COVID-19 has impacted every public transport agency in the world, but in inconsistent and unpredictable ways: trips to central business districts, business parks, universities and tourist attractions are down, while trips serving industrial areas, healthcare facilities might be up. Off-peak travel is often less impacted than peak travel.
Agencies are challenged to do more with less, and need to allocate scarce resources strategically, both to meet demand and customer expectations to provide an experience as free of crowding as possible.
Traditional tools used by agencies such as travel surveys, APC data and tap-on data fail to provide the granularity needed to make service planning decisions at the route level.
The reason why so many agencies retain a fare structure designed decades ago lies in the difficulty in making the case for change without good data. Decision-makers will ask: how much more ridership and revenue will this create? Who will be impacted and how?
While the world’s crystal balls all seem to be stuck in “cloudy” mode at the moment, the next best thing is to have solid information about the detailed impact of a pricing change or promotional campaign. How did it impact demand? Where? How often? Who responded best? Even with a small sample size, the data can be extrapolated to the known user base. For example, a small cohort in FAIRTIQ can test a new pricing structure or campaign, for broader rollout to all customers.
In areas where historical data is available, price simulations can be run on large sets of historical travel data. For example, FAIRTIQ simulated 5 different fare models to determine the impact on farebox revenue and on ticket costs for users.
FAIRTIQ is known as a mobile ticketing solution, but the technology can also be used as a mobility journal to map out trips for planning purposes. With the use of small incentives, agencies can recruit a cohort of riders to provide key usage insights by mapping their journeys with FAIRTIQ. Even with a fairly small cohort, agencies can generate insights about shifts in demand or assess how customers use passes. For example, SBB has used FAIRTIQ Lab to track consumption of Annual Pass (GA) users to find out how many are below/above the breakeven point and by how much.