点式的
计算机科学
稳健性(进化)
可扩展性
卷积神经网络
点态互信息
卷积(计算机科学)
频道(广播)
数据挖掘
深度学习
流量(计算机网络)
人工智能
期限(时间)
算法
机器学习
人工神经网络
相互信息
数学
数学分析
计算机网络
生物化学
化学
物理
计算机安全
量子力学
数据库
基因
作者
Ruijun Feng,Mingzhou Chen,Yu Song
标识
DOI:10.1016/j.eswa.2023.122468
摘要
In the construction of intelligent transportation systems, short-term traffic flow prediction is of great significance for the advancement of traffic network management. But due to the presence of many complex factors in both spatial and temporal domains, it remains a complex and challenging task. Existing literature usually employs the convolutional neural network (CNN)-based methods in capturing spatiotemporal correlations. These CNN-based methods often use a single-channel data structure to represent different periodic patterns, which makes the model susceptible to over-parameterization when capturing periodic dependencies and prone to information loss after convolution. To overcome these limitations, this paper presents a hybrid deep learning method for short-term traffic flow prediction. In this method, a video-shaped multi-channel data structure is designed to represent different periodic patterns more efficiently. Next, a new mixed-pointwise convolution is introduced for capturing periodic dependencies without the negative impacts mentioned above. Lastly, an improved channel attention mechanism is proposed to learn channel inter-dependencies with controllable parameter usage. The proposed method is lightweight, yet highly effective. Compared to the state-of-the-art baseline method, it reduces the root mean squared error by up to 6.7% on the PeMSD4 dataset and 13.3% on the PeMSD7 dataset, while also achieving substantial improvement in two additional metrics, exhibiting strong robustness and great scalability across various settings.
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