16.7.2.7.1 Traffic Flow Prediction, Forecast

Quek, C., Pasquier, M., Lim, B.L.B.,
POP-TRAFFIC: A Novel Fuzzy Neural Approach to Road Traffic Analysis and Prediction,
ITS(7), No. 2, June 2006, pp. 133-146.
IEEE DOI 0606
BibRef

Sun, S.L.[Shi-Liang], Zhang, C.S.[Chang-Shui], Yu, G.Q.[Guo-Qiang],
A bayesian network approach to traffic flow forecasting,
ITS(7), No. 1, March 2006, pp. 124-132.
IEEE DOI 0604
BibRef

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

Sun, S., Xu, X.,
Variational Inference for Infinite Mixtures of Gaussian Processes With Applications to Traffic Flow Prediction,
ITS(12), No. 2, June 2011, pp. 466-475.
IEEE DOI 1101
BibRef

Ramezani, A., Moshiri, B., Abdulhai, B., Kian, A.R.,
Distributed maximum likelihood estimation for flow and speed density prediction in distributed traffic detectors with gaussian mixture model assumption,
IET-ITS(6), No. 2, 2012, pp. 215-222.
DOI Link 1206
BibRef

Chang, H., Lee, Y., Yoon, B., Baek, S.,
Dynamic near-term traffic flow prediction: systemoriented approach based on past experiences,
IET-ITS(6), No. 2, 2012, pp. 292-305.
DOI Link 1209
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

Chen, C., Liu, Z., Lin, W.H., Li, S., Wang, K.,
Distributed Modeling in a MapReduce Framework for Data-Driven Traffic Flow Forecasting,
ITS(14), No. 1, March 2013, pp. 22-33.
IEEE DOI 1303
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

Kong, Q.J., Xu, Y., Lin, S., Wen, D., Zhu, F., Liu, Y.C.[Yun-Cai],
UTN-Model-Based Traffic Flow Prediction for Parallel-Transportation Management Systems,
ITS(14), No. 3, 2013, pp. 1541-1547.
IEEE DOI 1309
CORSIM BibRef

Xu, Y.Y.[Yan-Yan], Kong, Q.J.[Qing-Jie], Klette, R.[Reinhard], Liu, Y.C.[Yun-Cai],
Accurate and Interpretable Bayesian MARS for Traffic Flow Prediction,
ITS(15), No. 6, December 2014, pp. 2457-2469.
IEEE DOI 1412
Markov processes 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

Wang, Y.B.[Yu-Bin], van Schuppen, J.H., Vrancken, J.,
Prediction of Traffic Flow at the Boundary of a Motorway Network,
ITS(15), No. 1, February 2014, pp. 214-227.
IEEE DOI 1403
adaptive control BibRef

Asif, M.T., Dauwels, J., Goh, C.Y., Oran, A., Fathi, E., Xu, M., Dhanya, M.M., Mitrovic, N., Jaillet, P.,
Spatiotemporal Patterns in Large-Scale Traffic Speed Prediction,
ITS(15), No. 2, April 2014, pp. 794-804.
IEEE DOI 1404
Accuracy BibRef

Bonnin, S., Weisswange, T.H., Kummert, F., Schmuedderich, J.,
General Behavior Prediction by a Combination of Scenario-Specific Models,
ITS(15), No. 4, August 2014, pp. 1478-1488.
IEEE DOI 1410
behavioural sciences computing BibRef

Tahmasbi, R., Hashemi, S.M.,
Modeling and Forecasting the Urban Volume Using Stochastic Differential Equations,
ITS(15), No. 1, February 2014, pp. 250-259.
IEEE DOI 1403
differential equations 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

Lopes, S.B.[Simone Becker], Brondino, N.C.M.[Nair Cristina Margarido], Rodrigues da Silva, A.N.[Antônio Nélson],
GIS-Based Analytical Tools for Transport Planning: Spatial Regression Models for Transportation Demand Forecast,
IJGI(3), No. 2, 2014, pp. 565-583.
DOI Link 1405
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

Hou, Y., Edara, P., Sun, C.,
Traffic Flow Forecasting for Urban Work Zones,
ITS(16), No. 4, August 2015, pp. 1761-1770.
IEEE DOI 1508
Feedforward neural networks 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

Abadi, A., Rajabioun, T., Ioannou, P.A.,
Traffic Flow Prediction for Road Transportation Networks With Limited Traffic Data,
ITS(16), No. 2, April 2015, pp. 653-662.
IEEE DOI 1504
Estimation BibRef

Lv, Y.S.[Yi-Sheng], Duan, Y.J.[Yan-Jie], Kang, W.W.[Wen-Wen], Li, Z.X.[Zheng-Xi], Wang, F.Y.[Fei-Yue],
Traffic Flow Prediction With Big Data: A Deep Learning Approach,
ITS(16), No. 2, April 2015, pp. 865-873.
IEEE DOI 1504
Adaptation models BibRef

Du, W.B.[Wen-Bo], Chen, S.W.[Shen-Wen], Li, H.T.[Hai-Tao], Li, Z.S.[Zhi-Shuai], Cao, X.B.[Xian-Bin], Lv, Y.S.[Yi-Sheng],
Airport Capacity Prediction With Multisource Features: A Temporal Deep Learning Approach,
ITS(24), No. 1, January 2023, pp. 615-630.
IEEE DOI 2301
Airports, Feature extraction, Atmospheric modeling, Predictive models, Prediction algorithms, Clustering algorithms, deep learning BibRef

Chen, Y., Lv, Y., Wang, X., Li, L., Wang, F.,
Detecting Traffic Information From Social Media Texts With Deep Learning Approaches,
ITS(20), No. 8, August 2019, pp. 3049-3058.
IEEE DOI 1908
Data mining, Twitter, Feature extraction, Support vector machines, Predictive models, Real-time systems, Deep learning, text mining BibRef

Oh, S.D.[Se-Do], Kim, Y.J.[Young-Jin], Hong, J.S.[Ji-Sun],
Urban Traffic Flow Prediction System Using a Multifactor Pattern Recognition Model,
ITS(16), No. 5, October 2015, pp. 2744-2755.
IEEE DOI 1511
Gaussian processes BibRef

Dell'Acqua, P., Bellotti, F., Berta, R., de Gloria, A.,
Time-Aware Multivariate Nearest Neighbor Regression Methods for Traffic Flow Prediction,
ITS(16), No. 6, December 2015, pp. 3393-3402.
IEEE DOI 1512
Artificial neural networks BibRef

Oh, S., Byon, Y.J., Yeo, H.,
Improvement of Search Strategy With K-Nearest Neighbors Approach for Traffic State Prediction,
ITS(17), No. 4, April 2016, pp. 1146-1156.
IEEE DOI 1604
Acceleration 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

Hou, Z., Li, X.,
Repeatability and Similarity of Freeway Traffic Flow and Long-Term Prediction Under Big Data,
ITS(17), No. 6, June 2016, pp. 1786-1796.
IEEE DOI 1606
Analytical models BibRef

Zhao, J., Sun, S.,
High-Order Gaussian Process Dynamical Models for Traffic Flow Prediction,
ITS(17), No. 7, July 2016, pp. 2014-2019.
IEEE DOI 1608
Gaussian processes 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

Abbasi, O.R.[Omid Reza], Alesheikh, A.A.[Ali Asghar], Sharif, M.[Mohammad],
Ranking the City: The Role of Location-Based Social Media Check-Ins in Collective Human Mobility Prediction,
IJGI(6), No. 5, 2017, pp. xx-yy.
DOI Link 1706
BibRef

Tang, J., Liu, F., Zou, Y., Zhang, W., Wang, Y.,
An Improved Fuzzy Neural Network for Traffic Speed Prediction Considering Periodic Characteristic,
ITS(18), No. 9, September 2017, pp. 2340-2350.
IEEE DOI 1709
Gaussian processes, forecasting theory, fuzzy neural nets, fuzzy reasoning, Takagi-Sugeno type fuzzy rules, BibRef

Xiao, Z., Ponnambalam, L., Fu, X., Zhang, W.,
Maritime Traffic Probabilistic Forecasting Based on Vessels-Waterway Patterns and Motion Behaviors,
ITS(18), No. 11, November 2017, pp. 3122-3134.
IEEE DOI 1711
Artificial intelligence, Data mining, Forecasting, Planning, Prediction algorithms, Stability analysis, Transportation, Data mining, knowledge discovery, knowledge engineering, marine, transportation 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

Zhang, Y.[Yaying], Huang, G.[Guan],
traffic flow prediction model based on deep belief network and genetic algorithm,
IET-ITS(12), No. 6, August 2018, pp. 533-541.
DOI Link 1807
BibRef

Zhang, D.[Da], Kabuka, M.R.[Mansur R.],
Combining weather condition data to predict traffic flow: A GRU-based deep learning approach,
IET-ITS(12), No. 7, September 2018, pp. 578-585.
DOI Link 1808
BibRef

Besse, P.C., Guillouet, B., Loubes, J.M., Royer, F.,
Destination Prediction by Trajectory Distribution-Based Model,
ITS(19), No. 8, August 2018, pp. 2470-2481.
IEEE DOI 1808
Trajectory, Public transportation, Predictive models, Roads, Data models, Markov processes, Vehicles, Trajectory classification, final destination prediction BibRef

