Back to the Future: Predicting Traffic Shockwave Formation and Propagation Using a Convolutional Encoder-Decoder Network
Back to the Future: Predicting Traffic Shockwave Formation and Propagation Using a Convolutional Encoder-Decoder Network
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DOI：
10.1109/itsc.2019.8917430
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发表时间：
2019-05
期刊：
影响因子：
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通讯作者：
M. Khajeh-Hosseini;Alireza Talebpour
中科院分区：
文献类型：
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作者：
M. Khajeh-Hosseini;Alireza Talebpour
本研究提出一种深度学习方法来预测交通冲击波的传播。深度神经网络的输入是研究段的时空图，网络的输出是预测（未来）冲击波以时空图的形式在研究段上的传播。该方法的主要特点是能够提取嵌入在时空图中的特征来预测交通冲击波的传播。
This study proposes a deep learning methodology to predict the propagation of traffic shockwaves. The input to the deep neural network is time-space diagram of the study segment, and the output of the network is the predicted (future) propagation of the shockwave on the study segment in the form of time-space diagram. The main feature of the proposed methodology is the ability to extract the features embedded in the time-space diagram to predict the propagation of traffic shockwaves.