Spatiotemporal attention mechanism-based multistep traffic volume prediction model for highway toll stations
-
Published:2022-03-31
Issue:61
Volume:1
Page:21-38
-
ISSN:0866-9546
-
Container-title:Archives of Transport
-
language:
-
Short-container-title:AoT
Author:
Huang Zijing1, Lin Peiqun1, Lin Xukun2, Zhou Chuhao1, Huang Tongge3
Affiliation:
1. School of Civil Engineering and Transportation, South China University of Technology, Guangzhou, China 2. School of Civil Engineering and Transportation, South China University of Technology, Guangzhou, China; Department of Transportation of Guangdong Guangzhou, China 3. Institute of Software, Chinese Academy of Sciences, Beijing, China
Abstract
As the fundamental part of other Intelligent Transportation Systems (ITS) applications, short-term traffic volume prediction plays an important role in various intelligent transportation tasks, such as traffic management, traffic signal control and route planning. Although Neural-network-based traffic prediction methods can produce good results, most of the models can’t be explained in an intuitive way. In this paper, we not only proposed a model that increase the short-term prediction accuracy of the traffic volume, but also improved the interpretability of the model by analyzing the internal attention score learnt by the model. we propose a spatiotemporal attention mechanism-based multistep traffic volume prediction model (SAMM). Inside the model, an LSTM-based Encoder-Decoder network with a hybrid attention mechanism is introduced, which consists of spatial attention and temporal attention. In the first level, the local and global spatial attention mechanisms considering the micro traffic evolution and macro pattern similarity, respectively, are applied to capture and amplify the features from the highly correlated entrance stations. In the second level, a temporal attention mechanism is employed to amplify the features from the time steps captured as contributing more to the future exit volume. Considering the time-dependent characteristics and the continuity of the recent evolutionary traffic volume trend, the timestamp features and historical exit volume series of target stations are included as the external inputs. An experiment is conducted using data from the highway toll collection system of Guangdong Province, China. By extracting and analyzing the weights of the spatial and temporal attention layers, the contributions of the intermediate parameters are revealed and explained with knowledge acquired by historical statistics. The results show that the proposed model outperforms the state-of-the-art model by 29.51% in terms of MSE, 13.93% in terms of MAE, and 5.69% in terms of MAPE. The effectiveness of the Encoder-Decoder framework and the attention mechanism are also verified.
Publisher
Index Copernicus
Subject
Transportation,Automotive Engineering
Reference47 articles.
1. Connor, J. T., Martin, R. D., & Atlas, L. E. (1994). Recurrent neural networks and robust time series prediction. IEEE Transactions on Neural Networks, 5(2), 240-253. DOI: 10.1109/72.279188 2. Cui, Z., Henrickson, K., Ke, R., & Wang, Y. (2020). Traffic graph convolutional recurrent neural network: A deep learning framework for network-scale traffic learning and forecasting. IEEE Transactions on Intelligent Transportation Systems, 21(11), 4883-4894. DOI: 10.1109/TITS.2019.2950416 3. Du, S., Li, T., Gong, X., & Horng, S.-J. (2020). A hybrid method for traffic flow forecasting using multimodal deep learning. International Journal of Computational Intelligence Systems, 13(1), 85-97. DOI: 10.2991/ijcis.d.200120.001 4. Du, S., Li, T., Yang, Y., Gong, X., & Horng, S.-J. (2019). An lstm based encoder-decoder model for multistep traffic flow prediction. In 2019 international joint conference on neural networks, ijcnn 2019, july 14, 2019 - july 19, 2019 (Vol. 2019-July). Institute of Electrical and Electronics Engineers Inc. DOI: 10.1109/IJCNN.2019.8851928 5. Feng, X., Ling, X., Zheng, H., Chen, Z., & Xu, Y. (2019). Adaptive multi-kernel svm with spatial-temporal correlation for short-term traffic flow prediction. IEEE Transactions on Intelligent Transportation Systems, 20(6), 2001-2013. DOI: 10.1109/TITS.2018.2854913
Cited by
3 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
|
|