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.


How radial is general travel in Melbourne? (part 2)

Wed 11 September, 2019

In part 1 of this series, I looked at the radialness of general travel around Melbourne based on the VISTA household travel survey. This part 2 digs deeper into radialness by time of the day and week, and maps radialness and mode share for general travel around Melbourne.

A brief recap on measuring radialness: I’ve been measuring the difference in angle between the bearing of a trip, and a straight line to the CBD from the trip endpoint that is furthest from the CBD (origin or destination). An angle of 0° means the trip is perfectly radial (directly towards or away from the CBD) while 90° means the trip is entirely orbital relative to the CBD. An average angle in the low 40s means that there isn’t really any bias towards radial travel. I’ve been calling this two-way off-radial angle. Refer to part 1 if you need more of a refresher.

How does trip radialness vary by time of week?

The first chart shows the average two-way off-radial angle for trips within Greater Melbourne by time and type of day, for private transport, public transport, and walking.

Technical notes: I’ve had to aggregate weekend data into two hour blocks to avoid issues with small sample sizes. I’m only showing data where there are at least 100 trips for a mode and time (that’s still not a huge sample size so there is some “noise”). Trips times are assigned by the clock hour of the middle of the trip duration. For example, a trip starting at 7:50 am and finishing at 9:30 am has a mid-trip time of 8:40 am and therefore is counted in 8 – 9 am for one hour intervals, and 8 – 10 am for two hour intervals.

You can see:

  • Public transport trips are much more radial at all times of the week, but most particularly in the early AM peak and in the PM commuter peak. They are least radial in the period 3-4 pm on weekdays (PM school peak), which no doubt reflects school student travel, which is generally less radial.
  • Private transport trips are more radial before 8 am on weekdays, and in the early morning and late evening on weekends. Curiously private transport trips in the PM peak don’t show up as particularly radial, possibly because there is more of a mix of commuter and other trips at that time.
  • Walking trips show very little radial bias, except perhaps in the commuter peak times on weekdays.

When I drill down into specific modes, the sample sizes get smaller, so I have used 2 hour intervals on weekdays, and 3 hour intervals on weekends. Also to note is that VISTA assigns a “link mode” to each trip, being the most important mode used in the journey (generally train is highest, followed by tram, bus, vehicle driver, vehicle passenger, bicycle, walking only). I am using this “link mode” in the following charts.

Some observations:

  • Train trips are the most radial, followed by tram trips (no surprise as these networks are highly radial).
  • Bicycle trips are generally the third most radial mode, except at school times.
  • Public bus trips are more radial in the commuter peak periods, and much less radial in the middle of the day on weekdays. The greater radialness in commuter peaks will likely reflect people using buses in non-rail corridors to travel to the city centre (particularly along the Eastern Freeway corridor). Most of Melbourne’s bus routes run across suburbs, rather than towards the city centre, which will likely explain bus-only trips being less radial than train and tram, particularly off-peak.

How does radialness vary by trip purpose and time of week?

The following chart shows the average two-way off-radial angle of trips by trip purpose (at destination) and time of day:

Some observations:

  • Work related trips are generally the most radial, particularly in the AM peak (as you might expect), but less so on weekdays afternoons.
  • Weekday education trips are the next most radial (excluding trips to go home in the afternoon and evening), except at school times (school travel being less radially biased than tertiary education travel).
  • Social trips become much more radial late at night on weekends, probably reflecting inner city destinations.
  • Recreational trips are the least radial on weekends.
  • Otherwise most other trip purposes average around 35-40° – which is only slightly weighted towards radial travel.

What is the distribution of off-radial angles by time of day?

So far my analysis has been looking at radialness, without regard to whether trips are towards or away from the CBD. I’ve also used average off-radial angles which hides the underlying distribution of trip radialness.

I’m curious as to whether modes are dominated by inbound or outbound trips at any times of the week (particularly private transport), and the distribution of trips across various off-radial angles.

So to add the inbound/outbound component of radialness, I am going to use a slightly different measure, which I call the “one-way off-radial angle”. For this I am using a scale of 0° to 180°, with 0° being directly towards the CBD, and 180° being directly away from the CBD, and 90° being a perfectly orbital trip with regard to the CBD. For inbound trips, the one-way off-radial angle will be the same as the two-way off-radial angle, but outbound trips will instead fall in the 90° to 180° range.

One-way off-radial angles are still calculated relative to the trip end point (origin or destination) that is furthest from the CBD. I explained this in part 1.

