期刊
TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW
卷 125, 期 -, 页码 203-221出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.tre.2019.03.013
关键词
Air transport; Flight delay prediction; Delay propagation; Advanced transportation information; Deep belief learning
类别
资金
- National Natural Science Foundation of China [71571026]
- Liaoning Excellent Talents in University [LR2015008]
- Social Research Platform Grant from La Trobe University
This study analyzes high-dimensional data from Beijing International Airport and presents a practical flight delay prediction model. Following a multifactor approach, a novel deep belief network method is employed to mine the inner patterns of flight delays. Support vector regression is embedded in the developed model to perform a supervised fine-tuning within the presented predictive architecture. The proposed method has proven to be highly capable of handling the challenges of large datasets and capturing the key factors influencing delays. This ultimately enables connected airports to collectively alleviate delay propagation within their network through collaborative efforts (e.g., delay prediction synchronization).
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据