Huang, W., Jia, W., Guo, J., Williams, B.M., Shi, G., Wei, Y., Cao, J.,
Real-Time Prediction of Seasonal Heteroscedasticity in Vehicular Traffic Flow Series,
ITS(19), No. 10, October 2018, pp. 3170-3180.
IEEE DOI 1810
Predictive models, Adaptation models, Data models, Forecasting, Analytical models, Real-time systems, Transportation, adaptive Kalman filter BibRef

Maeda, T.N.[Takashi Nicholas], Mori, J.[Junichiro], Ochi, M.[Masanao], Sakimoto, T.[Tetsuo], Sakata, I.[Ichiro],
Measurement of Opportunity Cost of Travel Time for Predicting Future Residential Mobility Based on the Smart Card Data of Public Transportation,
IJGI(7), No. 11, 2018, pp. xx-yy.
DOI Link 1812
BibRef

Zhan, H., Gomes, G., Li, X.S., Madduri, K., Sim, A., Wu, K.,
Consensus Ensemble System for Traffic Flow Prediction,
ITS(19), No. 12, December 2018, pp. 3903-3914.
IEEE DOI 1812
Predictive models, Computational modeling, Forecasting, Time series analysis, Data models, traffic flow prediction BibRef

Xue, Z.L.[Ze-Long], Xue, Y.[Yang],
Multi Long-Short Term Memory Models for Short Term Traffic Flow Prediction,
IEICE(E101-D), No. 12, December 2018, pp. 3272-3275.
WWW Link. 1812
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.[Shifen], 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.[Haowen], 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

Guo, S.N.[Sheng-Nan], Lin, Y.F.[You-Fang], Li, S.J.[Shi-Jie], Chen, Z.M.[Zhao-Ming], Wan, H.Y.[Huai-Yu],
Deep Spatial-Temporal 3D Convolutional Neural Networks for Traffic Data Forecasting,
ITS(20), No. 10, October 2019, pp. 3913-3926.
IEEE DOI 1910
Correlation, Forecasting, Data models, Feature extraction, Predictive models, Solid modeling, recalibration block BibRef

Zhao, Y.[Yiji], Lin, Y.F.[You-Fang], Wen, H.M.[Hao-Min], Wei, T.L.[Tong-Long], Jin, X.Y.[Xi-Yuan], Wan, H.Y.[Huai-Yu],
Spatial-Temporal Position-Aware Graph Convolution Networks for Traffic Flow Forecasting,
ITS(24), No. 8, August 2023, pp. 8650-8666.
IEEE DOI 2308
Correlation, Convolution, Forecasting, Predictive models, Feature extraction, Roads, Data models, Traffic flow forecasting, graph convolution networks BibRef

Song, Y., Wang, X., Wright, G., Thatcher, D., Wu, P., Felix, P.,
Traffic Volume Prediction With Segment-Based Regression Kriging and its Implementation in Assessing the Impact of Heavy Vehicles,
ITS(20), No. 1, January 2019, pp. 232-243.
IEEE DOI 1901
Roads, Maintenance engineering, Predictive models, Australia, Correlation, Estimation, Sociology, Geostatistics, road maintenance BibRef

Chu, K., Saigal, R., Saitou, K.,
Real-Time Traffic Prediction and Probing Strategy for Lagrangian Traffic Data,
ITS(20), No. 2, February 2019, pp. 497-506.
IEEE DOI 1902
Data models, Real-time systems, Predictive models, Adaptation models, Computational modeling, Stochastic processes, adaptive data collection 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

Pamula, T.,
Impact of Data Loss for Prediction of Traffic Flow on an Urban Road Using Neural Networks,
ITS(20), No. 3, March 2019, pp. 1000-1009.
IEEE DOI 1903
Roads, Neurons, Neural networks, Machine learning, Predictive models, Data models, Adaptation models, Deep learning, sensitivity to loss of data 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

Ghanim, M.S.[Mohammad S.], Abu-Lebdeh, G.[Ghassan],
Projected state-wide traffic forecast parameters using artificial neural networks,
IET-ITS(13), No. 4, April 2019, pp. 661-669.
DOI Link 1903
BibRef

Chen, X., Zhang, S., Li, L., Li, L.,
Adaptive Rolling Smoothing With Heterogeneous Data for Traffic State Estimation and Prediction,
ITS(20), No. 4, April 2019, pp. 1247-1258.
IEEE DOI 1904
State estimation, Smoothing methods, Time measurement, Vehicle dynamics, Cameras, Microwave measurement, urban expressway 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

Lin, Y., Dai, X., Li, L., Wang, F.,
Pattern Sensitive Prediction of Traffic Flow Based on Generative Adversarial Framework,
ITS(20), No. 6, June 2019, pp. 2395-2400.
IEEE DOI 1906
Predictive models, Biological system modeling, Data models, Automation, Machine learning, Industries, Traffic flow prediction, generative adversarial network 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

Ganapathy, J.[Jayanthi], Paramasivam, J.[Jothilakshmi],
Prediction of traffic volume by mining traffic sequences using travel time based PrefixSpan,
IET-ITS(13), No. 7, July 2019, pp. 1199-1210.
DOI Link 1906
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

Yang, D.[Di], Li, S.J.[Song-Jiang], Peng, Z.[Zhou], Wang, P.[Peng], Wang, J.H.[Jun-Hui], Yang, H.M.[Hua-Min],
MF-CNN: Traffic Flow Prediction Using Convolutional Neural Network and Multi-Features Fusion,
IEICE(E102-D), No. 8, August 2019, pp. 1526-1536.
WWW Link. 1908
BibRef

Liu, Y.[Yu], Liu, Z.[Zhao], Li, X.G.[Xiu-Gang], Huang, W.[Wei], Wei, Y.[Yun], Cao, J.[Jinde], Guo, J.H.[Jian-Hua],
Dynamic traffic demand uncertainty prediction using radio-frequency identification data and link volume data,
IET-ITS(13), No. 8, August 2019, pp. 1309-1317.
DOI Link 1908
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

Zheng, Z., Yang, Y., Liu, J., Dai, H., Zhang, Y.,
Deep and Embedded Learning Approach for Traffic Flow Prediction in Urban Informatics,
ITS(20), No. 10, October 2019, pp. 3927-3939.
IEEE DOI 1910
Meteorology, Predictive models, Urban areas, Deep learning, Sensors, Roads, Informatics, Urban informatics, traffic flow prediction, deep learning 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

Chang, B., Chiou, J.,
Cloud Computing-Based Analyses to Predict Vehicle Driving Shockwave for Active Safe Driving in Intelligent Transportation System,
ITS(21), No. 2, February 2020, pp. 852-866.
IEEE DOI 2002
Cloud computing, Microscopy, Real-time systems, Delays, 5G mobile communication, Roads, Sensors, VCC, MEC, macroscopic and microscopic analyses 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

Zheng, C.P.[Chuan-Pan], Fan, X.L.[Xiao-Liang], Wen, C.L.[Cheng-Lu], Chen, L.B.[Long-Biao], Wang, C.[Cheng], Li, J.[Jonathan],
DeepSTD: Mining Spatio-Temporal Disturbances of Multiple Context Factors for Citywide Traffic Flow Prediction,
ITS(21), No. 9, September 2020, pp. 3744-3755.
IEEE DOI 2008
Neural networks, Meteorology, Predictive models, Deep learning, Urban areas, Intelligent transportation systems, intelligent transportation systems BibRef

Zhao, L.[Ling], Song, Y.J.[Yu-Jiao], Zhang, C.[Chao], Liu, Y.[Yu], Wang, P.[Pu], Lin, T.[Tao], Deng, M.[Min], Li, H.F.[Hai-Feng],
T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction,
ITS(21), No. 9, September 2020, pp. 3848-3858.
IEEE DOI 2008
Predictive models, Forecasting, Roads, Data models, Task analysis, Logic gates, Kalman filters, Traffic forecasting, temporal dependence BibRef

Bai, J.D.[Jian-Dong], Zhu, J.W.[Jia-Wei], Song, Y.J.[Yu-Jiao], Zhao, L.[Ling], Hou, Z.X.[Zhi-Xiang], Du, R.H.[Rong-Hua], Li, H.F.[Hai-Feng],
A3T-GCN: Attention Temporal Graph Convolutional Network for Traffic Forecasting,
IJGI(10), No. 7, 2021, pp. xx-yy.
DOI Link 2108
BibRef

Zhu, J.W.[Jia-Wei], Han, X.[Xing], Deng, H.[Hanhan], Tao, C.[Chao], Zhao, L.[Ling], Wang, P.[Pu], Lin, T.[Tao], Li, H.F.[Hai-Feng],
KST-GCN: A Knowledge-Driven Spatial-Temporal Graph Convolutional Network for Traffic Forecasting,
ITS(23), No. 9, September 2022, pp. 15055-15065.
IEEE DOI 2209
Roads, Forecasting, Predictive models, Data models, Semantics, Correlation, Mathematical models, Traffic flow forecasting, spatial-temporal graph convolutional networks BibRef

Sun, B.[Bo], Sun, T.[Tuo], Zhang, Y.J.[Yu-Jia], Jiao, P.P.[Peng-Peng],
Urban traffic flow online prediction based on multi-component attention mechanism,
IET-ITS(14), No. 10, October 2020, pp. 1249-1258.
DOI Link 2009
BibRef

Xu, D.W.[Dong-Wei], Peng, P.[Peng], Wei, C.C.[Chen-Chen], He, D.F.[De-Feng], Xuan, Q.[Qi],
Road traffic network state prediction based on a generative adversarial network,
IET-ITS(14), No. 10, October 2020, pp. 1286-1294.
DOI Link 2009
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

