Qu, L.,
Li, L.,
Zhang, Y.,
Hu, J.,
PPCA-Based Missing Data Imputation for Traffic Flow Volume:
A Systematical Approach,
ITS(10), No. 3, September 2009, pp. 512-522.
IEEE DOI
0909
BibRef
Tak, S.,
Woo, S.,
Yeo, H.,
Data-Driven Imputation Method for Traffic Data in Sectional Units of
Road Links,
ITS(17), No. 6, June 2016, pp. 1762-1771.
IEEE DOI
1606
Correlation
BibRef
Li, L.C.[Lin-Chao],
Zhang, J.[Jian],
Yang, F.[Fan],
Ran, B.[Bin],
Robust and flexible strategy for missing data imputation in intelligent
transportation system,
IET-ITS(12), No. 2, March 2018, pp. 151-157.
DOI Link
1801
BibRef
Zhuang, Y.F.[Yi-Fan],
Ke, R.M.[Rui-Min],
Wang, Y.H.[Yin-Hai],
Innovative method for traffic data imputation based on convolutional
neural network,
IET-ITS(13), No. 4, April 2019, pp. 605-613.
DOI Link
1903
BibRef
Rodrigues, F.,
Henrickson, K.,
Pereira, F.C.,
Multi-Output Gaussian Processes for Crowdsourced Traffic Data
Imputation,
ITS(20), No. 2, February 2019, pp. 594-603.
IEEE DOI
1902
Gaussian processes, Data models, Uncertainty, Probes, Bayes methods,
Roads, Predictive models, Traffic data, imputation, missing data,
crowdsourced data
BibRef
Wang, S.,
Mao, G.,
Missing Data Estimation for Traffic Volume by Searching an Optimum
Closed Cut in Urban Networks,
ITS(20), No. 1, January 2019, pp. 75-86.
IEEE DOI
1901
Roads, Detectors, Correlation, Estimation, Probabilistic logic,
Tensile stress, Traffic data imputation, optimum closed cut, NHA, k-NN
BibRef
Li, L.,
Zhang, J.,
Wang, Y.,
Ran, B.,
Missing Value Imputation for Traffic-Related Time Series Data Based
on a Multi-View Learning Method,
ITS(20), No. 8, August 2019, pp. 2933-2943.
IEEE DOI
1908
Time series analysis, Logic gates, Sensors, Data models,
Road transportation, Learning systems, Databases,
temporal and spatial views
BibRef
Chen, Y.,
Lv, Y.,
Wang, F.,
Traffic Flow Imputation Using Parallel Data and Generative
Adversarial Networks,
ITS(21), No. 4, April 2020, pp. 1624-1630.
IEEE DOI
2004
Generators, Data models,
Generative adversarial networks, Training, Loss measurement,
deep learning
BibRef
Wang, S.,
Mao, G.,
Fundamental Limits of Missing Traffic Data Estimation in Urban
Networks,
ITS(21), No. 3, March 2020, pp. 1191-1203.
IEEE DOI
2003
Cramer-Rao lower bound (CRLB), squared flow error bound (SFEB),
fisher matrix, spatial-temporal kriging
BibRef
Lu, W.Q.[Wen-Qi],
Zhou, T.[Tian],
Li, L.H.[Lin-Heng],
Gu, Y.L.[Yuan-Li],
Rui, Y.K.[Yi-Kang],
Ran, B.[Bin],
An improved tucker decomposition-based imputation method for
recovering lane-level missing values in traffic data,
IET-ITS(16), No. 3, 2022, pp. 363-379.
DOI Link
2202
BibRef
Liu, J.L.[Jie-Lun],
Ong, G.P.[Ghim Ping],
Chen, X.[Xiqun],
GraphSAGE-Based Traffic Speed Forecasting for Segment Network With
Sparse Data,
ITS(23), No. 3, March 2022, pp. 1755-1766.
