Tan, M.C.,
Wong, S.C.,
Xu, M.C.,
Guan, Z.R.,
Zhang, P.,
An Aggregation Approach to Short-Term Traffic Flow Prediction,
ITS(10), No. 1, March 2009, pp. 60-69.
IEEE DOI
0903
BibRef
Ghosh, B.,
Basu, B.,
O'Mahony, M.,
Multivariate Short-Term Traffic Flow Forecasting Using Time-Series
Analysis,
ITS(10), No. 2, June 2009, pp. 246-254.
IEEE DOI
0906
BibRef
Tchrakian, T.T.,
Basu, B.,
O'Mahony, M.,
Real-Time Traffic Flow Forecasting Using Spectral Analysis,
ITS(13), No. 2, June 2012, pp. 519-526.
IEEE DOI
1206
BibRef
Chan, K.Y.[Kit Yan],
Dillon, T.S.,
Singh, J.,
Chang, E.,
Neural-Network-Based Models for Short-Term Traffic Flow Forecasting
Using a Hybrid Exponential Smoothing and Levenberg-Marquardt Algorithm,
ITS(13), No. 2, June 2012, pp. 644-654.
IEEE DOI
1206
BibRef
Lippi, M.,
Bertini, M.,
Frasconi, P.,
Short-Term Traffic Flow Forecasting: An Experimental Comparison
of Time-Series Analysis and Supervised Learning,
ITS(14), No. 2, 2013, pp. 871-882.
IEEE DOI
1307
Graphical models; Time series analysis;
Intelligent transportation systems; traffic forecasting
BibRef
Jeong, Y.S.,
Byon, Y.J.,
Castro-Neto, M.M.,
Easa, S.M.,
Supervised Weighting-Online Learning Algorithm for Short-Term Traffic
Flow Prediction,
ITS(14), No. 4, 2013, pp. 1700-1707.
IEEE DOI
1312
Artificial neural networks
BibRef
Daraghmi, Y.,
Yi, C.,
Chiang, T.,
Negative Binomial Additive Models for Short-Term Traffic Flow
Forecasting in Urban Areas,
ITS(15), No. 2, April 2014, pp. 784-793.
IEEE DOI
1404
Additives
BibRef
Tselentis, D.I.,
Vlahogianni, E.I.,
Karlaftis, M.G.,
Improving short-term traffic forecasts:
To combine models or not to combine?,
IET-ITS(9), No. 2, 2015, pp. 193-201.
DOI Link
1504
autoregressive processes
BibRef
Kehagias, D.,
Salamanis, A.,
Tzovaras, D.,
Speed pattern recognition technique for short-term traffic
forecasting based on traffic dynamics,
IET-ITS(9), No. 6, 2015, pp. 646-653.
DOI Link
1509
forecasting theory
BibRef
Fusco, G.,
Colombaroni, C.,
Isaenko, N.,
Comparative analysis of implicit models for real-time short-term
traffic predictions,
IET-ITS(10), No. 4, 2016, pp. 270-278.
DOI Link
1606
Bayes methods
BibRef
Tan, H.,
Wu, Y.,
Shen, B.,
Jin, P.J.,
Ran, B.,
Short-Term Traffic Prediction Based on Dynamic Tensor Completion,
ITS(17), No. 8, August 2016, pp. 2123-2133.
IEEE DOI
1608
Data models
BibRef
Zhang, W.,
Tang, J.,
Kristian, H.,
Zou, Y.,
Wang, Y.,
Hybrid short-term prediction of traffic volume at ferry terminal
based on data fusion,
IET-ITS(10), No. 8, 2016, pp. 524-534.
DOI Link
1610
data handling
BibRef
Zheng, L.[Liang],
Zhu, C.[Chuang],
Zhu, N.[Ning],
He, T.[Tian],
Dong, N.[Ni],
Huang, H.[Helai],
Feature selection-based approach for urban short-term travel speed
prediction,
IET-ITS(12), No. 6, August 2018, pp. 474-484.
