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- W4313203280 abstract "Accurate traffic flow prediction is of great significance. Recently, lots of deep neural network models have been applied in predicting traffic flow, such as Attention based Spatial-Temporal Graph Neural Network(ASTGNN). However, although these models have good prediction performances, there are still some problems when they are applied to the increasingly expanding and complex traffic network, such as memory consumption and computing overheads. Therefore, this paper proposes a Distributed Traffic Flow Prediction Framework Based on Road Segmentation(DTFPRS). Specifically, it is consisted of Spatial Partitioning (SP) and Traffic Flow Prediction(TFP). SP segments the road into sub-roads according to the correlation of traffic flow, and TFP trains ASTGNN on each sub-road. Experiments on real-world traffic flow datasets demonstrate that DTFPRS can greatly shorten the training time and reduce the size of the model without affecting the accuracy of the prediction results which makes DTFPRS can be used to large traffic networks." @default.
- W4313203280 created "2023-01-06" @default.
- W4313203280 creator A5019988055 @default.
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- W4313203280 date "2022-10-29" @default.
- W4313203280 modified "2023-09-27" @default.
- W4313203280 title "DTFPRS: A Distributed Traffic Flow Prediction Framework Based on Road Segmentation" @default.
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- W4313203280 doi "https://doi.org/10.1109/besc57393.2022.9995636" @default.
- W4313203280 hasPublicationYear "2022" @default.
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