基于图形神经网络的旅游疏散路线建议的视觉分析
Lin Zhang1, Jifeng Xu2, Xiaotian Pan3
1School of Information Management and Artificial Intelligence, Zhejiang University of Finance and Economics, Hangzhou, 310018, China.
Scientific reports
|October 11, 2023
概括
过度拥挤的旅游景点带来了安全风险,并降低了旅游体验. 这项研究引入了一种新的图形神经网络算法 (PER-GCN),用于优化游客疏散路线,提高安全性和体验.
科学领域:
- 计算机科学 计算机科学
- 旅游管理 旅游管理
- 数据科学数据科学数据科学
背景情况:
- 景区的游客过度拥挤会损害安全,并降低旅行体验.
- 传统的管理方法,如限制访问,降低了旅游体验,而引导疏散忽视了用户的合作和效率.
- 现有的疏散策略未能充分平衡安全,效率和游客满意度.
研究的目的:
- 提出一个先进的旅游疏散路线推算法 (PER-GCN),考虑旅游风格相似性.
- 开发一个可视化系统,实时模拟和分析疏散效率.
- 为了提高风景点建议的准确性,并考虑实时条件,优化疏散路线.
主要方法:
- 使用图形挖掘构建了一个旅游景点交互矩阵,以捕捉高阶交互.
- 开发了一个图形神经网络 (PER-GCN),将旅游风格相似性纳入路线建议中.
- 通过考虑路线复杂性和实时景观点承载能力,优化疏散路线.
- 设计了一个可视化系统,用于实时监控和分析旅游流.
主要成果:
- PER-GCN算法在优化游客疏散路线方面表现出有效性,这一点在西湖案例研究中得到证实.
- 视觉化系统能够实时监控游客流量,并分析游客行为模式.
- 通过模拟各种参数来分析疏散效率,包括旅游合作意愿和疏散分批.
结论:
- PER-GCN算法在管理游客流动和确保高效,经验意识的疏散方面比传统方法有了显著的改进.
- 开发的可视化系统为实时人群管理和景区战略规划提供了有价值的工具.
- 这项研究通过将数据驱动的见解整合到疏散策略中,为更安全,更愉快的旅游业做出了贡献.
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