基于强化学习和网络协同效应的公共交通动态激励策略的优化
1Office of Academic Affairs, Ningbo Polytechnic University, Ningbo, 315800, China.
Scientific reports
|August 21, 2025
概括
这项研究引入了一个动态激励策略 - 异质响应协同模式 (DIS-HARM),以减少公共交通拥堵. 该模式显著减少了高峰时段的旅行,降低了成本,并优先考虑低收入通勤者的激励措施,促进绿色旅行和社会公平.
科学领域:
- 城市规划和交通科学
- 计算社会科学
- 行为经济学
背景情况:
- 城市公共交通面临高峰拥堵,不同用户行为和有限的网络协同作用的挑战.
- 现有的策略往往缺乏对用户异质性和实时条件的动态适应.
研究的目的:
- 为优化城市公共交通提供动态激励策略和多元响应协同模式 (DIS-HARM) 提出和评估.
- 解决乘客峰值拥堵,用户行为异质性和网络协同作用的挑战.
主要方法:
- 综合强化学习,用户异质模型和小世界网络协同作用.
- 采用动态激励生成器进行实时碳信用调整.
- 纳入弹性用户标识符以减少边际效用量化和天气减弱系数以优化社会影响.
主要成果:
- 实现了72.2%的高峰时段客流减少率,超过了静态策略.
- 平均每小时高峰成本降低了3.125%.
- 向低收入群体提供36.5%的激励资源,覆盖率为83.1%.
结论:
- DIS-HARM显著提高了公共交通系统的效率和公平性.
- 该模型为城市运输管理中的动态定价和差异化激励提供了理论框架.
- 支持可持续旅游和社会公平的双重目标.
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