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Title	Bilstm- And Gnn-Based Spatiotemporal Traffic Flow Forecasting With Correlated Weather Data
ID_Doc	12192
Authors	Alourani A.; Ashfaq F.; Jhanjhi N.Z.; Ali Khan N.
Year	2023
Published	Journal of Advanced Transportation, 2023
DOI	http://dx.doi.org/10.1155/2023/8962283
Abstract	The timely and accurate forecasting of urban road traffic is crucial for smart city traffic management and control. It can assist both drivers and traffic controllers in selecting efficient routes and diverting traffic to less congested roads. However, estimating traffic volume while taking into account external factors such as weather and accidents is still a challenge. In this research, we propose a hybrid deep learning framework, double attention graph neural network BiLSTM (DAGNBL), that utilizes a graph neural network to represent spatial characteristics and bidirectional LSTM units to capture temporal dependencies between features. Attention modules are added to the GNN and BLSTM to find high-impact attention weight values for the chosen road section. Our model offers the best prediction accuracy with a mean absolute percentage error of 5.21% and a root mean squared error of 4. It can be utilized as a useful tool for predicting traffic flow on certain stretches of road. © 2023 Abdullah Alourani et al.
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