The user time element in public transport service planning (Pt 1)

There’s been heavy discussion about the financial user cost of public transport, following the announcement of an 8.6 per cent fare rise from next month. This post is about another user cost; the longer time it sometimes takes relative to driving.

Amongst those with a choice, public transport attracts its highest share where it is time competitive with driving, notably work trips to the city. Where it’s uncompetitive passengers tend to be those with more time than money, typically due to low incomes and/or an inability to pay. This is the familiar skewed pattern of old and young in the off-peaks and city commuters during the peaks.

This is a long post, so is split into two parts. Part 1 discusses user financial costs, funding sources, trip time and trip time variability. Part 2 uses examples to illustrate the large gains possible from service and other improvements.

Two dimensions: time and money

One insight that comes from counting time as a cost is that it invites comparison with that other type of cost most commonly associated with public transport – fares. Fares are known and fixed whereas time value is less tangible but no less important.

Although lacking hard numbers, we can guess time value’s magnitude or draw inferences from surveys and modal share statistics. We know it varies with trip length, location and timing. Acceptance of slow trips and high costs varies but depends on the value placed on time and money respectively. Some rough plots of user time versus financial costs for various transport modes in Melbourne are on the graph below.

The ‘best’ is cheap and fast (bottom left) while the worst is dear and slow (top right). The other corners are occupied by fast expensive (top left) and slow cheap (bottom right). Overall user costs for each comprises dollar plus time costs, with this increasing from bottom left to top right.

Consensus that a particular mode represents good value is highest for those near the bottom left. Whereas top right is poor value and only worthwhile for those willing to bear the high overall cost. If they are not, they’ll change homes, jobs or schools to avoid it. The action is crucial; mere grumbling implies acceptance since bad transport may still be better than other choices available.

I take the view that most people are transport pragmatists, ie whichever available mode suits their time / cost value profile for a particular trip will be used. Most of the time the mode chosen (driving) is faster but dearer than public transport. This invites the question as to whether the overall user value of public transport would increase if it were faster, even if somewhat dearer.

Public transport’s place

Public transport’s detractors treat slowness as inherent but this is not necessarily so. Where transit is slow, this is due to decisions made on station spacing, route and timetable planning, street design, signal priority and allocation of road space between modes.

Skybus (also on the graph) is an example of fast public transport. Its fares are high relative to government-subsidised routes. But because it’s both fast and frequent its time costs are low. Add the two and you see that Skybus occupies a distinctive (and successful) position in the market, set apart from regular buses (with low fares but high time costs).

Is regular public transport’s position on the graph optimum?

The answer depends on what you want it to achieve.

What would happen if you changed either its user financial or time costs?

Making an already low-priced but infrequent bus service cheaper would not greatly reduce the overall user cost to the ‘average person’. This is because unless they value their time lowly, the time cost component probably outweighs the fare cost and slashing the latter won’t cut the total much.

However this conclusion ignores certain market segments. Fare cuts are more significant for those who value money more than time, ie the ‘captive passsenger’ end of the market. This segment tolerates the limited service and values the saving more than average so will probably use the service more, possibly even increasing revenue.

It’s a bit like arguments in favour of Reaganomics tax cuts; individuals pay less but incentive stimulates economic activity which increases overall tax take. Fare / patronage elasticity will likely be highest off-peak and weekends as there’s more discretionary trips made by a different passenger profile.

The above limited bus service isn’t attractive to car owners unless driving conditions are very poor. People on all but the lowest incomes may find the waiting not worth the saving. Passengers that a lowered fare attracts may have previously walked rather than driven. Cheaper buses might make individual travel more energy intensive, although public transport fuel efficiency (as measured by fuel use per passenger) improves with patronage (ie more passengers per litre).

Speeding up buses (achieved through directness, frequency and connectivity) but charging a higher fare should have a different effect. The better service may attract some who previously drove or got lifts. Existing passengers may use the improved, more capable service more. But a fare increase could mean that even though the service is better, price-sensitive passengers abandon it for their lower value discretionary trips.

Funding better transport

There’s at least three things that can be done.

