Optimal dynamic pricing for public transportation considering consumer social learning.

ABSTRACT: Effective public transportation pricing strategies are critical to reducing traffic congestion and meeting consumer demand for sustainable urban development. In this study, we construct a dynamic game pricing model and a social learning network model for consumers of three modes of public transportation including metro, bus, and pa-transit. In the model, the metro, bus, and pa-transit operators maximize their profits through dynamic pricing optimization, and consumers maximize their utility by adjusting their travel habits through social learning in the social network. The reinforcement learning algorithm is applied to simulate the model, and the results show that: (1) as consumers' perceived sensitivity to different modes of travel increases, the market share and price of each mode of tra

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC10830017 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature