Volume 26 Issue 2
Feb.  2026
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LIU Wei, ZHONG Can, CAO Wen-ming. Review of data-driven short-term prediction methods for continuous traffic flow in road networks[J]. Journal of Traffic and Transportation Engineering, 2026, 26(2): 24-43. doi: 10.19818/j.cnki.1671-1637.2026.141
Citation: LIU Wei, ZHONG Can, CAO Wen-ming. Review of data-driven short-term prediction methods for continuous traffic flow in road networks[J]. Journal of Traffic and Transportation Engineering, 2026, 26(2): 24-43. doi: 10.19818/j.cnki.1671-1637.2026.141
shu

Review of data-driven short-term prediction methods for continuous traffic flow in road networks

doi: 10.19818/j.cnki.1671-1637.2026.141
  • 1.

    College of Traffic & Transportation, Chongqing Jiaotong University, Chongqing 400074, China

  • 2.

    School of Mathematics and Statistics, Chongqing Jiaotong University, Chongqing 400074, China

Funds:

Key Project of Chongqing Natural Science Foundation Joint Fund for Innovation and Development CSTB2024NSCQ-LZX0139

More Information
  • Corresponding author: LIU Wei, professor, PhD, E-mail: neway119@qq.com
  • Received Date: 2025-08-22
  • Accepted Date: 2025-11-27
  • Rev Recd Date: 2025-10-16
  • Publish Date: 2026-02-28
    Abstract
  • In the context of the rapid development of artificial intelligence computing and real-time traffic data acquisition technology, the deep learning prediction models, data processing technology, and prediction performance for short-term traffic flow were reviewed and summarized to grasp the latest developments of data-driven short-term traffic flow prediction technology for road networks. The evolution of classical statistical models, machine learning models, and deep learning models for traffic flow prediction was reviewed, and the advantages and limitations of various models were emphatically analyzed. The research progress of short-term traffic flow prediction methods from 2024 to the present was summarized. Short-term traffic flow prediction models such as recurrent neural networks, graph convolutional networks, multi-head attention mechanisms and Transformer architectures, neural ordinary differential equations, hypergraph theory, and lightweight architectures were compared and investigated in detail, as well as data processing technologies for short-term traffic flow prediction including federated learning, transfer learning, generative adversarial networks, and multi-source data fusion. Based on the comparison of three core indicators including root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE), the performance of mainstream models on the standardized dataset PEMS was summarized, and the generalization capability and stability of representative models were evaluated. The research results show that deep learning methods have significant advantages over traditional models in terms of accuracy, generalization capability, and stability for short-term traffic flow prediction. Short-term traffic flow prediction models with characteristics such as dynamic spatio-temporal relationship modeling, multi-scale periodic data structures, computational efficiency improvement methods, and enhanced robustness mechanisms demonstrate superior performance. Data processing technologies can effectively mitigate practical problems such as data privacy, cross-regional differences, data scarcity, and abnormal missing values, enhancing the engineering application performance and scalability of short-term traffic flow prediction models. Future research can be deepened in aspects such as spatio-temporal feature mining, data fusion, model light weighting, knowledge transfer, and model engineering applications.

     

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    • References(89)
  • [1]
    SUN N. Intelligent transportation system planning in the age of artificial intelligence[C]//LI J, WU J, CHEN K. 2021 International Conference on Environmental and Engineering Management. Changsha: EDP Sciences, 2021: 1036-1041.
    [2]
    TEDJOPURNOMO D A, BAO Z, ZHENG B, et al. A survey on modern deep neural network for traffic prediction: Trends, methods and challenges[C]//IEEE. 2023 39th IEEE International Conference on Data Engineering (ICDE). New York: IEEE, 2023: 3795-3796.
    [3]
    LINGRAS P, SHARMA S, ZHONG M. Prediction of recreational travel using genetically designed regression and time-delay neural network models[J]. Transportation Research Record, 2002, 1805(1): 16-24. doi: 10.3141/1805-03
    [4]
    MA X L, TO Z M, WANG Y H, 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. doi: 10.1016/j.trc.2015.03.014
    [5]
    SHU W, CAI K, XIONG N N. A short-term traffic flow prediction model based on an improved gate recurrent unit neural network[J]. IEEE Transactions on Intelligent Transportation Systems, 2021(9): 1-12.
    [6]
    SHI X J, CHEN Z, WANG H, et al. Convolutional LSTM network: A machine learning approach for precipitation nowcasting[C]//Curran Associates. Advances in Neural Information Processing Systems 28. New York: Curran Associates, 2015: 802-810.
    [7]
    KIPF T N, WELLING M. Semi-supervised classification with graph convolutional networks[C]//ICLR. Proceedings of the 5th International Conference on Learning Representations. Toulon: ICLR, 2017: 1-14.
    [8]
    XIONG Zhang-you, LI Wei-jun, ZHU Xiao-juan, et al. Short-term traffic flow prediction based on deep learning[J]. Computer Engineering and Applications, 2025, 61(11): 67-82.
    [9]
    YAO Jun-feng, HE Rui, SHI Tong-tong, et al. Review on machine learning-based traffic flow prediction methods[J]. Journal of Traffic and Transportation Engineering, 2023, 23(3): 44-67. doi: 10.19818/j.cnki.1671-1637.2023.03.003
    [10]
    AHMED S F, KULDEEP S A, RAFA S J, et al. Enhancement of traffic forecasting through graph neural network-based information fusion techniques[J]. Information Fusion, 2024, 110: 102466. doi: 10.1016/j.inffus.2024.102466
    [11]
    STEPHANEDES Y J, MICHALOPOULOS P G, PLUM R A. Improved estimation of traffic flow for real time control[J]. Transportation Research Record Journal of the Transportation Research Board, 1980(795): 28-39.
    [12]
    AHMED M S, COOK A R. Analysis of freeway traffic time-series data by using Box-jenkins techniques[J]. Transportation Research Record, 1979(722): 1-9.
    [13]
    OKUTANI I, STEPHANEDES Y J. Dynamic prediction of traffic volume through Kalman filtering theory[J]. Transportation Research Part B Methodological, 1984, 18(1): 1-11. doi: 10.1016/0191-2615(84)90002-X
    [14]
    CAI P, WANG Y, LU G, et al. A spatiotemporal correlative k-nearest neighbor model for short-term traffic multistep forecasting[J]. Transportation Research Part C: Emerging Technologies, 2016, 62: 21-34. doi: 10.1016/j.trc.2015.11.002
    [15]
    HU W, YAN L, LIU K, et al. A short-term traffic flow forecasting method based on the hybrid PSO-SVR[J]. Neural Processing Letters, 2016, 43(1): 155-172. doi: 10.1007/s11063-015-9409-6
    [16]
    DOUGHERTY M S, KIRBY H R, BOYLE R D. The use of neural networks to recognize and predict traffic congestion[J]. Traffic engineering & control, 1993, 34(6): 311-314.
    [17]
    XU T, DENG J, MA R, et al. Hierarchical spatio-temporal graph ODE networks for traffic forecasting[J]. Information Fusion, 2025, 113: 102614. doi: 10.1016/j.inffus.2024.102614
    [18]
    SHI H, ZHANG W. Learning continuous spatial-temporal evolution via dynamic graph neural differential equations for traffic flow forecasting[J]. Information Sciences, 2025, 700: 121845. doi: 10.1016/j.ins.2024.121845
    [19]
    ZHAO L, GUO B, DAI C, et al. Multi-step trend aware graph neural network for traffic flow forecasting[J]. Big Data Research, 2024, 38: 100482. doi: 10.1016/j.bdr.2024.100482
    [20]
    KONG W, JU Y, ZHANG S, et al. Graph enhanced spatial-temporal transformer for traffic flow forecasting[J]. Applied Soft Computing, 2025, 170: 112698. doi: 10.1016/j.asoc.2025.112698
    [21]
    GUO Hai-feng, XU Hong-wei, ZHOU Zi-sheng. Urban traffic prediction based on deep spatio-temporal hybrid graph convolution[J]. Mini-Micro Systems, 2025, 46(1): 97-103.
    [22]
    YIN Bao-cai, WANG Jing-cheng, ZHANG Yong, et al. Traffic flow prediction model based on spectral hypergraph convolutional network[J]. Journal of Beijing University of Technology, 2024, 50(2): 152-164.
