph-based analysis of public transport networks to develop several indicators of network performance. One indicator that measures network accessibility is closeness centrality, which looks and speed and frequency of services to connect to other nodes in the network (it actually uses inter-peak frequencies and speeds, but they probably correlate fairly well with services in peak periods). A lower score indicates better accessibility.

I’ve extracted the closeness centrality scores for public transport nodes in each employment clusters (from the nearest available data to 2016 at the time of writing, some as old as 2011 so not perfect) and compared this with private transport mode shares to these clusters:

Some clusters were not really centred on a public transport node in the SNAMUTS analysis (eg Osborne Park in Perth, Clayton in Melbourne) and hence are not included in this analysis. These clusters have very high private transport mode shares, and would likely be towards the top right of the chart.

There’s clearly a relationship between the closeness centrality and private mode shares, with low private more shares only occurring where there is high accessibility by public transport. But it’s not super-strong, so there are other factors at play.

Some of the outliers in the bottom right of the distribution include Upper Mount Gravatt (based on a large shopping centre but also on a busway), Murdoch (dominated by hospitals a moderate walk from the station), Nedlands (also dominated by hospitals), Chermside (a combination of hospital and large shopping centre, with the bus interchange remote from the hospital), and Bedford Park (where 63% of jobs are in health) . Again, the pattern of higher private transport mode shares to hospitals is evident.

So do you need strong public transport access to support higher job densities? Here’s the relationship between closeness centrality and weighted job density:

There are no clusters with poor public transport access and high job density, which is not surprising. But this does suggest it could be difficult to significantly increase job densities in clusters currently in the top left of this chart without significantly improving public transport access.

Interestingly, Box Hill in Melbourne does have a similar closeness centrality score to Parramatta and Chatswood in Sydney, suggesting it might be able to support significantly higher job density. However, it only has rapid (train) public transport from two directions. It might be more challenging to maintain bus and tram travel times from other directions if there is significant jobs growth.

Melbourne’s largest cluster – Clayton – is not on the chart because it is not centred on a public transport node. There is however a bus interchange on the southern edge of the cluster at Monash University, which has a relatively low closeness centrality score of 64. I suspect the main employment area would probably have a higher closeness centrality score if it were to be measured because it not connected to the train network by a high frequency express shuttle service and has fewer bus routes. That would place it in the top-left part of the above chart (2016 weighted job density being 63 jobs/ha).

Do higher density clusters have fewer car parks?

The higher density centres certainly tended to have lower private mode shares, but does that mean they don’t have much car parking?

Well I don’t know how many car parking spaces each centre had, but I do know how many people travelled to work by car only, and from that I can calculate a density of car-only journeys (and I’ve calculated a weighted average of the destination zones in each centre). That’s probably a reasonable proxy for car park density.

Here’s how it compares to jobs density (note: log scales on both axes):

There is a very strong correlation between the two – in general centres with higher job density also have higher car density. The strongest correlation I can find is for a quadratic curve that flattens out at higher job densities (as drawn, with R-squared = 0.77), which simply suggests you get lower private mode shares in higher density clusters (in general).

The clusters on the bottom side of the curve have lower car mode shares, and so have a lower car density. Many are inner city locations with better public transport access, but also many nearby residents.

Heidelberg (a hospital-based cluster in Melbourne), has the highest car density of all centres and a high job density, but isn’t a large centre.

Do walking mode shares increase when there are many nearby residents?

If there are many residents living within walking distance of a cluster, relative to the size of that cluster, then you might expect a higher walking mode share, as more employees of the cluster are likely to live nearby.

I’ve roughly summed the number of residents who travelled to work (anywhere) and lived within 1km of each cluster. I’ve then taken the ratio of those nearby working residents to the number of journeys into the cluster, and then compared that with walk-only mode shares for 2016:

Yes, there’s definitely a relationship (although not strong), and this may explain some of the outliers in the previous charts such as Randwick, Marrickville, St Leonards and Bondi Junction.

Is there a relationship between parking costs and mode shares?

It’s quite difficult to definitively answer this question because I don’t have parking prices for 2016, and many car commuters might not be paying retail prices (eg employer-provided free or subsidised parking).

I’ve done a quick survey using Parkopedia of parking prices for parking 8:30 am to 5:30 pm on Monday 2 July 2018, and picked the best price available in each cluster. Of course not everyone will be able park in the cheapest car park so it’s certainly not an ideal measure. An average price might be a slightly better measure but that would be some work to calculate.

