|
References
[1] LIN J X. Traffic prediction of road speed based on conventional
neural network. Computer Knowedge and Technology, 2019, 15(9): 176 - 178 (in Chinese).
[2] CONNOR J T, MARTIN R D, ATLAS L E. Recurrent neural
networks and robust time series prediction. IEEE Transactions on Neural Networks, 1994, 5(2): 240 - 254.
[3] HOCHREITER S, SCHMIDHUBER J. Long short-term memory.
Neural Computation, 1997, 9(8): 1735 - 1780.
[4] CHO K, VAN MERRINBOER B, GULCEHRE C, et al. Learning
phrase representations using RNN encoder-decoder for statistical machine translation. Proceedings of the 2014 Conference on
Empirical Methods in Natural Language Processing (EMNLP'14),
2014, Oct 25 - 29, Doha, Qatar. Stroudsburg, PA, USA:
Association for Computational Linguistics, 2014: 1724 - 1734.
[5] FU R, ZHANG Z, LI L. Using LSTM and GRU neural network
methods for traffic flow prediction. Proceedings of the 31st Youth Academic Annual Conference of Chinese Association of Automation
(YAC'16), 2016, Nov 11 - 13, Wuhan, China. Piscataway,
NJ, USA: IEEE, 2016: 324 - 328.
[6] WU Z H, PAN S R, CHEN F W, et al. A comprehensive survey
on graph neural networks. IEEE Transactions on Neural Networks and Learning Systems, 2021, 32(1): 4 - 24.
[7] KIPF T N, WELLING M. Semi-supervised classification with
graph convolutional networks. arXiv Preprint, arXiv: 1609.
02907, 2016.
[8] ZHAO L, SONG Y J, ZHANG C, et al. T-GCN: A temporal
graph convolutional network for traffic prediction. IEEE
Transactions on Intelligent Transportation Systems, 2020, 21(9):
3848 - 3858.
[9] ZHANG J N, SHI X J, XIE J Y, et al. GaAN: Gated attention
networks for learning on large and spatiotemporal graphs. arXiv preprint, arXiv: 1803. 07294, 2018.
[10] YANG B, KANG Y, YUAN Y, et al. STLBAGAN: Spatio-temporal learnable bidirectional attention generative adversarial
networks for missing traffic data imputation. Knowledge-Based
Systems, 2021, 215: Article 106705.
[11] YAN H, FU L Y, QI Y, et al. Learning a robust classifier for
short-term traffic state prediction. Knowledge-Based Systems,
2022, 242: Article 108368.
[12] TA X X, LIU Z H, HU X, et al. Adaptive spatio-temporal graph
neural network for traffic forecasting. Knowledge-Based Systems,
2022, 242: Article 108199.
[13] REN Y L, JIANG H, JI N, et al. TBSM: A traffic burst-sensitive
model for short-term prediction under special events. Knowledge-Based Systems, 2022, 240: Article 108120.
[14] VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all
you need. Advances in Neural Information Processing Systems 30: Proceedings of the 31st International Conference on Neural
Information Processing Systems ( NIPS'17 ), 2017, Dec 4 - 9,
Long Beach, CA, USA. Red Hook, NY, USA: Curran Associates
Inc, 2017: 6000 - 6010.
[15] WANG X Y, MA Y, WANG Y Q, et al. Traffic flow prediction
via spatial temporal graph neural network. Proceedings of the 2020 Web Conference ( WWW'20 ), 2020, Apr 20 - 24, Taipei,
China. New York, NY, USA: ACM, 2020: 1082 - 1092.
[16] XU M X, DAI W R, LIU C M, et al. Spatial-temporal transformer
networks for traffic flow forecasting. arXiv preprint, arXiv: 2001.
02908, 2020.
[17] LIN G Y, DING J N, DING S T, et al. Passenger flow prediction with transformer: The Shenzhen metro case. Proceedings of the 2021 International Conference on Big Data Analysis and Computer
Science ( BDACS'21 ), 2021, Jun 25 - 27, Kunming, China. Piscataway, NJ, USA: IEEE, 2021: 97 - 100.
[18] LIU P J, SALEH M, POT E, et al. Generating Wikipedia by
summarizing long sequences. arXiv preprint, arXiv: 1801. 10198, 2018.
[19] PARMAR N, VASWANI A, USZKOREIT J, et al. Image
transformer. Proceedings of the 35th International Conference on Machine Learning ( PMLR'18 ), 2018, Jul 10 - 15, Stockholm
Sweden. Stroudsburg, PA, USA: International Machine Learning Society, 2018: 4055 - 4064.
[20] ZHOU L W, ZHOU Y B, CORSO J J, et al. End-to-end dense
video captioning with masked transformer. Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern
Recognition ( CVPR'18 ), 2018, Jun 18 - 23, Salt Lake City,
UT, USA. Piscataway, NJ, USA: IEEE, 2018: 8739 - 8748.
[21] RUAN H C, FENG X X, ZHENG H F. Graph transformer
attention networks for traffic flow prediction. Proceedings of the 7th International Conference on Computer and Communications
(ICCC'21), 2021, Dec 10 - 13, Chengdu, China. Piscataway,
NJ, USA: IEEE, 2021: 1778 - 1782.
[22] XIE Y L, NIU J J, ZHANG Y, et al. Multisize patched spatial-temporal transformer network for short-and long-term crowd flow prediction. IEEE Transactions on Intelligent Transportation
Systems, 2022, 23(11): 21548 - 21568.
[23] LI Y, REN Q Q, JIN H, et al. LSTN: Long short-term traffic flow
forecasting with transformer networks. Proceedings of the 26th International Conference on Pattern Recognition ( ICPR'22 ),
2022, Aug 21 - 25, Montreal, Canada. Piscataway, NJ, USA:
IEEE, 2022: 4793 - 4800.
[24] RAMANA K, SRIVASTAVA G, KUMAR M R, et al. A vision
transformer approach for traffic congestion prediction in urban
areas. IEEE Transactions on Intelligent Transportation Systems,
2023, 24(4): 3922 - 3934.
[25] CHEN C L, LIU Y B, CHEN L, et al. Bidirectional spatial-temporal adaptive transformer for urban traffic flow forecasting.
IEEE Transactions on Neural Networks and Learning Systems,
Early Access Article, DOI: 10.1109/TNNLS.2022.3183903.
[26] PU B, LIU J S, KANG Y, et al. MVSTT: A multiview spatial-temporal transformer network for traffic-flow forecasting. IEEE Transactions on Cybernetics, Early Access Article, DOI: 10.1109/TCYB.2022.3223918.
|