Cui, Z., Henrickson, K., Ke, R., Wang, Y.,
Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting,
ITS(21), No. 11, November 2020, pp. 4883-4894.
IEEE DOI 2011
Convolution, Forecasting, Predictive models, Roads, Machine learning, Feature extraction, Artificial neural networks, recurrent neural network 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

Zhang, Y., Wang, S., Chen, B., Cao, J., Huang, Z.,
TrafficGAN: Network-Scale Deep Traffic Prediction With Generative Adversarial Nets,
ITS(22), No. 1, January 2021, pp. 219-230.
IEEE DOI 2012
Roads, Predictive models, Correlation, Data models, Deep learning, Forecasting, Traffic prediction, deep learning 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

Guo, K.[Kan], Hu, Y.L.[Yong-Li], Qian, Z.[Zhen], Liu, H.[Hao], Zhang, K.[Ke], Sun, Y.F.[Yan-Feng], Gao, J.B.[Jun-Bin], Yin, B.C.[Bao-Cai],
Optimized Graph Convolution Recurrent Neural Network for Traffic Prediction,
ITS(22), No. 2, February 2021, pp. 1138-1149.
IEEE DOI 2102
Roads, Convolution, Recurrent neural networks, Training, Graph convolution network, recurrent neural network, traffic prediction BibRef

Sun, Y.F.[Yan-Feng], Jiang, X.H.[Xiang-Heng], Hu, Y.L.[Yong-Li], Duan, F.Q.[Fu-Qing], Guo, K.[Kan], Wang, B.Y.[Bo-Yue], Gao, J.B.[Jun-Bin], Yin, B.C.[Bao-Cai],
Dual Dynamic Spatial-Temporal Graph Convolution Network for Traffic Prediction,
ITS(23), No. 12, December 2022, pp. 23680-23693.
IEEE DOI 2212
Roads, Convolution, Predictive models, Correlation, Data models, Vehicle dynamics, Transportation, Graph convolution network, intelligent transportation systems BibRef

Low, R., Cheah, L., You, L.,
Commercial Vehicle Activity Prediction With Imbalanced Class Distribution Using a Hybrid Sampling and Gradient Boosting Approach,
ITS(22), No. 3, March 2021, pp. 1401-1410.
IEEE DOI 2103
Frequency modulation, Predictive models, Vehicles, Boosting, Data models, Prediction algorithms, Feature extraction, imbalanced dataset BibRef

Wang, Y., Yu, X., Zhang, S., Zheng, P., Guo, J., Zhang, L., Hu, S., Cheng, S., Wei, H.,
Freeway Traffic Control in Presence of Capacity Drop,
ITS(22), No. 3, March 2021, pp. 1497-1516.
IEEE DOI 2103
Traffic control, Optimal control, Data models, Analytical models, Merging, Mathematical model, Feedback control, macroscopic traffic flow modeling BibRef

Ma, D.F.[Dong-Fang], Song, X.[Xiang], Li, P.[Pu],
Daily Traffic Flow Forecasting Through a Contextual Convolutional Recurrent Neural Network Modeling Inter- and Intra-Day Traffic Patterns,
ITS(22), No. 5, May 2021, pp. 2627-2636.
IEEE DOI 2105
Forecasting, Machine learning, Time series analysis, Data mining, Predictive models, Recurrent neural networks, Context modeling, long short-term memory BibRef

Jia, T.[Tao], Yan, P.G.[Peng-Gao],
Predicting Citywide Road Traffic Flow Using Deep Spatiotemporal Neural Networks,
ITS(22), No. 5, May 2021, pp. 3101-3111.
IEEE DOI 2105
BibRef
And: Correction: ITS(22), No. 6, June 2021, pp. 3900-3900.
IEEE DOI 2106
Roads, Spatiotemporal phenomena, Predictive models, Neural networks, Autoregressive processes, Image segmentation, trajectory data BibRef

Lv, M.Q.[Ming-Qi], Hong, Z.X.[Zhao-Xiong], Chen, L.[Ling], Chen, T.M.[Tie-Ming], Zhu, T.T.[Tian-Tian], Ji, S.[Shouling],
Temporal Multi-Graph Convolutional Network for Traffic Flow Prediction,
ITS(22), No. 6, June 2021, pp. 3337-3348.
IEEE DOI 2106
Correlation, Roads, Semantics, Predictive models, Machine learning, Convolution, Analytical models, Traffic flow prediction, graph fusion BibRef

Chen, L.[Ling], Shao, W.[Wei], Lv, M.Q.[Ming-Qi], Chen, W.Q.[Wei-Qi], Zhang, Y.D.[You-Dong], Yang, C.H.[Cheng-Hu],
AARGNN: An Attentive Attributed Recurrent Graph Neural Network for Traffic Flow Prediction Considering Multiple Dynamic Factors,
ITS(23), No. 10, October 2022, pp. 17201-17211.
IEEE DOI 2210
Correlation, Sensors, Data models, Roads, Predictive models, Sensor phenomena and characterization, Semantics, urban computing BibRef

Chen, C.[Chen], Liu, Z.Y.[Zi-Ye], Wan, S.H.[Shao-Hua], Luan, J.T.[Jin-Tai], Pei, Q.Q.[Qing-Qi],
Traffic Flow Prediction Based on Deep Learning in Internet of Vehicles,
ITS(22), No. 6, June 2021, pp. 3776-3789.
IEEE DOI 2106
Roads, Predictive models, Machine learning, Feature extraction, Prediction algorithms, Computational modeling, Urban areas, traffic compression BibRef

Bhanu, M.[Manish], Mendes-Moreira, J.[João], Chandra, J.[Joydeep],
Embedding Traffic Network Characteristics Using Tensor for Improved Traffic Prediction,
ITS(22), No. 6, June 2021, pp. 3359-3371.
IEEE DOI 2106
Tensile stress, Urban areas, Matrix decomposition, Forecasting, Public transportation, Predictive models, Traffic prediction, reciprocity BibRef

Chen, M.[Meng], Zuo, Y.X.[Yi-Xuan], Jia, X.Y.[Xiao-Yi], Liu, Y.[Yang], Yu, X.H.[Xiao-Hui], Zheng, K.[Kai],
CEM: A Convolutional Embedding Model for Predicting Next Locations,
ITS(22), No. 6, June 2021, pp. 3349-3358.
IEEE DOI 2106
Trajectory, Predictive models, Data models, Roads, Recurrent neural networks, Context modeling, Convolution, traffic trajectory data 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

Liu, J.[Jin], Wu, N.Q.[Nai-Qi], Qiao, Y.[Yan], Li, Z.W.[Zhi-Wu],
A scientometric review of research on traffic forecasting in transportation,
IET-ITS(15), No. 1, 2021, pp. 1-16.
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, C.[Chenyu], Chan, W.K.(.[Wai Kin (Victor)],
Spatial-temporal attention wavenet: A deep learning framework for traffic prediction considering spatial-temporal dependencies,
IET-ITS(15), No. 4, 2021, pp. 549-561.
DOI Link 2106
BibRef

Azad, A.[Abul], Wang, X.[Xin],
Land Use Change Ontology and Traffic Prediction through Recurrent Neural Networks: A Case Study in Calgary, Canada,
IJGI(10), No. 6, 2021, pp. xx-yy.
DOI Link 2106
BibRef

Liu, M.H.[Meng-Hang], Li, L.N.[Lu-Ning], Li, Q.[Qiang], Bai, Y.[Yu], Hu, C.[Cheng],
Pedestrian Flow Prediction in Open Public Places Using Graph Convolutional Network,
IJGI(10), No. 7, 2021, pp. xx-yy.
DOI Link 2108
BibRef

Ren, C.[Chang], Tang, L.[Luliang], Long, J.[Jed], Kan, Z.[Zihan], Yang, X.[Xue],
Modelling Place Visit Probability Sequences during Trajectory Data Gaps Based on Movement History,
IJGI(10), No. 7, 2021, pp. xx-yy.
DOI Link 2108
BibRef

Shi, X.M.[Xiao-Ming], Qi, H.[Heng], Shen, Y.M.[Yan-Ming], Wu, G.[Genze], Yin, B.C.[Bao-Cai],
A Spatial-Temporal Attention Approach for Traffic Prediction,
ITS(22), No. 8, August 2021, pp. 4909-4918.
IEEE DOI 2108
Correlation, Neural networks, Predictive models, Roads, Convolution, Semantics, Time series analysis, Attention mechanism, neural networks BibRef

Giammarino, V.[Vittorio], Baldi, S.[Simone], Frasca, P.[Paolo], Monache, M.L.D.[Maria Laura Delle],
Traffic Flow on a Ring With a Single Autonomous Vehicle: An Interconnected Stability Perspective,
ITS(22), No. 8, August 2021, pp. 4998-5008.
IEEE DOI 2108
Asymptotic stability, Autonomous vehicles, Stability criteria, Numerical stability, Transfer functions, Structural rings, ring roadway 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

Yi, H.[Hongsuk], Bui, K.H.N.[Khac-Hoai Nam],
An Automated Hyperparameter Search-Based Deep Learning Model for Highway Traffic Prediction,
ITS(22), No. 9, September 2021, pp. 5486-5495.
IEEE DOI 2109
Road transportation, Predictive models, Training, Tuning, Optimization, Bayes methods, Deep learning, meta learning BibRef