DOI Link
1807
BibRef
Sun, B.[Bin],
Cheng, W.[Wei],
Goswami, P.[Prashant],
Bai, G.H.[Guo-Hua],
Short-term traffic forecasting using self-adjusting k-nearest
neighbours,
IET-ITS(12), No. 1, February 2018, pp. 41-48.
DOI Link
1801
BibRef
Ding, C.,
Duan, J.,
Zhang, Y.,
Wu, X.,
Yu, G.,
Using an ARIMA-GARCH Modeling Approach to Improve Subway Short-Term
Ridership Forecasting Accounting for Dynamic Volatility,
ITS(19), No. 4, April 2018, pp. 1054-1064.
IEEE DOI
1804
Analytical models, Forecasting, Mathematical model,
Predictive models, Public transportation, Reliability,
prediction interval
BibRef
Cheng, S.F.[Shi-Fen],
Lu, F.[Feng],
Peng, P.[Peng],
Wu, S.[Sheng],
A Spatiotemporal Multi-View-Based Learning Method for Short-Term
Traffic Forecasting,
IJGI(7), No. 6, 2018, pp. xx-yy.
DOI Link
1806
BibRef
Wang, X.X.[Xiang-Xue],
Xu, L.H.[Lun-Hui],
Wavelet-based short-term forecasting with improved threshold
recognition for urban expressway traffic conditions,
IET-ITS(12), No. 6, August 2018, pp. 463-473.
DOI Link
1807
BibRef
Zhou, T.[Teng],
Jiang, D.Z.[Da-Zhi],
Lin, Z.Z.[Zhi-Zhe],
Han, G.Q.[Guo-Qiang],
Xu, X.M.[Xue-Miao],
Qin, J.[Jing],
Hybrid dual Kalman filtering model for short-term traffic flow
forecasting,
IET-ITS(13), No. 6, June 2019, pp. 1023-1032.
DOI Link
1906
BibRef
Cui, Z.H.[Zhi-Han],
Huang, B.[Boyu],
Dou, H.W.[Hao-Wen],
Tan, G.[Guanru],
Zheng, S.Q.[Shi-Qiang],
Zhou, T.[Teng],
GSA-ELM: A hybrid learning model for short-term traffic flow
forecasting,
IET-ITS(16), No. 1, 2022, pp. 41-52.
DOI Link
2112
BibRef
Cai, L.R.[Ling-Ru],
Chen, Q.[Qian],
Cai, W.H.[Wei-Hong],
Xu, X.M.[Xue-Miao],
Zhou, T.[Teng],
Qin, J.[Jing],
SVRGSA: a hybrid learning based model for short-term traffic flow
forecasting,
IET-ITS(13), No. 9, September 2019, pp. 1348-1355.
DOI Link
1908
BibRef
Chen, X.Q.M.[Xi-Qun Michael],
Zhang, S.C.[Shuai-Chao],
Li, L.[Li],
Multi-model ensemble for short-term traffic flow prediction under
normal and abnormal conditions,
IET-ITS(13), No. 2, February 2019, pp. 260-268.
DOI Link
1902
BibRef
Diao, Z.,
Zhang, D.,
Wang, X.,
Xie, K.,
He, S.,
Lu, X.,
Li, Y.,
A Hybrid Model for Short-Term Traffic Volume Prediction in Massive
Transportation Systems,
ITS(20), No. 3, March 2019, pp. 935-946.
IEEE DOI
1903
Predictive models, Forecasting, Transportation,
Autoregressive processes, Discrete wavelet transforms,
Gaussian process (GP)
BibRef
Mackenzie, J.,
Roddick, J.F.,
Zito, R.,
An Evaluation of HTM and LSTM for Short-Term Arterial Traffic Flow
Prediction,
ITS(20), No. 5, May 2019, pp. 1847-1857.
