评估智能城市公园管理的新框架:基于社交媒体数据和深度学习的研究
Sijia Liu1, Chuandong Tan1, Feiyang Deng2
1College of Horticulture and Forestry, Huazhong Agricultural University, Wuhan, 430070, China.
Scientific reports
|February 13, 2024
概括
本研究引入了使用社交媒体评论进行智能城市公园管理评估的深度学习框架. 它提供了对公园性能和游客满意度的实时洞察,提高了效率.
科学领域:
- 城市规划和城市管理.
- 数据科学和人工智能数据科学和人工智能
- 环境科学环境科学
背景情况:
- 传统的城市公园评估方法在评估公园使用和环境条件方面缺乏全面性.
- 社交媒体和大数据为了解公众行为和公园特征提供了潜力,但对公园管理评估的研究是有限的.
- 智能管理对于优化城市公园运营和服务质量至关重要.
研究的目的:
- 通过使用社交媒体评论数据,提出和验证基于深度学习的智能城市公园管理评估框架.
- 量化评估城市公园在设施,安全,环境,活动和服务等各个方面的表现.
- 确定影响游客满意度和公园管理效率的关键因素.
主要方法:
- 开发了一个深度学习框架来分析来自武汉市七个城市公园的社交媒体评论数据.
- 该框架评估了从宏观到微观层面的公园管理,重点关注设施,安全,环境,活动和服务.
- 进行了定量分析,以评估公园的整体状态,性能,并确定管理问题.
主要成果:
- 该研究量化评估了七个城市公园的整体状态和表现,揭示了具体的管理挑战.
- 结果表明公园类型,季节和事件 (例如改造) 对游客满意度和公园特征的影响.
- 深度学习框架为公园使用和游客反提供了实时洞察力,超越了传统的评估限制.
结论:
- 拟议的深度学习框架可以实时智能评估城市公园管理,提高服务质量和效率.
- 这种数据驱动的方法为改善城市公园运营和游客体验提供了宝贵的见解.
- 该研究为智能城市发展提供了重要的参考,通过基于大数据和人工智能的智能公园管理.
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