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

Abstract

In a Shared Mobility on Demand Service (SMoDS), dynamic pricing plays an important role in the form of an incentive for the decision of the empowered passenger on the ride offer. Strategies for determining the dynamic tariff should be suitably designed so that the incurred demand and supply are balanced and therefore economic efficiency is achieved. In this manuscript, we formulate a discrete time Markov Decision Process (MDP) to determine the probability desired by the SMoDS platform corresponding to the acceptance rate of each empowered 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 goal that EWT be regulated around a target value. We then develop a Dynamic Programming (DP) algorithm to derive the optimal policy of the MDP that regulates EWT around the target value. Computational experiments are conducted that demonstrate the regulation of EWT is effective, through various scenarios. The overall demonstration is carried out offline. The MDP formulation together with the DP algorithm can be utilized to an online determination of the dynamic tariff by integrating with our earlier works on Cumulative Prospect Theory based passenger behavioral modeling and the AltMin dynamic routing algorithm, and form the subject of future works.

An Application of a Deep Q-Network Based Dynamic Fare Bidding System to Improve the Use of Taxi Services during Off-Peak Hours in Seoul

“…Based on this, studies on dynamic pricing have been conducted to maximize the efficient supply of services and the resulting benefits [24,25]. Among them, many platform-based ride-sharing service studies were conducted as a fare-setting model to reduce traffic demand through fare adjustment to induce waiting time and travel time, or to induce supply by improving driver profitability [26][27][28]. Furthermore, operational data from real-world mobility services enabled verification of the dynamic pricing strategy's effectiveness [29][30][31].…”

“…Many studies have reduced passenger's waiting time by reducing traffic demand through surcharges in high-demand situations. At the same time, it was used as a fare-setting model to induce supply by improving driver profitability [24][25][26][27][28][29][30][31][32][33].…”

“…Therefore, this study aims to derive application measures for fare discounts for nonpeak hours, which are relatively low in demand, in consideration of the traffic characteristics of each time zone in Korea. In particular, while a number of studies have addressed surcharges to induce supply [24][25][26][27][28][29][46][47][48][49], this study is differentiated in that it provides discount measures to induce demand from the opposite perspective. Furthermore, in the simulation process, we consider interaction between drivers with micro-approaches using multi agent-based reinforcement learning methodologies.…”