| [1] |
Fang W, Zhuo W, Song Y, Yan J, Zhou T, et al. 2023. Δfree-LSTM: an error distribution free deep learning for short-term traffic flow forecasting. Neurocomputing 526:180−90 doi: 10.1016/j.neucom.2023.01.009 |
| [2] |
Li H, Yang S, Song Y, Luo Y, Li J, et al. 2023. Spatial dynamic graph convolutional network for traffic flow forecasting. Applied Intelligence 53:14986−98 doi: 10.1007/s10489-022-04271-z |
| [3] |
Kaysi I, Ben-Akiva M, Koutsopoulos H. 1993. Integrated approach to vehicle routing and congestion prediction for real-time driver guidance. Transportation Research Record 1408. pp 66−74. doi: https://onlinepubs.trb.org/Onlinepubs/trr/1993/1408/1408-009.pdf |
| [4] |
Zare Moayedi H, Masnadi-Shirazi MA. 2008. ARIMA model for network traffic prediction and anomaly detection. 2008 International Symposium on Information Technology, Kuala Lumpur, Malaysia, 26−28 August 2008. USA: IEEE. pp. 1−6. doi: 10.1109/ITSIM.2008.4631947 |
| [5] |
Peng Y, Lei M, Li JB, Peng XY. 2014. A novel hybridization of echo state networks and multiplicative seasonal ARIMA model for mobile communication traffic series forecasting. Neural Computing and Applications 24:883−90 doi: 10.1007/s00521-012-1291-9 |
| [6] |
Zhou T, Jiang D, Lin Z, Han G, Xu X, et al. 2019. Hybrid dual Kalman filtering model for short-term traffic flow forecasting. IET Intelligent Transport Systems 13:1023−1032 doi: 10.1049/iet-its.2018.5385 |
| [7] |
Cai L, Zhang Z, Yang J, Yu Y, Zhou T, et al. 2019. A noise-immune Kalman filter for short-term traffic flow forecasting. Physica A: Statistical Mechanics and its Applications 536:122601 doi: 10.1016/j.physa.2019.122601 |
| [8] |
Zhang S, Song Y, Jiang D, Zhou T, Qin J. 2019. Noise-identified Kalman filter for short-term traffic flow forecasting. 2019 15 th International Conference on Mobile Ad-Hoc and Sensor Networks (MSN), Shenzhen, China, 11−13 December 2019. USA: IEEE, pp. 462−66. doi: 10.1109/MSN48538.2019.00093 |
| [9] |
Zhou T, Han G, Xu X, Han C, Huang Y, et al. 2019. A learning-based multimodel integrated framework for dynamic traffic flow forecasting. Neural Processing Letters 49:407−30 doi: 10.1007/s11063-018-9804-x |
| [10] |
Cai L, Yu Y, Zhang S, Song Y, Xiong Z, et al. 2020. A sample-rebalanced outlier-rejected k-nearest neighbor regression model for short-term traffic flow forecasting. IEEE Access 8:22686−96 doi: 10.1109/ACCESS.2020.2970250 |
| [11] |
Zheng S, Zhang S, Song Y, Lin Z, Jiang D, et al. 2021. A noise-immune boosting framework for short-term traffic flow forecasting. Complexity. 2021:5582974 doi: 10.1155/2021/5582974 |
| [12] |
Cai L, Chen Q, Cai W, Xu X, Zhou T, et al. 2019. SVRGSA: a hybrid learning based model for short-term traffic flow forecasting. IET Intelligent Transport Systems 13:1348−55 doi: 10.1049/iet-its.2018.5315 |
| [13] |
Cui Z, Huang B, Dou H, Tan G, Zheng S, et al. 2022. GSA-ELM: A hybrid learning model for short-term traffic flow forecasting. IET Intelligent Transport Systems 16(1):41−52 doi: 10.1049/itr2.12127 |
| [14] |
Chai W, Zheng Y, Tian L, Qin J, Zhou T. 2023. GA-KELM: Genetic-Algorithm-Improved Kernel Extreme Learning Machine for Traffic Flow Forecasting. Mathematics 11:3574 doi: 10.3390/math11163574 |
| [15] |
Wu K, Xu C, Yan J, Wang F, Lin Z, et al. 2023. Error-distribution-free kernel extreme learning machine for traffic flow forecasting. Engineering Applications of Artificial Intelligence 123:106411 doi: 10.1016/j.engappai.2023.106411 |
| [16] |
Zhou T, Han G, Xu X, Lin Z, Han C, et al. 2017. δ-agree AdaBoost stacked autoencoder for short-term traffic flow forecasting. Neurocomputing 247:31−38 doi: 10.1016/j.neucom.2017.03.049 |
| [17] |
Lu H, Huang D, Song Y, Jiang D, Zhou T, et al. 2020. ST-TrafficNet: a spatial-temporal deep learning network for traffic forecasting. Electronics 9:1474 doi: 10.3390/electronics9091474 |
