Journal of Shandong University(Engineering Science) ›› 2023, Vol. 53 ›› Issue (5): 29-36.doi: 10.6040/j.issn.1672-3961.0.2022.073
• Machine Learning & Data Mining • Previous Articles
CHEN Lei1,2,3, ZHAO Yaoshuai3,4,*, LIN Yan1,2, GUO Shengnan1,2, WAN Huaiyu1,2, LIN Youfang1,2
CLC Number:
[1] VLAHOGIANNI E I, KARLAFTIS M G, GOLIAS J C. Short-term traffic forecasting: where we are and where were going[J]. Transportation Research Part C: Emerging Technologies, 2014, 43: 3-19. [2] LAENGKVIST M, KARLSSON L, LOUTIFI A. A review of unsupervised feature learning and deep learning for time-series modeling[J]. Pattern Recognition Letters, 2014, 42(1):11-24. [3] TEDJOPURNOMO D A, BAO Z, ZHENG B, et al. A survey on modern deep neural network for traffic prediction: trends, methods and challenges[J]. IEEE Transactions on Knowledge and Data Engineering, 2020, 14(8):1-20. [4] WILLIAMS B M, HOEL L A. Modeling and forecasting vehicular traffic flow as a seasonal arima process: theoretical basis and empirical results[J]. Journal of Transportation Engineering, 2003, 129(6):664-672. [5] CHROBOK R, WAHLE J, SCHRECKENBERG M. Traffic forecast using simulations of large scale networks[C] // Proceedings of 2001 IEEE Intelligent Transportation Systems(ITSC). Oakland, USA: IEEE, 2001: 434-439. [6] CASTRO-NETO M, JEONG Y S, JEONG M K, et al. Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions[J]. Expert Systems with Applications, 2009, 36(3): 6164-6173. [7] SCHMIDHUBER J. Deep learning in neural networks: an overview[J]. Neural Networks, 2015, 61: 85-117. [8] 余凯,贾磊,陈雨强,等. 深度学习的昨天、今天和明天[J]. 计算机研究与发展,2013,50(9): 1799-1804. YU Kai, JIA Lei, CHEN Yuqiang, et al. Deep learning: yesterday, today, and tomorrow[J]. Journal of Computer Research and Development, 2013, 50(9): 1799-1804. [9] 刘知远,孙茂松,林衍凯,等. 知识表示学习研究进展[J]. 计算机研究与发展, 2016, 53(2): 247-261. LIU Zhiyuan, SUN Maosong, LIN Yankai, et al. Knowledge representation learning: a review[J]. Journal of Computer Research and Development, 2016, 53(2): 247-261. [10] KUMAR S V, VANAJAKSHI L. Short-term traffic flow prediction using seasonal arima model with limited input data[J]. European Transport Research Review, 2015, 7(3):1-9. [11] JEONG Y S, BYON Y J, CASTRO-NETO M M, et al. Supervised weighting-online learning algorithm for short-term traffic flow prediction[J]. IEEE Transactions on Intelligent Transportation Systems, 2013, 14(4):1700-1707. [12] GUO S, LIN Y, FENG N, et al. Attention based spatial-temporal graph convolutional networks for traffic flow forecasting[C] //Proceedings of the 33rd AAAI Conference on Artificial Intelligence. Honolulu, USA: AAAI Press, 2019:922-929. [13] VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[C] //Proceedings of the 31st International Conference on Neural Information Processing Systems. Long Beach, USA: Curran Associates Inc., 2017: 6000-6010. [14] ZHENG C, FAN X, WANG C, et al. Gman: a graph multi-attention network for traffic prediction[C] //Proceedings of the 34th AAAI Conference on Artificial Intelligence. New York, USA: AAAI Press, 2020:1234-1241. [15] ZHANG J, ZHENG Y, QI D. Deep spatio-temporal residual networks for citywide crowd flows prediction[C] //Proceedings of the 31st AAAI Conference on Artificial Intelligence. San Francisco, USA: AAAI Press, 2017:1655-1661. [16] YU B, YIN H, ZHU Z. Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting[C] //Proceedings of the 27th International Joint Conference on Artificial Intelligence. Stockholm, Sweden: AAAI Press, 2018:3634-3640. [17] GENG X, LI Y, WANG L, et al. Spatiotemporal multi-graph convolution network for ride-hailing demand forecasting[C] //Proceedings of the 33rd AAAI Conference on Artificial Intelligence. Honolulu, USA: AAAI Press, 2019:3656-3663. [18] WU Z, PAN S, LONG G, et al. Graph wavenet for deep spatial-temporal graph modeling[C] //Proceedings of the 28th International Joint Conference on Artificial Intelligence. Macao, China: AAAI Press, 2019:1907-1913. [19] LIN H, BAI R, JIA W, et al. Preserving dynamic attention for long-term spatial-temporal prediction[C] //Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA: Association for Computing Machinery, 2020:36-46. [20] TANG J, QU M, WANG M, et al. Line: large-scale information network embedding[C] //Proceedings of the 24th International Conference on World Wide Web. Florence, Italy: Association for Computing Machinery, 2015: 1067-1077. [21] KE G, HE D, LIU T. Rethinking the positional encoding in language pre-training[EB/OL].