A Ridepooling Bidding Model Considering Service Quality from the Perspective of Cournot Game Theory

Abstract

Pricing for ridepooling services may be affected by travel needs, penalties, and ride service quality. Firstly, this paper explores pricing methods based on the perceived value of the ride as compared to the distance–time prices and penalties and proposes a ridepooling travel utility function for pricing strategies. Then this paper develops a combined optimization ridepooling scheduling model integrating order allocation and path optimization (CORM), and designs a hybrid simulated annealing (HSA) heuristic algorithm for solving this model. Based on the Cournot game theory, this paper further develops a ridepooling bidding model (RBM) that can simultaneously optimize the seekers’ utility and the profit of the ridepool platform. The effectiveness of the CORM and HSA are verified, and the superiority of the RBM has also been proved by a numerical simulation example. The results show the following: (1) neither the low distance–time price and high penalty price strategy nor the high distance–time and low penalty price strategy is an absolute advantage strategy for ridepooling passengers and the platform; (2) compared with the CORM, the RBM can not only effectively improve the profit of ridepooling platform, but also improve the success rate of ridepooling.