[1] |
MOROCHO-CAYAMCELA M E, LEE H, LIM W. Machine learning for 5G/B5G mobile and wireless communications: Potential, limitations, and future directions[J]. IEEE Access, 2019,7:137184-137206.
|
[2] |
ZHAO J H, NI S J, YANG L H, et al. Multiband cooperation for 5G HetNets: A promising network paradigm[J].IEEE Vehicular Technology Magazine, 2019,14(4):85-93.
|
[3] |
OH S, BYON Y J, JANG K, et al. Short-term travel-time prediction on highway: A review of the data-driven approach[J]. Transport Reviews, 2015,35(1):4-32.
|
[4] |
MA X T, ZHAO J H, GONG Y, et al. Carrier sense multiple access with collision avoidance-aware connectivity quality of downlink broadcast in vehicular relay networks[J]. IET Microwaves, Antennas & Propagation, 2019,13(8):1096-1103.
|
[5] |
LIN X F, HUANG Y Z. Short-term high-speed traffic flow prediction based on ARIMA-GARCH-M model[J]. Wireless Personal Communications, 2021,117(4):3421-3430.
|
[6] |
赵伟. 基于SOA-LSSVM的短时交通流量预测[J]. 计算机与现代化, 2015(6):27-31.
|
[7] |
LIU Z, DU W, YAN D M, et al. Short-term traffic flow forecasting based on combination of K-nearest neighbor and support vector regression[J]. Journal of Highway and Transportation Research and Development(English Edition), 2018,12(1):89-96.
|
[8] |
LU W Q, YI Z W, LIU W, et al. Efficient deep learning based method for multi-lane speed forecasting: A case study in Beijing[J]. IET Intelligent Transport Systems, 2020,14(14):2073-2082.
|
[9] |
DOGAN E. LSTM training set analysis and clustering model development for short-term traffic flow prediction[J]. Neural Computing and Applications, 2021,33(17):11175-11188.
|
[10] |
马焱棋,林群,赵昱程,等. 基于深度学习LSTM对交通流状态的预测[J]. 数学的实践与认识, 2021,51(4):47-56.
|
[11] |
李巧茹,赵蓉,陈亮. 基于SVM与自适应时空数据融合的短时交通流量预测模型[J]. 北京工业大学学报, 2015,41(4):597-602.
|
[12] |
姚思佳,桂智明,郭黎敏. 基于改进eRCNN的局部路网交通流预测[J]. 计算机与现代化, 2021(7):49-53.
|
[13] |
李磊,张青苗,赵军辉,等. 基于改进CNN-LSTM组合模型的分时段短时交通流预测[J]. 应用科学学报, 2021,39(2):185-198.
|
[14] |
LI P, ABDEL-ATY M, YUAN J H. Real-time crash risk prediction on arterials based on LSTM-CNN[J]. Accident Analysis & Prevention, 2020,135. DOI:10.1016/j.app.2019.105371.
|
[15] |
SUN P, BOUKERCHE A, TAO Y J. SSGRU: A novel hybrid stacked GRU-based traffic volume prediction approach in a road network[J]. Computer Communications, 2020,160:502-511.
|
[16] |
卢生巧,黄中祥. 基于深度学习的短时交通流预测模型[J]. 交通科学与工程, 2020,36(3):74-80.
|
[17] |
ALI A, ZHU Y M, ZAKARYA M. Exploiting dynamic spatio-temporal correlations for citywide traffic flow prediction using attention based neural networks[J]. Information Sciences, 2021,577:852-870.
|
[18] |
VIJAYALAKSHMI B, RAMAR K, JHANJHI N Z, et al. An attention-based deep learning model for traffic flow prediction using spatiotemporal features towards sustainable smart city[J]. International Journal of Communication Systems, 2021,34(3). DOI:10.1002/dac.4609.
|
[19] |
桂智明,李壮壮,郭黎敏. 基于ACGRU模型的短时交通流预测[J]. 计算机工程与应用, 2020, 56(21):260-265.
|
[20] |
CHEN X Q, CHEN H X, YANG Y S, et al. Traffic flow prediction by an ensemble framework with data denoising and deep learning model[J]. Physica A: Statistical Mechanics and its Applications, 2021,565. DOI:10.1016/j.physa.2020.125574.
|
[21] |
HUANG H C, CHEN J Y, HUO X T, et al. Effect of multi-scale decomposition on performance of neural networks in short-term traffic flow prediction[J]. IEEE Access, 2021,9:50994-51004.
|
[22] |
聂铃,张剑,胡茂政. 基于CEEMDAN分解的短时交通流组合预测[J/OL]. 计算机工程与应用:1-9[2022-05-23].
|
[23] |
CHEN K, CHEN F, LAI B S, et al. Dynamic spatio-temporal graph-based CNNs for traffic flow prediction[J]. IEEE Access, 2020,8:185136-185145.
|
[24] |
李幼军,黄佳进,王海渊,等. 基于SAE和LSTM RNN的多模态生理信号融合和情感识别研究[J]. 通信学报, 2017,38(12):109-120.
|
[25] |
ZHAO Z, ZHANG Y Y. A traffic flow prediction approach: LSTM with detrending[C]// 2018 IEEE International Conference on Progress in Informatics and Computing. 2018:101-105.
|
[26] |
杨祎玥,伏潜,万定生. 基于深度循环神经网络的时间序列预测模型[J]. 计算机技术与发展, 2017,27(3):35-38.
|
[27] |
HUANG N E, SHEN Z, LONG S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proceedings of the Royal Society of London, Series A, 1998,454(1971):903-995.
|
[28] |
王静,李维德. 基于CEEMD和GWO的超短期风速预测[J]. 电力系统保护与控制, 2018,46(9):69-74.
|