Journal of Guangdong University of Technology ›› 2024, Vol. 41 ›› Issue (01): 86-92.doi: 10.12052/gdutxb.230005

• Computer Science and Technology • Previous Articles     Next Articles

Traffic Flow Prediction Based on Recurrent Independent Mechanisms

Wen Wen, Jiang Jian-qiang, Cai Rui-chu, Hao Zhi-feng   

  1. School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, China
  • Received:2023-01-09 Online:2024-01-25 Published:2024-02-01

HTML

PDF (PC)

470

Abstract: Traffic flow prediction is an important issue of the intelligent traffic control and management systems. However, traffic flow data has nonlinear and complex characteristics in both time and space, making it challenging to accurately predict it. In this regard, this paper proposes a Graph temopral recurrent independent mechanisms (G-tRIM) model, which uses Graph attention networks (GAT) to effectively capture the spatial dependencies of traffic flow data, and uses Recurrent independent mechanisms (RIM) to accurately characterize the latent state of traffic flow data. We conduct experiments on the Beijing and Guizhou datasets, and the experimental results show that our proposed G-tRIM outperforms the baseline models on both datasets in terms of MSE and MAE.

Key words: traffic flow prediction, graph attention networks, recurrent independent mechanisms

CLC Number: 

  • TP183
[1] YAO H, TANG X, WEI H, et al. Revisiting spatial-temporal similarity: a deep learning framework for traffic prediction[C]//Proceedings of the AAAI Conference on Artificial Intelligence. Hawaii State: AAAI, 2019. 33(1) : 5668-5675.
[2] ZHANG J B, ZHENG Y, SUN J K, et al. Flow prediction in spatio-temporal networks based on multitask deep learning [J]. IEEE Transactions on Knowledge and Data Engineering, 2019, 32(3): 468-478.
[3] HAMED M M, AL-MASAEID H R, SAID Z M B. Short-term prediction of traffic volume in urban arterials [J]. Journal of Transportation Engineering, 1995, 121(3): 249-254.
[4] 姚智胜, 邵春福, 高永亮. 基于支持向量回归机的交通状态短时预测方法研究[J]. 北京交通大学学报, 2006, 30(3): 19-22.
YAO Z S, SHAO C F, GAO Y L. Research on methods of short-term traffic forecasting based on support vector regression [J]. Journal of Beijing Jiaotong University, 2006, 30(3): 19-22.
[5] 张晓利, 贺国光, 陆化普. 基于K-邻域非参数回归短时交通流预测方法[J]. 系统工程学报, 2009, 24(2): 178-183.
ZHANG X L, HE G G, LU H P. Short-term traffic flow forecasting based on K-nearest neighbors non-parametric regression [J]. Journal of Systems Engineering, 2009, 24(2): 178-183.
[6] ZHANG J B, ZHENG Y, QI D K. Deep spatio-temporal residual networks for citywide crowd flows prediction[C]//Proceedings of the AAAI Conference on Artificial Intelligence. San Francisco: AAAI. 2017, 31(1) .
[7] WU Z H, PAN S R, CHEN F W, et al. A comprehensive survey on graph neural networks [J]. IEEE Transactions on Neural Networks and Learning Systems, 2020, 32(1): 4-24.
[8] 冯宁, 郭晟楠, 宋超, 等. 面向交通流量预测的多组件时空图卷积网络[J]. 软件学报, 2019, 30(3): 759-769.
FENG N, GUO S N, SONG C, et al. Multi-component spatial-temporal graph convolution networks for traffic flow forecasting [J]. Journal of Software, 2019, 30(3): 759-769.
[9] KIPF T N, WELLING M. Semi-supervised classification with graph convolutional networks[C]//International Conference on Learning Representations. Toulon: ICLR, 2017.
[10] HOCHREITER S, SCHMIDHUBER J. Long short-term memory [J]. Neural Computation, 1997, 9(8): 1735-1780.
[11] CHO K, VAN MERRIËNBOER B, BAHDANAU D, et al. On the properties of neural machine translation: encoder–decoder approaches[EB/OL]. arXiv: 1409.1259(2014-10-07)[2023-01-01]. https://arxiv.org/abs/1409.1259.
[12] JORDAN M I. SERIAL ORDER: A parallel distributed processing approach[M]//Advances in Psychology. Amsterdam: North-Holland, 1997, 121: 471-495.
[13] GOYAL A, LAMB A, HOFFMANN J, et al. Recurrent independent mechanisms[C]//International Conference on Learning Representations. Online: ICLR, 2021.
[14] YU B, YIN H T, ZHU Z X. Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting[C]//Proceedings of the 27th International Joint Conference on Artificial Intelligence. Stockholm: IJCAI, 2018: 3634-3640.
[15] VELICKOVIC P, CUCURULL G, CASANOVA A, et al. Graph attention networks [J]. International Conference on Learning Representations, 2018, 1050: 4.
[16] ZHAO L, SONG Y, ZHANG C, et al. T-GCN: a temporal graph convolutional network for traffic prediction [J]. IEEE Transactions on Intelligent Transportation Systems, 2019, 21(9): 3848-3858.
[17] GUO S, LIN Y, FENG N, et al. Attention based spatial-temporal graph convolutional networks for traffic flow forecasting[C]//Proceedings of the AAAI Conference on Artificial Intelligence. Hawaii State: AAAI, 2019, 33(1) : 922-929.
[1] Lai Zhi-mao, Zhang Yun, Li Dong. A Survey of Deepfake Detection Techniques Based on Transformer [J]. Journal of Guangdong University of Technology, 2023, 40(06): 155-167.
[2] Zhang Yun, Wang Xiao-dong. A Review and Thinking of Deep Learning with a Restricted Number of Samples [J]. Journal of Guangdong University of Technology, 2022, 39(05): 1-8.
[3] Yuan Jun, Zhang Yun, Zhang Gui-dong, Li Zhong, Chen Zhe, Yu Sheng-long. A Survey of Energy Management System Based on Adaptive Dynamic Programming [J]. Journal of Guangdong University of Technology, 2022, 39(05): 21-28.
[4] Qiu Zhan-chun, Fei Lun-ke, Teng Shao-hua, Zhang Wei. Palmprint Recognition Based on Cosine Similarity [J]. Journal of Guangdong University of Technology, 2022, 39(03): 55-62.
[5] Hu Bin, Guan Zhi-hong, Xie Kan, Chen Guan-rong. Dynamics and Intelligent Control of Complex Networks [J]. Journal of Guangdong University of Technology, 2021, 38(06): 9-19.
[6] Lai Jun, Liu Zhen-yu, Liu Sheng-hai. A Small Sample Data Prediction Method Based on Global Data Shuffling [J]. Journal of Guangdong University of Technology, 2021, 38(03): 17-21.
[7] Wu Jia-hu, Xiong Hua, Zong Rui, Zhao Yao, Zhou Xian-zhong. Target Turning Maneuver Type Recognition Based on Recurrent Neural Networks [J]. Journal of Guangdong University of Technology, 2020, 37(02): 67-73.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Copyright © 2017 Journal of Guangdong University of Technology
Add：729 East Dongfeng RD., Guangzhou PRC. Postcode: 510090
Tel：020-37626653,020-37626139,020-37626396
Email：xbzrb@gdut.edu.cn
Supported:Beijing Magtech