Unlimited-ride bike-share pass pricing revenue management for casual riders using only public data

Abstract

Despite the proliferation of publicly available Big Data in Mobility-as-a-Service systems, few studies in the urban mobility service literature deal with unlimited usage price plan strategies. We conduct an experimental case study to design such a strategy: an unlimited-ride X-Day pass pricing for bike-share usage especially targeting short-term casual users. Public data from Citi Bike is used to estimate a pass choice model for bike-share services. As disaggregate data for riders have not been available due to privacy concerns, their travel behaviors are veiled and kept confidential. The estimation is made possible using bootstrap method to resample average numbers of daily trips for individuals from coefficients of a linear regression model that distributes daily trips to 1-Day and 3-Day Pass users. The analysis suggests 1-Day Pass users make 2.8 trips per day at an average cost of $4.29/trip while 3-Day Pass users make 1.8 trips per day at an average cost of $4.47/trip. The estimated pass choice model from the choice-based sample has a cost per trip coefficient of −2.884. The model is used in designing a new pricing plan of ($18.50, $12) for (3-Day, 1-Day) compared to a benchmark of ($24, $12). By using only public data, we can nonetheless show that this pricing plan should increase monthly revenue for Citi Bike by at least 5.5% and increase consumer surplus by at least $0.09/trip/short-term pass customer.

Keywords

• Bike-share
• Unlimited-ride pass
• Bootstrap method
• Revenue management
• Logit model