IEEE DOI
2203
Forecasting, Roads, Correlation, Probes, Trajectory, Data models,
Predictive models, Urban road network, recovery of missing data,
deep learning
BibRef
Joelianto, E.[Endra],
Fathurrahman, M.F.[Muhammad Farhan],
Sutarto, H.Y.[Herman Yoseph],
Semanjski, I.[Ivana],
Putri, A.[Adiyana],
Gautama, S.[Sidharta],
Analysis of Spatiotemporal Data Imputation Methods for Traffic Flow
Data in Urban Networks,
IJGI(11), No. 5, 2022, pp. xx-yy.
DOI Link
2206
BibRef
Wang, S.F.[Shao-Fan],
Zhao, Y.B.[Yong-Bo],
Zhang, Y.[Yong],
Hu, Y.L.[Yong-Li],
Yin, B.C.[Bao-Cai],
Spatiotemporal traffic data imputation via tensorial weighted
Schatten-p norm minimization,
IET-ITS(16), No. 7, 2022, pp. 926-939.
DOI Link
2206
BibRef
Lei, M.Y.[Meng-Ying],
Labbe, A.[Aurelie],
Wu, Y.K.[Yuan-Kai],
Sun, L.J.[Li-Jun],
Bayesian Kernelized Matrix Factorization for Spatiotemporal Traffic
Data Imputation and Kriging,
ITS(23), No. 10, October 2022, pp. 18962-18974.
IEEE DOI
2210
Spatiotemporal phenomena, Kernel, Data models,
Computational modeling, Task analysis, Correlation, Bayes methods,
Markov chain Monte Carlo
BibRef
Zhu, Y.T.[Yi-Ting],
Wang, J.[Jiyu],
Wang, J.[Junbo],
He, Z.C.[Zhao-Cheng],
Multitask Neural Tensor Factorization for Road Traffic Speed-Volume
Correlation Pattern Learning and Joint Imputation,
ITS(23), No. 12, December 2022, pp. 24550-24560.
IEEE DOI
2212
Tensors, Correlation, Task analysis, Roads, Solid modeling, Detectors,
Data models, Multi-task learning, neural tensor factorization,
automatic vehicle identification
BibRef
Zhang, X.Y.[Xin-Yu],
Zhang, Y.[Yong],
Wei, X.[Xiulan],
Hu, Y.L.[Yong-Li],
Yin, B.C.[Bao-Cai],
Traffic forecasting with missing data via low rank dynamic mode
decomposition of tensor,
IET-ITS(16), No. 9, 2022, pp. 1164-1176.
DOI Link
2208
BibRef
Zhang, W.B.[Wei-Bin],
Zhang, P.[Pulin],
Yu, Y.H.[Ying-Hao],
Li, X.Y.[Xi-Ying],
Biancardo, S.A.[Salvatore Antonio],
Zhang, J.Y.[Jun-Yi],
Missing Data Repairs for Traffic Flow With Self-Attention Generative
Adversarial Imputation Net,
ITS(23), No. 7, July 2022, pp. 7919-7930.
IEEE DOI
2207
Data models, Time series analysis, Maintenance engineering,
Training, Task analysis, Generative adversarial networks, self-attention
BibRef
Ben Said, A.[Ahmed],
Erradi, A.[Abdelkarim],
Spatiotemporal Tensor Completion for Improved Urban Traffic
Imputation,
ITS(23), No. 7, July 2022, pp. 6836-6849.
IEEE DOI
2207
Tensors, Spatiotemporal phenomena, Sparse matrices, Meteorology,
Data models, Forecasting, Traffic tensor, tensor completion, CANDECOMP/PARAFAC
BibRef
Xing, J.P.[Ji-Ping],
Liu, R.H.[Rong-Hui],
Anish, K.[Khadka],
Liu, Z.Y.[Zhi-Yuan],
A Customized Data Fusion Tensor Approach for Interval-Wise Missing
Network Volume Imputation,
ITS(24), No. 11, November 2023, pp. 12107-12122.
IEEE DOI
2311
fill in missing data.
BibRef
Wang, A.[Ao],
Ye, Y.C.[Yong-Chao],
Song, X.Z.[Xiao-Zhuang],
Zhang, S.[Shiyao],
Yu, J.J.Q.[James J. Q.],
Traffic Prediction With Missing Data: A Multi-Task Learning Approach,
ITS(24), No. 4, April 2023, pp. 4189-4202.
