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- W4387423218 abstract "Traffic flow prediction plays an important role in Intelligent Transportation Systems(ITS). To improve the accuracy of traffic flow prediction, this paper proposes a multi-location based on Trend-Seasonal Decomposition and GCN Traffic Flow Forecasting Models for the task of multi-location traffic flow prediction. In this paper, the proposed model mainly consists of two functions: First, the Trend-Seasonal component decomposes the temporal data of traffic flow into a more predictable trend part and a seasonal or periodic part. Second, GCN is used to obtain spatial information between different observation points and improve the accuracy of multi-position prediction. Finally, the experiments for the PeMS04 and PeMS08 data sets are carried out to verify the effectiveness of proposed model." @default.
- W4387423218 created "2023-10-08" @default.
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- W4387423218 date "2023-01-01" @default.
- W4387423218 modified "2023-10-08" @default.
- W4387423218 title "A Model Based on Trend-Seasonal Decomposition and GCN for Traffic Flow Prediction" @default.
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- W4387423218 doi "https://doi.org/10.1007/978-981-99-6847-3_53" @default.
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