    [23]
    ZHANG J, ZHENG Y, QI D. Deep spatio-temporal residual networks for citywide crowd flows prediction[C]//AAAI Press. Proceedings of the 31st AAAI Conference on Artificial Intelligence. San Francisco: AAAI Press, 2017: 1655-1661.
    [24]
    MIHAITA A S, LI H, HE Z, et al. Motorway traffic flow prediction using advanced deep learning[C]//IEEE. 2019 IEEE Intelligent Transportation Systems Conference (ITSC). New York: IEEE, 2019: 1683-1690.
    [25]
    WANG P, FENG L, ZHU Y, et al. Hybrid spatial-temporal graph neural network for traffic forecasting[J]. Information Fusion, 2025, 118: 102978. doi: 10.1016/j.inffus.2025.102978
    [26]
    ZHANG Hong, GONG Lei, CAO Jie, et al. Research on traffic speed forecasting with spatio-temporal fusion based on mayfly optimization algorithm[J]. Journal of Harbin Engineering University, 2025, 46(4): 764-771.
    [27]
    YANG Ping, LI Cheng-xin, LIU Yi-cheng, et al. Spatio-temporal graph model for multi-step forecasting of dynamic traffic flow[J]. Computer Engineering and Design, 2024, 45(4): 1195-1201.
    [28]
    XIAO Pei-cheng, CAO Yang, SHEN Qin-qin, et al. Short-term traffic flow prediction based on spatial-temporal graph convolutional network with ARMA filter[J]. Computer Engineering and Applications, 2024, 60(21): 308-314.
    [29]
    LUO Q, HE S, HAN X, et al. LSTTN: A long-short term transformer-based spatiotemporal neural network for traffic flow forecasting[J]. Knowledge-Based Systems, 2024, 293: 111637. doi: 10.1016/j.knosys.2024.111637
    [30]
    LOU J, ZHANG X, WANG R, et al. HSFE: A hierarchical spatial-temporal feature enhanced framework for traffic flow forecasting[J]. Information Sciences, 2024, 679: 121070. doi: 10.1016/j.ins.2024.121070
    [31]
    WANG B, LONG Z, SHENG J, et al. Spatial-temporal similarity fusion graph adversarial convolutional networks for traffic flow forecasting[J]. Journal of the Franklin Institute, 2024, 361(17): 107299. doi: 10.1016/j.jfranklin.2024.107299
    [32]
    XIAO Lin, CHEN Hong-chao, ZOU Fu-min. Traffic flow prediction based on multi-head attention spatiotemporal graph neural network[J]. Journal of Chongqing Jiaotong University (Natural Science), 2024, 43(9): 78-85.
    [33]
    TIAN J, HAN L, CHEN M, et al. MFGCN: Multi-faceted spatial and temporal specific graph convolutional network for traffic-flow forecasting[J]. Knowledge-Based Systems, 2024, 306: 112671. doi: 10.1016/j.knosys.2024.112671
    [34]
    TIAN Jun-shan, ZENG Jun-cheng, DING Feng, et al. Highway traffic flow forecasting based on spatiotemporal relationship[J]. Journal of Engineering Science, 2024, 46(9): 1623-1629.
    [35]
    LIU A, ZHANG Y. An efficient spatial-temporal transformer with temporal aggregation and spatial memory for traffic forecasting[J]. Expert Systems with Applications, 2024, 250: 123884. doi: 10.1016/j.eswa.2024.123884
    [36]
    WANG Quan, LU Qi-xiang, SHI Pei. Multi graph diffusion attention network for traffic flow prediction[J]. Journal of Computer Applications, 2025, 45(5): 1472-1479.
    [37]
    DENG Han-you, CHEN Hong-mei, XIAO Qing, et al. Dynamic graph convolution network with multi-head attention for traffic flow prediction[J]. Journal of Taiyuan University of Technology, 2024, 55(1): 172-183.
    [38]
    XU C, XU C Q. Local spatial and temporal relation discovery model based on attention mechanism for traffic forecasting[J]. Neural Networks, 2024, 176: 106365. doi: 10.1016/j.neunet.2024.106365
    [39]
    ZHAI Zhi-peng, CAO Yang, SHEN Qin-qin, et al. Traffic flow prediction model based on multiple spatio-temporal graph fusion and dynamic attention[J]. Computer Engineering, 2025, 51(9): 139-148.
