计算机科学
卷积神经网络
人工智能
均方误差
人工神经网络
深度学习
弹道
时间序列
系列(地层学)
国家(计算机科学)
循环神经网络
机器学习
数据挖掘
模式识别(心理学)
算法
数学
生物
统计
天文
物理
古生物学
作者
Ping Huang,Chao Wen,Liping Fu,Qiyuan Peng,Yixiong Tang
标识
DOI:10.1016/j.ins.2019.12.053
摘要
Dynamical systems that contain moving objects generate multi-attribute data, including static, time-series, and spatiotemporal formats. The diversity of the data formats creates challenges for the accurate modeling of these systems, for example, the state/location/trajectory prediction of moving objects. We developed a deep learning (DL) approach that combines 3-dimensional convolutional neural networks (3D CNN), long short-term memory (LSTM) recurrent neural network, and fully-connected neural network (FCNN) architectures to address this problem. The proposed model, named CLF-Net, uses individual factors with different attributes as input to achieve better predictions. The spatiotemporal features are fed into the 3D CNN, the time-series variables are fed into the LSTM, and the non-time-series factors are fed into the FCNN, respectively. A case study of train delay prediction for four railway lines with different operational features shows that the CLF-Net outperforms conventional machine learning models and the state-of-the-art DL models with regard to the performance metrics of the root mean squared error and mean absolute error.
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