(2020-01-28)[2021-03-01]. https://arxiv.org/abs/2006.15595. [22] XU D, RUAN C, KORPEOGLU E, et al. Inductive representation learning on temporal graphs[EB/OL].(2020-02-19)[2021-03-01]. https://arxiv.org/abs/2002.07962. [23] LI Y, YU R, SHAHABI C, et al. Diffusion convolutional recurrent neural network: data-driven traffic forecasting[EB/OL].(2017-07-06)[2021-03-01]. https://arxiv.org/abs/1707.01926. [24] SONG C, LIN Y, GUO S, et al. Spatial-temporal synchronous graph convolutional networks: a new framework for spatial-temporal network data forecasting[C] //Proceedings of the 34th AAAI Conference on Artificial Intelligence. New York, USA: AAAI Press, 2020: 914-921. [25] BAI L, YAO L, LI C, et al. Adaptive graph convolutional recurrent network for traffic forecasting[C] //Proceedings of the 33rd International Conference on Neural Information Processing Systems. Red Hook, USA: Curran Associates Inc., 2020: 17804-17815. [26] LI M, ZHU Z. Spatial-temporal fusion graph neural networks for traffic flow forecasting[C] //Proceedings of the 35th AAAI Conference on Artificial Intelligence. Vancouver, Canada: AAAI Press, 2021: 4189-4196. |
[1] | Ying LI,Jiankun WANG. The classification of mild cognitive impairment based on supervised graph regularization and information fusion [J]. Journal of Shandong University(Engineering Science), 2023, 53(4): 65-73. |
[2] | LIU Xing, YANG Lu, HAO Fanchang. Finger vein image retrieval based on multi-feature fusion [J]. Journal of Shandong University(Engineering Science), 2023, 53(2): 118-126. |
[3] | YU Yixuan, YANG Geng, GENG Hua. Multimodal hierarchical keyframe extraction method for continuous combined motion [J]. Journal of Shandong University(Engineering Science), 2023, 53(2): 42-50. |
[4] | HUANG Huajuan, CHENG Qian, WEI Xiuxi, YU Chuchu. Adaptive crow search algorithm with Jaya algorithm and Gaussian mutation [J]. Journal of Shandong University(Engineering Science), 2023, 53(2): 11-22. |
[5] | LIU Fangxu, WANG Jian, WEI Benzheng. Auxiliary diagnosis algorithm for pediatric pneumonia based on multi-spatial attention [J]. Journal of Shandong University(Engineering Science), 2023, 53(2): 135-142. |
[6] | ZHANG Hao, LI Ziling, LIU Tong, ZHANG Dawei, TAO Jianhua. A technology prediction model based on fuzzy Bayesian networks with sociological factors [J]. Journal of Shandong University(Engineering Science), 2023, 53(2): 23-33. |
[7] | WU Yanli, LIU Shuwei, HE Dongxiao, WANG Xiaobao, JIN Di. Poisson-gamma topic model of describing multiple underlying relationships [J]. Journal of Shandong University(Engineering Science), 2023, 53(2): 51-60. |
[8] | YU Mingjun, DIAO Hongjun, LING Xinghong. Online multi-object tracking method based on trajectory mask [J]. Journal of Shandong University(Engineering Science), 2023, 53(2): 61-69. |
[9] | Yue YUAN,Yanli WANG,Kan LIU. Named entity recognition model based on dilated convolutional block architecture [J]. Journal of Shandong University(Engineering Science), 2022, 52(6): 105-114. |
[10] | Xiaobin XU,Qi WANG,Bin GAO,Zhiyu SUN,Zhongjun LIANG,Shangguang WANG. Pre-allocation of resources based on trajectory prediction in heterogeneous networks [J]. Journal of Shandong University(Engineering Science), 2022, 52(4): 12-19. |
[11] | Yinfeng MENG,Qingfang LI. Recognition learning based on multivariate functional principal component representation [J]. Journal of Shandong University(Engineering Science), 2022, 52(3): 1-8. |
[12] | Xiushan NIE,Yuling MA,Huiyan QIAO,Jie GUO,Chaoran CUI,Zhiyun YU,Xingbo LIU,Yilong YIN. Survey on student academic performance prediction from the perspective of task granularity [J]. Journal of Shandong University(Engineering Science), 2022, 52(2): 1-14. |
[13] | Tongyu JIANG, Fan CHEN, Hongjie HE. Lightweight face super-resolution network based on asymmetric U-pyramid reconstruction [J]. Journal of Shandong University(Engineering Science), 2022, 52(1): 1-8. |
[14] | Jun HU,Dongmei YANG,Li LIU,Fujin ZHONG. Cross social network user alignment via fusing node state information [J]. Journal of Shandong University(Engineering Science), 2021, 51(6): 49-58. |
[15] | Ye LIANG,Nan MA,Hongzhe LIU. Image-dependent fusion method for saliency maps [J]. Journal of Shandong University(Engineering Science), 2021, 51(4): 1-7. |
|