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Aug 30, 2023
Attention-Based Convolutional Recurrent Neural Network for Traffic Index Prediction
Authors: Xiaolei Liu [email protected], Zhengyu Duan [email protected], Songwen Luo [email protected], and Yixin Zhao [email protected]Author Affiliations
Publication: CICTP 2023
ABSTRACT
Accurate and real-time prediction of traffic indexes are a basis for mastering the change of road network state, making traffic control measures, and travel planning reasonable. The ACRNN model which combines an attention mechanism, convolution neural network, and recurrent neural network is proposed to predict the traffic index with multiple factors. First, the eXtreme Gradient Boosting model is used to calculate the importance of features. Then, the ACRNN model is constructed, the spatial and temporal features of the traffic index are captured by a convolution neural network and long short-term memory, and an attention mechanism is introduced to capture the global change trend. The performances of ACRNN model are tested by using 2 months of data from Shenzhen. The results show that the proposed model performs well and the prediction accuracy reaches 89.6%, which is better than ARIMA models and other neural network models such as BP, LSTM, CNN, and CRNN.
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Published online: Aug 30, 2023
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ASCE Technical Topics:
- Artificial intelligence and machine learning
- Computer programming
- Computing in civil engineering
- Engineering fundamentals
- Highway and road management
- Highway transportation
- Highways and roads
- Infrastructure
- Model accuracy
- Models (by type)
- Neural networks
- Traffic engineering
- Traffic management
- Traffic models
- Transportation engineering
- Transportation management
- Transportation networks
Authors
Affiliations
1Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji Univ., Shanghai, China. Email: [email protected]
2Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji Univ., Shanghai, China. Email: [email protected]
3Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji Univ., Shanghai, China. Email: [email protected]
4China Academy of Urban Planning and Design, Beijing, China. Email: [email protected]
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Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.