IEEE DOI
1905
Roads, Neural networks, Prediction algorithms, Traffic control,
Predictive models, Timing, Arterial road networks,
traffic-flow prediction
BibRef
Feng, X.,
Ling, X.,
Zheng, H.,
Chen, Z.,
Xu, Y.,
Adaptive Multi-Kernel SVM With Spatial-Temporal Correlation for
Short-Term Traffic Flow Prediction,
ITS(20), No. 6, June 2019, pp. 2001-2013.
IEEE DOI
1906
Kernel, Support vector machines, Predictive models, Correlation,
Prediction algorithms, Real-time systems, Forecasting,
spatial-temporal correlation
BibRef
Zhu, Z.,
Chen, X.,
Zhang, X.,
Zhang, L.,
Probabilistic Data Fusion for Short-Term Traffic Prediction With
Semiparametric Density Ratio Model,
ITS(20), No. 7, July 2019, pp. 2459-2469.
IEEE DOI
1907
Data integration, Predictive models, Probabilistic logic,
Data models, Time series analysis, Artificial neural networks,
probability distribution
BibRef
Duan, P.,
Mao, G.,
Liang, W.,
Zhang, D.,
A Unified Spatio-Temporal Model for Short-Term Traffic Flow
Prediction,
ITS(20), No. 9, September 2019, pp. 3212-3223.
IEEE DOI
1909
Roads, Predictive models, Correlation, Data models,
Computational modeling, Neural networks, Network topology,
unified
BibRef
Ma, D.F.[Dong-Fang],
Sheng, B.[Bowen],
Ma, X.L.[Xiao-Long],
Jin, S.[Sheng],
Fuzzy hybrid framework with dynamic weights for short-term traffic flow
prediction by mining spatio-temporal correlations,
IET-ITS(14), No. 2, February 2020, pp. 73-81.
DOI Link
2002
BibRef
Gu, Y.,
Lu, W.,
Xu, X.,
Qin, L.,
Shao, Z.,
Zhang, H.,
An Improved Bayesian Combination Model for Short-Term Traffic
Prediction With Deep Learning,
ITS(21), No. 3, March 2020, pp. 1332-1342.
IEEE DOI
2003
Predictive models, Deep learning, Correlation, Neural networks,
Bayes methods, Data models, Roads, Urban road,
microwave data
BibRef
Han, L.[Lei],
Huang, Y.S.[Yi-Shao],
Short-term traffic flow prediction of road network based on deep
learning,
IET-ITS(14), No. 6, June 2020, pp. 495-503.
DOI Link
2005
BibRef
Tong, X.H.[Xiao-Hua],
Wang, R.J.[Run-Jie],
Shi, W.Z.[Wen-Zhong],
Li, Z.Y.[Zhi-Yuan],
An Approach for Filter Divergence Suppression in a Sequential Data
Assimilation System and Its Application in Short-Term Traffic Flow
Forecasting,
IJGI(9), No. 6, 2020, pp. xx-yy.
DOI Link
2006
BibRef
Li, Z.,
Zheng, Z.,
Washington, S.,
Short-Term Traffic Flow Forecasting: A Component-Wise Gradient
Boosting Approach With Hierarchical Reconciliation,
ITS(21), No. 12, December 2020, pp. 5060-5072.
IEEE DOI
2012
Forecasting, Boosting, Predictive models, Correlation, Australia,
Urban areas, Roads, Traffic volume forecasting, gradient boosting,
hierarchical reconciliation
BibRef
Wang, R.J.[Run-Jie],
Shi, W.Z.[Wen-Zhong],
Liu, X.[Xianglei],
Li, Z.Y.[Zhi-Yuan],
An Adaptive Cutoff Frequency Selection Approach for Fast Fourier
Transform Method and Its Application into Short-Term Traffic Flow
Forecasting,
IJGI(9), No. 12, 2020, pp. xx-yy.
DOI Link
2012
BibRef
Zhang, Z.[Zhao],
Jiao, X.H.[Xiao-Hong],
A deep network with analogous self-attention for short-term traffic
flow prediction,
IET-ITS(15), No. 7, 2021, pp. 902-915.
