Short-term traffic flow prediction based on whale optimization algorithm optimized BiLSTM_Attention

Xing Xu, Chengxing Liu, Yun Zhao*, Xiaoshu Lv

*Tämän työn vastaava kirjoittaja

Tutkimustuotos: LehtiartikkeliArticleScientificvertaisarvioitu

1 Sitaatiot (Scopus)

Abstrakti

With the growths in population and vehicles, traffic flow becomes more complex and uncertain disruptions occur more often. Accurate prediction of urban traffic flow is important for intelligent decision-making and warning, however, remains a challenge. Many researchers have applied neural network methods, such as convolutional neural networks and recurrent neural networks, for traffic flow prediction modeling, but training the conventional network that can obtain the best network parameters and structure is difficult, different hyperparameters lead to different network structures. Therefore, this article proposes a traffic flow prediction model based on the whale optimization algorithm (WOA) optimized BiLSTM_Attention structure to solve this problem. The traffic flow is predicted first using the BiLSTM_Attention network which is then optimized by using the WOA to obtain its four best parameters, including the learning rate, the training times, and the numbers of the nodes of two hidden layers. Finally, the four best parameters are used to build a WOA_BiLSTM_Attention model. The proposed model is compared with both conventional neural network model and neural network model optimized by the WOA. Based on the evaluation metrics of MAPE, RMSE, MAE, and R2, the WOA_BiLSTM_Attention model proposed in this article presents the best performance.

AlkuperäiskieliEnglanti
Artikkeli6782
Sivumäärä16
JulkaisuCONCURRENCY AND COMPUTATION: PRACTICE AND EXPERIENCE
Vuosikerta34
Numero10
Varhainen verkossa julkaisun päivämäärä12 tammik. 2022
DOI - pysyväislinkit
TilaJulkaistu - 1 toukok. 2022
OKM-julkaisutyyppiA1 Julkaistu artikkeli, soviteltu

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