* The first is to maximise effective service levels from current budgets. For example delete routes that duplicate others and use saved resources where more worthwhile. This requires a strong service planning culture, and for co-ordinating agencies to take a network view; both factors in which Melbourne has been weak. Like tidying an over-grown garden before selling your house, good service planning has high payoffs, due to this legacy. And participatory public engagement, including illustrating the service gains possible, should help build acceptance.

* Secondly there’s the possibility of differential off-peak pricing. This gives the price-sensitive a discount while preserving revenue from the less price sensitive. This segments the market and probably optimises patronage, though at expense to legibility. A difficulty here is if peak fares must rise disproportionately to retain overall revenue, and here we merge into the next point.

* The third are other measures to generate revenue. Possibilities include development of rail air space, parking taxes, hypothecated property levies or above-CPI fare increases.

All are politically difficult, especially if the fare increase has not followed tangible reliability and service improvements. Rises skewed toward peak period commuters (without whom capacity building infrastructure would not be needed) may be economically most rational, but send the wrong signals with regards modal choice if not accompanied by similar increases for driving (whether through fuel, parking or road space pricing).

This combined approach may be better transport policy but probably harder than fare hikes alone due to the larger numbers affected. While not impossible, it may require a trust in government that is lacking, particularly in states that propose then cancel major transport projects, such as New South Wales with its various metro plans.

Whereas people are willing to individually pay more for quicker travel (as witnessed by the popularity of driving versus public transport), sourcing such funds for collectively funded network improvements raises hackles. How do we break this deadlock?

Seeing improvements differently

It may be worth reviewing how we regard transport improvements.

Large transport plans may be centred on infrastructure. Its purpose is typically to relieve ‘bottlenecks’ caused by extrapolating present commuting patterns forward. Time costs may be used to justify it; studies may cite rising congestion costs and ‘lost productivity’. Infrastructure’s tangible nature invites us to draw lines on maps and build models to imagine a faster future.

Another way of thinking is to set a public transport journey time reduction target and use the best combination of infrastructure and services to achieve it. The bigger the target the higher the cost, but the more likely that public transport would win the modal share shifts envisaged in Melbourne 2030 (an increase from 9 to 20% or a patronage trebling).

A 20 to 50% cut in average public transport travel times may seem far-fetched. But considering how it could happen would sharpen our thinking about which improvements are really worthwhile. Plus it familiarises planners with the real source of delays, which passengers know but they may not.

Overall travel time and its variability

Rules for such analysis need to match where and when people live and travel. For example they must acknowledge that people live where they do and not at railway stations. They want to leave when they want, without checking a timetable. And the selection of trips used must represent travel made by all motorised modes, and not skewed towards public transport’s current profile. These rules are observed by (i) counting waiting and transfer times, (ii) assuming random arrival at the stop or station, and (iii) including a representative spread of trips (including cross-suburban, night and weekend travel, which together form a majority of all trips).

Adding the first two produces a more useful average travel time (random arrival end to end travel time) than ‘headline’ minimum in-vehicle travel times. The latter is often a dubious selling point for ‘fast rail’ projects. It’s a bit like quoting air fares without all the extras and surcharges; meaningless at best and misleading at worst.

Travel time variability also needs to be known. Which transit system is better? One where a particular trip takes anywhere between 10 and 40 minutes. Or one that at best is slower (eg 15 minutes) but at worst is faster (25 minutes). It’s no contest really; the latter’s lower variability means better reliability, especially for time-critical trips.

Average random arrival end-to-end travel time and travel time variability (for a representative sample of trips in the area) are the to key numbers planners need to pick the best from a number of route and timetable options.

Train planners may have to juggle with express running versus frequency. Service delivery is also critical; when passengers start having to catch an earlier train due to the risk of the one they want being cancelled, effective travel speeds can easily halve, especially when a connection from a bus is involved.

Tram planners have little scope for express running and frequency is generally already high. Their main problems are externally caused, especially when in mixed traffic. Although there are trade-offs between whether all services are run to the terminus or some terminate early to provide greater frequency and capacity in the busier inner core.

Bus planners have more flexibility. They must balance walking time, coverage, number of routes, use of transfers, directness and frequency. However, like trams, priority on the roads can speed travel and reduce variability. This is especially where bus priority operates at all times and not just when the bus is running late.

Part 2 will follow with examples