Here is the distribution of one-way off-radial angles by time of day for trips where train was the main mode:

A reminder: only time intervals with a sample of at least 100 trips are shown.

In the morning, trips are very much inbound radial, with around three-quarters being angles of 0°-10°. Likewise in the PM peak, almost three-quarters of train trips are very outbound radial with angles 170°-180°.

As per the second chart in this post, train trips remain very radial throughout the day. But there is slightly more diversity in off-radial angles 3-4 pm on weekdays, when many school students use trains for journeys home from school that are less radially biased. Less radial trips could be a result of using two train lines, using bus in combination with train, or using a short section of the train network that isn’t as radial (eg Eltham to Greensborough, Williamstown to Newport, or a section of the Alamein line).

On weekends it’s interesting to see that there are many more inbound than outbound journeys between 12 pm and 2 pm on weekends. The “flip time” when outbound journeys outnumber inbound journeys is probably around 2 pm. This is consistent with CBD pedestrian counters that show peak activity in the early afternoon.

One problem with the chart above is that volumes of train travel vary considerably across the day. So here’s the same data, but as (estimated) average daily trips:

You can see the intense peak periods on weekdays, and a gradual switch from inbound trips to outbound trips around 1 pm on weekdays. There’s also a mini-peak in the “contra-peak” directions (outbound trips in the AM peak and inbound trips in the PM peak).

The weekend volumes are for two hour intervals so not directly comparable to weekdays (which are calculated for one hour intervals), but you can see higher volumes of inbound trips until around 2 pm, and then outbound trip volumes are higher.

Those results for trains were probably not surprising, but what about private vehicle driver trips?

There is much more diversity in off-radial angles at all times of the day, and a less severe change between inbound and outbound trips across the day.

On both weekday and weekend mornings there is a definite bias towards inbound travel. Afternoons and evenings are biased towards outbound travel, but not nearly as much (it’s much stronger late at night). This is consistent with the higher average two-way off-radial angle seen for private transport in the PM peak compared to the AM peak.

Here is the same data again but in volumes:

This shows the weekday AM peak spread concentrated between 8 and 9 am, while the PM peak is more spread over three hours (beginning with the end of school).

Here are the same two charts for tram trips (the survey sample is smaller, so we can only see results for weekdays):

Again there is a strong bias to inbound trips in the morning and outbound in the afternoon, with slightly more diversity in the PM school peak, and early evening.

Next up public bus (a separate category to school buses, however many school students do travel on public buses):

There is a lot more diversity in off-radial angles (particularly 2-4 pm covering the end of school), but also the same trend of more inbound trips in the morning and outbound trips in the afternoon.

Next up, bicycle:

There’s a fair amount of diversity, across the day, with inbound trips dominating the AM peak and outbound trips in the PM commuter peak (but not as strongly in the PM school peak). Weekend late afternoon trips show a little more diversity than morning and early afternoon trips, but the volumes are relatively small.

Next is walking trips:

There is considerable diversity in off-radial angles across most of the week, although outbound trips have a larger share in the late evening.

Walking volumes on weekdays peak at school times. On weekends walking seems to peak between 10 am and 12 pm and again 4 pm to 6 pm, but not considerably compared to the rest of the day time.

Mapping mode shares and radialness

So far I’ve been looking at radialness for modes by time of day. This section next section looks at radialness and mode shares by origins and destinations within Melbourne.

In recent posts I’ve had fun mapping journeys to work from census data (see: Mapping Melbourne’s journeys to work), so I’ve been keep to explore what’s possible for general travel.

VISTA is only a survey of travel (rather than a census), so if you want to map mode shares of trips around the city, you unfortunately need to lose a lot of geographic resolution to get reasonable sample sizes.

The following map shows private transport mode shares for journeys between SA3s (which are roughly the size of municipalities), where there were at least 80 surveyed trips (yes, that is a small sample size so confidence intervals are wider, but I’m also showing mode shares in 10% ranges). Dots indicate trips within an SA3, and lines indicate trips between SA3s. I’ve animated the map to make try to make it slightly easier to call out the high and low private mode shares.

You can see lower private transport mode shares for radial travel involving the central city (Melbourne City SA3), particularly from inner and middle suburbs (less so from outer suburbs). Radial travel that doesn’t go to the city centre generally has high private transport mode shares.

I also have origin and destination SA1s for surveyed trips. Here is a map showing all SA1-SA1 survey trip combinations by main mode, animated to show intervals of two-way off-radial angles:

It’s certainly not a perfect representation because of the all the overlapping lines (I have used a high degree of transparency). You can generally see more blue lines (public transport) in the highly radial angles, and almost entirely red (private transport) and short green lines (active transport) for larger angle ranges. This is consistent with charts in my last post (see: How radial is general travel in Melbourne? (Part 1)).

