期刊
NEUROCOMPUTING
卷 500, 期 -, 页码 329-340出版社
ELSEVIER
DOI: 10.1016/j.neucom.2022.05.083
关键词
Deep learning; Temporal fusion transformer; Traffic speed; Multistep prediction
资金
- National Natural Science Foun-dation of China [52172331]
- Fundamental Research Funds for the Central Universities [22120220013]
This study adopts a novel architecture called TFT to predict freeway speed, which can capture short-term and long-term temporal dependence and improve prediction accuracy by incorporating various types of inputs.
Accurate short-term freeway speed prediction is a key component for intelligent transportation management and can help travelers plan travel routes. However, very few existing studies focus on predicting one-hour ahead or longer freeway speed. In this study, a novel architecture called Temporal Fusion Transformer (TFT) is adopted to predict freeway speed with the prediction horizons from 5 min to 150 min. The TFT can capture short-term and long-term temporal dependence by a multi-head attention mechanism. Moreover, the TFT utilizes the fusion decoder to import various types of inputs which can improve the prediction accuracy. To demonstrate the advantage of the TFT, traffic speed data collected from an interstate freeway in Minnesota are used to train and test the prediction model. The TFT prediction performance is compared with several classic traffic prediction methods, and the results reveal that the TFT performs best in speed prediction when the prediction horizon is longer than 30 min.(c) 2022 Elsevier B.V. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据