IEEE DOI
2304
Task analysis, Predictive models, Training, Multitasking,
Feature extraction, Deep learning, Data mining,
multi-task learning
BibRef
Miao, M.[Meng],
Kang, M.Y.[Ming-Yu],
Qian, X.[Xusheng],
Chen, D.[Duxin],
Wu, W.J.[Wei-Jiang],
Yu, W.W.[Wen-Wu],
Improving traffic time-series predictability by imputing continuous
non-random missing data,
IET-ITS(17), No. 10, 2023, pp. 1925-1934.
DOI Link
2310
artificial intelligence, big data,
intelligent transportation systems, prediction theory
BibRef
Cai, L.[Li],
Sha, C.[Cong],
He, J.[Jing],
Yao, S.W.[Shao-Wen],
Spatial-Temporal Data Imputation Model of Traffic Passenger
Flow Based on Grid Division,
IJGI(12), No. 1, 2023, pp. xx-yy.
DOI Link
2301
BibRef
Cai, S.[Siteng],
Liu, G.[Gang],
He, J.[Jing],
Du, Y.[Yulun],
Si, Z.C.[Zhi-Chao],
Jiang, Y.H.[Yun-Hao],
Temporal-Spatial Traffic Flow Prediction Model Based on Prompt
Learning,
IJGI(14), No. 1, 2025, pp. 11.
DOI Link
2501
BibRef
Wu, P.L.[Peng-Ling],
Ding, M.[Meng],
Zheng, Y.B.[Yu-Bang],
Spatiotemporal traffic data imputation by synergizing low tensor ring
rank and nonlocal subspace regularization,
IET-ITS(17), No. 9, 2023, pp. 1908-1923.
DOI Link
2310
data analysis, intelligent transportation systems,
interpolation, management and control, traffic modeling
BibRef
Su, X.[Xing],
Sun, W.J.[Wen-Jie],
Song, C.[Chenting],
Cai, Z.[Zhi],
Guo, L.M.[Li-Min],
A Latent-Factor-Model-Based Approach for Traffic Data Imputation with
Road Network Information,
IJGI(12), No. 9, 2023, pp. 378.
DOI Link
2310
BibRef
Zeng, Z.[Zeyu],
Liu, B.[Bin],
Feng, J.[Jun],
Yang, X.L.[Xiao-Lin],
Low-Rank Tensor and Hybrid Smoothness Regularization-Based Approach
for Traffic Data Imputation With Multimodal Missing,
ITS(25), No. 10, October 2024, pp. 13014-13026.
IEEE DOI
2410
Tensors, Imputation, Spatiotemporal phenomena, Data models,
Matrix decomposition, TV, hybrid total variation
BibRef
Zeng, Z.[Zeyu],
Feng, J.[Jun],
Huang, Z.[Zhang],
Liu, B.[Bin],
Zhou, B.[Bin],
A Flexible Approach Based on Hybrid Global Low-Rankness and
Smoothness Regularization With Nonlocal Structure for Traffic Data
Imputation,
ITS(26), No. 6, June 2025, pp. 8864-8879.
IEEE DOI
2506
Tensors, Imputation, Correlation, Spatiotemporal phenomena,
Data models, Accuracy, Matrix decomposition, Transportation,
complex missing data scenarios
BibRef
Xu, C.[Chen],
Wang, Q.[Qiang],
Zhang, W.Q.[Wen-Qi],
Sun, C.[Chen],
Spatiotemporal Ego-Graph Domain Adaptation for Traffic Prediction
With Data Missing,
ITS(25), No. 12, December 2024, pp. 20804-20819.
IEEE DOI
2412
Adaptation models, Predictive models, Data models, Tensors,
Spatiotemporal phenomena, Roads, Feature extraction,
domain adaptation
BibRef
Zhao, M.[Meng],
Gahrooei, M.R.[Mostafa Reisi],
Ilbeigi, M.[Mohammad],
Change Detection in Partially Observed Large-Scale Traffic Network
Data,
ITS(25), No. 11, November 2024, pp. 18913-18924.