You can also see that few trips fall into the 80-90° interval, which is because I’m measuring radialness relative to the trip endpoint furthest from the CBD. An angle of 80-90° requires the origin and destination to be about the same distance from the CBD and for the trip to be relatively short.

So there you go, almost certainly more than you ever wanted or needed to know about the radialness of travel in Melbourne. I suspect many of the patterns would also be found in other cities, although some aspects – such the as the geography of Port Phillip Bay – will be unique to Melbourne.

Again, I want to the thank the Department of Transport for sharing the full VISTA data set with me to enable this analysis.


How radial is general travel in Melbourne? (Part 1)

Mon 22 July, 2019

In a recent post I found that journeys to work are generally quite radial relative to city CBDs. But what about travel for other purposes, travel on different days of the week, and travel to different types of places?

This post explores the radialness of general travel around Melbourne using data from Melbourne’s household travel survey (VISTA – the Victorian Integrated Survey of Travel and Activity), which captures all types of personal travel by residents.

In this post (part 1), I will look at measuring radialness, radialness of weekday and weekend trips, radialness of total distance travelled, and how radialness varies by mode, distance from the CBD, different places, ages and sex.

Part 2 of this analysis will look at radialness at different times of day and also visualising radialness on maps.

Measuring radialness for general travel

Unlike the census of population and housing that only captures journeys from home to work, VISTA measures trips in all directions, including to and from survey home locations, so I need a slightly different measure to my previous post (see: How radial are journeys to work in Australian cities?).

When considering the radialness of a trip, I want to compare the difference between the trip’s alignment and a trip that would head directly towards (or away from) the CBD. I am calling the difference between these alignments the off-radial angle.

So, in the following simple example, the off-radial angle is the difference between the bearing from the origin to destination and the bearing from the origin to the CBD.

The trip is quite radial and the off-radial angle is small. A perfectly radial trip would have an off-radial angle of 0°, while a perfectly orbital trips would have an off-radial angle of 90°.

Unfortunately it’s not always that simple. Let’s consider an trip that is the exact opposite of the example above. If I don’t care about whether the trip is towards or away from the CBD, then I want to get the same radialness measure as the first example because it is equally as radial.

To get the same measure as the first example, I need to measure the off-radial angle with respect to the destination rather the origin. If I measure the off-radial angle for the second trip with respect to its origin then I’d get an angle of around 100°, suggesting a very non-radial trip, which isn’t really the case.

So for this post I am always going to measure the off-radial with respect to the trip end that is furthest from the CBD, whether than be the origin or destination. When I don’t care whether the trip is inbound or outbound, the off-radial angle will always be in the range 0° to 90°.

Just in case you need more convincing, consider the following trip:

The origin is so close to the CBD that this is a very radial trip so I want a small angle, but the off-radial angle with respect to the origin is almost 90°.

If you think about a trip originating in the CBD, the off-radial angle with respect to the origin has everything to do with the location within the CBD compared to the GPO (the actual point I am measuring against), when really a trip from anywhere in the CBD to the suburbs is very radial.

This approach does introduce a slight bias towards smaller off-radial angles. Using the trip end that is furthest from the CBD means that even if you had a completely random distribution of trip origins and destinations, there would be more trips with smaller off-radial angles and fewer trips with angles near 90°. In fact, to get close to a 90° angle, the origin and destination would have to be almost exactly the same distance from the CBD, and the trip be not be very long – an unlikely scenario. So for a truly random distribution of trip directions the off-radial angles will be slightly biased towards smaller angles and the average would be less than 45°. I don’t expect this bias would be large, and we will get a feel for this bias shortly.

Another slight complication is that a short trip within the CBD will have a fairly arbitrary off-radial angle which isn’t very meaningful or relevant. So I am not going to bother considering trips that start and finish within (an arbitrary) 1.5 km of the GPO.

I should point out that a “trip” in VISTA is considered a journey between two places of activity for a person. It may have multiple stops along the way for the purposes of changing mode (e.g. bus to train), but for this post I’m looking at the geometry of the end-to-end trip.

Finally, to differentiate this radialness measure from a slightly different radialness measure I will introduce in part 2 of my analysis, I’m calling it the “two-way off-radial angle” (two-way because I don’t care whether the trip is inbound or outbound with respect to the CBD).

How radial is travel on weekdays and weekends?

