用户偏好在乘车共享的数学模型中,用于增强匹配
Zahra Dastani1, Hamidreza Koosha2, Hossein Karimi3
1Department of Industrial Engineering, Ferdowsi University of Mashhad, Mashhad, 9177948974, Iran.
Scientific reports
|November 9, 2024
概括
将用户偏好整合到乘客转移的乘车共享中,大大提高了系统效率和用户满意度. 这种方法提高了匹配,减少了响应时间,并增加了整体需求和收入.
科学领域:
- 运营研究 运营研究
- 运输科学 运输科学
- 计算机科学 计算机科学
背景情况:
- 乘车共享服务面临着诸如过度拥挤,资源限制和环境影响等挑战.
- 乘坐共享的乘客转移可以克服匹配限制,特别是在人口密度较低的地区.
- 纳入用户偏好对于提高乘车共享系统效率和用户满意度至关重要.
研究的目的:
- 开发一个数学编程模型,将用户偏好集成到乘客转移的乘车共享中.
- 引入基于偏好匹配和高效解决方案生成的算法.
- 在真实场景中评估拟议模型的性能.
主要方法:
- 开发一个包含用户偏好的数学编程模型.
- 建议使用偏好驱动匹配算法来捕获用户偏好并识别潜在匹配.
- 介绍了一种代增强和优化算法,用于快速,高质量的解决方案生成.
主要成果:
- 与其他方法相比,采用集成用户偏好的拟议模型表现出优异的性能.
- 算法有效地捕获了用户偏好,并生成了最佳匹配.
- 对实体场景的评估证实了拟议方法的效率和有效性.
结论:
- 用户偏好对于优化乘车共享系统,平衡效率和满意度至关重要.
- 开发的模型提高了用户满意度,系统响应性和运营效率.
- 这种方法通过为更大的用户群提供服务,从而增加了需求和收入.
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