铁道货运2024，Vol.42Issue(6)：52-59,8.DOI:10.16669/j.cnki.issn.1004-2024.2024.06.08
基于异质时空图注意力网络的铁路车站货运量预测
Freight Volume Prediction of Railway Station Based on Heterogeneous Spatial-Temporal Graph Attention Network
张海山 1王文斌 1周瑾2
作者信息
- 1. 中国神华能源股份有限公司 煤炭运输部,北京 100040
- 2. 北京全路通信信号研究设计院 通信信息技术研究院,北京 100070
- 折叠
摘要
Abstract
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.关键词
重载铁路/车站货运量/时空图注意力网络/时序预测/注意力机制Key words
Railway Freight/Station Freight Volume/Spatial-Temporal Graph Attention Network/Time-series Prediction/Attention Mechanism分类
交通工程引用本文复制引用
张海山,王文斌,周瑾..基于异质时空图注意力网络的铁路车站货运量预测[J].铁道货运,2024,42(6):52-59,8.