But for what it worth, here is the relationship between July 2018 all day parking prices and 2016 private transport mode shares:

You might expect an inverse correlation between the two. Certainly clusters with very cheap or free parking had very high private transport mode shares, but other centres are scattered in the distribution.

Looking at outliers in the top right: I suspect Bedford Park (63% health workers), Heidelberg (hospital precinct), Tooronga (with one major employer being the Coles HQ), Chermside (including Prince Charles Hospital), and Rhodes will have significantly cheaper parking for employees (with visitors paying the prices listed on Parkopedia). Indeed, I could not find many parking prices listed for Rhodes, but there are clearly multi-storey parking garages near the office towers not on Parkopedia.

Looking at outliers in the bottom left: Relatively cheap $15 parking is available at multiple car parks in Bondi Junction. The $10 price in Chatswood was only available at one car park, with higher prices at others, so it is probably below the average price paid. Maybe traffic congestion is enough of a disincentive to drive to work in these centres?

For interest, here’s the relationship between weighted car density and parking prices:

The relationship is again not very strong – I suspect other factors are at play such as unlisted employer provided car parking, as discussed above.

So does job growth in suburban employment clusters lead to lower overall private transport mode shares?

Here is a chart showing the effective private mode share of net new trips in each job cluster, plus the inner 4 km of each city:

(Fremantle, Dandenong, Burwood, and Woden had a net decline in jobs between 2011 and 2016 and so have been excluded from this chart)

The chart shows that although many suburban jobs clusters had a low private mode share of net new trips, it was always higher than for the inner 4 km of that city.

Here’s a summary of net new trips for each city:

So every new 100 jobs in suburban employment clusters did generate many more private transport trips than new jobs in the inner city, particularly for Sydney (45 : 10), Melbourne (68 : 13), and Canberra (84: 18). But then new jobs in suburban employment clusters had significantly lower private transport mode shares than new jobs elsewhere in each city.

So arguably if you wanted to minimise new private transport journeys to work, you’d aim for a significant portion of your employment growth in the central city, and most of the rest in employment clusters (ideally clusters that have excellent access by rapid public transport). Of course you would also want to ensure your central city and employment clusters were accessible by high quality / rapid public transport links (not to forget active transport links for shorter distance commutes).

One argument for growing jobs in suburban employment clusters is that new public transport trips to suburban employment clusters will often be on less congested sections of the public transport network – particularly on train networks (some would even involve contra-peak travel relative to central city). On the other hand, new jobs in the central city have much higher public transport mode shares, but relatively expensive capacity upgrades may be required to facilitate the growth.

New active transport trips to the central city and employment clusters probably requires the least in terms of new infrastructure, and there are probably very few congested commuter cycleways in Australian cities at present.

Another argument for suburban employment clusters is to bring jobs closer to people living in the outer suburbs.

Are new private transport trips to suburban employment clusters much shorter than new private transport trips to the central city, and therefore perhaps not as bad from a congestion / emissions point of view?

Certainly many of these clusters will have congested roads in peak periods, but the distance question is worth investigating.

So how far do people travel to work in different employment clusters?

The 2016 census journey to work data now includes on-road commuting distances (thanks ABS!).

Of course for any jobs cluster there will be a range of people making shorter and longer distance trips and it is difficult to summarise the distribution in one statistic. Averages are not great because they are skewed by a small number of very long distance commutes. For the want of something better, I’ve calculated medians, and here are calculations for Sydney job clusters:

(I’ve added a “Sydney” jobs cluster which is the “Sydney – Haymarket – The Rocks” SA2 that covers the CBD area).

There’s a lot going on in this data:

  • Median distances for private transport commutes to most employment clusters are longer than to the CBD (particularly the big clusters of Macquarie Park and Parramatta).
  • The clusters of Brookvale, Bondi Junction, and Randwick near the east coast have lower medians for motorised modes, probably reflecting smaller catchments. Randwick and Brookvale also do not have rail access, which might explain their low median public transport commute distances.
  • Public transport median commute distances were longer in the rail-based near-CBD clusters of Bondi Junction, Alexandria – Mascot, and St Leonards, but also in some further out rail-based clusters, including Parramatta, Westmead and Penrith.
  • Penrith – the cluster furthest from the Sydney CBD – curiously had the longer public transport median commute distance, which probably reflects good access from longer distance rail services (but public transport mode share was only 14%).
  • Active transport medians vary considerably, and this might be impacted by the mix of shorter walking and longer cycling trips. For example, North Ryde saw more cycling than walking trips, but also had only 1% active transport mode share.