This first chart looks at the distribution of trips by mode and two-way off-radial angle interval, with a histogram for each day type:

Technical note: As per usual, I’ve classified any journey involving public transport as “Public”, any journey not involving motorised transport as “Active”, and any journey involving a motorised road-based vehicle as “Private”. With VISTA a quite small number of trips are classified as “other”, which I have excluded.

You can see that on all day types, the most common two-way off-radial angle group is 0°-10° – which are very radial trips. The most radial day type is a school holiday weekday, where there are still many work trips to the central, but a lot fewer non-radial trips to schools.

You can also see that active transport trips are fairly well distributed across the angle intervals (with only a slightly bias towards radial trips), while public transport trips are highly radial on all day types.

Public transport mode share is only really significant for highly radial trips. This probably reflects most (but not all) high quality public transport lines being highly radial and running to the central city where car parking costs are generally much higher.

What surprised me a little is that weekend trips are only slightly less radial than weekdays, even for private transport trips (good quality roads exist in multiple travel directions in most of Melbourne).

What is different between weekday and weekend travel?

Here is a VISTA destination density map of travel around Melbourne by private transport (excluding trips to places of accommodation – such as homes) animated to alternate between school weekdays and weekends. The red areas have the highest concentration of trip destinations.

The central city area dominates weekday destination density, but major suburban shopping centres are also significant destinations on weekends (as you might expect).

Here is a chart showing the distribution of destinations by distance from the CBD by day type:

You can see that trips on non-school weekdays are more likely to be to destinations closer to the CBD (indeed around 19% were to destinations within 2 km of the GPO). This reduces for school weekdays, and further for Saturdays and Sundays.

But as we saw in the first chart, there are still a large number of very radial trips on weekends, so where are these trips going? Are they still mostly going to the CBD, even though fewer people are travelling to the CBD when compared to weekdays?

The following chart is similar to the last, but is filtered for very radial trips – being those with off-radial angles 0° to 10°:

Destinations within 0-2 km of the CBD are the largest category for very radial trips, but actually a minority of (non-home) destinations, even on weekdays. On weekends around two-thirds of very radial trips have a (non-home) destination more than 2 km from the CBD. There are lots of very radial trips on weekdays and weekends, but most of them are to destinations more than 2 km from the CBD.

So are these very radial trips shorter on weekends? Here’s a chart showing the median and average distance of trips by two-way off-radial angle:

Technical note regarding weighted v unweighted: VISTA trips are weighted so that they can be summed to represent travel by the total residential population – with survey home types that are under-represented given a larger weighting. In all my averages I’ve used this weighting, but unfortunately at the time of writing Tableau cannot calculate weighted medians (or percentiles), so I’ve had to calculate unweighted medians instead (I’ve manually checked the weighted medians for 0-10° and they calculate as 9.98 km on weekdays and 7.99 km on weekends – fairly close to the unweighted medians).

For very radial trips (0°-10°), weekend trips are shorter by both measures, but for the next interval (10°-20°) weekend trips are curiously longer. There’s not a huge difference with subsequent angle intervals and I don’t think we should get too excited about them because there will be some noise in the sample.

How radial are trips by distance from the city centre?

If we want to measure radialness against multiple variables, then more histogram charts aren’t going to be practical. So instead I’m going to calculate a single summary statistic: the average two-way off-radial angle.

If the average is small, then trips are very radial, whereas an average near 40-45° would suggest no radial bias.

Here’s a look at radialness by origin distances from the CBD, main mode, and weekdays v weekends:

(in case you are wondering, this chart looks very similar if the X-axis is destination distance from the CBD)

Trips starting closer to the CBD are much more radial for all modes (although much less so for walking), suggesting the CBD is dominant for travel from the inner city.

Public transport trips are the most radial compared to other modes (as we also saw earlier). They are followed by bicycle trips, which probably reflects relatively better cycling infrastructure in the central city, and the fact bicycles can be ridden and parked in the central city for free (unlike public and private transport).

Walking trips are the least radial on average, with almost no radial-bias showing at all. Earlier in this post I pointed out that two-way off-radial angles measured will be slightly biased towards smaller angles, even if trips were truly randomly orientated. I think there’s a good chance that walking trips in the middle and outer suburbs have no radial bias, and the fact they mostly have an average angle of between 40° and 45° suggests the inherent measurement bias to smaller angles is not particularly strong.

In the chart you can also see that within each main mode there is little difference between weekday and weekend trips (for walking trips the smaller weekend sample size is likely introducing some noise).

