Freight Volume Prediction for Logistics Sorting Centers Using an Integrated GCN-BiLSTM- Transformer Model

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In this paper, a comprehensive learning model is proposed for predicting cargo volume at sorting centers, which integrates GCN (Graph Convolutional Network), BiLSTM (Bidirectional Long Short-Term Memory Network), ARIMA (Autoregressive Integrated Moving Average model), and Transformer models. To achieve this, a directed weighted graph is constructed considering the transport network and average cargo volume of each sorting center. The GCN model is employed to extract spatial features from the transport connection information of the sorting centers and these features are then fed into the BiLSTM network. The BiLSTM network leverages bidirectional information flow to learn the temporal characteristics of the data. Subsequently, the GCN-BiLSTM model, which combines spatial and temporal features, is used to predict the daily cargo volume for the next 30 days. The results demonstrate that the GCN-BiLSTM model and the ARIMA-BiLSTM integrated model significantly enhance prediction performance compared to single-model approaches.