DOI Link
2106
BibRef
Tao, Y.Y.[Yan-Yun],
Wang, X.[Xiang],
Zheng, J.Y.[Jian-Ying],
E, W.J.[Wen-Juan],
Zhao, P.[Po],
Meng, S.W.[Shi-Wei],
Deep tree neural network for multiple-time-step prediction of
short-term speed and confidence estimation,
IET-ITS(15), No. 3, 2021, pp. 446-462.
DOI Link
2106
BibRef
Tian, Z.[Zhongda],
Approach for Short-Term Traffic Flow Prediction Based on Empirical
Mode Decomposition and Combination Model Fusion,
ITS(22), No. 9, September 2021, pp. 5566-5576.
IEEE DOI
2109
Predictive models, Roads, Training, Empirical mode decomposition,
Optimization, Prediction algorithms, Biological system modeling,
improved fruit fly optimization algorithm
BibRef
Yu, Y.D.[Ya-Dong],
Zhang, Y.[Yong],
Qian, S.[Sean],
Wang, S.F.[Shao-Fan],
Hu, Y.L.[Yong-Li],
Yin, B.C.[Bao-Cai],
A Low Rank Dynamic Mode Decomposition Model for Short-Term Traffic
Flow Prediction,
ITS(22), No. 10, October 2021, pp. 6547-6560.
IEEE DOI
2110
Predictive models, Detectors, Neural networks, Roads,
Time series analysis, Machine learning, Data models,
low rank representation
BibRef
Cheng, S.[Shifen],
Lu, F.[Feng],
Peng, P.[Peng],
Short-Term Traffic Forecasting by Mining the Non-Stationarity of
Spatiotemporal Patterns,
ITS(22), No. 10, October 2021, pp. 6365-6383.
IEEE DOI
2110
Roads, Predictive models, Spatiotemporal phenomena,
Adaptation models, Forecasting, Vehicle dynamics,
temporal non-stationarity
BibRef
Qu, Z.W.[Zhao-Wei],
Haitao, L.[Li],
Li, Z.H.[Zhi-Hui],
Tao, Z.[Zhong],
Short-Term Traffic Flow Forecasting Method With M-B-LSTM Hybrid
Network,
ITS(23), No. 1, January 2022, pp. 225-235.
IEEE DOI
2201
Forecasting, Machine learning, Predictive models, Data models,
Uncertainty, Recurrent neural networks, Probability distribution,
data stochasticity
BibRef
Liu, J.[Jin],
Wu, N.Q.[Nai-Qi],
Qiao, Y.[Yan],
Li, Z.W.[Zhi-Wu],
Short-Term Traffic Flow Forecasting Using Ensemble Approach Based on
Deep Belief Networks,
ITS(23), No. 1, January 2022, pp. 404-417.
IEEE DOI
2201
Forecasting, Predictive models, Object oriented modeling,
Machine learning, Neural networks, Transportation,
traffic flow forecasting
BibRef
Zhao, L.[Leina],
Wen, X.Y.[Xin-Yu],
Wang, Y.P.[Yan-Peng],
Shao, Y.M.[Yi-Ming],
A novel hybrid model of ARIMA-MCC and CKDE-GARCH for urban short-term
traffic flow prediction,
IET-ITS(16), No. 2, 2022, pp. 206-217.
DOI Link
2201
BibRef
Sun, Z.Y.[Zhao-Yun],
Hu, Y.J.[Yuan-Jiao],
Li, W.[Wei],
Feng, S.W.[Shao-Wei],
Pei, L.[Lili],
Prediction model for short-term traffic flow based on a K-means-gated
recurrent unit combination,
IET-ITS(16), No. 5, 2022, pp. 675-690.
DOI Link
2204
BibRef
Ma, C.X.[Chang-Xi],
Dai, G.[Guowen],
Zhou, J.[Jibiao],
Short-Term Traffic Flow Prediction for Urban Road Sections Based on
Time Series Analysis and LSTM_BILSTM Method,
ITS(23), No. 6, June 2022, pp. 5615-5624.