Xiao, X.[Xiao], Jin, Z.[Zhiling], Hui, Y.L.[Yi-Long], Xu, Y.[Yueshen], Shao, W.[Wei],
Hybrid Spatial-Temporal Graph Convolutional Networks for On-Street Parking Availability Prediction,
RS(13), No. 16, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Chen, K.Q.[Kai-Qi], Deng, M.[Min], Shi, Y.[Yan],
A Temporal Directed Graph Convolution Network for Traffic Forecasting Using Taxi Trajectory Data,
IJGI(10), No. 9, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Das, A.[Arindam], Gangwar, M.K.[Manoj Kumar], Ghosh, D.[Devleena], Mandal, C.[Chittaranjan], Sengupta, A.[Anirban], Waris, M.M.[M. Mubashshir],
Automatic Generation of Route Control Chart From Validated Signal Interlocking Plan,
ITS(22), No. 10, October 2021, pp. 6516-6525.
IEEE DOI 2110
Rail transportation, Layout, Tools, Graphical user interfaces, Safety, Control charts, Standards, Railway signalling, validation and verification (V&V) 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

Wang, Z.M.[Zhu-Mei], Su, X.[Xing], Ding, Z.M.[Zhi-Ming],
Long-Term Traffic Prediction Based on LSTM Encoder-Decoder Architecture,
ITS(22), No. 10, October 2021, pp. 6561-6571.
IEEE DOI 2110
Predictive models, Forecasting, Deep learning, Calibration, Market research, Neural networks, Prediction algorithms, attention BibRef

Guo, Z.G.[Zhen-Gang], Zhang, Y.F.[Ying-Feng], Lv, J.X.[Jing-Xiang], Liu, Y.[Yang], Liu, Y.[Ying],
An Online Learning Collaborative Method for Traffic Forecasting and Routing Optimization,
ITS(22), No. 10, October 2021, pp. 6634-6645.
IEEE DOI 2110
Roads, Real-time systems, Forecasting, Routing, Optimization, Predictive models, Collaboration, Online learning, cyber-physical systems 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

He, Z.X.[Zhi-Xiang], Chow, C.Y.[Chi-Yin], Zhang, J.D.[Jia-Dong],
STNN: A Spatio-Temporal Neural Network for Traffic Predictions,
ITS(22), No. 12, December 2021, pp. 7642-7651.
IEEE DOI 2112
Roads, Predictive models, Time series analysis, Neural networks, Data models, Computational modeling, Decoding, Traffic predictions, attention model BibRef

Liu, L.B.[Ling-Bo], Zhen, J.J.[Jia-Jie], Li, G.B.[Guan-Bin], Zhan, G.[Geng], He, Z.C.[Zhao-Cheng], Du, B.[Bowen], Lin, L.[Liang],
Dynamic Spatial-Temporal Representation Learning for Traffic Flow Prediction,
ITS(22), No. 11, November 2021, pp. 7169-7183.
IEEE DOI 2112
Predictive models, Urban areas, Neural networks, Task analysis, Feature extraction, Market research, attentional recurrent neural network BibRef

Liu, Y.[Yang], Wang, K.[Keze], Liu, L.B.[Ling-Bo], Lan, H.Y.[Hao-Yuan], Lin, L.[Liang],
TCGL: Temporal Contrastive Graph for Self-Supervised Video Representation Learning,
IP(31), 2022, pp. 1978-1993.
IEEE DOI 2202
Code, Action Recognition.
WWW Link. Task analysis, Representation learning, Discrete cosine transforms, Legged locomotion, spatial-temporal data analysis BibRef

Luo, X.Y.[Xiao-Yi], Peng, J.H.[Jia-Heng], Liang, J.[Jun],
Directed hypergraph attention network for traffic forecasting,
IET-ITS(16), No. 1, 2022, pp. 85-98.
DOI Link 2112
BibRef

Zhaowei, Q.[Qu], Haitao, L.[Li], Zhi-Hui, L.[Li], 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

Liu, D.[Di], Baldi, S.[Simone], Yu, W.[Wenwu], Cao, J.[Jinde], Huang, W.[Wei],
On Training Traffic Predictors via Broad Learning Structures: A Benchmark Study,
SMCS(52), No. 2, February 2022, pp. 749-758.
IEEE DOI 2201
Training, Prediction algorithms, Zinc, Testing, Real-time systems, Learning systems, traffic flow prediction 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

Ren, Y.J.[Ya-Jie], Zhao, D.[Dong], Luo, D.[Dan], Ma, H.D.[Hua-Dong], Duan, P.R.[Peng-Rui],
Global-Local Temporal Convolutional Network for Traffic Flow Prediction,
ITS(23), No. 2, February 2022, pp. 1578-1584.
IEEE DOI 2202
Convolution, Market research, Feature extraction, Predictive models, Data models, Urban areas, Machine learning, neural network BibRef

Li, Z.S.[Zhi-Shuai], Xiong, G.[Gang], Tian, Y.L.[Yong-Lin], Lv, Y.S.[Yi-Sheng], Chen, Y.Y.[Yuan-Yuan], Hui, P.[Pan], Su, X.[Xiang],
A Multi-Stream Feature Fusion Approach for Traffic Prediction,
ITS(23), No. 2, February 2022, pp. 1456-1466.
IEEE DOI 2202
Feature extraction, Roads, Monitoring, Predictive models, Convolution, Neural networks, Computational modeling, data-driven adjacent matrix BibRef

Wu, S.F.[Shao-Fei],
Spatiotemporal Dynamic Forecasting and Analysis of Regional Traffic Flow in Urban Road Networks Using Deep Learning Convolutional Neural Network,
ITS(23), No. 2, February 2022, pp. 1607-1615.
IEEE DOI 2202
Convolution, Roads, Forecasting, Predictive models, Feature extraction, Convolutional neural networks, BiLSTM 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

Zhang, C.[Chi], Zhou, H.Y.[Hong-Yu], Qiu, Q.[Qiang], Jian, Z.C.[Zhi-Chun], Zhu, D.[Daoye], Cheng, C.Q.[Cheng-Qi], He, L.S.[Lie-Song], Liu, G.P.[Guo-Ping], Wen, X.[Xiang], Hu, R.[Runbo],
Augmented Multi-Component Recurrent Graph Convolutional Network for Traffic Flow Forecasting,
IJGI(11), No. 2, 2022, pp. xx-yy.
DOI Link 2202
BibRef

Li, X.L.[Xiao-Long], Xia, J.[Jing], Chen, X.Y.[Xiao-Yong], Tan, Y.B.[Yong-Bin], Chen, J.[Jing],
SIT: A Spatial Interaction-Aware Transformer-Based Model for Freeway Trajectory Prediction,
IJGI(11), No. 2, 2022, pp. xx-yy.
DOI Link 2202
BibRef

Wang, Y.[Yi], Jing, C.F.[Chang-Feng],
Spatiotemporal Graph Convolutional Network for Multi-Scale Traffic Forecasting,
IJGI(11), No. 2, 2022, pp. xx-yy.
DOI Link 2202
BibRef

Shin, Y.[Yuyol], Yoon, Y.[Yoonjin],
Incorporating Dynamicity of Transportation Network With Multi-Weight Traffic Graph Convolutional Network for Traffic Forecasting,
ITS(23), No. 3, March 2022, pp. 2082-2092.
IEEE DOI 2203
Forecasting, Predictive models, Convolution, Roads, Data models, Deep learning, Deep learning, graph convolutional network, transportation network 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

Huo, G.Y.[Guang-Yu], Zhang, Y.[Yong], Wang, B.Y.[Bo-Yue], Hu, Y.L.[Yong-Li], Yin, B.C.[Bao-Cai],
Text-to-Traffic Generative Adversarial Network for Traffic Situation Generation,
ITS(23), No. 3, March 2022, pp. 2623-2636.
IEEE DOI 2203
Social networking (online), Generative adversarial networks, Data models, Semantics, Generators, Blogs, Predictive models, condition generative adversarial network BibRef

Chen, Y.Y.[Yuan-Yuan], Chen, H.Y.[Hong-Yu], Ye, P.J.[Pei-Jun], Lv, Y.S.[Yi-Sheng], Wang, F.Y.[Fei-Yue],
Acting as a Decision Maker: Traffic-Condition-Aware Ensemble Learning for Traffic Flow Prediction,
ITS(23), No. 4, April 2022, pp. 3190-3200.
IEEE DOI 2204
Predictive models, Feature extraction, Training, Data models, Spatiotemporal phenomena, Stacking, Convolution, deep learning BibRef

Lai, Y.X.[Yong-Xuan], Xu, Y.F.[Yi-Fan], Mai, D.[Duojian], Fan, Y.[Yi], Yang, F.[Fan],
Optimized Large-Scale Road Sensing Through Crowdsourced Vehicles,
ITS(23), No. 4, April 2022, pp. 3878-3889.
IEEE DOI 2204
Sensors, Task analysis, Crowdsensing, Roads, Costs, Urban areas, Fans, Vehicular crowdsensing, maximal weighted sensing paths, least-interrupted urban sensing 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

Avedisov, S.S.[Sergei S.], Bansal, G.[Gaurav], Orosz, G.[Gábor],
Impacts of Connected Automated Vehicles on Freeway Traffic Patterns at Different Penetration Levels,
ITS(23), No. 5, May 2022, pp. 4305-4318.
IEEE DOI 2205
Vehicle-to-everything, Connected vehicles, Vehicle dynamics, Automobiles, Numerical models, Aerodynamics, traffic flow BibRef

