| 基于门控递归单元神经网络的高速公路行程时间预测 |
| |
| 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 |
| |
| 作者姓名： | 刘松 彭勇 邵毅明 宋乾坤 |
| |
| 作者单位： | 1重庆交通大学 交通运输学院, 重庆 400074;2山地城市交通系统与安全实验室, 重庆 400074;3重庆交通大学 数学系, 重庆 400074 |
| |
| 基金项目： | 教育部人文社会科学研究规划基金(17YJA630079) |
| |
| 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.
|
| 关 键 词： | 高速公路 行程时间预测 门控递归单元 神经网络 |
| 收稿时间： | 2019-06-14 |
Expressway Travel Time Prediction Based on Gated Recurrent Unit Neural Networks |
| |
| Affiliation: | 1College of Traffic & Transportation, Chongqing Jiaotong University, Chongqing 400074, P.R.China;2Chongqing Key Lab of Traffic System & Safety in Mountain Cities, Chongqing 400074, P.R.China;3College of Mathematics and Statistics,Chongqing Jiaotong University, Chongqing 400074, P.R.China |
| |
| Abstract: | To efficiently predict the travel time on the expressway, the travel time was studied with the gated recurrent neural network through collection of the swiping data of vehicles at toll gates on the expressway. By means of the developed prediction computer program, the effects of the proposed method were then tested with the charging data of the Guangzhou Airport south expressway. The results show that the prediction effects are satisfying. Comparison with the LSTM neural network and the BP neural network indicates that, the gated recurrent neural network is better in prediction accuracy. |
| |
| Keywords: | |
|
| 点击此处可从《应用数学和力学》浏览原始摘要信息 |
|
点击此处可从《应用数学和力学》下载免费的PDF全文 |
|
