Freight Volume Prediction of Railway Station Based on Heterogeneous Spatial-Temporal Graph Attention Network
The short-term prediction of station freight volume helps stations and dispatching departments to understand the trend of volume changes in advance,adjust the allocation of transportation resources,and improve transportation organization efficiency.The railway freight stations of the National Energy Group were focused and a freight volume prediction model was constructed based on the heterogeneous spatial-temporal graph attention network in this study.In the graph network,the stations were treated as nodes,whereas the physical adjacency relationships,the waybill demand relationships,and the train operation relationships between stations were abstracted as heterogeneous edges between nodes.The model utilized graph attention mechanisms to capture the spatial correlations between stations and their neighbors within a single graph network and used heterogeneous node feature fusion mechanisms to integrate information among three sub-graphs The obtained spatial features were then put into Gated Recurrent Unit network to update time-series features.Actual freight volume data from various railway stations of the National Energy Group were selected for experimentation,and the results demonstrate that the proposed model is more accurate in prediction and can effectively assist in scheduling and statistical work.
国家科技期刊平台
NSTL
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