1 |
中商情报网.2022年中国汽车及新能源汽车保有量数据统计情况[EB/OL]. [2023-01-12]. .
|
|
China Business Intelligence Network. In 2022, the statistics of the amount of data in China automobile and new energy vehicles [EB/OL]. [2023-01-12]. .
|
2 |
姚俊峰, 何瑞, 史童童, 等. 基于机器学习的交通流预测方法综述[J]. 交通运输工程学报, 2023, 23(3): 44-67.
|
|
YAO J F, HE R, SHI T T, et al. Review on machine learning-based traffic flow prediction methods [J]. Journal of Traffic and Transportation Engineering, 2023, 23(3): 44-67.
|
3 |
罗向龙,李丹阳,杨彧,等.基于KNN-LSTM的短时交通流预测[J].北京工业大学学报,2018,44(12):1521-1527.
|
|
LUO X L, LI D Y, YANG Y, et al. Short-time traffic flow prediction based on KNN-LSTM [J]. Journal of Beijing University of Technology, 2018,44(12): 1521-1527.
|
4 |
PEARL J. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference [M]. San Francisco: Morgan Kaufmann, 1988: 117-124.
|
5 |
CORTES C, VAPNIK V. Support-vector networks [J]. Machine Learning, 1995, 20(3): 273-297.
|
6 |
WU Q, JIANG Z, HONG K, et al. Tensor-based recurrent neural network and multi-modal prediction with its applications in traffic network management [J]. IEEE Transactions on Network and Service Management, 2021, 18(1): 780-792.
|
7 |
MA X, TAO Z, WANG Y, et al. Long short-term memory neural network for traffic speed prediction using remote microwave sensor data [J]. Transportation Research Part C: Emerging Technologies, 2015, 54: 187-197.
|
8 |
CHUNG J, GULCEHRE C, CHO K H, et al. Empirical evaluation of gated recurrent neural networks on sequence modeling [EB/OL]. [2023-04-29]. .
|
9 |
MA X, DAI Z, HE Z, et al. Learning traffic as images: a deep convolutional neural network for large-scale transportation network speed prediction [J]. Sensors, 2017, 17(4): No. 818.
|
10 |
CAO X, ZHONG Y, ZHOU Y, et al. Interactive temporal recurrent convolution network for traffic prediction in data centers[J]. IEEE Access, 2017, 6: 5276-5289.
|
11 |
SHI X, CHEN Z, 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.
|
12 |
TIAN Y, WEI C, XU D. Traffic flow prediction based on stack autoencoder and long short-term memory network [C]// Proceedings of the 2020 IEEE 3rd International Conference on Automation, Electronics and Electrical Engineering. Piscataway: IEEE, 2020: 385-388.
|
13 |
ZHANG J, ZHENG Y, QI D,et al. Predicting citywide crowd flows using deep spatio-temporal residual networks [J]. Artificial Intelligence, 2018, 259: 147-166.
|
14 |
GUO S, LIN Y, FENG N, et al. Attention based spatial-temporal graph convolutional networks for traffic flow forecasting [J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2019, 33(1): 922-929.
|
15 |
ZHENG C, FAN X, WANG C, et al. GMAN: a graph multi-attention network for traffic prediction [J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2020, 34(1): 1234-1241.
|
16 |
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. Red Hook: Curran Associates Inc., 2017: 6000-6010.
|
17 |
SZEGEDY C, LIU W, JIA Y, et al. Going deeper with convolutions [C]// Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2015: 1-9.
|
18 |
HUANG G, LIU Z, VAN DER MAATEN L, et al. Densely connected convolutional networks [C]// Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2017: 2261-2269.
|
19 |
Caltrans performance measurement system [DS/OL]. [2022-12-23]. .
|
20 |
SUTSKEVER I, VINYALS O, LE Q V. Sequence to sequence learning with neural networks [C]// Proceedings of the 27th International Conference on Neural Information Processing Systems — Volume 2. Cambridge: MIT Press, 2014: 3104-3112.
|
21 |
BAHDANAU D, CHO K, BENGIO Y. Neural machine translation by jointly learning to align and translate [EB/OL]. [2023-01-29]. .
|