A5102904093- Traffic Prediction and Management Techniques
- Transportation Planning and Optimization
- Iron and Steelmaking Processes
- Time Series Analysis and Forecasting
- Traffic control and management
- Human Mobility and Location-Based Analysis
- Geochemistry and Geologic Mapping
- Geoscience and Mining Technology
Institute of Automation
2023
Chinese Academy of Sciences
2023
Shandong Institute of Automation
2023
Traffic prediction is an important component of intelligent transportation systems. However, due to the dynamic and complex dependence time space, traffic extremely challenging. In terms correlation, impact external events such as accidents weather conditions, there are non-stationary signals in sequence signals. This means that future data cannot be inferred only from past periodicity, its state may suddenly change at any moment. The existing methods still have two limitations. Firstly,...
Timely and accurate traffic prediction is crucial for public safety rational allocation of resources such as roads. However, it still remains an open challenge timely forecasting, due to the highly nonlinear temporal correlation dynamical spatial dependence data. In order fully capture dependences, we propose a dual-channel spatio-temporal wavelet transform graph neural network (DSTwave) forecasting. Specifically, used obtain low- high-frequency parts from original sequence signals, in...
Accurately predicting CO and CO2 content in blast furnace gas (BFG) holds immense significance, ensuring stable operation improving energy utilization. However, due to the variable operating conditions of (BF) ironmaking complex chemical reactions BF, it is difficult accurately predict changing trend BFG. To solve this problem, study proposes a temporal double graph convolutional network (TDGCN) model for prediction. It consists three parts: convolution, hypergraph TimesNet. Specifically, we...