Su, J.[Jie], Jin, Z.F.[Zhong-Fu], Ren, J.[Jie], Yang, J.[Jiandang], Liu, Y.[Yong],
GDFormer: A Graph Diffusing Attention based approach for Traffic Flow Prediction,
PRL(156), 2022, pp. 126-132.
Elsevier DOI 2205
Graph neural network, Diffusion process, Attention mechanism, Traffic flow prediction BibRef

Paliwal, C.[Charul], Bhatt, U.[Uttkarsha], Biyani, P.[Pravesh], Rajawat, K.[Ketan],
Traffic Estimation and Prediction via Online Variational Bayesian Subspace Filtering,
ITS(23), No. 5, May 2022, pp. 4674-4684.
IEEE DOI 2205
Bayes methods, Estimation, Prediction algorithms, Tensors, Roads, Probability density function, Predictive models, robust matrix completion 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.[Zeyang], Lu, J.[Jian], Zhou, H.J.[Hua-Jian], Zhang, Y.[Yibin], 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

Yin, X.[Xueyan], Wu, G.[Genze], Wei, J.Z.[Jin-Ze], Shen, Y.M.[Yan-Ming], Qi, H.[Heng], Yin, B.C.[Bao-Cai],
Deep Learning on Traffic Prediction: Methods, Analysis, and Future Directions,
ITS(23), No. 6, June 2022, pp. 4927-4943.
IEEE DOI 2206
Deep learning, Correlation, Predictive models, Data models, Convolution, Roads, Learning systems, Traffic prediction, spatial-temporal dependency modeling BibRef

Manibardo, E.L.[Eric L.], Laña, I.[Ibai], del Ser, J.[Javier],
Deep Learning for Road Traffic Forecasting: Does it Make a Difference?,
ITS(23), No. 7, July 2022, pp. 6164-6188.
IEEE DOI 2207
Forecasting, Deep learning, Predictive models, Roads, Data models, Time series analysis, Task analysis, Machine learning, spatio-temporal data mining 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

Li, D.[Duo], Lasenby, J.[Joan],
Spatiotemporal Attention-Based Graph Convolution Network for Segment-Level Traffic Prediction,
ITS(23), No. 7, July 2022, pp. 8337-8345.
IEEE DOI 2207
Roads, Feature extraction, Spatiotemporal phenomena, Deep learning, Predictive models, Meteorology, Detectors, Traffic prediction, attention mechanism BibRef

Zhang, S.[Shaokun], Guo, Y.[Yao], Zhao, P.[Peize], Zheng, C.P.[Chuan-Pan], Chen, X.Q.[Xiang-Qun],
A Graph-Based Temporal Attention Framework for Multi-Sensor Traffic Flow Forecasting,
ITS(23), No. 7, July 2022, pp. 7743-7758.
IEEE DOI 2207
Roads, Predictive models, Forecasting, Data models, Correlation, Topology, Network topology, Traffic flow prediction, road network distance BibRef

Yu, J.J.Q.[James J. Q.], Markos, C.[Christos], Zhang, S.Y.[Shi-Yao],
Long-Term Urban Traffic Speed Prediction With Deep Learning on Graphs,
ITS(23), No. 7, July 2022, pp. 7359-7370.
IEEE DOI 2207
Correlation, Deep learning, Transportation, Forecasting, Data mining, Predictive models, Training, Traffic speed prediction, data mining BibRef

Abdelraouf, A.[Amr], Abdel-Aty, M.[Mohamed], Yuan, J.[Jinghui],
Utilizing Attention-Based Multi-Encoder-Decoder Neural Networks for Freeway Traffic Speed Prediction,
ITS(23), No. 8, August 2022, pp. 11960-11969.
IEEE DOI 2208
Predictive models, Feature extraction, Roads, Decoding, Computational modeling, Traffic control, Data mining, explainable neural networks 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

Shi, R.Y.[Rong-Ye], Mo, Z.B.[Zhao-Bin], Huang, K.[Kuang], Di, X.[Xuan], Du, Q.[Qiang],
A Physics-Informed Deep Learning Paradigm for Traffic State and Fundamental Diagram Estimation,
ITS(23), No. 8, August 2022, pp. 11688-11698.
IEEE DOI 2208
Mathematical model, Data models, Maximum likelihood estimation, Physics, Deep learning, Urban areas, Predictive models, physics-informed deep learning 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

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

Pavlyuk, D.[Dmitry],
Robust and Responsive Learning of Spatiotemporal Urban Traffic Flow Relationships,
ITS(23), No. 9, September 2022, pp. 14524-14541.
IEEE DOI 2209
Spatiotemporal phenomena, Forecasting, Predictive models, Roads, Feature extraction, Heuristic algorithms, Time series analysis, variable selection 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

Yu, J.J.Q.[James J. Q.],
Graph Construction for Traffic Prediction: A Data-Driven Approach,
ITS(23), No. 9, September 2022, pp. 15015-15027.
IEEE DOI 2209
Correlation, Training, Convolution, Laplace equations, Deep learning, Predictive models, Transportation, Traffic graph construction, data mining BibRef

Ge, L.F.[Liang-Fu], Dan, D.H.[Dan-Hui], Liu, Z.J.[Zi-Jia], Ruan, X.[Xin],
Intelligent Simulation Method of Bridge Traffic Flow Load Combining Machine Vision and Weigh-in-Motion Monitoring,
ITS(23), No. 9, September 2022, pp. 15313-15328.
IEEE DOI 2209
Bridges, Load modeling, Length measurement, Data models, Position measurement, Machine vision, Axles, Traffic flow load, Intelligent Driver Model 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

Wang, H.Q.[Han-Qiu], Zhang, R.Q.[Rong-Qing], Cheng, X.[Xiang], Yang, L.Q.[Liu-Qing],
Hierarchical Traffic Flow Prediction Based on Spatial-Temporal Graph Convolutional Network,
ITS(23), No. 9, September 2022, pp. 16137-16147.
IEEE DOI 2209
Predictive models, Data models, Protocols, Roads, Correlation, Urban areas, Support vector machines, traffic flow prediction BibRef

Hu, H.[Hexuan], Lin, Z.Z.[Zhen-Zhou], Hu, Q.[Qiang], Zhang, Y.[Ye],
Attention Mechanism With Spatial-Temporal Joint Model for Traffic Flow Speed Prediction,
ITS(23), No. 9, September 2022, pp. 16612-16621.
IEEE DOI 2209
Predictive models, Deep learning, Data models, Mathematical models, Roads, Task analysis, Urban areas, attention mechanism 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

Xing, L.[Lumin], Liu, W.J.[Wen-Jian],
A Data Fusion Powered Bi-Directional Long Short Term Memory Model for Predicting Multi-Lane Short Term Traffic Flow,
ITS(23), No. 9, September 2022, pp. 16810-16819.
IEEE DOI 2209
Predictive models, Roads, Spatiotemporal phenomena, Data models, Hidden Markov models, Correlation, Real-time systems, DFBD-LSTM 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

Wu, X.Y.[Xiang-Yang], Zhu, W.[Weite], Liu, Z.[Zhen], Zhang, Z.[Zhen],
A Novel Vehicle Destination Prediction Model With Expandable Features Using Attention Mechanism and Variational Autoencoder,
ITS(23), No. 9, September 2022, pp. 16548-16557.
IEEE DOI 2209
Predictive models, Data models, Vehicle driving, Trajectory, Feature extraction, Mathematical models, Load modeling, attentional mechanisms BibRef

Su, Y.C.[Yu-Chao], Du, J.[Jie], Li, Y.M.[Yuan-Man], Li, X.[Xia], Liang, R.Q.[Rong-Qin], Hua, Z.Y.[Zhong-Yun], Zhou, J.T.[Jian-Tao],
Trajectory Forecasting Based on Prior-Aware Directed Graph Convolutional Neural Network,
ITS(23), No. 9, September 2022, pp. 16773-16785.
IEEE DOI 2209
Trajectory, Predictive models, Directed graphs, Generative adversarial networks, Topology, Feature extraction, asymmetric interactions BibRef

Wang, Y.[Yang], Zheng, J.[Jin], Du, Y.Q.[Yu-Qi], Huang, C.[Cheng], Li, P.[Ping],
Traffic-GGNN: Predicting Traffic Flow via Attentional Spatial-Temporal Gated Graph Neural Networks,
ITS(23), No. 10, October 2022, pp. 18423-18432.
IEEE DOI 2210
Predictive models, Graph neural networks, Logic gates, Bidirectional control, Task analysis, Correlation, Message passing, traffic flow prediction BibRef

Dai, F.[Fei], Huang, P.G.[Peng-Gui], Mo, Q.[Qi], Xu, X.L.[Xiao-Long], Bilal, M.[Muhammad], Song, H.[Houbing],
ST-InNet: Deep Spatio-Temporal Inception Networks for Traffic Flow Prediction in Smart Cities,
ITS(23), No. 10, October 2022, pp. 19782-19794.
IEEE DOI 2210
Correlation, Mathematical models, Data models, Predictive models, Learning systems, Matrix converters, Support vector machines, inception networks 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

