B2C online ride-hailing pricing and service optimization under competitions

Qingfeng Meng; Wenjing Li; Zhen Li; Changzhi Wu

doi:10.3934/jimo.2021147

Article Contents

2022, Volume 18, Issue 6: 4049-4069. Doi: 10.3934/jimo.2021147

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B2C online ride-hailing pricing and service optimization under competitions

1.
School of Management, Jiangsu University, Zhenjiang, 212013, China
2.
School of Management, Guangzhou University, Guangzhou, 510006, China

^* Corresponding author: Changzhi Wu
^* Corresponding author: Changzhi Wu

Received: January 31, 2021

Revised: May 31, 2021

Early access: August 2021

Published: September 2022

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Abstract

B2C online ride-hailing is to provide customers with anytime, anywhere and on-call ride services by professional vehicles and professional drivers. How to maintain good service quality and reasonable pricing under competition is of importance to a platform. In this paper, we will integrate pricing and service together to maximize the profit of a platform through Nash game theory. Specifically, we will establish the models for there scenarios: the demand market competition under decline of ride demand, the supply market competition under surge of ride demand, and the coexistence of demand and supply market competition for stable ride demand. Then, the Nash equilibriums are derived for the three models which are corresponding to minimize ride price, optimize quality of efforts and maximize profit. Our results uncover that the driver's incentive amount is conducive to the profit of both platform and the drivers for the case of demand market competition. The platforms and drivers achieve the highest profit under supply market competition, and the strategy through minimizing price and maximizing service can effectively adjust the balance between market supply and demand.

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Mathematics Subject Classification: Primary: 58F15, 58F17; Secondary: 53C35.

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Figure 1. The operation process of the online ride-hailing platform

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Figure 2. The influence of the upper limit of driver's incentive amount on the optimal strategy

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Figure 3. The influence of the upper limit of driver's incentive amount on the optimal profit

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Figure 4. The influence of ride demand market competition on the optimal price change trajectory

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Figure 5. The influence of ride demand market competition on the optimal platform service change trajectory

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Figure 6. The influence of ride demand market competition on the optimal drives service change trajectory

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Figure 7. The influence of market competition of ride demand on the optimal profit change trajectory

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Table 1. Variable description

Variable	Variable description
$ p_j $	The ride price of online ride-hailing platform in the $ j $-th model
$ s_{1j} $	The quality of service effort of the online ride-hailing platform in the $ j $-th mdel
$ s_{2j} $	The quality of service efforts of the driver in the $ j $-th mdel
$ j $	$ j=l,u,b $ represent the demand market competition, the supply market completion, and the coexistence of demand and supply market competition, respectively
$ c $	The unit operating costs of the B2C online ride-hailing platforms
$ m $	The fixed delivery cost for drivers
$ D_0 $	The initial market ride demand
$ a $	The market ride demand fluctuation factor
$ q_1 $	The cost of platform unit quality of service improvement
$ q_2 $	The cost of driver unit quality of service improvement
$ M_1 $	The fixed development and operation costs of B2C online ride-hailing platform
$ M_2 $	The fixed training and management costs for drivers
$ \theta $	The ride price sensitivity coefficient
$ \beta $	The sensitivity coefficient of quality of service efforts of B2C online ride-hailing platforms and drivers
$ k_D $	The ride demand market competition coefficient
$ \lambda $	The incentive upper limit of drivers
$ \pi_{1j} $	The profit of the B2C online ride-hailing platforms respectively in the $ j $-th model
$ \pi_{2j} $	The profit of the drivers in the $ j $-th model
$ superscript^* $	The optimal variable value
$ p_j^* $, $ \pi_{1j}^* $, $ \pi_{2j}^* $	The optimal decision variable value

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