IEEE DOI
2206
Time series analysis, Predictive models, Fractals, Data models,
Correlation, Biological neural networks, Training,
urban road section
BibRef
Cheng, Z.Y.[Ze-Yang],
Lu, J.[Jian],
Zhou, H.J.[Hua-Jian],
Zhang, Y.B.[Yi-Bin],
Zhang, L.[Lin],
Short-Term Traffic Flow Prediction: An Integrated Method of
Econometrics and Hybrid Deep Learning,
ITS(23), No. 6, June 2022, pp. 5231-5244.
IEEE DOI
2206
Predictive models, Hidden Markov models, Deep learning,
Neural networks, Reactive power, Time series analysis, spatiotemporal heatmap
BibRef
Fang, M.Y.[Meng-Yuan],
Tang, L.[Luliang],
Yang, X.[Xue],
Chen, Y.[Yang],
Li, C.K.[Chao-Kui],
Li, Q.Q.[Qing-Quan],
FTPG: A Fine-Grained Traffic Prediction Method With Graph Attention
Network Using Big Trace Data,
ITS(23), No. 6, June 2022, pp. 5163-5175.
IEEE DOI
2206
Roads, Predictive models, Sensors, Estimation, Data models,
Urban areas, Real-time systems, Short-term traffic prediction, turn level
BibRef
Furtlehner, C.[Cyril],
Lasgouttes, J.M.[Jean-Marc],
Attanasi, A.[Alessandro],
Pezzulla, M.[Marco],
Gentile, G.[Guido],
Short-Term Forecasting of Urban Traffic Using Spatio-Temporal Markov
Field,
ITS(23), No. 8, August 2022, pp. 10858-10867.
IEEE DOI
2208
Data models, Predictive models, Belief propagation,
Markov processes, Indexes, Forecasting, Convergence,
machine learning
BibRef
Yan, H.[He],
Qi, Y.[Yong],
Ye, Q.L.[Qiao-Lin],
Yu, D.J.[Dong-Jun],
Robust Least Squares Twin Support Vector Regression With Adaptive FOA
and PSO for Short-Term Traffic Flow Prediction,
ITS(23), No. 9, September 2022, pp. 14542-14556.
IEEE DOI
2209
Predictive models, Data models, Support vector machines,
Computational modeling, Adaptation models, Optimization, Training,
short-term traffic flow prediction
BibRef
Jin, J.C.[Jun-Chen],
Rong, D.D.[Ding-Ding],
Zhang, T.[Tong],
Ji, Q.Y.[Qing-Yuan],
Guo, H.F.[Hai-Feng],
Lv, Y.S.[Yi-Sheng],
Ma, X.L.[Xiao-Liang],
Wang, F.Y.[Fei-Yue],
A GAN-Based Short-Term Link Traffic Prediction Approach for Urban
Road Networks Under a Parallel Learning Framework,
ITS(23), No. 9, September 2022, pp. 16185-16196.
IEEE DOI
2209
Roads, Predictive models, Data models, Recurrent neural networks,
Generators, Deep learning, Wasserstein generative adversarial network
BibRef
Tao, Q.H.[Qing-Hua],
Li, Z.[Zhen],
Xu, J.[Jun],
Lin, S.[Shu],
de Schutter, B.[Bart],
Suykens, J.A.K.[Johan A. K.],
Short-Term Traffic Flow Prediction Based on the Efficient Hinging
Hyperplanes Neural Network,
ITS(23), No. 9, September 2022, pp. 15616-15628.
IEEE DOI
2209
Predictive models, Artificial neural networks, Neurons,
Feature extraction, Analysis of variance, Data models, variables analysis
BibRef
Zhang, Y.[Yang],
Xin, D.R.[Dong-Rong],
A Diverse Ensemble Deep Learning Method for Short-Term Traffic Flow
Prediction Based on Spatiotemporal Correlations,
ITS(23), No. 9, September 2022, pp. 16715-16727.
