[1] CHEONG R C K, LIM J M Y, PARTHIBAN R. Missing traffic data imputation for artificial intelligence in intelligent transportation systems: review of methods, limitations, and challenges. IEEE Access, 2023, 11: 34080 - 34093.
[2] HASAN M K, ALAM M A, ROY S, et al. Missing value imputation affects the performance of machine learning: a review and analysis of the literature ( 2010 - 2021 ). Informatics in Medicine Unlocked, 2021, 27: Article 100799.
[3] MENG H C, CHEN S Y. A comparative analysis of data imputation methods for missing traffic flow data. Journal of Transport Information and Safety, 2018, 36 ( 2 ): 61 - 67 ( in Chinese)
[4] HU W, LIU J, CONG H, et al. Comparative study of real-time traffic flow missing data recovery method. Excellent Papers of the 8th Annual China Intelligent Transportation Conference, 2013, Sep 26 - 28, Hefei, China. Beijing, China: Publishing House of Electronics Industry, 2013 ( in Chinese)
[5] ZHAO Y Y, JI J. Missing data recovery for national and provincial highways based on Lagrange interpolation method. Wireless Internet Science and Technology, 2021, 18 ( 10 ): 97 - 100 ( in Chinese)
[6] DABERDAKU S, TAVAZZI E, DI CAMILLO B. A combined interpolation and weighted K-nearest neighbours approach for the imputation of longitudinal ICU laboratory data. Journal of Healthcare Informatics Research, 2020, 4: 174 - 188.
[7] KHOTIMAH B K, MISWANTO M, SUPRAJITNO H. Modeling Naïve Bayes imputation classification for missing data. IOP Conference Series: Earth and Environmental Science, Vol 243: Proceedings of the 1st International Conference on Environmental Geography and Geography Education ( ICEGE’18 ), 2018, Nov 17 - 18, Jember, Indonesia. Bristol, UK: IOP Publishing, 2019: Article 012111.
[8] QIN Y F, MA M H, WANG Y S, et al. A recovery method for abnormal traffic flow data based on improved KNN algorithm. Computer Measurement & Control, 2018, 26(12): 180 - 184 ( in Chinese)
[9] MARCHANG N, TRIPATHI R. KNN-ST: exploiting spatio- temporal correlation for missing data inference in environmental crowd sensing. IEEE Sensors Journal, 2020, 21 ( 3 ): 3429 -3436.
[10] CUI Z Y, KE R M, PU Z Y, et al. Stacked bidirectional and unidirectional LSTM recurrent neural network for forecasting network-wide traffic state with missing value. Transportation Research Part C: Emerging Technologies, 2020, 118: Article 102674.
[11] XIA Y, LIU M. Traffic flow prediction based on spatial-temporal attention convolutional neural network. Journal of Southwest Jiaotong University, 2023, 58(2): 340 - 347 ( in Chinese)
[12] HU Z A, DENG J C, HAN J L, et al. Review on application of graph neural network in traffic prediction. Journal of Traffic and Transportation Engineering, 2023, 23(5): 39 - 61 ( in Chinese)
[13] ZHANG X J, YU G J, CUI Y, et al. Short-term traffic flow prediction based on clustering algorithm and graph neural network. Journal of Jilin University ( Engineering and Technology Edition), 2022, 54(6): 1593 - 1600 ( in Chinese)
[14] ZHANG W B, ZHANG P L, SU Z Y, et al. Missing data repairs for road network traffic flow with self-attention graph auto-encoder networks. Journal of Transportation Systems Engineering and Information Technology, 2021, 21(4): 90 - 98 ( in Chinese)
[15] HOU Y, HAN C Y, ZHENG X, et al. Traffic flow data repair method based on spatial-temporal fusion graph convolution. Journal of Zhejiang University ( Engineering Science ), 2022, 56(7): 1394 - 1403 ( in Chinese)
[16] ZHANG W B, ZHANG P L, YU Y H, et al. Missing data repairs for traffic flow with self-attention generative adversarial imputation net. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(7): 7919 - 7930.
[17] GUO S N, LIN Y F, FENG N, et al. Attention based spatial- temporal graph convolutional networks for traffic flow forecasting. Proceedings of the 33rd AAAI Conference on Artificial Intelligence and 31st Innovative Applications of Artificial Intelligence Conference and 9th AAAI Symposium on Educational Advances in Artificial Intelligence ( AAAI’19 / IAAI’19 / EAAI’19 ), 2019, Jan 27 - Feb 1, Honolulu, HI, USA. Palo Alto, CA, USA: AAAI, 2019: 922 - 929.
[18] ZHANG X J, ZHANG G N, ZHANG X L, et al. Medium and long-term traffic flow prediction based on ACNN-Trans model. The Journal of China Universities of Posts and Telecommunications, 2023, 30(4): 21 - 32.
[19] LIU H C, DONG Z, JIANG R H, et al. Spatio-temporal adaptive embedding makes vanilla transformer SOTA for traffic forecasting. Proceedings of the 32nd ACM International Conference on Information and Knowledge Management ( CIKM’23 ), 2023, Oct 21 - 25, Birmingham, UK. New York, NY, USA: ACM, 2023: 4125 - 4129.
[20] GAO H T, JIANG R H, DONG Z, et al. Spatio-temporal- decoupled masked pre-training for traffic forecasting. Proceedings of the 33rd International Joint Conference on Artificial Intelligence Main Track ( IJCAI’24 ), 2024, Aug 3 - 9, Jeju, Republic of Korea. International Joint Conferences on Artificial Intelligence, 2024: 3998 - 4006.
[21] PAN Z F, WANG Y L, WANG K, et al. Imputation of missing values in time series using an adaptive-learned median-filled deep autoencoder. IEEE Transactions on Cybernetics, 2023, 53 ( 2 ): 695 - 706.
[22] CHOI J, CHOI H, HWANG J, et al. Graph neural controlled differential equations for traffic forecasting. Proceedings of the 36th AAAI Conference on Artificial Intelligence ( AAAI’22 ), 2022, Feb 22-Mar 1, Online. Palo Alto, CA, USA: AAAI, 2022: 6367 - 6374.
[23] PENG W C, LIN Y F, GUO S N, et al. Generative-contrastive- attentive spatial-temporal network for traffic data imputation. Proceedings of the 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining ( PAKDD’23 ): Part IV, 2023, May 25 - 28, Osaka, Japan. Cham: Springer Nature Switzerland, 2023: 45 - 56.
[24] DUAN Y X, WANG C C, WANG C, et al. Traffic volume imputation using attention-based spatiotemporal generative adversarial imputation network. Transportation Safety and Environment, 2024: tdae008.
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