Reliable and quantitative forecasts for the future
What migration patterns will arise in a particular public transport concession when product A is introduced, and will that result in an increase in the number of journeys? What volume effects can be expected with the introduction of a hyperspire? What is the price effect for students if product B disappears from the range, but at the same time product C is reduced in price? How much does the kilometer tariff (on a specific line bundle) have to increase in order to be yield-neutral next year if paper sales on the bus are abolished, and how much in two, three or four years?
The above and many other questions with regard to the expected effects of possible changes in the product and tariff house in a particular public transport concession (or region) are almost continuously relevant to both carriers and concession providers. A scenario study can provide answers to (among other things) these questions and estimate the effects on the revenues of the transporter, the costs for the traveler and the migration effects, both in terms of revenues and volumes, in various proposed scenarios.
Hypercube has developed a scenario tool for carrying out a scenario study, which uses usage data from the public transport chip card, supplemented (if necessary) with information about the sale of non-chipped products, such as PIN transactions on the bus, and the revenues from standing charges. This scenario tool uses the principle of backcasting. By this we mean that the travel pattern of a traveler from the past is projected on the new proposed fare policy. The scenario tool determines the chances with which each traveler will travel with a particular product under that rate policy, and the extent to which. In this way, the scenario tool provides a solid foundation for the effects on the transporter’s revenues, the costs for the traveler and the migration effects in various scenarios, both on revenues and volumes.
To make adjustments to the proposed rate policy, the scenario tool contains a wide range of setting options, such as:
Prices (kilometer rates, product prices, discount percentages);
Validity of the range (age, peak and valley windows, working / weekend days, area);
Add new assortment;
Autonomous developments (product use, numbers of travelers).
Settings can be adjusted up to the level of specific time windows and lines. Think of a line-dependent rush-hour rate or a product with a discount percentage that depends on the chosen modality and the time. It is also possible to report at the same level of detail.
The projection of new policy on the full picture of actually made trips and the price paid for it based on the range chosen by the traveler, provides a very useful picture of the effects on passenger revenues and development. These images become sharper with the correct settings regarding parameters such as price sensitivity, product preference and willingness to change. The setting of these parameters can be adjusted in consultation with the customer, for example on the basis of additional expertise about the region. But the scenario tool by default uses the intelligence in this area collected by Hypercube over the years.
Passenger transport is an inelastic market. This means that there is less travel when prices rise and more when prices fall, but that an increase in volumes with a price decrease is insufficient to maintain the level of revenues. The default settings in the scenario tool with regard to price sensitivity are based on a large number of national and international (scientific) reports, combined with insights from (recent) practice. In terms of price sensitivity, we make a distinction, for example, between travel period (peak or off-peak), age and travel frequency. In addition, we take into account that the price sensitivity is lower in the short term than in the long term.
Not every traveler will always choose the cheapest product. In addition to price, other characteristics of a product, such as the convenience that the product provides, influence a traveler’s preference for a particular product. In addition, there also appears to be a difference in the actual price differences and the perception thereof by the traveler, and the travel pattern’s assessment of his travel pattern when purchasing the product does not always correspond to the travel pattern realized afterwards. Hypercube determines these product preferences based on the current situation in a region and applies them explicitly in the scenario tool.
The last parameter in our model is the willingness to change. Even if an alternative to the current product would be preferred at the moment when a trade-off between these products should now be made.