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View all- Huang FWang JYi PPeng JXiong XLiu Y(2024)SCSQInformation Sciences: an International Journal10.1016/j.ins.2024.120730674:COnline publication date: 1-Jul-2024
A Multi-Layer Model Based on Transformer and Deep Learning for Traffic Flow Prediction
Pages 443 - 451
Abstract
Using traffic data to accurately predict the traffic flow at a certain time in the future can alleviate problems such as traffic congestion, which plays an important role in the healthy transportation and economic development of cities. However, current traffic flow prediction models rely on human experience and only consider the advantages of single machine learning model. Therefore, in this work, we propose a multi-layer model based on transformer and deep learning for traffic flow prediction (MTDLTFP). The MTDLTFP model first draws on the idea of transformer model, which uses multiple encoders and decoders to perform feature extraction on the initial traffic data without human experience. In addition, in the prediction stage, the MTDLTFP model using deep learning technology, which input the hidden features into the convolutional neural network (CNN) and multi-layer feedforward neural network (MFNN) to obtain the prediction score respectively. The CNN model can captures the correlation information between the hidden features, and the MFNN can captures the nonlinear relationship between the features. Finally, we use a linear model to combine the two prediction scores, which can make the final prediction value take into account the common advantages of both models. Multiple experimental results on two real datasets demonstrate the effectiveness of the MTDLTFP model. The experimental results on the <inline-formula> <tex-math notation="LaTeX">$WorkDay$ </tex-math></inline-formula> dataset are as follows, with the RMSE value of 0.191, MAE value of 0.165. The experimental results on the <inline-formula> <tex-math notation="LaTeX">$HoliDay$ </tex-math></inline-formula> dataset are as follows, with RMSE value of 0.227, MAE value of 0.192.
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Published: 22 September 2023
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View all- Huang FWang JYi PPeng JXiong XLiu Y(2024)SCSQInformation Sciences: an International Journal10.1016/j.ins.2024.120730674:COnline publication date: 1-Jul-2024