IEEE DOI
2209
Roads, Predictive models, Correlation, Deep learning,
Spatiotemporal phenomena, Prediction algorithms,
convolutional neural network
BibRef
Shu, W.N.[Wan-Neng],
Cai, K.[Ken],
Xiong, N.N.[Neal Naixue],
A Short-Term Traffic Flow Prediction Model Based on an Improved Gate
Recurrent Unit Neural Network,
ITS(23), No. 9, September 2022, pp. 16654-16665.
IEEE DOI
2209
Predictive models, Adaptation models, Time series analysis,
Prediction algorithms, Mathematical model,
short-term traffic flow prediction
BibRef
Mirzahossein, H.[Hamid],
Gholampour, I.[Iman],
Sajadi, S.R.[Sayed Reza],
Zamani, A.H.[Amir Hossein],
A hybrid deep and machine learning model for short-term traffic
volume forecasting of adjacent intersections,
IET-ITS(16), No. 11, 2022, pp. 1648-1663.
DOI Link
2210
BibRef
Jiang, Y.L.[Yun-Liang],
Fan, J.B.[Jin-Bin],
Liu, Y.[Yong],
Zhang, X.T.[Xiong-Tao],
Deep Graph Gaussian Processes for Short-Term Traffic Flow Forecasting
From Spatiotemporal Data,
ITS(23), No. 11, November 2022, pp. 20177-20186.
IEEE DOI
2212
Gaussian processes, Feature extraction, Spatiotemporal phenomena,
Monitoring, Kernel, Predictive models, Data models,
traffic flow forecasting
BibRef
Huang, S.[Shuai],
Sun, D.[Dihua],
Zhao, M.[Min],
Chen, J.[Jin],
Chen, R.[Rui],
Short-term traffic flow prediction approach incorporating vehicle
functions from RFID-ELP data for urban road sections,
IET-ITS(17), No. 1, 2023, pp. 144-164.
DOI Link
2301
BibRef
Xia, M.R.[Meng-Ran],
Jin, D.W.[Da-Wei],
Chen, J.Y.[Jing-Yu],
Short-Term Traffic Flow Prediction Based on Graph Convolutional
Networks and Federated Learning,
ITS(24), No. 1, January 2023, pp. 1191-1203.
IEEE DOI
2301
Predictive models, Data models, Forecasting, Training,
Computational modeling, Roads, Data privacy,
horizontal local road network
BibRef
Zhou, S.H.[Sheng-Han],
Wei, C.F.[Chao-Fan],
Song, C.F.[Chao-Fei],
Pan, X.[Xing],
Chang, W.B.[Wen-Bing],
Yang, L.C.[Lin-Chao],
Short-Term Traffic Flow Prediction of the Smart City Using 5G
Internet of Vehicles Based on Edge Computing,
ITS(24), No. 2, February 2023, pp. 2229-2238.
IEEE DOI
2302
Roads, 5G mobile communication, Smart cities, Transportation,
Predictive models, Prediction algorithms, Computational modeling,
smart city
BibRef
Varga, B.[Balázs],
Pereira, M.[Mike],
Kulcsár, B.[Balázs],
Pariota, L.[Luigi],
Péni, T.[Tamás],
Data-Driven Distance Metrics for Kriging-Short-Term Urban Traffic
State Prediction,
ITS(24), No. 6, June 2023, pp. 6268-6279.
IEEE DOI
2306
Prediction algorithms, Measurement, Detectors, Kernel, Deep learning,
Correlation, Neural networks, Kriging, spatio-temporal prediction,
traffic flow prediction
BibRef
Yang, Z.J.[Zi-Jing],
Wang, C.[Cheng],
Short-term traffic flow prediction based on AST-MTL-CNN-GRU,
IET-ITS(17), No. 11, 2023, pp. 2205-2220.
DOI Link
2311
convolutional neural network, gate recurrent unit,
short-term traffic flow prediction,
spatiotemporal separation attention mechanism
BibRef
Li, K.[Kai],
Bai, W.H.[Wei-Hua],
Huang, S.W.[Shao-Wei],
Tan, G.[Guanru],
Zhou, T.[Teng],
Li, K.Q.[Ke-Qin],
Lag-related noise shrinkage stacked LSTM network for short-term
traffic flow forecasting,
IET-ITS(18), No. 2, 2024, pp. 244-257.
