Convolutional Neural Network-Based Bidirectional Gated Recurrent Unit–Additive Attention Mechanism Hybrid Deep Neural Networks for Short-Term Traffic Flow Prediction

Song Liu, Wenting Lin, Yue Wang, Dennis Z. Yu, Yong Peng () and Xianting Ma
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Song Liu: Institute for Key Laboratory of Traffic System, Chongqing Jiaotong University, Chongqing 400074, China
Wenting Lin: School of Traffic and Transportation, Chongqing Jiaotong University, Chongqing 400074, China
Yue Wang: Highway Service Center of Yongchuan District, Chongqing 402160, China
Dennis Z. Yu: The David D. Reh School of Business, Clarkson University, Potsdam, NY 13699, USA
Yong Peng: School of Traffic and Transportation, Chongqing Jiaotong University, Chongqing 400074, China
Xianting Ma: Institute for Key Laboratory of Traffic System, Chongqing Jiaotong University, Chongqing 400074, China

Sustainability, 2024, vol. 16, issue 5, 1-15

Abstract: To more accurately predict short-term traffic flow, this study posits a sophisticated integrated prediction model, CNN-BiGRU-AAM, based on the additive attention mechanism of a convolutional bidirectional gated recurrent unit neural network. This model seeks to enhance the precision of traffic flow prediction by integrating both historical and prospective data. Specifically, the model achieves prediction through two steps: encoding and decoding. In the encoding phase, convolutional neural networks are used to extract spatial correlations between weather and traffic flow in the input sequence, while the BiGRU model captures temporal correlations in the time series. In the decoding phase, an additive attention mechanism is introduced to weigh and fuse the encoded features. The experimental results demonstrate that the CNN-BiGRU model, coupled with the additive attention mechanism, is capable of dynamically capturing the temporal patterns of traffic flow, and the introduction of isolation forests can effectively handle data anomalies and missing values, improving prediction accuracy. Compared to benchmark models such as GRU, the CNN-BiGRU-AAM model shows significant improvement on the test set, with a 47.49 reduction in the Root Mean Square Error (RMSE), a 30.72 decrease in the Mean Absolute Error (MAE), and a 5.27% reduction in the Mean Absolute Percentage Error (MAPE). The coefficient of determination ( R 2 ) reaches 0.97, indicating the high accuracy of the CNN-BiGRU-AAM model in traffic flow prediction. It provides a good solution for short-term traffic flow with spatio-temporal features, thereby enhancing the efficiency of traffic management and planning and promoting the sustainable development of transportation.

Keywords: sustainable transportation; short-term traffic flow; convolutional neural network; bidirectional gated recurrent unit; additive attention mechanism; combinatorial predictive model (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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