Duan, S.J.[Si-Jing], Lyu, F.[Feng], Ren, J.[Ju], Wang, Y.F.[Yi-Feng], Yang, P.[Peng], Zhang, D.S.[De-Sheng], Zhang, Y.X.[Yao-Xue],
Multitype Highway Mobility Analytics for Efficient Learning Model Design: A Case of Station Traffic Prediction,
ITS(23), No. 10, October 2022, pp. 19484-19496.
IEEE DOI 2210
Transportation, Predictive models, Analytical models, Roads, Urban areas, Logistics, Data analysis, Multitype highway mobility, traffic prediction model BibRef

Liu, F.Q.[Fu-Qiang], Wang, J.W.[Jia-Wei], Tian, J.[Jingbo], Zhuang, D.[Dingyi], Miranda-Moreno, L.[Luis], Sun, L.J.[Li-Jun],
A Universal Framework of Spatiotemporal Bias Block for Long-Term Traffic Forecasting,
ITS(23), No. 10, October 2022, pp. 19064-19075.
IEEE DOI 2210
Predictive models, Convolution, Forecasting, Spatiotemporal phenomena, Computational modeling, Logic gates, residual connection BibRef

Wang, J.C.[Jing-Cheng], Zhang, Y.[Yong], Wang, L.[Lixun], Hu, Y.L.[Yong-Li], Piao, X.L.[Xing-Lin], Yin, B.C.[Bao-Cai],
Multitask Hypergraph Convolutional Networks: A Heterogeneous Traffic Prediction Framework,
ITS(23), No. 10, October 2022, pp. 18557-18567.
IEEE DOI 2210
Task analysis, Multitasking, Data models, Predictive models, Neural networks, Public transportation, Deep learning, multi-task learning BibRef

Huo, J.B.[Jin-Biao], Wu, X.H.[Xin-Hua], Lyu, C.[Cheng], Zhang, W.B.[Wen-Bo], Liu, Z.Y.[Zhi-Yuan],
Quantify the Road Link Performance and Capacity Using Deep Learning Models,
ITS(23), No. 10, October 2022, pp. 18581-18591.
IEEE DOI 2210
Business process re-engineering, Roads, Estimation, Neural networks, Transportation, Calibration, Deep learning, macroscopic and microscopic traffic modeling BibRef

Zang, D.[Di], Chen, X.[Xihao], Lei, J.T.[Jun-Tao], Wang, Z.Q.[Zeng-Qiang], Zhang, J.Q.[Jun-Qi], Cheng, J.[Jiujun], Tang, K.[Keshuang],
A multi-channel geometric algebra residual network for traffic data prediction,
IET-ITS(16), No. 11, 2022, pp. 1549-1560.
DOI Link 2210
BibRef

Badu-Marfo, G.[Godwin], Farooq, B.[Bilal], Patterson, Z.[Zachary],
Composite Travel Generative Adversarial Networks for Tabular and Sequential Population Synthesis,
ITS(23), No. 10, October 2022, pp. 17976-17985.
IEEE DOI 2210
Statistics, Sociology, Data models, Generative adversarial networks, Training, Linear programming, agent based modelling BibRef

Zeng, Z.[Zeng], Zhao, W.[Wei], Qian, P.S.[Pei-Sheng], Zhou, Y.J.[Ying-Jie], Zhao, Z.Y.[Zi-Yuan], Chen, C.[Cen], Guan, C.T.[Cun-Tai],
Robust Traffic Prediction From Spatial-Temporal Data Based on Conditional Distribution Learning,
Cyber(52), No. 12, December 2022, pp. 13458-13471.
IEEE DOI 2212
Training, Convolution, Graph neural networks, Probability distribution, Learning systems, Deep learning, traffic prediction 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

Zhao, Y.J.[Yi-Ji], Lin, Y.F.[You-Fang], Zhang, Y.K.[Yong-Kai], Wen, H.M.[Hao-Min], Liu, Y.X.[Yun-Xiao], Wu, H.[Hao], Wu, Z.H.[Zhi-Hao], Zhang, S.C.[Shuai-Chao], Wan, H.Y.[Huai-Yu],
Traffic Inflow and Outflow Forecasting by Modeling Intra- and Inter-Relationship Between Flows,
ITS(23), No. 11, November 2022, pp. 20202-20216.
IEEE DOI 2212
Feature extraction, Correlation, Predictive models, Forecasting, Data models, Convolutional neural networks, Training, graph convolutional networks BibRef

Huang, J.[Jing], Luo, K.[Kun], Cao, L.B.[Long-Bing], Wen, Y.Q.[Yuan-Qiao], Zhong, S.Y.[Shu-Yuan],
Learning Multiaspect Traffic Couplings by Multirelational Graph Attention Networks for Traffic Prediction,
ITS(23), No. 11, November 2022, pp. 20681-20695.
IEEE DOI 2212
Couplings, Predictive models, Roads, Data models, Hidden Markov models, Forecasting, Deep learning, traffic signal coupling BibRef

Li, Y.Q.[Yi-Qun], Chai, S.J.[Song-Jian], Wang, G.B.[Gui-Bin], Zhang, X.[Xian], Qiu, J.[Jing],
Quantifying the Uncertainty in Long-Term Traffic Prediction Based on PI-ConvLSTM Network,
ITS(23), No. 11, November 2022, pp. 20429-20441.
IEEE DOI 2212
Predictive models, Deep learning, Logic gates, Uncertainty, Reliability, Probabilistic logic, Feature extraction, traffic flow prediction BibRef

Yan, H.Y.[Hao-Yang], Ma, X.L.[Xiao-Lei], Pu, Z.Y.[Zi-Yuan],
Learning Dynamic and Hierarchical Traffic Spatiotemporal Features With Transformer,
ITS(23), No. 11, November 2022, pp. 22386-22399.
IEEE DOI 2212
Forecasting, Roads, Predictive models, Feature extraction, Deep learning, Spatiotemporal phenomena, Heuristic algorithms, graph-based model BibRef

Liu, Z.Y.[Zhi-Yuan], Lyu, C.[Cheng], Huo, J.B.[Jin-Biao], Wang, S.A.[Shuai-An], Chen, J.[Jun],
Gaussian Process Regression for Transportation System Estimation and Prediction Problems: The Deformation and a Hat Kernel,
ITS(23), No. 11, November 2022, pp. 22331-22342.
IEEE DOI 2212
Kernel, Optimization, Transportation, Gaussian processes, Strain, Estimation, Bayes methods, Hat kernel, hyperparameter optimization, lower bound BibRef

Fang, Y.C.[Yu-Chen], Zhao, F.[Fang], Qin, Y.J.[Yan-Jun], Luo, H.Y.[Hai-Yong], Wang, C.X.[Chen-Xing],
Learning All Dynamics: Traffic Forecasting via Locality-Aware Spatio-Temporal Joint Transformer,
ITS(23), No. 12, December 2022, pp. 23433-23446.
IEEE DOI 2212
Forecasting, Correlation, Convolution, Roads, Transformers, Predictive models, Task analysis, Traffic forecasting, diffusion convolution network BibRef

Liang, M.[Maohan], Liu, R.W.[Ryan Wen], Zhan, Y.[Yang], Li, H.H.[Huan-Huan], Zhu, F.H.[Feng-Hua], Wang, F.Y.[Fei-Yue],
Fine-Grained Vessel Traffic Flow Prediction With a Spatio-Temporal Multigraph Convolutional Network,
ITS(23), No. 12, December 2022, pp. 23694-23707.
IEEE DOI 2212
Feature extraction, Correlation, Artificial intelligence, Trajectory, Data mining, Predictive models, Learning systems, automatic identification system (AIS) BibRef

Li, Z.L.[Zi-Long], Ren, Q.Q.[Qian-Qian], Chen, L.[Long], Li, J.B.[Jin-Bao], Li, X.K.[Xiao-Kun],
Multi-scale convolutional networks for traffic forecasting with spatial-temporal attention,
PRL(164), 2022, pp. 53-59.
Elsevier DOI 2212
Traffic forecasting, Spatial-temporal attention, Convolutional networks BibRef

Li, Z.L.[Zi-Long], Ren, Q.Q.[Qian-Qian], Chen, L.[Long], Sui, X.H.[Xiao-Hong], Li, J.B.[Jin-Bao],
Multi-Hierarchical Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting,
ICPR22(4913-4919)
IEEE DOI 2212
Correlation, Convolution, Transportation, Traffic control, Predictive models, Transformers, Feature extraction, traffic flow forecasting BibRef

Deng, X.D.[Xing-Dong], Zhang, J.[Ji], Liao, S.Y.[Shun-Yi], Zhong, C.J.[Chu-Jie], Gao, F.[Feng], Teng, L.[Li],
Interactive Impacts of Built Environment Factors on Metro Ridership Using GeoDetector: From the Perspective of TOD,
IJGI(11), No. 12, 2022, pp. xx-yy.
DOI Link 2301
BibRef

Nadarajan, J.[Jeba], Sivanraj, R.[Rathi],
Attention-Based Multiscale Spatiotemporal Network for Traffic Forecast with Fusion of External Factors,
IJGI(11), No. 12, 2022, pp. xx-yy.
DOI Link 2301
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

Abdelraouf, A.[Amr], Abdel-Aty, M.[Mohamed], Mahmoud, N.[Nada],
Sequence-to-Sequence Recurrent Graph Convolutional Networks for Traffic Estimation and Prediction Using Connected Probe Vehicle Data,
ITS(24), No. 1, January 2023, pp. 1395-1405.
IEEE DOI 2301
Probes, Estimation, Data models, Sensors, Detectors, Predictive models, Roads, Traffic estimation, traffic prediction, recurrent neural networks 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

