1 |
ZHANG J P, WANG F Y, WANG K F, et al. Data-driven intelligent transportation systems: a survey[J]. IEEE Transactions on Intelligent Transportation Systems, 2011, 12(4): 1624-1639. 10.1109/tits.2011.2158001
|
2 |
吕明琪,洪照雄,陈铁明. 一种融合时空关联与社会事件的交通流预测方法[J]. 计算机科学, 2021, 48(2): 264-270. 10.11896/jsjkx.200300098
|
|
LYU M Q, HONG Z X, CENG T M. Traffic flow forecasting method combining spatio-temporal correlations and social events[J]. Computer Science, 2021, 48(2): 264-270. 10.11896/jsjkx.200300098
|
3 |
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. [S.l.]: IJCAI Organization, 2018: 3634-3640. 10.24963/ijcai.2018/505
|
4 |
LI Y G, YU R, SHAHABI C, et al. Diffusion convolutional recurrent neural network: data-driven traffic forecasting[EB/OL]. (2018-02-22) [2021-03-10].. 10.1109/ictai50040.2020.00063
|
5 |
KIPF T N, WELLING M. Semi-supervised classification with graph convolutional networks[EB/OL]. (201702-22) [2021-03-10]..
|
6 |
HOCHREITER S, SCHMIDHUBER J. Long short-term memory[J]. Neural Computation, 1997, 9(8): 1735-1780. 10.1162/neco.1997.9.8.1735
|
7 |
CHUNG J, GULCEHRE C, CHO K, et al. Empirical evaluation of gated recurrent neural networks on sequence modeling[EB/OL]. (2014-12-11) [2021-03-10].. 10.1007/978-3-662-44848-9_34
|
8 |
LeCUN Y, BOTTOU L, BENGIO Y, et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11): 2278-2324. 10.1109/5.726791
|
9 |
SONG C, LIN Y F, GUO S N, 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. Palo Alto, CA: AAAI Press, 2020: 914-921. 10.1609/aaai.v34i01.5438
|
10 |
FENG Y F, YOU H X, ZHANG Z Z, et al. Hypergraph neural networks[C]// Proceedings of the 33rd AAAI Conference on Artificial Intelligence. Palo Alto: CA: AAAI Press, 2019: 3558-3565. 10.1609/aaai.v33i01.33013558
|
11 |
KAMARIANAKIS Y, PRASTACOS P. Forecasting traffic flow conditions in an urban network: comparison of multivariate and univariate approaches[J]. Transportation Research Record, 2003, 1857(1): 74-84. 10.3141/1857-09
|
12 |
VOORT M VAN DER, DOUGHERTY M, WATSON S. Combining Kohonen maps with ARIMA time series models to forecast traffic flow[J]. Transportation Research Part C: Emerging Technologies, 1996, 4(5): 307-318. 10.1016/s0968-090x(97)82903-8
|
13 |
翁钢民,李凌雁. 旅游客流量预测:基于季节调整的PSO-SVR模型研究[J]. 计算机应用研究, 2014, 31(3): 692-695.
|
|
WENG G M, LI L Y. Study on tourism flow forecast: based on seasonal adjustment’ PSO-SVR model[J]. Application Research of Computers, 2014, 31(3): 692-695.
|
14 |
林培群,周楠楠. 基于多特征GBDT模型的收费站短时交通流量预测[J]. 广西大学学报(自然科学版), 2018, 43(3): 340-347.
|
|
LIN P Q, ZHOU N N. Short-term traffic flow prediction at toll stations based on multi-feature GBDT model[J]. Journal of Guangxi University (Natural Science Edition), 2018, 43(3): 340-347.
|
15 |
ZHANG J B, ZHENG Y, QI D K. Deep spatio-temporal residual networks for citywide crowd flows prediction[C]// Proceedings of the 31st AAAI Conference on Artificial Intelligence. Palo Alto, CA: AAAI Press, 2017: 1655-1661. 10.1016/j.artint.2018.03.002
|
16 |
GUO S N, LIN Y F, LI S J, et al. Deep spatial-temporal 3D convolutional neural networks for traffic data forecasting[J]. IEEE Transactions on Intelligent Transportation Systems, 2019, 20(10): 3913-3926. 10.1109/tits.2019.2906365
|
17 |
SHI X J, CHEN Z R, WANG H, et al. Convolutional LSTM network: a machine learning approach for precipitation nowcasting[C]// Proceedings of the 28th International Conference on Neural Information Processing Systems. Cambridge: MIT Press, 2015: 802-810.
|
18 |
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. 10.13328/j.cnki.jos.005697
|
|
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. 10.13328/j.cnki.jos.005697
|
19 |
GUO S N, LIN Y F, 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. Palo Alto, CA: AAAI Press, 2019: 922-929. 10.1609/aaai.v33i01.3301922
|
20 |
WU Z H, PAN S R, LONG G D, et al. Graph WaveNet for deep spatial-temporal graph modeling[C]// Proceedings of the 28th International Joint Conference on Artificial Intelligence. [S.l.]: IJCAI Organization, 2019: 1907-1913. 10.24963/ijcai.2019/264
|
21 |
WU Z H, PAN S R, LONG G D, et al. Connecting the dots: multivariate time series forecasting with graph neural networks[C]// Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2020: 753-763. 10.1145/3394486.3403118
|
22 |
ZHANG Q, CHANG J L, MENG G F, et al. Spatio-temporal graph structure learning for traffic forecasting[C]// Proceedings of the 34th AAAI Conference on Artificial Intelligence. Palo Alto, CA: AAAI Press, 2020: 1177-1185. 10.1609/aaai.v34i01.5470
|
23 |
LI M Z, ZHU Z X. Spatial-temporal fusion graph neural networks for traffic flow forecasting[EB/OL]. (2021-03-06) [2021-03-10].. 10.1109/ijcnn52387.2021.9534420
|
24 |
DONG Y, SAWIN W, BENGIO Y. HNHN: hypergraph networks with hyperedge neurons[EB/OL]. (2020-06-22) [2021-03-10]..
|