    [40]
    MA Y, LOU H, YAN M, et al. Spatio-temporal fusion graph convolutional network for traffic flow forecasting[J]. Information Fusion, 2024, 104: 102196. doi: 10.1016/j.inffus.2023.102196
    [41]
    LI Z, ZHOU J, LIN Z, et al. Dynamic spatial aware graph transformer for spatiotemporal traffic flow forecasting[J]. Knowledge-based systems, 2024, 297: 111946. doi: 10.1016/j.knosys.2024.111946
    [42]
    LI Yun, GAO Ya, YAO Zhi-xiu, et al. Intelligent traffic flow prediction for data scarcity scenarios[J]. Journal of Software, 2025, 36(8): 3787-3801.
    [43]
    MA Fei, YANG Zhi-jie, WANG Jiang-bo, et al. Short-term traffic flow velocity prediction method based on multi-channel fusion of meteorological and transportation data[J]. Journal of Traffic and Transportation Engineering, 2024, 24(6): 183-196. doi: 10.19818/j.cnki.1671-1637.2024.06.013
    [44]
    SANG W, ZHANG H, KANG X, et al. Dynamic multi-granularity spatial-temporal graph attention network for traffic forecasting[J]. Information Sciences, 2024, 662: 120230. doi: 10.1016/j.ins.2024.120230
    [45]
    LI G, WANG J, ZHAO Z, et al. Advancing complex urban traffic forecasting: A fully attentional spatial-temporal network enhanced by graph representation[J]. International Journal of Applied Earth Observation and Geoinformation, 2024, 134: 104237. doi: 10.1016/j.jag.2024.104237
    [46]
    ZHANG Yang, WANG Zi-liang, YAO Fang-yu, et al. IDBiLSTM short-term traffic flow prediction with fused hysteretic extreme learning machine[J]. Journal of Chongqing Jiaotong University (Natural Science), 2024, 43(6): 39-46.
    [47]
    ZHAO Zi-qi, YANG Bin, ZHANG Yuan-guang. Hierarchical traffic flow prediction model based on graph autoencoder and GRU network[J]. Computer Science, 2024, 51(S1): 692-697.
    [48]
    ZHANG Jun-xi, QU Shi-ru, ZHANG Zhi-teng, et al. A spatial-temporal dependent short-term traffic flow prediction model for road networks[J]. Journal of Beijing Jiaotong University, 2024, 48(3): 74-82.
    [49]
    LUO Xiang-long, XU Zhong-cheng, SU Yong-dong, et al. Short-term traffic flow prediction based on improved dilated temporal-spatio graph convolutional Network[J]. Computer Engineering, 2025, 51(12): 346-356.
    [50]
    ZHANG X, CHEN X, TANG H, et al. AdpSTGCN: Adaptive spatial-temporal graph convolutional network for traffic forecasting[J]. Knowledge-Based Systems, 2024, 301: 11.
    [51]
    ZHENG X, SHAO H, YAN S, et al. Multiscale information enhanced spatial-temporal graph convolutional network for multivariate traffic flow forecasting via magnifying perceptual scope[J]. Engineering Applications of Artificial Intelligence, 2024, 136: 109010. doi: 10.1016/j.engappai.2024.109010
    [52]
    XIA Z, ZHANG Y, YANG J, et al. Dynamic spatial-temporal graph convolutional recurrent networks for traffic flow forecasting[J]. Expert Systems with Applications, 2024, 240: 122381. doi: 10.1016/j.eswa.2023.122381
    [53]
    HOU Yue, ZHANG Xin, WU Yue. Traffic flow prediction based on spatio-temporal dynamic constraint graph feedback[J/OL]. Journal of Jilin University (Engineering and Technology Edition), 2024, https://doi.org/10.13229/j.cnki.jdxbgxb.20240640.
    [54]
    FU Qi, BA Bing, HUANG Chong-jing, et al. Dynamic spatiotemporal graph convolutional networks for short-term traffic flow prediction[J]. Journal of Hunan University of Science & Technology (Natural Science Edition), 2024, 39(1): 70-79.
    [55]
    WU Yong-qing, JIANG Zheng-yu. Traffic flow prediction based on decoupled dynamic spatial-temporal convolutional recurrent network[J/OL]. Computer Engineering, 2024, https://doi.org/10.19678/j.issn.1000-3428.0070319.
