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- W3126873503 abstract "Short-term traffic flow prediction plays an important role in intelligent traffic management system, such as preventing traffic jam and relieving traffic pressure. Deep neural network algorithm has the ability of using big data to predict short-term traffic flow, but it is still a problem to improve the accuracy of prediction by fully extracting the effective spatiotem-poral characteristics of traffic data. To this end, this paper presents a deeply densely connected inception network model with an attention mechanism to predict traffic flow. This model uses multi-scale convolution and pooling of sparse structures combined with dense connections, which increases the depth and width of the network to ensure the fit of the network, and reduces the complexity of the network. The attention mechanism used internally in the model can enhance the propagation of useful information and improve the prediction accuracy of the model. The data in Caltrans Performance Measurement System (PEMS) is used in simulation to verify the accuracy of the prediction model and the beneficial effects of attention mechanism and dense connection structure by comparing with the prediction indexes of different prediction models." @default.
- W3126873503 created "2021-02-15" @default.
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- W3126873503 date "2020-11-06" @default.
- W3126873503 modified "2023-09-24" @default.
- W3126873503 title "A Dense Inception Network with Attention Mechanism for Short-term Traffic Flow Prediction" @default.
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- W3126873503 doi "https://doi.org/10.1109/cac51589.2020.9327049" @default.
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