| [18] |
Lu H, Ge Z, Song Y, Jiang D, Zhou T, et al. 2021. A temporal-aware LSTM enhanced by loss-switch mechanism for traffic flow forecasting. Neurocomputing 427:169−78 doi: 10.1016/j.neucom.2020.11.026 |
| [19] |
Huang B, Dou H, Luo Y, Li J, Wang J, et al. 2023. Adaptive spatiotemporal transformer graph network for traffic flow forecasting by IoT loop detectors. IEEE Internet of Things Journal 10:1642−53 doi: 10.1109/JIOT.2022.3209523 |
| [20] |
Lv Y, Duan Y, Kang W, Li Z, Wang FY. 2015. Traffic flow prediction with big data: a deep learning approach. IEEE Transactions on Intelligent Transportation Systems 16:865−73 doi: 10.1109/TITS.2014.2345663 |
| [21] |
Cui Z, Huang B, Dou H, Cheng Y, Guan J, et al. 2022. A Two-Stage Hybrid Extreme Learning Model for Short-term Traffic Flow Forecasting. Mathematics 10:2087 doi: 10.3390/math10122087 |
| [22] |
Qu L, Lyu J, Li W, Ma D, Fan H. 2021. Features injected recurrent neural networks for short-term traffic speed prediction. Neurocomputing 451:290−304 doi: 10.1016/j.neucom.2021.03.054 |
| [23] |
Hochreiter S, Schmidhuber J. 1997. Long short-term memory. Neural Computation 9:1735−80 doi: 10.1162/neco.1997.9.8.1735 |
| [24] |
Fang W, Zhuo W, Yan J, Song Y, Jiang D, et al. 2022. Attention Meets Long Short-term Memory: A Deep Learning Network for Traffic Flow Forecasting. Physica A: Statistical Mechanics and its Applications 587:126485 doi: 10.1016/j.physa.2021.126485 |
| [25] |
Yang S, Li H, Luo Y, Li J, Song Y, et al. 2022. Spatiotemporal Adaptive Fusion Graph Network for Short-Term Traffic Flow Forecasting. Mathematics 10:1594 doi: 10.3390/math10091594 |
| [26] |
Zhao L, Wang Q, Jin B, Ye C. 2020. Short-term traffic flow intensity prediction based on CHS-LSTM. Arabian Journal for Science and Engineering 45:10845−57 doi: 10.1007/s13369-020-04862-3 |
| [27] |
Cai L, Lei M, Zhang S, Yu Y, Zhou T, et al. 2020. A noise-immune LSTM network for short-term traffic flow forecasting. Chaos 30:023135 doi: 10.1063/1.5120502 |
| [28] |
Chen B, Wang X, Lu N, Wang S, Cao J, et al. 2018. Mixture correntropy for robust learning. Pattern Recognition 79:318−27 doi: 10.1016/j.patcog.2018.02.010 |
| [29] |
Principe JC. 2010. Information Theoretic Learning: Renyi's Entropy and Kernel Perspectives. New York: Springer. 448 pp. doi: 10.1007/978-1-4419-1570-2 |
| [30] |
Chen B, Wang X, Li Y, Principe JC. 2019. Maximum correntropy criterion with variable center. IEEE Signal Processing Letters 26:1212−16 doi: 10.1109/LSP.2019.2925692 |
| [31] |
Zheng Y, Chen B, Wang S, Wang W, Qin W. 2022. Mixture correntropy-based kernel extreme learning machines. IEEE Transactions on Neural Networks and Learning Systems 33:811−25 doi: 10.1109/TNNLS.2020.3029198 |
| [32] |
Xie Y, Zhang Y, Ye Z. 2007. Short-term traffic volume forecasting using Kalman with discrete wavelet decomposition. Computer-Aided Civil and Infrastructure Engineering 22:326−34 doi: 10.1111/j.1467-8667.2007.00489.x |
| [33] |
Wang Y, van Schuppen JH, Vrancken J. 2013. Prediction of traffic flow at the boundary of a motorway network. IEEE Transactions on Intelligent Transportation Systems 15:214−27 doi: 10.1109/TITS.2013.2278192 |
| [34] |
Cai W, Yang J, Yu Y, Song Y, Zhou T, et al. 2020. PSO-ELM: A Hybrid Learning Model for Short-term Traffic Flow Forecasting. IEEE Access 8:6505−6514 doi: 10.1109/ACCESS.2019.2963784 |
| [35] |
Cai W, Yang J, Yu Y, Song Y, Zhou T, et al. 2024. SSA-ELM: a hybrid learning model for short-term traffic flow forecasting. Mathematics 12(12):1895 doi: 10.3390/math12121895 |
| [36] |
Zhu JZ, Cao JX, Zhu Y. 2014. Traffic volume forecasting based on radial basis function neural network with the consideration of traffic flows at the adjacent intersections. Transportation Research Part C: Emerging Technologies 47:139−54 doi: 10.1016/j.trc.2014.06.011 |