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Multi-Platform dynamic game and operation of hybrid Bike-Sharing systems based on reinforcement learning

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  • Shi, Ziyi
  • Xu, Meng
  • Song, Yancun
  • Zhu, Zheng

Abstract

The advent of electric bikes, or ebikes, has significantly enhanced competitiveness of bike-sharing systems, providing benefits to both riders (comfort during uphill and long-distance rides), platforms (more profit), and the environment. Operating such a hybrid bike-sharing system, i.e., with both bikes and ebikes, in a competitive multi-platform market, can be challenging due to the complex and unpredictable interplay among heterogeneous market participants, which becomes more pronounced with the ebike varying battery, and dynamic demand. Most related research is predicated on the assumption of a monopoly market, which is not always the case: in worldwide capital-oriented markets, many firms will quickly imitate and join in rapidly developing fields for profits. Thus, this paper addresses platforms’ hybrid bike-sharing system operation problem with time-varying ebike pricing and rebalancing strategy in consideration of competition. We consider two docked hybrid bike-sharing platforms with charging stations at the site. Platforms utilize trucks for their own rebalancing operation including bike, ebike and mixed bike/ebike relocation tasks. We combine the Markov decision process (MDP) model with game theory, and establish the dual-platform MDP framework in which one mainstream platform and one competing platform optimize their profits by dynamic pricing and bike/ebike rebalancing based on highly dynamic and stochastic demand. Users’ choice is described by a modified nested logit model and the endogenous demand is generated. We develop the tailored double dueling deep Q-network for solving dynamic gaming. A series of experiments are conducted based on the real-world dataset in Shenzhen and several strategy combinations are compared. The results show the win–win situation where both platforms improve profits with a higher market ratio and demonstrate how to introduce and operate ebikes in the system by analyzing detailed strategies in different games.

Suggested Citation

  • Shi, Ziyi & Xu, Meng & Song, Yancun & Zhu, Zheng, 2024. "Multi-Platform dynamic game and operation of hybrid Bike-Sharing systems based on reinforcement learning," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 181(C).
    • Handle: RePEc:eee:transe:v:181:y:2024:i:c:s1366554523003629
    DOI: 10.1016/j.tre.2023.103374
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    References listed on IDEAS

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