Optimal dynamic pricing for public transportation considering consumer social learning

Effective public transportation pricing strategies are critical to reducing traffic congestion and meeting consumer demand for sustainable urban development. In this study, the authors 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 travel adjust accordingly. (2) When taking into account consumers' social learning behavior, the market share of metros remains high, while the market shares of buses and pa-transit are relatively low. (3) As consumers become more sensitive to their perception of each travel mode, operators invest more resources in improving service quality to gain market share, which in turn affects the price of each travel mode. The authors' results provide decision support for optimal pricing of urban public transportation.

• Record URL:
  • https://doi.org/10.1371/journal.pone.0296263
• Supplemental Notes:
  • © 2024 Zhang, Zhao. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
• Authors:
  • Zhang, Yihua
  • Zhao, Zhan
• Publication Date: 2024

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: e0296263
• Serial:
  • PLoS One
  • Volume: 19
  • Issue Number: 1
  • Publisher: Public Library of Science
  • EISSN: 1932-6203
  • Serial URL: https://journals.plos.org/plosone/
• Publication flags:

  Open Access (libre)

Subject/Index Terms

• TRT Terms: Bus transit; Consumer behavior; Market share; Pricing; Public transit; Rail transit; Urban transit
• Subject Areas: Economics; Finance; Passenger Transportation; Public Transportation;

Filing Info

• Accession Number: 01907917
• Record Type: Publication
• Files: TRIS
• Created Date: Feb 12 2024 11:23AM