Liuzhou Railway Vocational Technical College, Liuzhou, Guangxi, China.
Shandong University Stomatology Hospital, Jinan, Shandong, China.
PLoS One. 2024 Jan 31;19(1):e0296263. doi: 10.1371/journal.pone.0296263. eCollection 2024.
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 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. Our results provide decision support for optimal pricing of urban public transportation.
有效的公共交通定价策略对于减少交通拥堵和满足消费者对可持续城市发展的需求至关重要。在这项研究中，我们构建了一个地铁、公共汽车和拼车三种公共交通模式的动态博弈定价模型和社会学习网络模型。在模型中，地铁、公共汽车和拼车运营商通过动态定价优化来最大化利润，消费者通过社会网络中的社会学习来调整出行习惯以最大化效用。我们应用强化学习算法来模拟模型，结果表明：（1）随着消费者对不同出行模式的感知敏感度的提高，每种出行模式的市场份额和价格都会相应调整。（2）考虑到消费者的社会学习行为，地铁的市场份额仍然较高，而公共汽车和拼车的市场份额相对较低。（3）随着消费者对每种出行模式的感知敏感度的提高，运营商投入更多资源来提高服务质量以获得市场份额，这反过来又会影响每种出行模式的价格。我们的研究结果为城市公共交通的最优定价提供了决策支持。
