A hybrid deep learning model with 1DCNN-LSTM-attention networks for short-term traffic flow prediction

2021 ◽  
pp. 126293
Author(s):  
Ke Wang ◽  
Changxi Ma ◽  
Yihuan Qiao ◽  
Xijin Lu ◽  
Weining Hao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Traffic Flow ◽  
Learning Model ◽  
Short Term ◽  
Attention Networks ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Deep Learning Model

Traffic flow prediction by an ensemble framework with data denoising and deep learning model

2021 ◽  
Vol 565 ◽  
pp. 125574
Author(s):  
Xinqiang Chen ◽  
Huixing Chen ◽  
Yongsheng Yang ◽  
Huafeng Wu ◽  
Wenhui Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Traffic Flow ◽  
Learning Model ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Deep Learning Model

An attention‐based deep learning model for traffic flow prediction using spatiotemporal features towards sustainable smart city

2020 ◽  
Vol 34 (3) ◽  
Author(s):  
Balachandran Vijayalakshmi ◽  
Kadarkarayandi Ramar ◽  
NZ. Jhanjhi ◽  
Sahil Verma ◽  
Madasamy Kaliappan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Traffic Flow ◽  
Smart City ◽  
Learning Model ◽  
Spatiotemporal Features ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Deep Learning Model

scholarly journals Traffic Volume Prediction: A Fusion Deep Learning Model Considering Spatial–Temporal Correlation

2021 ◽  
Vol 13 (19) ◽  
pp. 10595
Author(s):  
Yan Zheng ◽  
Chunjiao Dong ◽  
Daiyue Dong ◽  
Shengyou Wang
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Traffic Flow ◽  
Temporal Correlation ◽  
Learning Model ◽  
Flow Data ◽  
Urban Road ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Deep Learning Model

In this paper, a fusion deep learning model considering spatial–temporal correlation is proposed to solve the problem of urban road traffic flow prediction. Firstly, this paper holds that the traffic flow of a section in the urban road network not only depends on the fluctuation of its own time series, but is also related to the traffic flow of other sections in the whole region. Therefore, a traffic flow similarity measurement method based on wavelet decomposition and dynamic time warping is proposed to screen the sections which are similar to the traffic flow state of the target section. Secondly, in order to improve the prediction accuracy, the unstable time series are reconstructed into stationary time series by differential method. Finally, taking the extracted traffic flow data of a similar section as an independent variable and the traffic flow data of target section as dependent variable, we input the above variables into the proposed CNN-LSTM fusion deep learning model for traffic flow prediction. The results show that the proposed model has a higher accuracy and stability than the other benchmark models. The MAPE can reach 92.68%, 93.39%, 85.14%, and 76.14% at a time interval of 5 min, 15 min, 30 min, and 60 min, and the other evaluation indexes are also better than the rest of the benchmark models.

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