Furthermore, average two-way off-radial angles are mostly flat for each mode between beyond 10 km from the CBD, with two exceptions:

  • The weekend public transport average two-way off-radial angle for 40-45 km from the CBD was 23°, influenced by many local non-work trips (and there is insufficient sample size for public transport trips commencing further out).
  • Trips in outer Melbourne (55+ km) are actually slightly biased towards non-radial travel – i.e. average two-way off-radial angle higher than 45°. It turns out most of them are on the Mornington Peninsula where trip bearings are heavily influenced by the shape of the peninsula.

To visualise this, here is a map showing the average two-way off-radial angle for trip origin SA2s across Greater Melbourne:

Technical note: the map is drawn using SA1 areas, and only SA1s with a trip origin are included, which explains the many small gaps.

Point Nepean (at the bottom of the map) has an average off-radial angle of 52°, which reflects the thin non-radial geography of the end of the peninsula.

Some relative outliers of interest include:

  • Melbourne Airport has a relatively low average angle of 23°, probably reflecting higher travel volumes to the CBD and wealthy inner city and south-east suburbs where regular air travellers might be more likely to live. However this would be offset by airport workers who tend to live nearby, although those in Sunbury are actually making fairly radial trips. Note that non-resident airport users are not included in VISTA, and I’ve filtered out flights (because almost all of them don’t stay within Greater Melbourne).
  • Wandin – Seville in the outer east has an average off-radial angle of 18°, probably reflecting that many of the urban settlements in this area are along the east-west (radial) Warburton Highway.
  • Melton and Melton South have a high average angle of 42° – probably reflecting many local trips within the township that would have no radial bias.
  • Mount Dandenong – Olinda has a higher average angle of 42° also – probably reflecting some north-south geographic barriers in the area (ie steeper mountain slopes on the western edge).
  • Several bay-side SA2s have higher radialness, probably reflecting the coast line being fairly radially aligned with the CBD, and the coast being a natural barrier to non-radial travel.

How does radialness vary by trip purpose?

Firstly, here’s a chart showing average two-way off-radial angles by (destination) trip purpose for weekdays and weekends:

  • Work related trips are generally the most radial, followed by social and then education trips (except weekend education trips, of which there are few).
  • The least radial trip type is recreational trips on weekends.
  • Curiously, weekend trips to accompany, pick up or drop off someone were more radial than weekdays, perhaps because more people are not working and able to do this for others, and/or weekend public transport service levels are lower.

VISTA designates a “link mode” to each trip – being generally the highest ranked mode in the journey (trains being highest ranked, followed by trams, buses, vehicle driver, vehicle passenger, bicycle, walking). Actual trips may involve multiple modes, and the off-radial angle is measured for the end-to-end trip, not the part of the trip that used the “link mode”.

The following table shows average two-way off-radial angles for combinations of link mode and trip purpose:

Technical note: gaps in the table are where there were insufficient trips sampled of that trip purpose and mode combination (less than 100).

The table shows that:

  • Train trips are the most radial, followed by trams, taxis, motorcycles, buses, bicycles, private vehicle travel and finally walking.
  • Education trips by train and tram are less radial than other trip purposes on trains and trams, I suspect because many will be to schools and universities not in the central city.

How does radialness vary by different destination types?

Here is a chart of average two-way off-radial angles for trips to most VISTA destination place types (those with a sample of at least 100 trips):

Some observations:

  • Trips to tourist places were the most radial, with most of those destinations in the central city.
  • Markets attract quite radial trips, probably because many popular markets are in the inner city (eg Queen Victoria, South Melbourne, Prahran, Footscray).
  • Trips to the bay or beach are the least radial – which makes sense given much of the coastlines are radial in orientation.
  • Within education places, primary schools have the least radialness, and tertiary institutions the most, with secondary schools in between.

I’ll let you make your own further observations.

How does radialness vary by age and sex?

I expect the patterns are related to the most common occupations and trip purposes for males and females at different ages. For example, 20-24 year-olds might be more likely to be studying at university or working (both resulting in more radial trips), while children are more likely to travel to school which is not generally particularly radial. Females over 30 are less likely to be in the workforce so more likely to make local trips that are not radially biased.

How radial is total distance travelled?

Not all trips are the same length or duration. Longer trips will have more impact on the transport network. So what does radialness look like in terms of total distance travelled?

Here’s a similar chart to the first chart, but the Y-axis is the proportion of distance travelled on that day type, rather than proportion of trips:

A much larger proportion of distance travelled is accounted for by very radial trips (angles 0°-10°) compared to the proportion of trips. In fact it is around 42% on school weekdays, 35% on Saturdays and 37% on Sundays. On this measure weekend travel is again less radial than weekdays, but still heavily biased towards radial travel.

