AAAI Publications, Thirty-Second AAAI Conference on Artificial Intelligence

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Optimal Pricing for Distance-Based Transit Fares
Richard Hoshino, Jeneva Beairsto

Last modified: 2018-04-27

Abstract


Numerous urban planners advocate for differentiated transit pricing to improve both ridership and service equity. Several metropolitan cities are considering switching to a more "fair fare system," where passengers pay according to the distance travelled, rather than a flat fare or zone boundary scheme that discriminates against various marginalized groups. In this paper, we present a two-part optimal pricing formula for switching to distance-based transit fares: the first formula maximizes forecasted revenue given a target ridership, and the second formula maximizes forecasted ridership given a target revenue. Both formulas hold for all price elasticities. Our theory has been successfully tested on the SkyTrain mass transit network in Metro Vancouver, British Columbia, with over 400,000 daily passengers. This research has served Metro Vancouver's transportation authority as they consider changing their fare structure for the first time in over 30 years.

Keywords


Constraint Optimization; quadratic programming; quadratic optimization; transit;

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