Here’s the same for Melbourne (with a cluster created for the CBD):

Clayton, Dandenong, and Melbourne CBD median commute distances were very similar, whereas median commutes to other clusters were mostly shorter.

Here are results for clusters in the smaller cities:

In Perth, Joondalup had shorter median commuter distances, while Osborne Park and Murdoch (both near rapid train lines) had the longest median public transport journey distances (but not very high public transport mode shares: 7% and 15% respectively). Half of the suburban clusters had a longer median private transport distance than the CBD, and half were shorter.

In Brisbane, median private commute distances were shorter in Chermside, but similar to the city centre for other clusters.

Coming back to our question, only some suburban employment clusters have shorter median private transport commute distances. I expect the slightly shorter distances for those clusters would not cancel out the much higher private transport mode shares, and therefore new suburban cluster jobs would be generating more vehicle kms than new central city jobs.

But perhaps what matters more is the distance travelled by new commuters. New trips from the growing urban fringe to a CBD would be very long in all cities. While ABS haven’t provided detailed journey distance data for 2011, some imperfect analysis of 2011 and 2016 straight line commuter distances between SA2s (sorry not good enough to present in detail) suggests average commuter distances are increasing by 1-2 kms across Sydney and 2-3 kms across Melbourne, and these increases are fairly consistent across the city (including the central city). This may reflect urban sprawl (stronger in Melbourne than Sydney), with new residents on the urban fringe a long way from most jobs.

So did private transport mode shares reduce in suburban employment clusters?

Yes, they did reduce in most clusters, but some saw an increase of up to 2%.

The cluster with the biggest shift away from private transport was Rhodes in Sydney (relatively small and only moderately dense), followed by Perth’s fastest growing hospital cluster of Murdoch.

But perhaps more relevant is how fast each cluster is growing and the mode share of new jobs:

If you want to reduce private transport travel growth, then you don’t want to see many clusters in the top right of this chart (growing fast with high dependency on private transport). Those centres could be experiencing increasing traffic congestion, and may start to hit growth limits unless they get significantly improved public transport access.

Of the cluster in the top-right:

  • Bella Vista – Norwest – Castle Hill will soon have a rapid rail service with Sydney Metro.
  • Murdoch’s high private transport mode share might reflect the fairly long walking distance between the station and hospitals (up to 10 minutes through open space with no tree canopy), but also hospital shift workers who may find private transport more convenient.
  • Clayton might reflect most jobs being remote from the train line (although it is served by three SmartBus routes that have high frequency and some on-road priority). Note: my Monash cluster unfortunately does not include the Monash Medical Centre that is closer to Clayton Station and very job dense. The hospital precinct had its own destination zone in 2016 with 88% private transport mode share, but was washed out in a larger destination zone in 2011 which made it difficult to include in the cluster (for the record, that 2011 destination zone also had an 88% private transport mode share).
  • Joondalup is a large but not particularly dense employment area, and I suspect many jobs are remote from the train/bus interchange, and some local bus frequencies are low.

Can you predict mode shares with a mathematical model?

I have put the data used above into a regression model trying to explain private transport mode shares in the clusters. I found that only weighted job density, walking catchment size, and distance from CBD were significant variables, but this might be for want of a better measure of the quality of public transport accessibility (SNAMUTS Closeness Centrality scores are not available for many centres).

I also tested the percentage of jobs in health care and social assistance (looking for a hospital effect), the surrounding population up to 10km (nearby population density), median travel distances, and the size of clusters, but these did not show up as significant predictors.

Can you summarise all that?

