Hybrid graph convolution neural network and branch-and-bound optimization for traffic flow forecasting
Peer reviewed, Journal article
Published version
Date
2023Metadata
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Original version
Future Generation Computer Systems. 2023, 139, 100-108. 10.1016/j.future.2022.09.018Abstract
In this study, we combine graph optimization and prediction in a single pipeline to investigate an innovative convolutional graph-based neural network for urban traffic flow prediction in an edge IoT environment. Pre-processing of the linked graph is first performed to remove noise from the set of original road networks of urban traffic data. Outlier detection strategy is used to efficiently explore the road network and remove irrelevant patterns and noise. The resulting graph is then implemented to train an extended graph convolutional neural network to estimate the traffic flow in the city. To accurately tune the hyperparameter values of the proposed framework, a new optimization technique is developed based on branch and bound. For comparison, an intensive evaluation is conducted with multiple datasets and baseline methods. The results show that the proposed framework outperforms the baseline solutions, especially when the number of nodes in the graph is large.