People aren’t agents: We need to consider user experience when designing policies and programs

Posted November 03, 2020

SGS Economics and Planning Gerard Lind

Complexity in public transport fares is not a unique policy problem. A recent study by SGS Economics and Planning and the Behavioural Insights Team has helped shape Infrastructure Victoria’s proposal for a new public transport fare system. It also highlights the critical role of user experience in policy and program design.

Infrastructure Victoria proposed a new public transport system in their latest report, Fair Move: Better Public Transport Fares for Melbourne. The variable fare structure would deliver Melburnians and visitors discounts for travelling outside peak times and for choosing less crowded services like the bus network or weekend trains.

Our study How People Respond to Complexity in Public Transport Fares added the human element to IV’s transport modelling. Through expert interviews, focus groups, interviews and an online experiment, we looked at how users interact with public transport fares and systems of differing complexity. The findings in our report about preferences, thinking styles and the limits of system design helped shape IV’s recommended fare system.

SGS Economics and Planning Melbourne Bourke St Train

There is a lot to consider when designing public transport fares

Some prefer a flat fare structure for simplicity. Others want fares to reflect the trip distance. This delivers what economists call ‘allocative efficiency’ because when the price of a service matches the marginal cost of delivering that service, it also matches the value users derive from it.

Others still argue against higher fares for longer trips on social equity grounds; more disadvantaged people live on the urban fringes where fares would be highest.

The transport departments who fund the services want to maintain financial sustainability, so they are interested in cost recovery. This means that they might prefer buses, which are cheaper to run than trams and trains.

Of course, there is also environmental sustainability to think about. If people aren’t catching the bus or the train, are they driving? Public transport has an important role in reducing the negative externalities of car travel.

And it’s not just the costs that people think about

The experts we spoke to said that convenience, frequency, safety and available seats were all more important factors for most travellers than small changes and complexity in pricing.

We can think about these factors in order of priority in the figure below. Like the food pyramid or Maslow’s hierarchy of needs, this pyramid reflects the factors that impact user decisions from the most fundamental at the base to higher order considerations at the top.

At the base are the most influential factors, like where you live, the local transport options that are available and the opportunity costs of the options. Moving up the pyramid are factors that are considered within the bounds of the prior constraints.

Users are likely to make safer and more reliable choices once they know all the available trips from their place of residence. The safe option that best matches the time that users must arrive at work could be the next consideration. This will be especially important when we reach 'COVID normal'.

Remember how transport departments like buses because they are cheaper to run? The problem is they perceived as less reliable, less comfortable and, in some ways, less safe. This is why they are deeply unpopular with users in many cities.

The complexity of the fares is likely to be a later factor, while comfort and over-crowding might follow price. These factors could also switch importance for users depending on the time of day, the nature of the trip and individual preferences.

SGS Economics and Planning Transport Fares diagram 1
Source: BIT and SGS (2020) How people respond to complexity in public transport fares

Just how complex can public transport fares get?

Quite complex. There are flat fares. There are mode-based fares for trains, trams and buses. There are distance-based fares which charge per kilometre. There are zone-based fares which split up the distance into bigger chunks (zones). There are also time-based, quality-based, cost-based, route-based and consumer-based fares (eager readers can see our full report for those).

Then you can look at different subsidies and incentives. This might mean peak hour surcharges, off-peak discounts, bulk-buy discounts, daily or weekend caps, and targeted subsidies for students, seniors, people with disability and other concession cardholders.

These are all the different levers we can pull to achieve the desired public transport system. The econocrats and policy wonks crunch the numbers, run the models and work out which levers to pull. The models tell us the likely social and economic outcomes from a precise combination of settings and help to identify which settings lead to the best outcomes.

Upon crunching those numbers, you might end up with a system that charges by distance for allocative efficiency, incentivises buses for cost recovery, smoothes demand with peak-pricing and assists disadvantaged travellers with targeted subsidies. Not to mention discounts for multi-model trip fares so those who need to catch a bus home when they get off the train don’t get penalised.

Pushing and pulling all the levers on the machine might lead to good outcomes on paper. But in the real world they would leave quite the mess, a mess that is difficult to understand and interpret.

The classical economist view doesn’t see this as a problem. It imagines a world of super rational agents optimising their every move to reduce costs and maximise utility. Of course, we know the real world isn’t like that. Policy design is catching up to the behaviouralist understanding that the world is complex and uncertain and us human beings, far from being ‘agents’, can only just keep up.

We have biases. We make mistakes. We often rely on simple rules of thumb – heuristics – for day-to-day decisions. Rather than super rational humans, we can maintain only a bounded rationality, at best.

