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高速公路货运在货运体系中持续占据重要地位，相较于其他交通数据，门架数据准确性更高，但由于其难以获取，现有的预测模型较少使用门架数据来预测高速公路货车流量。针对以上问题，提出基于门架数据的高速公路货车流量短时预测模型。首先，对高速公路货车数据进行预处理。其次，将注意力机制与自适应图卷积网络(AGCN)相融合，挖掘高速公路货车数据中的空间相关性，并通过残差神经网络(ResNet)与长短期记忆(LSTM)网络来挖掘高速公路货车数据中的时间相关性。最后，通过特征融合得到最终高速公路货车流量预测结果。通过对比实验，所提模型与LSTM、STNN等基线模型相比，在短期的高速公路货车流量预测上有更高的准确度。
Abstract：Highway freight always occupy a large share in the freight system. Compared with other traffic sources, data collected from gantry were more accurate. But the data were difficult to obtain, so the existing forecasting models rarely used gantry data to predict highway freight traffic. To address this issue, a short-term prediction model for highway freight traffic based on gantry data was proposed. Initially, the highway freight data were preprocessed. Then, an integration of attention mechanisms with AGCN was employed to excavate spatial correlations within the data, while ResNet and LSTM were utilized to uncover temporal dependencies. Finally, feature fusion was applied to derive the predicted highway freight traffic results. By comparative experiments, it was demonstrated that the proposed model exhibited higher accuracy in short-term highway freight traffic forecasting compared to baseline models such as LSTM and STNN.
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基本信息:
DOI：10.13705/j.issn.1671-6841.2024025
中图分类号:U495
引用信息:
[1]田钊,程钰婕,李姝婕,等.基于门架数据的高速公路货车流量短时预测[J].郑州大学学报(理学版),2025,57(06):58-64.DOI:10.13705/j.issn.1671-6841.2024025.
基金信息:
综合交通运输大数据应用技术交通运输行业重点实验室开放课题(2022B1201); 河南省高等学校重点科研项目(24A520045)