IEEE DOI
2411
Monitoring, Tensors, Spatiotemporal phenomena, Roads, Streams,
Imputation, Traffic control, statistical monitoring
BibRef
Huo, G.Y.[Guang-Yu],
Zhang, Y.[Yong],
Wang, B.Y.[Bo-Yue],
Gao, J.B.[Jun-Bin],
Hu, Y.L.[Yong-Li],
Yin, B.C.[Bao-Cai],
Hierarchical Spatio-Temporal Graph Convolutional Networks and
Transformer Network for Traffic Flow Forecasting,
ITS(24), No. 4, April 2023, pp. 3855-3867.
IEEE DOI
2304
Forecasting, Transformers, Convolution, Roads, Task analysis,
Predictive models, Network topology, transformer
BibRef
Guo, K.[Kan],
Tian, D.X.[Da-Xin],
Hu, Y.L.[Yong-Li],
Sun, Y.F.[Yan-Feng],
Qian, Z.S.[Zhen Sean],
Zhou, J.[Jianshan],
Gao, J.B.[Jun-Bin],
Yin, B.C.[Bao-Cai],
Contrastive learning for traffic flow forecasting based on multi
graph convolution network,
IET-ITS(18), No. 2, 2024, pp. 290-301.
DOI Link
2402
intelligent transportation systems, traffic information systems
BibRef
Hu, Y.L.[Yong-Li],
Peng, T.[Ting],
Guo, K.[Kan],
Sun, Y.F.[Yan-Feng],
Gao, J.B.[Jun-Bin],
Yin, B.C.[Bao-Cai],
Graph transformer based dynamic multiple graph convolution networks
for traffic flow forecasting,
IET-ITS(17), No. 9, 2023, pp. 1835-1845.
DOI Link
2310
intelligent transportation systems, traffic information systems
BibRef
Guo, K.[Kan],
Hu, Y.L.[Yong-Li],
Qian, Z.[Zhen],
Sun, Y.F.[Yan-Feng],
Gao, J.B.[Jun-Bin],
Yin, B.C.[Bao-Cai],
Dynamic Graph Convolution Network for Traffic Forecasting Based on
Latent Network of Laplace Matrix Estimation,
ITS(23), No. 2, February 2022, pp. 1009-1018.
IEEE DOI
2202
Forecasting, Roads, Convolution, Feature extraction, Data models,
Artificial neural networks, Dynamic graph convolution network,
Laplace matrix latent network
BibRef
Wei, X.[Xiulan],
Zhang, Y.[Yong],
Wang, S.F.[Shao-Fan],
Zhao, X.[Xia],
Hu, Y.L.[Yong-Li],
Yin, B.C.[Bao-Cai],
Self-Attention Graph Convolution Imputation Network for
Spatio-Temporal Traffic Data,
ITS(25), No. 12, December 2024, pp. 19549-19562.
IEEE DOI
2412
Imputation, Convolution, Data models, Time series analysis,
Interpolation, Deep learning, Correlation, Learning systems,
diffusion graph convolution
BibRef
Yang, H.S.[Heng-Shuo],
Lin, M.W.[Ming-Wei],
Chen, H.[Hong],
Luo, X.[Xin],
Xu, Z.[Zeshui],
Latent Factor Analysis Model With Temporal Regularized Constraint for
Road Traffic Data Imputation,
ITS(26), No. 1, January 2025, pp. 724-741.
IEEE DOI
2501
Imputation, Data models, Tensors, Analytical models, Training,
Convergence, Time series analysis, Accuracy, Matrix decomposition,
latent factor analysis
BibRef
Chen, P.[Peng],
Li, F.[Fang],
Wei, D.L.[De-Liang],
Lu, C.H.[Chang-Hong],
Low-Rank and Deep Plug-and-Play Priors for Missing Traffic Data
Imputation,
ITS(26), No. 2, February 2025, pp. 2690-2706.
IEEE DOI Code:
WWW Link.
