Short‐term traffic congestion prediction with Conv–BiLSTM considering spatio‐temporal features

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Abstract
For many intelligent transportation applications, traffic congestion prediction is quite essential. If traffic congestion on the road ahead can be accurately and promptly predicted, and routes can be planned reasonably based on the prediction results, traffic congestion can be effectively alleviated. Aiming at the spatio‐temporal correlation and evolution characteristics of traffic flow data, the Conv–BiLSTM module comprising a convolutional neural network (CNN) and a bidirectional long short‐term memory (BiLSTM) is proposed, considering the spatio‐temporal features. Firstly, the obtained traffic speed data is folded according to spatio‐temporal features, and a three‐dimensional matrix is constructed as the input of the prediction network module. After the spatial features are extracted by the CNN, the temporal features and alignment features are extracted by the BiLSTM, followed by which the prediction results are obtained as an output. Prediction and evaluation experiments on the traffic data of the highway in Shanghai prove that the traffic congestion state predicted by this method is largely consistent with the actual state. The results demonstrate that the proposed method has a higher prediction accuracy compared with the conventional and state‐of‐the‐art methods and is an efficient method of traffic congestion prediction.
Funding Information
  • National Natural Science Foundation of China (71901196)

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