In our study, we helped the Victorian Government to understand the human side of the fare design challenge. We teamed up with BIT to run a series of experiments, focus groups and interviews to examine how people respond to complexity in public transport fares. Our study looked at different combinations of ‘levers’: peak, mode-based, distance-based and zone-based pricing.

SGG Economics and Planning Transport Fares Diagram2
Source: BIT and SGS (2020) How people respond to complexity in public transport fares

Our study revealed five useful insights about user engagement

1. Simpler elements are easier to understand

The relatively simple modal and peak charges were easier to understand than the relatively complex distance charges, which required grater mental calculus. Given the difficult nature of this calculation, participants often left consideration of this fare element till last when estimating their trip fare.

2. As complexity increases, comprehension declines

Extensive literature supports the idea that consumers do not deal with complexity well, and typically do not choose the best prices when faced with more complex pricing structures.

Combining multiple elements in the one fare structure led to a decline in comprehension. Most users could handle two elements in the one fare. Three elements seemed to be a tipping point, especially when the third element was a distance charge.

Some participants also showed frustration and even disengaged when presented with the most complex fares. This indicates that public transport users may not fully engage and respond to a system that is overly complex and combines multiple elements. However, regular users would probably learn just enough over repeated trips to respond and choose the best option for them.

3. Different users have different approaches

In the focus groups, we identified five different styles when thinking about fares:

  • Problem solvers: working out the correct value of the fare to the cent using mental arithmetic.
  • Fixators: assuming certain elements make such a big impact on the fare total that other elements are not important, e.g. some assumed that off-peak is always cheaper, regardless of any other fare structure elements.
  • Feelers: some participants chose their answers based on what they felt may be cheaper using a loose understanding of the overall fare structure.
  • Screeners: some ruled out information they perceived as irrelevant, e.g. they might not consider the base fare if it was similar enough across two options.
  • Avoiders: some ignored the trickiest and most complex fare element (most often, the distance component) and hoped that the other elements would be sufficient for the calculation.

Some of these thinking styles appeared to bear out in BIT’s experimental data. The histogram below shows the distribution of task scores with one peak in the centre and one to the right. Our hypothesis is that these peaks represent at least two different approaches to the task.

The peak on the right may represent ‘problem solvers’ who do the math and the centre peak may represent a broad group that takes a less calculated approach. They likely rely more on heuristics that allow a person to approximate what the right answer is. This group could have been made up of the ‘fixators’, ‘feelers’ and ‘screeners’ identified in the focus groups.

Histogram of experiment scores (out of 20)
SGG Economics and Planning Transport Fares Diagram3
Source: BIT and SGS (2020) How people respond to complexity in public transport fares

4. There is potential for learning, but it depends on feedback

There was mixed evidence of learning amongst the participants, which depended on the design of the focus groups and experiment. Evidence from the literature and our discussions with experts, suggests that whilst people may not be adept at learning broader systems, they can typically learn about how those systems impact them individually, especially if they interact with the system regularly.

5. Framing and presentation will impact comprehension in the real world

More intuitive structures are more easily understood. The experiment found that modal differences were very easy to understand (the differences were very salient). For peak charges, there was some confusion about exactly when peak charges applied. Hence, how the elements of the fare structure are presented, and what supporting information and promotion there is in the system, will greatly impact understanding.

Simple lessons for complex systems

In short, the findings tell us that fares cannot be too complex and should probably only combine two different elements. People will think about systems in different ways and those systems should be accessible to many members of our diverse communities.

These findings directly influenced Infrastructure Victoria's proposed new system. They only pulled two key levers of the many at their disposal – mode and peak pricing – and they excluded distance-based pricing:

“The research found that modal and peak charges were relatively easy to understand for participants in both the experiment and user consultation. Distance charges were the hardest element to understand. For this reason, and due to a low level of evidence that distance pricing significantly contributes to the social cost of additional public transport use, we have not included distance based fares in our recommendations.” Infrastructure Victoria (2020) Fair Move: Better Public Transport Fares for Melbourne.

Complexity in public transport fares is not a unique policy problem. While these insights might appear straightforward, they can also be easy to forget when facing a plethora of intricate program and policy design decisions.

Our work shows that simple experiments and user engagement can improve policy and program design. They help identify which design elements are the easiest to understand and the best value for money.

This thinking can be applied to all policy areas. In planning, it can shape how to set and implement developer contributions. In government programs, it can help engage job seekers in the employment services system. And during COVID-19, it can help people make informed and safe work and life decisions.

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