  • Compared to other cities, Sydney has many more clusters and they are larger, more dense, and generally have much lower private transport mode shares.
  • With the exception of Canberra, less than half of all jobs in each city were in either the inner city area or a dense suburban jobs cluster. In Perth it was as low as 32%, while Sydney was 45%, and Canberra 54%.
  • Higher density clusters correlate with lower private transport mode shares.
  • Only higher density clusters centred on train stations with strong connections to the broader train network achieve relatively high public transport mode shares of journeys to work.
  • High quality bus services can boost mode shares in clusters, but the highest bus-only mode share was 15% (in 2016).
  • High-frequency express shuttle bus services can boost public transport mode shares in off-rail clusters.
  • Walk-only mode shares for journeys to work are generally very low (typically 2-5%) but generally higher in clusters where there are many nearby residents.
  • Private transport mode shares are generally 90%+ in clusters with free parking.
  • I suspect there is a relationship between parking prices and private mode share, but it’s hard to get complete data to prove this. Subsidised employer provided parking probably leads to higher private transport mode shares, and may be common at hospitals. However unexpectedly cheap parking in Bondi Junction and Chatswood needs to be explained (perhaps an oversupply, or just horrible traffic congestion?).
  • There is some evidence to suggest hospitals are prone to having higher private transport mode shares, possibly due to significant numbers of shift workers who need to commute at times when public transport service levels are lower.
  • Private transport shares in suburban clusters are much higher than central cities, but lower than elsewhere in cities. The private transport mode share of net new jobs in clusters is much higher than for central city areas, but generally lower than elsewhere in cities.
  • High density clusters still have large amounts of car parking.
  • Median commuter distances to suburban employment clusters are sometimes longer and sometimes shorter than median commuter distances to each cities CBD.
  • The clusters of Joondalup, Clayton, Murdoch, and Bella Vista – Norwest – Castle Hill have grown significantly in size with very high private transport mode shares. These centres might be experiencing increased traffic congestion, and their growth might be limited without significant improvements in public transport access.

What could this mean for Melbourne’s “National Employment and Innovation Clusters”?

One motivation for this research was getting insights into the future of Melbourne’s National Employment and Innovation Clusters (NEICs). What follows is intended to be observations about the research, rather than commentary about the whether any plans should be changed, or certain projects should or should not be built.

Firstly, the “emerging” NEICs of Sunshine and Werribee didn’t meet my (arguably) low criteria for dense employment clusters in 2016 (too small). The same is true for the Dandenong South portion of the “Dandenong” cluster (not dense enough).

Parkville and Fishermans Bend would have qualified had I not excluded areas within 4km of the CBD.

Significant sections of the Parkville, Fishermans Bend, Dandenong, Clayton, and La Trobe NEICs are currently beyond walking distance of Melbourne’s rapid transit network. Of these currently off-rail clusters:

  • Parkville: a new rail link is under construction
  • Fishermans Bend: new light and heavy rail links are proposed. In the short term, paid parking is to be introduced in some parts in 2018 (which had commuter densities of 47-63 per hectare in 2016). The longer term vision is for 80% of transport movements by public or active transport.
  • Clayton: New light and heavy rail links are proposed. The Monash University campus has had paid parking for some time, but there appears to be free parking for employees in the surrounding industrial areas to the north and east. It will be interesting to see if/when paid parking becomes a reality in the industrial area (commuter densities ranged from 48 to 74 jobs/ha in 2016, not dissimilar to Fishermans Bend). The Monash Medical Centre area is relatively close to Clayton train station, has very high commuter density (329 per hectare in 2016 before a new children’s hospital opened in 2017) and had 88% private transport mode share in 2016. No doubt car parking will be an ongoing challenge/issue for this precinct.
  • La Trobe: No rapid transit links are currently proposed to the area around the university, which had an 83% private transport mode share in 2016. There is a currently a frequent express shuttle bus from Reservoir station to the university campus, and a high frequency tram route touches the western edge of the campus.
  • Dandenong South: The area is dominated by industrial rather than office facilities, and the job density ranges from 7 to 33 commuters per hectare, which is relatively low compared to the clusters in my study. There are no commercial car parks listed on Parkopedia so I assume pretty much all employees currently get free parking. No rapid transit stations are proposed for the area. The area is served by a few bus routes, including one high frequency SmartBus route, but 98% of new jobs between 2011 and 2016 were accounted for by private transport trips. This suggests it is difficult even for high-frequency (but non-rapid) public transport to complete with free parking in such areas.

Another potential challenge is connectivity to Melbourne’s broader train network. Parkville (and Fishermans Bend should Melbourne Metro 2 be built) will be well connected to the broader network by the nature of their central location. The area around Sunshine station has excellent rail access from four directions (with a fifth proposed with Melbourne Airport Rail). Dandenong, La Trobe and Werribee are on or near 1 or 2 radial train lines.

You can read more about Melbourne’s employment clusters in this paper by Prof John Stanley, Dr Peter Brain, and Jane Cunningham, which suggests there would be productivity gains from improved public transport access to such clusters.

I hope this post provides some food for thought.


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.