Why is travel so radial in Melbourne?

This analysis has found that travel in Melbourne is biased towards radial trips, even on weekends. I think a few things can explain the more radial nature of general travel, including:

  • The central and inner city is a major destination for many trips, including those to work, universities, entertainment, and medical facilities. Also, many specialised activities and services are only available in the central and inner city. Trips to these destinations will inherently be more radial.
  • Other large population-serving destinations such as hospitals, large shopping centres, and universities are more likely to be located in the inner and middle suburbs, resulting in fairly radial travel to them from the outer suburbs.
  • Melbourne’s urban form (like many cities) has several outer radial corridors, often orientated around train lines. Local travel within these corridors is going to be more radial on average because there are fewer non-radial trip destinations with the urbanised area.
  • The coastlines of Port Phillip Bay are largely radial in orientation relative to the CBD. In areas near the coastline, it is possible to make radial trips but non-radial trips are restricted by the coastline (as we saw above).

Part 2 of this analysis will focus on radialness of trips at different times of day for different travel purposes and modes, as well as visualising radialness on maps.

Finally, I want to thank the Victorian Department of Transport for sharing the VISTA data set with me and allowing me to publish this analysis.


Mapping Melbourne’s journeys to work

Mon 24 June, 2019

The unwritten rules of mapping data include avoiding too much data and clutter, and not using too much colour. This blog often violates those rules, and when it comes to visualising journeys to work, I think we can learn a lot about cities with somewhat cluttered colourful animated maps.

This post maps journeys to work in Melbourne, using data from the 2016 census. I will look at which types of home-work pairs have different public, private and active transport mode shares and volumes.

Although this post will focus on Melbourne, I will include a brief comparison to Sydney at the end.

Where are public transport journeys to work in Melbourne?

First I need to explain the maps you are about to see.

So that I can show mode shares, I’ve grouped journeys between SA2s (which are roughly the size of a suburb). Lines are drawn from the population centroid of the home SA2 (thin end) to the employment centroid of the work SA2 (thicker end). Centroids are calculated as the weighted average location residents/jobs in each SA2 (using mesh block / destination zone data). This generally works okay for urban areas, but be aware that actual trips will be distributed across SA2s, and some SA2s on the urban fringe are quite large.

The thickness of each line at the work end is roughly proportional to the number of journeys by the mode of interest between the home-work pair (refer legends), but it’s difficult to use a scale that is meaningful for smaller volumes. Unfortunately there’s only so much you can do on a 2-D chart.

I’ve not drawn lines where there are fewer than 50 journeys in total (all modes), or where there were no journeys of the mode that is the subject of the map. This threshold of 50 isn’t perfect either as SA2s are not consistently sized within and between cities, so larger SA2s are more likely to generate lines on the map.

To try to help deal with the clutter, I’ve made the lines somewhat transparent, and also animated the map to highlight trips with different mode share intervals. For frames showing all lines, the lines with highest mode share are drawn on top.

So here is an animated map showing public transport journeys to work in Melbourne, by different mode share ranges and overall:

Technical note: I have included journeys to work that are internal to an SA2. Usually these appear as simple circles, but sometimes they appear as small teardrops where the population and employment centroids are sufficiently far apart.

You can see that the highest PT shares and largest PT volumes are for journeys to the central city, and generally from SA2s connected to Melbourne CBD by train (including many outer suburbs).

As the animation moves to highlight lower PT mode share ranges, the lines become a little less radial, a little shorter on average, and the lowest (non-zero) PT mode shares are mostly for suburban trips.

A notable exception is journeys to Port Melbourne Industrial SA2 (also known as Fishermans Bend), which is located at the junction of two major motorways and is remote from rapid public transport (it does however have a couple of high frequency bus lines from the CBD).

The lowest PT mode shares are seen for trips around the outer suburbs. The maps above unfortunately aren’t very good at differentiating small volumes. The following animated map shows public transport journeys with a filter progressively applied to remove lines with small numbers of public transport journeys (refer blue text in title):

You can see that most of the outer suburban lines quickly disappear as they have very small volumes. Inter-suburban lines with more than 50 public transport journeys go to centres including Dandenong, Clayton, Box Hill, and Heidelberg.