Diao, C.Y.[Chun-Yan], Zhang, D.[Dafang], Liang, W.[Wei], Li, K.C.[Kuan-Ching], Hong, Y.J.[Yu-Jie], Gaudiot, J.L.[Jean-Luc],
A Novel Spatial-Temporal Multi-Scale Alignment Graph Neural Network Security Model for Vehicles Prediction,
ITS(24), No. 1, January 2023, pp. 904-914.
IEEE DOI 2301
Convolution, Correlation, Vehicle dynamics, Forecasting, Roads, Predictive models, Time series analysis, vehicle prediction BibRef

Luo, D.[Dan], Zhao, D.[Dong], Cao, Z.J.[Zi-Jian], Wu, M.Y.[Ming-Yao], Liu, L.[Liang], Ma, H.D.[Hua-Dong],
M3AN: Multitask Multirange Multisubgraph Attention Network for Condition-Aware Traffic Prediction,
ITS(24), No. 1, January 2023, pp. 218-232.
IEEE DOI 2301
Roads, Data models, Correlation, Predictive models, Adaptation models, Deep learning, deep learning BibRef

Zhang, W.F.[Wei-Feng], Wu, Z.[Zhe], Zhang, X.F.[Xin-Feng], Song, G.[Guoli], Wang, Y.[Yaowei], Chen, J.[Jie],
Robust and Hierarchical Spatial Relation Analysis for Traffic Forecasting,
ITS(24), No. 1, January 2023, pp. 201-217.
IEEE DOI 2301
Market research, Feature extraction, Forecasting, Time series analysis, Transportation, Deep learning, temporal convolution network BibRef

Luo, G.Y.[Gui-Yang], Zhang, H.[Hui], Yuan, Q.[Quan], Li, J.L.[Jing-Lin], Wang, F.Y.[Fei-Yue],
ClusterST: Clustering Spatial-Temporal Network for Traffic Forecasting,
ITS(24), No. 1, January 2023, pp. 706-717.
IEEE DOI 2301
Feature extraction, Forecasting, Correlation, Laplace equations, Sensor phenomena and characterization, Predictive models, over-smoothing 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

Ji, J.Z.[Jun-Zhong], Yu, F.[Fan], Lei, M.[Minglong],
Self-Supervised Spatiotemporal Graph Neural Networks With Self-Distillation for Traffic Prediction,
ITS(24), No. 2, February 2023, pp. 1580-1593.
IEEE DOI 2302
Predictive models, Spatiotemporal phenomena, Task analysis, Data models, Feature extraction, Self-supervised learning, self-distillation BibRef

Li, M.X.[Ming-Xi], Tang, Y.H.[Yi-Hong], Ma, W.[Wei],
Few-Sample Traffic Prediction With Graph Networks Using Locale as Relational Inductive Biases,
ITS(24), No. 2, February 2023, pp. 1894-1908.
IEEE DOI 2302
Data models, Predictive models, Urban areas, Task analysis, Roads, Numerical models, Training, Traffic prediction, intelligent transportation systems BibRef

Li, N.[Na], Sheng, H.T.[Hao-Tian], Wang, P.Y.[Ping-Yao], Jia, Y.L.[Yu-Lin], Yang, Z.[Zaili], Jin, Z.H.[Zhi-Hong],
Modeling Categorized Truck Arrivals at Ports: Big Data for Traffic Prediction,
ITS(24), No. 3, March 2023, pp. 2772-2788.
IEEE DOI 2303
Containers, Predictive models, Data models, Seaports, Deep learning, Logic gates, Task analysis, Container terminal, truck arrival, big data BibRef

Jiang, W.W.[Wei-Wei], Luo, J.[Jiayun], He, M.[Miao], Gu, W.X.[Wei-Xi],
Graph Neural Network for Traffic Forecasting: The Research Progress,
IJGI(12), No. 3, 2023, pp. xx-yy.
DOI Link 2303
BibRef

Mahajan, V.[Vishal], Cantelmo, G.[Guido], Rothfeld, R.[Raoul], Antoniou, C.[Constantinos],
Predicting network flows from speeds using open data and transfer learning,
IET-ITS(17), No. 4, 2023, pp. 804-824.
DOI Link 2304
deep learning, open data, traffic forecasting, traffic prediction, traffic state estimation, transfer learning BibRef

Lai, Q.F.[Qi-Feng], Tian, J.[Jinyu], Wang, W.[Wei], Hu, X.P.[Xi-Ping],
Spatial-Temporal Attention Graph Convolution Network on Edge Cloud for Traffic Flow Prediction,
ITS(24), No. 4, April 2023, pp. 4565-4576.
IEEE DOI 2304
Cloud computing, Data models, Predictive models, Servers, Convolution, Training, Feature extraction, Egde cloud, graph convolution network BibRef

Zeng, J.[Jie], Tang, J.J.[Jin-Jun],
Modeling Dynamic Traffic Flow as Visibility Graphs: A Network-Scale Prediction Framework for Lane-Level Traffic Flow Based on LPR Data,
ITS(24), No. 4, April 2023, pp. 4173-4188.
IEEE DOI 2304
Predictive models, Roads, Correlation, Spatiotemporal phenomena, Data models, Logic gates, Task analysis, license plate recognition 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

Wang, Y.[Yi], Jing, C.F.[Chang-Feng], Huang, W.[Wei], Jin, S.Y.[Shi-Yuan], Lv, X.X.[Xin-Xin],
Adaptive Spatiotemporal InceptionNet for Traffic Flow Forecasting,
ITS(24), No. 4, April 2023, pp. 3882-3907.
IEEE DOI 2304
Spatiotemporal phenomena, Feature extraction, Forecasting, Roads, Predictive models, Data mining, Convolution, graph pooling 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

Ma, Q.W.[Qi-Wei], Sun, W.[Wei], Gao, J.[Junbo], Ma, P.W.[Peng-Wei], Shi, M.J.[Meng-Jie],
Spatio-temporal adaptive graph convolutional networks for traffic flow forecasting,
IET-ITS(17), No. 4, 2023, pp. 691-703.
DOI Link 2304
BibRef

Liao, Z.H.[Zhu-Hua], Huang, H.K.[Hao-Kai], Zhao, Y.J.[Yi-Jiang], Liu, Y.Z.[Yi-Zhi], Zhang, G.Q.[Guo-Qiang],
Analysis and Forecast of Traffic Flow between Urban Functional Areas Based on Ride-Hailing Trajectories,
IJGI(12), No. 4, 2023, pp. 144.
DOI Link 2305
BibRef

Liu, M.Z.[Ming-Zhe], Zhu, T.[Tongyu], Ye, J.[Junchen], Meng, Q.X.[Qing-Xin], Sun, L.L.[Lei-Lei], Du, B.[Bowen],
Spatio-Temporal AutoEncoder for Traffic Flow Prediction,
ITS(24), No. 5, May 2023, pp. 5516-5526.
IEEE DOI 2305
Convolution, Time series analysis, Feature extraction, Decoding, Data mining, Correlation, History, Traffic flow prediction, hidden state extraction BibRef

Chondrodima, E.[Eva], Pelekis, N.[Nikos], Pikrakis, A.[Aggelos], Theodoridis, Y.[Yannis],
An Efficient LSTM Neural Network-Based Framework for Vessel Location Forecasting,
ITS(24), No. 5, May 2023, pp. 4872-4888.
IEEE DOI 2305
Trajectory, Forecasting, Artificial neural networks, Predictive models, Hidden Markov models, trajectory data augmentation 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

Weng, W.C.[Wen-Chao], Fan, J.[Jin], Wu, H.[Huifeng], Hu, Y.J.[Yu-Jie], Tian, H.[Hao], Zhu, F.[Fu], Wu, J.[Jia],
A Decomposition Dynamic graph convolutional recurrent network for traffic forecasting,
PR(142), 2023, pp. 109670.
Elsevier DOI 2307
Traffic forecasting, Dynamic graph generation, Residual decomposition, Segmented learning, Graph convolution network BibRef

Li, H.R.[Hao-Ran], Yuan, Z.Z.[Zhen-Zhou], Chen, S.Y.[Si-Yuan], Zhu, C.[Chuang],
Exploring the effects of measures of performance and calibration strategies on calibrating traffic microsimulation model: A quantitative analysis approach,
IET-ITS(17), No. 6, 2023, pp. 1200-1219.
DOI Link 2307
calibration, performance evaluation, traffic engineering computing, transport modelling and microsimulation BibRef

Sun, H.R.[Hao-Ran], Wei, Y.L.[Yan-Ling], Huang, X.L.[Xue-Liang], Gao, S.[Shan], Song, Y.H.[Yu-Hang],
Global spatio-temporal dynamic capturing network-based traffic flow prediction,
IET-ITS(17), No. 6, 2023, pp. 1220-1228.
DOI Link 2307
complex networks, decision making, management and control, traffic modelling BibRef

Li, Z.X.[Zhen-Xin], Han, Y.[Yong], Xu, Z.Y.[Zhen-Yu], Zhang, Z.H.[Zhi-Hao], Sun, Z.X.[Zhi-Xian], Chen, G.[Ge],
PMGCN: Progressive Multi-Graph Convolutional Network for Traffic Forecasting,
IJGI(12), No. 6, 2023, pp. xx-yy.
DOI Link 2307
BibRef

