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Integrated ridesharing services with chance-constrained dynamic pricing and demand learning

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Document pages: 20 pages

Abstract: The design of integrated mobility-on-demand services requires jointlyconsidering the interactions between traveler choice behavior and operators operation policies to design a financially sustainable pricing scheme. However,most existing studies focus on the supply side perspective, disregarding theimpact of customer choice behavior in the presence of co-existing transportnetworks. We propose a modeling framework for dynamic integratedmobility-on-demand service operation policy evaluation with two serviceoptions: door-to-door rideshare and rideshare with transit transfer. A newconstrained dynamic pricing model is proposed to maximize operator profit,taking into account the correlated structure of different modes of transport.User willingness to pay is considered as a stochastic constraint, resulting ina more realistic ticket price setting while maximizing operator profit. Unlikemost studies, which assume that travel demand is known, we propose a demandlearning process to calibrate customer demand over time based on customers historical purchase data. We evaluate the proposed methodology throughsimulations under different scenarios on a test network by considering theinteractions of supply and demand in a multimodal market. Different scenariosin terms of customer arrival intensity, vehicle capacity, and the variance ofuser willingness to pay are tested. Results suggest that the proposedchance-constrained assortment price optimization model allows increasingoperator profit while keeping the proposed ticket prices acceptable.

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