酒店行业的选址选择预测方法基于超级学习算法和运输可访问性
Na Li1, Huaishi Wu2
1Tianjin Chengjian University, Tianjin, 300384, China.
Scientific reports
|May 9, 2025
概括
这项研究使用人工智能和超级学习来预测天津的酒店位置. 人工智能模型实现了高精度,根据交通可达性和需求确定了最佳位置.
科学领域:
- 城市规划 城市规划
- 地理信息系统 (GIS) 是一个地理信息系统.
- 人工智能的人工智能
背景情况:
- 优化城市空间结构和旅游服务需要合理的酒店位置选择.
- 人工智能为这一关键决策过程提供了数据驱动的方法.
研究的目的:
- 为天津的星级酒店提出使用元学习和交通方便的短拍酒店位置预测方法.
- 提高城市环境中酒店位置选择的准确性和效率.
主要方法:
- 一个超学习算法被用来生成初始的酒店位置预测.
- 空间语法被用来构建用于二次选的运输可访问性模型.
- 根据确定的需求水平,创建了一个适当性分布图.
主要成果:
- 该元模型实现了90.45%的分类准确度和91.90%的位置适配度,比基线模型提高了11%.
- 交通便利性是一个重要的因素,在星级酒店分销中的分类信息中贡献了45%.
- 推的投资区域包括小宝路,大旺庄路和武大道街.
结论:
- 拟议的超级学习方法在少数拍摄的酒店位置场景中表现出卓越的表现.
- 该模型为数据驱动的酒店定位决策提供了实际框架,优化了城市发展和旅游业.
- 未来的酒店投资应在战略上针对具有高交通可达性和需求的地区.
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