田钊,程钰婕,李姝婕,张乾钟,邵凯凯,杨艳芳

• 1:郑州大学网络空间安全学院
• 2:郑州市区块链与数据智能重点实验室
• 3:交通运输部科学研究院
• 4:综合交通运输大数据应用技术交通运输行业重点实验室

摘要(Abstract)：

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。

关键词(KeyWords)： 短时流量预测;门架数据;深度学习;残差神经网络;长短期记忆网络

Abstract:

Keywords:

基金项目(Foundation): 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)

作者(Author): 田钊,程钰婕,李姝婕,张乾钟,邵凯凯,杨艳芳

DOI: 10.13705/j.issn.1671-6841.2024025

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