期刊
TRANSPORTMETRICA B-TRANSPORT DYNAMICS
卷 10, 期 1, 页码 499-524出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/21680566.2021.2008280
关键词
Traffic flow prediction; adverse weather; deep learning; hybrid method; CNN; GRU
资金
- Open Foundation of Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport of China Academy of Transportation Sciences [2020B1203]
- National Natural Science Foundation of China [52172314]
- Fundamental Research Funds for the Central Universities of Ministry of Education of China [DUT20JC40]
The study developed a hybrid deep learning model DLW-Net to predict traffic flow under adverse weather, with target analysis and global analysis parts using various neural network models. The results showed different impacts of different adverse weather conditions on traffic flow, with DLW-Net performing the best under all conditions.
To predict traffic flow under adverse weather, a hybrid deep learning model concerning adverse weather (DLW-Net) is formulated. The DLW-Net model consists of the target and global analysis parts. For the target analysis part, the spatio-temporal characteristics of traffic flow data are analyzed using the convolutional neural network (CNN), the long short-term memory (LSTM) and gated recurrent unit (GRU) neural networks. For the global analysis part, the variation rules of traffic flow and weather data are extracted using the LSTM model. Additionally, the characteristics of traffic flow under normal and adverse weather are also discussed. The developed model is verified using three cases. The results show that traffic volume and speed would reduce under heavy rain compared to normal weather, however, drizzle has little impact on traffic flow patterns; the rules of traffic speed data are disturbed by strong wind; and the DLW-Net model performs best under all the conditions.
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