Here’s another animation that builds up the map starting with low public transport mode share lines, and then progressively adds lines with higher public transport mode shares:

As an aside, here is a chart showing journeys to work by straight line distance (between SA2 centroids), public transport mode share, work distance from the CBD and home-work volume:

The black dots represent journeys to the inner 5km of the city, and you can see public transport has a high mode share of longer trips. Public transport mode share falls away for shorter journeys to the inner city as people are more likely to use active transport. A dot on the top left of the curve is 8,874 journeys from Docklands to Melbourne – which benefits from the free tram zone and the distances can be 1-2 km. Most of the longer journeys with low public transport mode share are to workplaces remote from the CBD (coloured dots).

Another way to deal with the clutter of overlapping lines around the CBD is to progressively remove lines to workplaces in and around the CBD. Here is another animated map that does exactly so that you can better see journeys in the nearby inner and middle suburbs.

As you strip away the CBD and inner suburbs, you lose most lines with high public transport mode shares and volumes. However some high public transport mode share lines remain, including the following outbound journeys:

  • Melbourne (CBD) to Melbourne Airport: 72% of 64 journeys
  • Melbourne (CBD) to Box Hill: 66% of 76 journeys
  • Melbourne (CBD) to Clayton: 57% of 82 journeys
  • South Yarra – East to Clayton: 57% of 173 journeys

Just keep in mind that these are all very small volumes compared to total journeys in Melbourne.

You might have noticed on the western edge of the map some yellow and orange lines from the Wyndham area (south-west Melbourne) that go off the map towards the south west. These journeys go to Geelong.

Here’s a map showing journeys around Geelong and between Geelong and Greater Melbourne (journeys entirely within Greater Melbourne excluded):

You can see very high public transport mode shares for journeys from the Geelong and Bellarine region to the Melbourne CBD and Docklands (and fairly large volumes), but no lines to Southbank, East Melbourne, Parkville or Carlton – all more remote from Southern Cross Station, the city terminus for regional trains.

(The other purple lines to the CBD are from Ballarat, Bacchus Marsh, Daylesford, Woodend, Kyneton, Castlemaine, Kilmore-Broadford and Warragul, with at least 60 journeys each.)

You can also see those orange and yellow lines from the Wyndham area to central Geelong, being mode shares of 20-40%. The Geelong train line provides frequent services between Tarneit, Wyndham Vale, and Geelong, and has proved reasonably popular with commuters to Geelong (frequency was significantly upgraded in June 2015 with the opening of the Regional Rail Link, just 14 months before the census of August 2016).

However, public transport mode shares for travel within Greater Geelong are very small – even for SA2 that are connected by trains. This might reflect Geelong train station being on the edge of its CBD, relatively cheap parking in central Geelong, limited stopping frequency at some stations (many at 40 minute base pattern), and/or limited walk-up population catchments at several of Geelong’s suburban train stations.

Does public transport have significant mode share for cross-suburban journeys to work?

To search for cross-suburban journeys with relatively high public transport mode shares, here is a map that only shows lines with public transport mode shares above 20% between homes and workplaces both more than 5 km from the CBD (yes, those are arbitrary thresholds):

Of these journeys, the highest mode shares are for journeys from the inner northern suburbs to St Kilda and Hawthorn. There’s also a 49% mode share from Footscray to Maribyrnong (connected by frequent trams and buses).

The tear drop to the north of the city is 114 people who used PT from Coburg to Brunswick (connected by two tram routes and one train route).

Most of the other links on this map are fairly well aligned with train, tram, or SmartBus routes, suggesting high quality services are required to attract significant mode shares.

But these trips are a tiny fraction of journeys to work around Melbourne. In fact 3.0% of journeys to work in Melbourne were by public transport to workplaces more than 5 km from the CBD. The same statistic for Sydney is more than double this, at 7.3%.

What about private transport journeys?

Firstly, here’s a map showing private transport mode shares and volumes, building up the map starting with low private mode share lines.

The links with lowest private transport mode shares are very radial as you might expect (pretty much the inverse of the public transport maps). Progressively less radial lines get added to the map before there is a big bang when the final private transport mode share band of 95-100% gets added, with large volumes of outer suburban trips.

For completeness, here’s an animation that highlights each mode share range individually.

There are some other interesting stories in this data. The following map shows private transport mode share of journeys to work, excluding workplaces up to 10 km from the CBD to remove some clutter.

If you look carefully you’ll see that there is a much lower density of trips that cross the Yarra River (which runs just south of Heidelberg and Eltham). There are limited bridge crossings, and this is probably inhibiting people considering such journeys.

The construction of the North East Link motorway will add considerable cross-Yarra road capacity, and I suspect it may induce more private transport journeys to work across the Yarra River (although tolls will be a disincentive).

