Prioritising Speed

Returning to statistics on transport in Melbourne the VISTA data set captures how people are choosing to travel, and for the most part they prioritise speed. This is entirely logical for the individual but the sum of our choices are shaping the direction of the city and it may not be quite what you expect.

The survey is conducted for the state government, sampling random households each for a single day. But unlike the census data which is captured across the country on a specific date these samples are taken almost evenly across the year and capture significantly more detail. Occupants of the house have all their transport trips recorded and from the start and end points both the time taken and the estimated distance between the points are stored while discarding the identifying details of exactly where the journey was from and to. Although there are allocations to different vehicles used within a trip (such as walking to a train station) only the majority mode is used here to capture the total speed of multimodal trips. All the data from 2012-2016 has been averaged together here and first we can look at the mode share of trips by their distance.

Cars dominate the transport environment throughout the day/week, with public transport stepping up mostly during the commuting peaks when they run more frequent services and express routes. Predictably walking drops off in popularity as distance increases, but even before journeys reach 1km its mode share has dropped below 50%. Even more disturbing:

For journeys in Melbourne of 100m or shorter cars maintain a greater than 10% share of trips.

The distance between origin and destination is recorded as the shortest network distance rather than the particular route taken, which underestimates the actual distance travelled but pedestrian permeability for short trips should be significantly better than for motor vehicles. This is before considering the additional time needed to get in and out of a vehicle, parking manoeuvres, etc which are not necessary when walking. Taking the reported durations for each journey the data can be presented as average speeds by distance travelled. Grouping by distance travelled an average speed was calculated for each mode. A solid line is plotted spanning the middle 90% of the journeys by that type by distance and that is extended with dashed lines to 99%

Once again private motor transport comes out on top as the fastest way to travel, almost twice as fast as public transport. This only slightly changes during peak hours, with public transport journeys becoming 10% quicker and private motor vehicles 10% slower when compared to their off peak or weekend travel. The most obvious difference between peak and off-peak periods are longer journeys by car and train being shifted away from the congested periods.

Both busses and trains follow almost identical trajectories, although this data already includes users choices of the most appropriate transport for each journey it appears busses are performing just as well as trains all the way to longer journeys. At the shorter end of trips trains quickly give way to busses and trams for journeys under 4km. Trams however even with their more extensive segregated network compared to busses are a slower option, slower even than leisurely cycling.

Leaving the shortest and longest 0.5% of journeys by each mode off the graphs still captures extremely short car journeys, but the stereotypical lycra clad cyclist training on the roads and covering 30-100km at a time appears completely absent. Either they are a much smaller minority than previously thought or, more likely, the methodologies of this survey have excluded them from the reporting.

Without any of the overheads of other modes it would be expected that walking would be a flat line. Even with the slowest walkers opting for other transport earlier there could be some gradient but the significant difference in speed between the shortest and longest distances is not fully explained by this or quantisation of the data.

Lastly the data is presented as relative number of trips by distance and mode:

From this 73% of all weekday trips are made predominantly by car and 79% on weekends. Repeated in each plot is a valley where public transport becomes unattractive at shorter distances, but the 4km is still too far for most people to consider walking. 42% of all weekday trips are under this 4km distance where still 67% of those journeys are made by car. If the journeys are limited to only shopping trips then busses appear to fill the gap:


For shopping trips the median distance was 2km compared to the 2.5km median over all trips. Despite shorter distances shopping trips are even more dominated by car use at 80% share against a further reduction in walking mode share and a jump to 20% of distances 100m and below using a motor vehicle. This heads down a spiral of positive feedback where shops increase their parking footprint to follow customer demand, further discouraging access by walking as the vehicles are given priority access to the site and walking distance or challenge increases.

From the reported journey times the public say it is quicker to drive these extremely short distances while the overheads of parking and traffic should make it slower than walking over the same travelled distance, this needs to be reversed. These last mile connections need to be heavily prioritised for pedestrians, as we’re all pedestrians once parked and out of our cars.

2 thoughts on “Prioritising Speed

  1. Fascinating. The second set of graphs illustrate well my experience of arriving at places by bicycle in around the same time it takes for a bus to get there; then when you take into account the waiting time of passengers at bus stops, and perhaps a walk at either end, I know why I prefer to be cycling.


    1. This is not uncommon for cities focused on private motor vehicles as the preferred mode of transport. In cities that prioritise walking and cycling cars get intentionally slowed/detoured so all choices are similarly attractive, not considering the weather!


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