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.
A Model Based on Trend-Seasonal Decomposition and GCN for Traffic Flow Prediction
Lect. Notes Electrical Eng.
Chinese Intelligent Systems Conference ; 2023 ; Ningbo, China October 14, 2023 - October 15, 2023
2023-10-08
8 pages
Article/Chapter (Book)
Electronic Resource
English
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