Sensors (Basel, Switzerland)
Accurate predictive modeling of traffic flow is critically important as it allows transportation users to make wise decisions to circumvent traffic congestion regions. The advanced development of sensing technology makes big data more affordable and accessible, meaning that data-driven methods have been increasingly adopted for traffic flow prediction. Although numerous data-driven methods have been introduced for traffic flow predictions, existing data-driven methods cannot consider the correlation of the extracted high-dimensional features and cannot use the most relevant part of the traffic flow data to make predictions. To address these issues, this work proposes a decoder convolutional LSTM network, where the convolutional operation is used to consider the correlation of the high-dimensional features, and the LSTM network is used to consider the temporal correlation of traffic flow data. Moreover, the multi-head attention mechanism is introduced to use the most relevant portion of the traffic data to make predictions so that the prediction performance can be improved. A traffic flow dataset collected from the Caltrans Performance Measurement System (PeMS) database is used to demonstrate the effectiveness of the proposed method.
attention mechanism, convolutional LSTM, deep learning, traffic flow prediction
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This work is licensed under a Creative Commons Attribution 4.0 License.
Industrial and Systems Engineering
Yupeng Wei and Hongrui Liu. "Convolutional Long-Short Term Memory Network with Multi-Head Attention Mechanism for Traffic Flow Prediction" Sensors (Basel, Switzerland) (2022). https://doi.org/10.3390/s22207994