Traffic flow prediction is a crucial aspect of transportation planning to prevent congestion. A good understanding of a traffic planning strategy will assist in avoiding traffic congestion. Complex factors, such as inter-region traffic conditions, vehicle relationships, and unexpected incidents, influence accurate traffic flow prediction. Thanks to embedded sensors in transportation systems, vehicle movement data can be widely collected and analyzed to predict the traffic flow. To this end, this study proposes a deep learning-based prediction algorithm called Deep learning-based traffic flow graph prediction (DeepTFGP) for forecasting traffic flow on each road within the road network. We modeled road networks and traffic flow data using the graph theory to generate a traffic flow graph (TFG) representing the diversion of vehicle flow in the traffic network. Through learning from the TFG dataset, DeepTFGP accurately predicts future traffic flow. DeepTFGP employs three independent residual neural network modules to model the temporal characteristics of traffic flow: closeness, period, and trend. Each module consists of multiple residual units, and the outputs are combined through a shared time prediction function module. The DeepTFGP parameters are automatically optimized using the gradient descent algorithm of neural networks to achieve accurate traffic flow prediction. We collected and constructed a relevant UK National Highways dataset from traffic detection data provided by the British Department of Transport on the UK highway network. We performed experiments and comparisons on this dataset with existing algorithms. Our final results demonstrate that DeepTFGP outperforms various benchmark comparison models in accuracy.
Bian et al. (Tue,) studied this question.