Page 193 - 《软件学报》2024年第4期
P. 193
赵文竹 等: 多视角融合的时空动态 GCN 城市交通流量预测 1771
References:
[1] Haydari A, Yılmaz Y. Deep reinforcement learning for intelligent transportation systems: A survey. IEEE Trans. on Intelligent
Transportation Systems, 2020, 23(1): 11−32.
[2] Meena G, Sharma D, Mahrishi M. Traffic prediction for intelligent transportation system using machine learning. In: Proc. of the
3rd Int’l Conf. on Emerging Technologies in Computer Engineering: Machine Learning and Internet of Things. 2020. 145−148.
[3] Li C, Xu P. Application on traffic flow prediction of machine learning in intelligent transportation. Neural Computing and
Applications, 2021, 33: 613−624.
[4] Reza S, Ferreira MC, Machado J, et al. A multi-head attention-based transformer model for traffic flow forecasting with a
comparative analysis to recurrent neural networks. Expert Systems with Applications, 2022, 202: 117275.
[5] Liu Y, Wu C, Wen J, et al. A grey convolutional neural network model for traffic flow prediction under traffic accidents.
Neurocomputing, 2022, 500: 761−775.
[6] Li H, Li X, Su L, et al. Deep spatio-temporal adaptive 3D convolutional neural networks for traffic flow prediction. ACM Trans. on
Intelligent Systems and Technology, 2022, 13(2): 1−21.
[7] Qu Z, Li H, Li Z, et al. Short-term traffic flow forecasting method with MB-LSTM hybrid network. IEEE Trans. on Intelligent
Transportation Systems, 2020, 23(1): 225−235.
[8] Mo J, Gong Z, Chen J. Attentive differential convolutional neural networks for crowd flow prediction. Knowledge-based Systems,
2022, 258: 110006.
[9] Feng N, Guo SN, Song C, et al. Multi-component spatial-temporal graph convolution networks for traffic flow forecasting. Ruan
Jian Xue Bao/Journal of Software, 2019, 30(3): 759−769 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/
5697.htm [doi: 10.13328/j.cnki.jos.005697]
[10] Lan S, Ma Y, Huang W, et al. Dstagnn: Dynamic spatial-temporal aware graph neural network for traffic flow forecasting. In: Proc.
of the 39th Int’l Conf. on Machine Learning, Vol.162. 2022. 11906−11917.
[11] Choi J, Choi H, Hwang J, et al. Graph neural controlled differential equations for traffic forecasting. In: Proc. of the 36th AAAI
Conf. on Artificial Intelligence. 2022. 6367−6374.
[12] Zhao L, Song Y, Zhang C, et al. T-GCN: A temporal graph convolutional network for traffic prediction. IEEE Trans. on Intelligent
Transportation Systems, 2019, 21(9): 3848−3858.
[13] Yu B, Yin H, Zhu Z. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv:1709.
04875, 2017.
[14] Li M, Zhu Z. Spatial-temporal fusion graph neural networks for traffic flow forecasting. In: Proc. of the 36th AAAI Conf. on
Artificial Intelligence. 2021. 4189−4196.
[15] Li Y, Shahabi C. A brief overview of machine learning methods for short-term traffic forecasting and future directions. Sigspatial
Special, 2018, 10(1): 3−9.
[16] Tedjopurnomo DA, Bao Z, Zheng B, et al. A survey on modern deep neural network for traffic prediction: Trends, methods and
challenges. IEEE Trans. on Knowledge and Data Engineering, 2020, 34(4): 1544−1561.
[17] Chen F, Chen Z, Biswas S, et al. Graph convolutional networks with kalman filtering for traffic prediction. In: Proc. of the 28th
Int’l Conf. on Advances in Geographic Information Systems. 2020. 135−138.
[18] Yin X, Wu G, Wei J, et al. Deep learning on traffic prediction: Methods, analysis, and future directions. IEEE Trans. on Intelligent
Transportation Systems, 2021, 23(6): 4927−4943.
[19] Lin G, Lin A, Gu D. Using support vector regression and K-nearest neighbors for short-term traffic flow prediction based on
maximal information coefficient. Information Sciences, 2022, 608: 517−531.
[20] Xu H, Jiang C. Deep belief network-based support vector regression method for traffic flow forecasting. Neural Computing and
Applications, 2020, 32: 2027−2036.
[21] Boukerche A, Wang J. Machine learning-based traffic prediction models for intelligent transportation systems. Computer Networks,
2020, 181: 107530.
[22] Minaee S, Boykov Y, Porikli F, et al. Image segmentation using deep learning: A survey. IEEE Trans. on Pattern Analysis and
Machine Intelligence, 2021, 44(7): 3523−3542.