Dynamic Optimal Pricing of Ridesharing Platforms under Network Externalities with Stochastic Demand

Wenjie Wang,Lei Xie,Baogui Xin

doi:10.1155/2021/6442797

Abstract

Ridesharing two-sided platforms link the stochastic demand side and the self-scheduling capacity supply side where there are network externalities. The main purpose of this paper is to establish the optimal pricing model of ridesharing platforms to dynamically coordinate uncertain supply and stochastic demand with network externalities in order to maximize platforms’ revenue and social welfare. We propose dynamic pricing strategies under two demand scenarios that minimize order loss in the surge demand period and maximize social welfare in the declining demand period. The numerical simulation results show that dynamic pricing strategies could stimulate the supply to reduce delayed orders in the surge demand scenario and adjust the demand to maximize social welfare under declining demand scenario. Additionally, we further find that the direct network externalities positively influence the platforms’ revenue, and the indirect network externalities have a negative effect on social welfare in the declining demand scenario, and a higher wage ratio cannot enhance the platforms’ revenue.

Highlights

With the rapid growth of sharing economy, ridesharing platforms, which offer service to consumers via sharing idle social labors, have entered in our lives and have deep and long-lasting impact on transportation [1]
E ridesharing platforms are facing the challenge on coordination self-scheduling capacity supply with stochastic demand. e sharing drivers who provide delivery capacity for ridesharing platforms are part-time social labors with high mobility and instability. e self-scheduling social drivers [5] who can provide service and decide working hours by themselves can be far less controlled by ridesharing platforms
Because consumers and drivers are sensitive to price and wage [6], a dynamic pricing strategy could be applied to effectively manage the stochastic demand and uncertain supply of the ridesharing platforms [7]

Summary

Introduction

With the rapid growth of sharing economy, ridesharing platforms, which offer service to consumers via sharing idle social labors, have entered in our lives and have deep and long-lasting impact on transportation [1]. Because consumers and drivers are sensitive to price and wage [6], a dynamic pricing strategy could be applied to effectively manage the stochastic demand and uncertain supply of the ridesharing platforms [7]. To answer these questions, our research focuses on the dynamic pricing modeling considering network externalities in order to balance the demand and supply to maximize ridesharing platforms’ revenue. We study the dynamic pricing strategies in two scenarios, which are the surge demand scenario in peak time to minimize the delayed order loss and the declining demand scenario in off-peak time to minimize idle drivers. We put forward dynamic pricing strategies for ridesharing platforms under surge demand scenario [5, 11, 12] and under declining demand scenario in which the idle drivers are reduced to maximize the social welfare.

Related Literature

Problem Formulation

Numerical Study