2502
Tensors, Imputation, Spatiotemporal phenomena, Data models,
Deep learning, Iterative methods, Accuracy, Correlation,
low-rank tensor completion
BibRef
Dong, H.X.[Han-Xuan],
Zhang, H.L.[Hai-Long],
Ding, F.[Fan],
Tan, H.C.[Hua-Chun],
Co-Evolving Traffic State Parameters Prediction Based on
Mechanism-Data Blending Driven Deep Learning,
ITS(26), No. 3, March 2025, pp. 3084-3100.
IEEE DOI
2503
Tensors, Time series analysis, Data models, Roads, Deep learning,
Predictive models, Accuracy, Prediction algorithms, Detectors,
data missing
BibRef
Sabzekar, S.[Sina],
Roudbari, A.[Asal],
Dehghani, A.[Arash],
Safaeiestalkhzir, A.[Artin],
Amini, Z.[Zahra],
The Impact of Network Indices Integration on Traffic Flow Imputation
Accuracy: A Machine Learning Approach,
ITS(26), No. 4, April 2025, pp. 5411-5421.
IEEE DOI
2504
Roads, Imputation, Accuracy, Machine learning, Feature extraction,
Transportation, Deep learning, Correlation, Training, Resilience,
machine learning
BibRef
Chen, H.[Hong],
Lin, M.W.[Ming-Wei],
Zhao, L.[Liang],
Xu, Z.[Zeshui],
Luo, X.[Xin],
Fourth-Order Dimension Preserved Tensor Completion With Temporal
Constraint for Missing Traffic Data Imputation,
ITS(26), No. 5, May 2025, pp. 6734-6748.
IEEE DOI
2505
Imputation, Accuracy, Tensors, Data models, Computational modeling,
Time series analysis, Matrix decomposition, Interpolation,
temporal constraint
BibRef
Cao, S.[Shan],
Song, C.Y.[Chun-Yue],
Zhang, J.[Jie],
Zhang, X.[Xiangrui],
Road Network Similarity-Based Transfer Learning Method for Traffic
Volume Estimation in Undetected Road Segments,
ITS(26), No. 6, June 2025, pp. 7700-7714.
IEEE DOI
2506
Roads, Transfer learning, Estimation, Volume measurement, Detectors,
Computational modeling, Urban areas, Solid modeling,
transfer learning
BibRef
Yu, L.F.[Lin-Fang],
Guan, C.Y.[Chen-Yu],
Wang, H.[Hao],
He, Y.X.[Yu-Xin],
Cao, W.M.[Wen-Ming],
Leung, C.S.[Chi-Sing],
Robust Tensor Ring Decomposition for Urban Traffic Data Imputation,
ITS(26), No. 6, June 2025, pp. 8707-8719.
IEEE DOI Code:
WWW Link.
2506
Tensors, Imputation, Noise, Matrix decomposition,
Spatiotemporal phenomena, Optimization, Transportation, AO-ADMM
BibRef
Zhou, W.F.[Wen-Feng],
Shen, G.J.[Guo-Jiang],
Zhang, Y.M.[Yi-Mei],
Deng, Z.L.[Zhao-Lin],
Kong, X.J.[Xiang-Jie],
Xia, F.[Feng],
Sequence-to-Sequence Traffic Missing Data Imputation via
Self-Supervised Contrastive Learning,
ITS(26), No. 7, July 2025, pp. 9948-9961.
IEEE DOI
2507
Data models, Imputation, Spatiotemporal phenomena,
Contrastive learning, Training, Deep learning, Decoding,
self-supervised contrastive learning
BibRef
Yoon, C.[Chanyoung],
Yim, S.[Soobin],
Yoo, S.[Sangbong],
Jung, C.[Chanyoung],
Yeon, H.[Hanbyul],
Jang, Y.[Yun],
V-DCRNN: Virtual Network-Based Diffusion Convolutional Recurrent
Neural Network for Estimating Unobserved Traffic Data,
ITS(26), No. 7, July 2025, pp. 10336-10352.
IEEE DOI
2507
Data models, Spatiotemporal phenomena, Global Positioning System,
Tensors, Reliability, Imputation, Deep learning,
diffusion convolutional recurrent neural network
BibRef
Chapter on Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities continues in
Traffic Origin-Destination Analysis .