What about active transport journeys?

Next is a map for active transport journeys, but this time I’ve progressively added a filter for the number of active transport journeys, as most of the lines on the full chart are for very small volumes.

As soon as the filter reaches a minimum of 50 active journeys most of the lines between SA2s in the middle and outer suburbs disappear. Note that journeys between SA2s are not necessarily long, they might just be a short trip over the boundary.

Then at minimum 200 journeys you can only see central city journeys plus intra-SA2 journeys in relatively dense centres such as Hawthorn, Heidelberg, Box Hill, Clayton, Frankston, Mornington, Footscray, and St Kilda. The large volume in the south of the map that hangs around is Hastings – Somers, where 882 used active transport (probably mostly walking to work on the HMAS Cerebus navy base).

Active transport journeys are mostly much shorter than private and public transport journeys – as you might expect as most people will only walk or ride a bicycle so far. But there are people who said they made very long active transport journeys to work – the map shows some journeys from Point Nepean, Torquay, Ballarat, Daylesford, and Castlemaine to Melbourne. That’s some keen cyclists, incredible runners, people who changed jobs in the week of the census (the census asks for work location the prior week, and modes used on census day), and/or people who didn’t fill in their census forms accurately. The volumes of these trips are very small (mostly less than 5).

That map is very congested around the central city, so here is a map zoomed into the inner suburbs and this time animated by building up the map starting with high active transport mode share lines.

The highest active transport mode shares are for travel within Southbank and from Carlton to Parkville, followed by journeys to places like the CBD, Docklands, South Yarra, South Melbourne, Carlton, Fitzroy, Parkville, and Carlton.

Then you see a lot of trips added from the inner northern suburbs, which are connected to the central city by dare-I-say “above average” cycling infrastructure across some relatively flat terrain. In particular, a thick red line on the map is for 471 active transport journeys from Brunswick to Melbourne (CBD) with a mode share of 17%. A second thick red line is Richmond to Melbourne (CBD) being 589 journeys with 16% active mode share.

Another way of summarising mode shares by work and home distance from the CBD

I’ve experimented with another visualisation approach to overcome the clutter issues. The next charts have home distances from the CBD on the Y axis, work distances from the CBD on the X axis, bubble size representing number of journeys, and colour showing mode shares. I’m drawing smaller journey volumes on top, and I’ve used some transparency to help a little with the clutter.

Firstly here is public transport (animated to show each mode share range individually):

The chart is roughly a V-shape with many trips on the left edge and along a diagonal (mostly representing intra-SA2 journeys), then with several vertical stripes being major suburban employment destinations (including Dandenong at 31 km, Clayton at 19 km, and Frankston at 40 km). Trips above the diagonal are roughly inbound, while trips below the diagonal are roughly outbound.

Some observations:

  • The diagonal line (mostly local journeys) has very low public transport mode shares (sometimes zero).
  • Higher PT mode shares are only seen on the far left and bottom left hand corner of the chart. Some outliers include Richmond to Box Hill (32%), Clayton to Malvern East (32%), and South Yarra – East to Clayton (57%).
  • PT mode shares of 80+% are only seen for journeys to the CBD from home SA2s at least 11 km out (with one exception of Melbourne CBD to St Kilda with 80% PT share).
  • Home-work pairs with zero public transport journeys are scattered around the middle and outer suburbs, most being longer distance journeys (home and work at different distances from the CBD).

Here’s the same chart for private transport:

The lowest private transport shares are seen for journeys to the CBD. The diagonal has many mode shares in the 80-90% range.

And here is active transport:

The highest active transport mode shares are seen in the central city area, followed by the diagonal mostly representing local journeys (with generally higher shares closer to the CBD). Some notable outliers include local trips within Clayton (1,298 active trips / 46% active mode share), Box Hill (914 / 40%), Hastings – Somers (1,762 / 27%), Flinders (240 / 24%), Glen Waverley – West (308 / 21%), and Mentone (226 / 23%).

How does Sydney compare to Melbourne?

Here is a chart with private transport mode share maps for both Melbourne and Sydney, animated in tandem to progressively add higher mode share journeys.

You can see that Sydney has a lot more trips at lower private transport mode shares, and that workplaces outside the city centre start to show up earlier in the animation in Sydney – being the dense transit-orientated suburban employment clusters that are largely unique to Sydney (see: Suburban employment clusters and the journey to work in Australian cities).

If time permits, I may do similar analysis for Sydney and other cities in future posts.