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基于门控递归单元神经网络的高速公路行程时间预测

刘松 彭勇 邵毅明 宋乾坤

刘松, 彭勇, 邵毅明, 宋乾坤. 基于门控递归单元神经网络的高速公路行程时间预测[J]. 应用数学和力学, 2019, 40(11): 1289-1298. doi: 10.21656/1000-0887.400187
引用本文: 刘松, 彭勇, 邵毅明, 宋乾坤. 基于门控递归单元神经网络的高速公路行程时间预测[J]. 应用数学和力学, 2019, 40(11): 1289-1298. doi: 10.21656/1000-0887.400187
LIU Song, PENG Yong, SHAO Yiming, SONG Qiankun. Expressway Travel Time Prediction Based on Gated Recurrent Unit Neural Networks[J]. Applied Mathematics and Mechanics, 2019, 40(11): 1289-1298. doi: 10.21656/1000-0887.400187
Citation: LIU Song, PENG Yong, SHAO Yiming, SONG Qiankun. Expressway Travel Time Prediction Based on Gated Recurrent Unit Neural Networks[J]. Applied Mathematics and Mechanics, 2019, 40(11): 1289-1298. doi: 10.21656/1000-0887.400187

基于门控递归单元神经网络的高速公路行程时间预测

doi: 10.21656/1000-0887.400187
基金项目: 教育部人文社会科学研究规划基金(17YJA630079)
详细信息
    作者简介:

    刘松(1986—),男,博士生(E-mail: 515044261@qq.com);彭勇(1973—),男,教授,博士,硕士生导师(通讯作者. E-mail: pengyong@cqjtu.edu.cn);邵毅明(1955—),男,教授,博士,博士生导师(E-mail: sym@cqjtu.edu.cn);宋乾坤( 1963—) ,男,教授,博士,博士生导师(E-mail: qiankunsong@163.com).

  • 中图分类号: U491

Expressway Travel Time Prediction Based on Gated Recurrent Unit Neural Networks

  • 摘要: 为了更高效地预测高速公路行程时间,以高速公路行程时间为研究对象,通过采集车辆在高速公路进出口收费站的刷卡数据获取行程时间,利用门控递归单元神经网络对行程时间进行预测.按照所设计的预测流程,利用广州市机场高速南线高速公路收费数据进行验证,结果显示,预测拟合效果较好,并与LSTM神经网路和BP神经网络进行了对比分析.结果表明:门控递归单元神经网络具有更好的预测准确度.
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出版历程
  • 收稿日期:  2019-06-14
  • 修回日期:  2019-07-19
  • 刊出日期:  2019-11-01

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