A Multi-Head Self-Attention Transformer-Based Model for Traffic Situation Prediction in Terminal Areas
Terminal operations management is an important part of air traffic management. Accurately detecting and predicting the operational status of the terminal area can help formulate more appropriate and efficient management methods. To achieve more accurate results in predicting the traffic situation, a...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10044658/ |
| _version_ | 1797900204919226368 |
|---|---|
| author | Zhou Yu Xingyu Shi Zhaoning Zhang |
| author_facet | Zhou Yu Xingyu Shi Zhaoning Zhang |
| author_sort | Zhou Yu |
| collection | DOAJ |
| description | Terminal operations management is an important part of air traffic management. Accurately detecting and predicting the operational status of the terminal area can help formulate more appropriate and efficient management methods. To achieve more accurate results in predicting the traffic situation, a ConvTrans-TCN (Convolutional Transformer with Temporal Convolutional Network) model is proposed in this paper. The model first constructs the feature extraction part using the causal-convolution multi-head self-attention module. It can effectively model the long-term dependency in the sequence and match the local patterns of the sequence, and it enhances the performance of feature extraction. Then the TCN (Temporal Convolutional Network) module is used to build the information fusion part to complete the fusion of feature data. The TCN architecture can accurately learn long-term and short-term dependencies in time series, and it has sufficient memory. Finally, the situation prediction is obtained by a feedforward neural network. The experiment’s results prove that this model is feasible and it performs better than the common models such as LSTM, BP, which can help air traffic managers to identify the operational status of the terminal area and provide decision support. |
| first_indexed | 2024-04-10T08:42:20Z |
| format | Article |
| id | doaj.art-2812ad7c6c014a049515972314dd975d |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-10T08:42:20Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-2812ad7c6c014a049515972314dd975d2023-02-23T00:01:05ZengIEEEIEEE Access2169-35362023-01-0111161561616510.1109/ACCESS.2023.324508510044658A Multi-Head Self-Attention Transformer-Based Model for Traffic Situation Prediction in Terminal AreasZhou Yu0https://orcid.org/0000-0002-2318-555XXingyu Shi1Zhaoning Zhang2College of Air Traffic Management, Civil Aviation University of China, Tianjin, ChinaCollege of Air Traffic Management, Civil Aviation University of China, Tianjin, ChinaCollege of Air Traffic Management, Civil Aviation University of China, Tianjin, ChinaTerminal operations management is an important part of air traffic management. Accurately detecting and predicting the operational status of the terminal area can help formulate more appropriate and efficient management methods. To achieve more accurate results in predicting the traffic situation, a ConvTrans-TCN (Convolutional Transformer with Temporal Convolutional Network) model is proposed in this paper. The model first constructs the feature extraction part using the causal-convolution multi-head self-attention module. It can effectively model the long-term dependency in the sequence and match the local patterns of the sequence, and it enhances the performance of feature extraction. Then the TCN (Temporal Convolutional Network) module is used to build the information fusion part to complete the fusion of feature data. The TCN architecture can accurately learn long-term and short-term dependencies in time series, and it has sufficient memory. Finally, the situation prediction is obtained by a feedforward neural network. The experiment’s results prove that this model is feasible and it performs better than the common models such as LSTM, BP, which can help air traffic managers to identify the operational status of the terminal area and provide decision support.https://ieeexplore.ieee.org/document/10044658/Intelligent transportation systemair transportationtraffic situation predictiontransformertemporal convolutional network |
| spellingShingle | Zhou Yu Xingyu Shi Zhaoning Zhang A Multi-Head Self-Attention Transformer-Based Model for Traffic Situation Prediction in Terminal Areas IEEE Access Intelligent transportation system air transportation traffic situation prediction transformer temporal convolutional network |
| title | A Multi-Head Self-Attention Transformer-Based Model for Traffic Situation Prediction in Terminal Areas |
| title_full | A Multi-Head Self-Attention Transformer-Based Model for Traffic Situation Prediction in Terminal Areas |
| title_fullStr | A Multi-Head Self-Attention Transformer-Based Model for Traffic Situation Prediction in Terminal Areas |
| title_full_unstemmed | A Multi-Head Self-Attention Transformer-Based Model for Traffic Situation Prediction in Terminal Areas |
| title_short | A Multi-Head Self-Attention Transformer-Based Model for Traffic Situation Prediction in Terminal Areas |
| title_sort | multi head self attention transformer based model for traffic situation prediction in terminal areas |
| topic | Intelligent transportation system air transportation traffic situation prediction transformer temporal convolutional network |
| url | https://ieeexplore.ieee.org/document/10044658/ |
| work_keys_str_mv | AT zhouyu amultiheadselfattentiontransformerbasedmodelfortrafficsituationpredictioninterminalareas AT xingyushi amultiheadselfattentiontransformerbasedmodelfortrafficsituationpredictioninterminalareas AT zhaoningzhang amultiheadselfattentiontransformerbasedmodelfortrafficsituationpredictioninterminalareas AT zhouyu multiheadselfattentiontransformerbasedmodelfortrafficsituationpredictioninterminalareas AT xingyushi multiheadselfattentiontransformerbasedmodelfortrafficsituationpredictioninterminalareas AT zhaoningzhang multiheadselfattentiontransformerbasedmodelfortrafficsituationpredictioninterminalareas |