    [56]
    CHEN Y, HUO J, LIN F, et al. Beyond homophily in spatial-temporal traffic flow forecasting[J]. Neural Networks, 2025, 183: 106950. doi: 10.1016/j.neunet.2024.106950
    [57]
    HOU Yue, ZHANG Xin, XI Zhu-tao, et al. Spatio-temporal heterogeneous traffic flow prediction under special road network topology deconstruction[J]. Journal of Railway Science and Engineering, 2025, 22(7): 2932-2945.
    [58]
    TANG Chen-jia, ZENG Wei, ZHAO Zhen-xing. Traffic flow prediction based on adaptive dynamic graph convolutional recurrent network[J]. Journal of Wuhan University of Technology (Transportation Science & Engineering), 2024, 48(3): 415-420.
    [59]
    LUO Y, ZHENG J, WANG X, et al. ST-GMLP: A concise spatial-temporal framework based on gated multi-layer perceptron for traffic flow forecasting[J]. Neural Networks, 2025, 184: 107074. doi: 10.1016/j.neunet.2024.107074
    [60]
    ZHANG Hong, ZHU Si-yu, ZHANG Xi-jun, et al. Research on non-stationary traffic flow forecasting based on adaptive graph convolution optimization meta-graph learning[J/OL]. Computer Engineering, 2024, https://doi.org/10.19678/j.issn.1000-3428.0070124.
    [61]
    LIANG Xiu-xia, XIA Man-man, HE Yue-yang, et al. Traffic flow prediction based on spatio-temporal multi-head graph attention network[J]. Acta Electronica Sinica, 2024, 52(2): 500-509.
    [62]
    WANG L, GUO D, WU H, et al. TC-GCN: Triple cross-attention and graph convolutional network for traffic forecasting[J]. Information Fusion, 2024, 105: 102229. doi: 10.1016/j.inffus.2024.102229
    [63]
    LU Si-yuan, SHEN Qin-qin, BAO Yin-xin, et al. Traffic flow prediction model based on spatial-temporal aware Transformer[J]. Electronic Measurement Technology, 2024, 47(10): 85-92.
    [64]
    GENG Z, XU J, WU R, et al. STGAFormer: Spatial-temporal gated attention transformer based graph neural network for traffic flow forecasting[J]. Information Fusion, 2024, 105: 102228. doi: 10.1016/j.inffus.2024.102228
    [65]
    YANG Guo-liang, XI Hao, GONG Jia-ren, et al. Spatio-temporal traffic flow prediction method based on multi-graph fusion[J]. Computer Applications and Software, 2024, 41(3): 169-173.
    [66]
    LIU Y, WU X, TANG Y, et al. Decomposition with feature attention and graph convolution network for traffic forecasting[J]. Knowledge-Based Systems, 2024, 300: 112193. doi: 10.1016/j.knosys.2024.112193
    [67]
    ZHUANG Wei-qing, CAO Yong-bo. Research on traffic flow prediction model based on improved spatial-temporal graph convolutional networks[J]. Journal of Systems Science and Mathematical Sciences, 2024, 44(1): 217-235.
    [68]
    ZHANG Jing, CHEN Nuo, BAO Qian-han, et al. Prediction of Short-term traffic flow parameter based on traffic event information[J]. Highway, 2024, 69(10): 241-250.
    [69]
    WANG Jiu-sheng, XU Hui, MIAO Zhong-yan. Study and application of multi fine-grained tunnel traffic flow prediction model based on spatio-temporal relation[J]. Journal of Highway and Transportation Research and Development, 2024, 41(11): 86-93.
    [70]
    LIU Yun-xiang, LIANG Zhi-chao. A highly efficient traffic prediction model for continuous time-series graph attention networks[J]. Computer Engineering, 2025, 51(4): 350-359.
    [71]
    YAN T, GONG H, ZHAN Y, et al. CAG-NSPDE: Continuous adaptive graph neural stochastic partial differential equations for traffic flow forecasting[J]. Neurocomputing, 2024, 603: 128256. doi: 10.1016/j.neucom.2024.128256
    [72]
    YAN Guang-hui, LI Hong-tao, ZHANG Bin, et al. Traffic prediction model considering spatial correlation of non-neighboring nodes[J]. Application Research of Computers, 2025, 42(3): 825-833.