Fan, A.[Aihua], Chen, X.[Xumei], Yu, L.[Lei], Li, M.[Ming],
Investigating heterogeneity in travel behaviour change when implementing soft transport interventions: A latent class choice model,
IET-ITS(17), No. 6, 2023, pp. 1072-1086.
DOI Link 2307
latent class choice model, soft transport intervention, travel behaviour change, traveller heterogeneity BibRef

Wang, B.[Binwu], Zhang, Y.D.[Yu-Dong], Shi, J.H.[Jia-Hao], Wang, P.K.[Peng-Kun], Wang, X.[Xu], Bai, L.[Lei], Wang, Y.[Yang],
Knowledge Expansion and Consolidation for Continual Traffic Prediction With Expanding Graphs,
ITS(24), No. 7, July 2023, pp. 7190-7201.
IEEE DOI 2307
Spatiotemporal phenomena, Data models, Predictive models, Roads, Knowledge engineering, Task analysis, Correlation, continuous learning BibRef

Huang, R.[Ru], Chen, Z.J.[Zi-Jian], Zhai, G.T.[Guang-Tao], He, J.H.[Jian-Hua], Chu, X.L.[Xiao-Li],
Spatial-temporal correlation graph convolutional networks for traffic forecasting,
IET-ITS(17), No. 7, 2023, pp. 1380-1394.
DOI Link 2307
management and control, neural net architecture, network topology, traffic modeling BibRef

Qi, X.Y.[Xiao-Yu], Mei, G.[Gang], Tu, J.Z.[Jing-Zhi], Xi, N.[Ning], Piccialli, F.[Francesco],
A Deep Learning Approach for Long-Term Traffic Flow Prediction With Multifactor Fusion Using Spatiotemporal Graph Convolutional Network,
ITS(24), No. 8, August 2023, pp. 8687-8700.
IEEE DOI 2308
Spatiotemporal phenomena, Predictive models, Deep learning, Convolution, Data models, Forecasting, Logic gates, deep learning BibRef

Yuan, X.M.[Xiao-Ming], Chen, J.[Jiahui], Yang, J.[Jiayu], Zhang, N.[Ning], Yang, T.T.[Ting-Ting], Han, T.[Tao], Taherkordi, A.[Amir],
FedSTN: Graph Representation Driven Federated Learning for Edge Computing Enabled Urban Traffic Flow Prediction,
ITS(24), No. 8, August 2023, pp. 8738-8748.
IEEE DOI 2308
Computational modeling, Servers, Predictive models, Data models, Collaborative work, Deep learning, Training, smart city BibRef

Chen, Y.[Yan], Shu, T.[Tian], Zhou, X.K.[Xiao-Kang], Zheng, X.[Xuzhe], Kawai, A.[Akira], Fueda, K.[Kaoru], Yan, Z.[Zheng], Liang, W.[Wei], Wang, K.I.K.[Kevin I-Kai],
Graph Attention Network With Spatial-Temporal Clustering for Traffic Flow Forecasting in Intelligent Transportation System,
ITS(24), No. 8, August 2023, pp. 8727-8737.
IEEE DOI 2308
Forecasting, Feature extraction, Convolution, Predictive models, Task analysis, Internet of Things, Data models, intelligent transportation system BibRef

Guo, C.Y.[Can-Yang], Chen, C.H.[Chi-Hua], Hwang, F.J.[Feng-Jang], Chang, C.C.[Ching-Chun], Chang, C.C.[Chin-Chen],
Fast Spatiotemporal Learning Framework for Traffic Flow Forecasting,
ITS(24), No. 8, August 2023, pp. 8606-8616.
IEEE DOI 2308
Spatiotemporal phenomena, Correlation, Convolution, Roads, Intelligent transportation systems, Kernel, Logic gates, traffic flow forecasting BibRef

Huang, Y.J.[Yun-Jie], Song, X.Z.[Xiao-Zhuang], Zhu, Y.S.[Yuan-Shao], Zhang, S.[Shiyao], Yu, J.J.Q.[James J. Q.],
Traffic Prediction With Transfer Learning: A Mutual Information-Based Approach,
ITS(24), No. 8, August 2023, pp. 8236-8252.
IEEE DOI 2308
Urban areas, Transfer learning, Roads, Task analysis, Data models, Forecasting, Predictive models, Traffic prediction, mutual information BibRef

Jin, G.Y.[Guang-Yin], Li, F.[Fuxian], Zhang, J.[Jinlei], Wang, M.[Mudan], Huang, J.[Jincai],
Automated Dilated Spatio-Temporal Synchronous Graph Modeling for Traffic Prediction,
ITS(24), No. 8, August 2023, pp. 8820-8830.
IEEE DOI 2308
Computational modeling, Correlation, Convolution, Predictive models, Adaptation models, Time series analysis, automated machine learning BibRef

Yao, Z.X.[Zhi-Xiu], Xia, S.C.[Shi-Chao], Li, Y.[Yun], Wu, G.F.[Guang-Fu], Zuo, L.L.[Lin-Li],
Transfer Learning with Spatial-Temporal Graph Convolutional Network for Traffic Prediction,
ITS(24), No. 8, August 2023, pp. 8592-8605.
IEEE DOI 2308
Roads, Transfer learning, Convolutional neural networks, Feature extraction, Task analysis, Data models, Convolution, adversarial domain adaptation BibRef

Wang, Q.[Qiang], Jiang, H.[Hao], Qiu, M.[Meikang], Liu, Y.F.[Yi-Feng], Ye, D.S.[Dong-Sheng],
TGAE: Temporal Graph Autoencoder for Travel Forecasting,
ITS(24), No. 8, August 2023, pp. 8529-8541.
IEEE DOI 2308
Forecasting, Task analysis, Transportation, Vehicle dynamics, Predictive models, Peer-to-peer computing, Heuristic algorithms, temporal networks BibRef

Manibardo, E.L.[Eric L.], Laña, I.[Ibai], Villar-Rodriguez, E.[Esther], Ser, J.D.[Javier Del],
A Graph-Based Methodology for the Sensorless Estimation of Road Traffic Profiles,
ITS(24), No. 8, August 2023, pp. 8701-8715.
IEEE DOI 2308
Roads, Data models, Estimation, Behavioral sciences, Predictive models, Urban areas, Feature extraction, traffic data generation BibRef

Li, W.[Wei], Zhan, X.[Xi], Liu, X.[Xin], Zhang, L.[Lei], Pan, Y.[Yu], Pan, Z.[Zhisong],
SASTGCN: A Self-Adaptive Spatio-Temporal Graph Convolutional Network for Traffic Prediction,
IJGI(12), No. 8, 2023, pp. 346.
DOI Link 2309
BibRef

Li, Y.[Yang], Ren, Q.Q.[Qian-Qian], Jin, H.[Hu], Han, M.[Meng],
LSTN:Long Short-Term Traffic Flow Forecasting with Transformer Networks,
ICPR22(4793-4800)
IEEE DOI 2212
Recurrent neural networks, Roads, Time series analysis, Transformers, Forecasting, Task analysis BibRef

Guo, K.[Ke], Liu, W.X.[Wen-Xi], Pan, J.[Jia],
End-to-End Trajectory Distribution Prediction Based on Occupancy Grid Maps,
CVPR22(2232-2241)
IEEE DOI 2210
Reinforcement learning, Predictive models, Transformers, Trajectory, Pattern recognition, Task analysis, Behavior analysis, Robot vision 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, Pattern recognition BibRef

Sun, Y.[Yiwen], Wang, Y.[Yulu], Fu, K.[Kun], Wang, Z.[Zheng], Zhang, C.S.[Chang-Shui], Ye, J.P.[Jie-Ping],
Constructing Geographic and Long-term Temporal Graph for Traffic Forecasting,
ICPR21(3483-3490)
IEEE DOI 2105
Measurement, Deep learning, Recurrent neural networks, Correlation, Roads, Traffic control, User experience BibRef

Ai, W., Su, Y., Xing, T., Liu, D.,
Phase Plane Analysis of Traffic Flow Evolution Based on Sticky Payne Model,
CVIDL20(237-240)
IEEE DOI 2102
road traffic, phase plane analysis method, nonlinear phenomena, traffic congestion, system instability, phase plan, system stability 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

Jiber, M., Lamouik, I., Ali, Y., Sabri, M.A.,
Traffic flow prediction using neural network,
ISCV18(1-4)
IEEE DOI 1807
neural nets, road traffic, traffic engineering computing, transportation, Moroccan center, neural network model, neural network BibRef

Hou, J., Chen, J., Liao, S., Wen, J., Xiong, Q.,
Predicting Traffic Flow via Ensemble Deep Convolutional Neural Networks with Spatio-temporal Joint Relations,
ICPR18(1487-1492)
IEEE DOI 1812
Predictive models, Data models, Optimization, Task analysis, Kernel, Convolutional neural networks BibRef

Zhang, Q., Jin, Q., Chang, J., Xiang, S., Pan, C.,
Kernel-Weighted Graph Convolutional Network: A Deep Learning Approach for Traffic Forecasting,
ICPR18(1018-1023)
IEEE DOI 1812
Kernel, Forecasting, Roads, Task analysis, Convolution, Convolutional neural networks BibRef

Priambodo, B.[Bagus], Ahmad, A.[Azlina],
Predicting Traffic Flow Based on Average Speed of Neighbouring Road Using Multiple Regression,
IVIC17(309-318).
Springer DOI 1711
BibRef

Chapter on Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities continues in
Emission Control Issues in Traffic Control .