Navigating the pricing structures of public transport systems in new cities is something even experienced travellers dread. There’s lots of different things to think about: the mode of transport you’d like to use; the distance you’re travelling; and even time of day changes to transport fares.
A behavioural insights lens can help us understand how people interpret information and online tests can help us understand how these insights might influence behaviour. We used an online environment where we could systematically control and vary information presented to people to understand how the information they are presented influences decisions. You can read the full report here.
What we did
We worked with Infrastructure Victoria and SGS Economics and Planning to understand how incorporating complexity via multiple pricing elements influences comprehension of the pricing structure. To do this, we conducted an online randomised controlled trial with 2,011 Australians where we showed respondents four different pricing structures. These pricing structures incorporated an increasingly complex set of elements:
- Making peak travel more expensive;
- Varying the price of certain modes of transport (i.e., bus, tram, or train; and making peak travel more expensive);
- Pricing based on distance travelled (i.e., an additional 10 cents per kilometre travelled, and whether or not the travel was at peak times) or;
- Having a structure that incorporated a peak travel surcharge, transport mode differences, and a distance-based charge.
We then asked respondents to think about a friend who is trying to navigate these transport systems, and to recommend the cheapest fare for their friend for a series of hypothetical public journeys. Our outcome was the total number of fares participants correctly selected as the cheapest (out of 20 hypothetical journeys), and participants were offered an additional incentive if they correctly indicated the cheapest fare on 80% of the journeys presented.
As complexity increases, comprehension decreases
We found that when complexity increased (i.e., when more elements were incorporated into a pricing structure), individuals were less able to choose the cheapest fare for their friend. This finding is not particularly groundbreaking. However, we were also able to uncover some surprising insights about how individuals use complex pricing structures to make decisions.
Not all types of complexity are created equal
One of the surprising things we found is that certain pricing elements are more difficult than others for individuals to use to make decisions. We asked participants to rate how difficult they found the pricing structure, and found that pricing structures that included distance-based pricing were rated as significantly more difficult than those that included peak-only or combined peak + mode pricing. We also found that the combined peak + mode pricing resulted in significantly more correct responses compared to the peak only pricing.
This difference in levels of understanding between the mode and distance elements of a pricing structure may be because distances are more difficult to visualise and use in decision-making than modes of transport. This suggests that pricing structures that incorporate distance-based elements may lead to some confusion, and that incorporating a simpler, more intuitive zone-based charge could aid in decision-making.
Different people approach complexity in different ways
Broadly speaking, we also saw different approaches to the task: intuitors, and calculators. Intuitors are those who have a sense of the pricing structure, but rely on mental shortcuts or heuristics to choose the cheapest fare. On the other hand, calculators are those who have internalised the pricing structure and its nuances and are happy to invest some time in working out the prices via doing the maths.
We found that as pricing complexity increased, the number of intuitors increased and the number of calculators decreased. These results were also reinforced by qualitative interviews that SGS Economics and Planning conducted with consumers.
This suggests that when faced with complexity, people might be more likely to ‘throw up their hands’. This can be a problem – often public transport fares are designed with some sort of price signal (e.g., a peak hour charge is designed to deter travel during peak times). But, if the system is too complex for people to understand, then it will fail to meet the ultimate policy objectives. It also suggests that when designing pricing schedules, we should make them as intuitive as we can, to capture those who will still try and ‘intuit’ their way through decisions.