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对于COVID-19数据挖掘的新方法:基于交通复兴指数树结构的深度时空预测模型
Zhiqiang Lv1,2, Xiaotong Wang1, Zesheng Cheng1
1College of Computer Science & Technology, Qingdao University, Qingdao 266071, China.
概括
这项研究引入了一种新的深度学习模型来预测交通复兴指数,这对于了解COVID-19后城市交通恢复至关重要. 拟议的模型显著提高了预测的准确性,帮助决策者在有效的城市规划.
科学领域:
- 运输科学 运输科学
- 流行病学 流行病学
- 数据科学数据科学数据科学
背景情况:
- COVID-19大流行严重影响了包括运输在内的全球行业.
- 中国政府旨在制COVID-19传播的政策最初限制了交通部门.
- 交通复兴指数是评估大流行后城市交通恢复的关键.
研究的目的:
- 为交通复兴指数开发一个准确的预测模型.
- 协助政府机构在宏观层面的城市交通评估和政策制定.
- 提高在疫情恢复期间对城市交通动态的理解.
主要方法:
- 提出了使用树结构的深度时空预测模型.
- 整合了一个空间卷积模块,具有树结构,用于定向和层次的城市节点特征.
- 开发了一个时间卷积模块,用于时间依赖的多层残余结构.
- 实施了矩阵数据融合模块,用于多层次整合COVID-19和交通数据.
主要成果:
- 与基线模型相比,拟议的模型表现出优越的性能.
- 在平均绝对误差 (MAE) 中实现了平均21%的改善.
- 在根平均平方误差 (RMSE) 中平均有18%的改善,在平均绝对百分比误差 (MAPE) 中平均有23%.
结论:
- 深度时空模型有效地预测了交通复兴指数.
- 该模型结合流行病和交通数据的能力提高了预测准确度.
- 调查结果支持为交通恢复和城市规划制定明智的政策.
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