One major U.S. transit agency was proposing a significant round of fare restructuring in the face of a structural deficit, and many fare policy options were being considered. Some of these proposed fare structures involved significant increases and expected diversions of current customers between different fare products (e.g. pay-per-ride tickets, discounted multi-ride tickets, and unlimited ride passes of differing durations), and an option to introduce higher peak fares was being discussed. This type of major change in fare structure was unusual in this metropolitan area and advocates were concerned about its equity impacts, as the proposals had the potential to shift the cost burden between different groups or geographic areas. We were tasked to combine existing fare media usage survey data, customer demographics data, U.S. Census data, and current farecard utilization data to determine impacts to different groups of customers, such as minority or low-income. For this assignment, we came up with two different approaches for impact analyses: a classical aggregate methodology based on known fare elasticities in the various markets, and an innovative disaggregate one utilizing all observed trips and individually simulating each rider’s choice of fare product based on the new fare structure and their daily travel pattern. From these results, the transit agency was able to choose a fare scenario that delivered the required revenue increases whilst minimizing impacts to protected demographics. These results were also provided to relevant authorities for establishing Title VI compliance of the fare restructuring process.
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