    [73]
    QI Y, HOSSAIN M S, NIE J, et al. Privacy-preserving blockchain-based federated learning for traffic flow prediction[J]. Future Generation Computer Systems, 2021, 17: 328-337.
    [74]
    GUO H, MEESE C, LI W, et al. B2sfl: A bi-level block chained architecture for secure federated learning-based traffic prediction[J]. IEEE Transactions on Services Computing, 2023, 16(6): 4360-4374. doi: 10.1109/TSC.2023.3318990
    [75]
    FENG J, YANG L T, ZHU Q, et al. Privacy-preserving tensor decomposition over encrypted data in a federated cloud environment[J]. IEEE Transactions on Dependable and Secure Computing, 2018, 17(4): 857-868.
    [76]
    ZHANG C, DANG S, SHIHADA B, et al. Dual attention-based federated learning for wireless traffic prediction[C]//IEEE. IEEE INFOCOM 2021-IEEE conference on computer communications. New York: IEEE, 2021: 1-10.
    [77]
    ZHI Hui, DUAN Miao-miao, YANG Li-xia, et al. A traffic flow prediction method based on the fusion of blockchain and federated learning[J]. Journal of Electronics & Information Technology, 2024, 46(9): 3777-3787.
    [78]
    WAN X, LIU H, XU H, et al. Network traffic prediction based on LSTM and transfer learning[J]. IEEE Access, 2022, 10: 86181-86190. doi: 10.1109/ACCESS.2022.3199372
    [79]
    WANG S, MIAO H, LI J, et al. Spatio-temporal knowledge transfer for urban crowd flow prediction via deep attentive adaptation networks[J]. IEEE Transactions on Intelligent Transportation Systems, 2021, 23(5): 4695-4705.
    [80]
    TANG Y, QU A, CHOW A H F, et al. Domain adversarial spatial-temporal network: A transferable framework for short-term traffic forecasting across cities[C]//ACM. Proceedings of the 31st ACM international conference on information & knowledge management. New York: ACM, 2022: 1905-1915.
    [81]
    YOON J, JORDON J, SCHAAR M. Gain: Missing data imputation using generative adversarial nets[C]//PMLR. International conference on machine learning. Princeton: PMLR, 2018: 5689-5698.
    [82]
    ZHANG Hong, YANG Jun-yi. Spatio-temporal generative adversarial clustering graph convolutional networks for anomalous traffic flow forecasting[J/OL]. Computer Engineering, 2025, https://doi.org/10.19678/j.issn.1000-3428.0070180.
    [83]
    LI L, BI J, YANG K, et al. Di-GraphGAN: An enhanced adversarial learning framework for accurate spatial-temporal traffic forecasting under data missing scenarios[J]. Information Sciences, 2024, 677: 120911. doi: 10.1016/j.ins.2024.120911
    [84]
    ZHANG Z, LIN X, LI M, et al. A customized deep learning approach to integrate network-scale online traffic data imputation and prediction[J]. Transportation Research Part C: Emerging Technologies, 2021, 132: 103372. doi: 10.1016/j.trc.2021.103372
    [85]
    WANG Qing-rong, MU Zhuang-zhuang, ZHU Chang-feng, et al. Research on short-term traffic flow prediction based on HDNNF-CAF[J]. Computer Engineering and Applications, 2025, 2025, 61 (15): 318-328.
    [86]
    LIU Si-lin, LIAO Zhu-hua, FU Qi, et al. Prediction of traffic flow speed under multiple influencing factors[J]. Journal of Hunan University of Science & Technology (Natural Science Edition), 2024, 39(3): 109-115.
    [87]
    KOU Min, ZHANG Meng-meng, ZHAO Jun-xue, et al. WOA-GRU expressway short-term traffic flow prediction incorporating weather features[J]. Highway, 2024, 69(4): 193-199.
    [88]
    HU Chun-hua, ZENG E-lan, RONG Hui-gui. Traffic flow prediction of digital twin based on two-graph convolution mechanism[J]. Acta Electronica Sinica, 2025, 53(1): 141-150.
    [89]
    LIU C, YANG S, XU Q, et al. Spatial-temporal large language model for traffic prediction[C]//IEEE. 2024 25th IEEE International Conference on Mobile Data Management (MDM). New York: IEEE, 2024: 31-40.
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