DOI Link
2402
intelligent transportation systems,
traffic information systems, traffic modeling, management and control
BibRef
Yan, X.[Xiao],
Gan, X.H.[Xiang-Hua],
Tang, J.J.[Jing-Jing],
Zhang, D.P.[Da-Peng],
Wang, R.[Rui],
ProSTformer: Progressive Space-Time Self-Attention Model for
Short-Term Traffic Flow Forecasting,
ITS(25), No. 9, September 2024, pp. 10802-10816.
IEEE DOI Code:
WWW Link.
2409
Forecasting, Transformers, Spatiotemporal phenomena,
Computational modeling, Predictive models,
spatial-temporal learning
BibRef
Wang, H.X.[Hao-Xu],
Wang, Z.W.[Zhi-Wen],
Li, L.[Long],
Yang, K.K.[Kang-Kang],
Zeng, J.X.[Jing-Xiao],
Zhao, Y.B.[Yi-Bin],
Zhang, J.[Jindou],
SARO-MB3-BiGRU: A novel model for short-term traffic flow forecasting
in the context of big data,
IET-ITS(18), No. 11, 2024, pp. 2097-2113.
DOI Link
2411
artificial intelligence, big data-driven, intelligent transportation systems,
optimisation, Short-term traffic flow prediction
BibRef
Liu, L.[Limei],
Duan, P.[Peibo],
Chen, Z.[Zhuo],
Zhang, J.H.[Jing-Hui],
Feng, S.Y.[Si-Yuan],
Yue, W.W.[Wen-Wei],
Wang, Y.[Yibo],
Rong, J.[Jia],
Spatiotemporal Generalization Graph Neural Network-Based Prediction
Models by Considering Morphological Diversity in Traffic Networks,
ITS(26), No. 7, July 2025, pp. 9993-10007.
IEEE DOI
2507
Correlation, Transfer learning, Spatiotemporal phenomena,
Predictive models, Topology, Network topology, Training,
causality
BibRef
Xu, C.C.[Cheng-Cheng],
Shao, Y.C.[Yong-Cheng],
Ma, C.X.[Chen-Xiang],
Han, M.[Mingmin],
Tong, H.[Hao],
Peng, C.[Chang],
A Geometric Deep Learning Approach to Traffic Flow Shockwave
Prediction on Freeways Using Vehicle Trajectory Data and HD Map,
ITS(26), No. 7, July 2025, pp. 9907-9917.
IEEE DOI
2507
Trajectory, Predictive models, Accuracy, Roads,
Graph neural networks, Attention mechanisms, Feature extraction,
wavelet transform
BibRef
Mallick, T.[Tanwi],
Balaprakash, P.[Prasanna],
Rask, E.[Eric],
Macfarlane, J.[Jane],
Transfer Learning with Graph Neural Networks for Short-Term Highway
Traffic Forecasting,
ICPR21(10367-10374)
IEEE DOI
2105
Road transportation, Training, Recurrent neural networks,
Transfer learning, Predictive models, Traffic control
BibRef
Li, Q.,
Wang, H.,
Elman short-term traffic flow prediction model based on association
rules,
CVIDL20(673-678)
IEEE DOI
2102
backpropagation, data mining, recurrent neural nets, road traffic,
time series, traffic engineering computing, time series factors,
Elman neural network
BibRef
Liu, D.,
Hui, S.,
Li, L.,
Liu, Z.,
Zhang, Z.,
A Method For Short-Term Traffic Flow Forecasting Based On GCN-LSTM,
CVIDL20(364-368)
IEEE DOI
2102
convolutional neural nets, data reduction,
intelligent transportation systems, LSTM
BibRef
Chapter on Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities continues in
Missing Data in Traffic Flow, Data Imputation, Prediction, Forecast .