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In a Shared Mobility on Demand Service (SMoDS), dynamic pricing plays animportant role in the form of an incentive for the decision of the empoweredpassenger on the ride offer. Strategies for determining the dynamic tariffshould be suitably designed so that the incurred demand and supply are balancedand therefore economic efficiency is achieved. In this manuscript, we formulatea discrete time Markov Decision Process (MDP) to determine the probabilitydesired by the SMoDS platform corresponding to the acceptance rate of eachempowered passenger at each state of the system. We use Estimated Waiting Time(EWT) as the metric for the balance between demand and supply, with the goalthat EWT be regulated around a target value. We then develop a DynamicProgramming (DP) algorithm to derive the optimal policy of the MDP thatregulates EWT around the target value. Computational experiments are conductedthat demonstrate the regulation of EWT is effective, through various scenarios.The overall demonstration is carried out offline. The MDP formulation togetherwith the DP algorithm can be utilized to an online determination of the dynamictariff by integrating with our earlier works on Cumulative Prospect Theorybased passenger behavioral modeling and the AltMin dynamic routing algorithm,and form the subject of future works.

Towards Dynamic Pricing for Shared Mobility on Demand using Markov Decision Processes and Dynamic Programming