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.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.[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.J.[Yi-Ji],
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
Zhou, L.,
Zhang, S.,
Yu, J.,
Chen, X.,
Spatial-Temporal Deep Tensor Neural Networks for Large-Scale Urban
Network Speed Prediction,
ITS(21), No. 9, September 2020, pp. 3718-3729.
IEEE DOI
2008
Roads, Predictive models, Stacking, Neural networks,
Time series analysis, Detectors, Speed prediction, deep learning
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.Y.[Chen-Yu],
Chan, W.K.V.[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.H.[Zi-Han],
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.L.[Zhi-Ling],
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,
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, 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
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
Liu, D.[Di],
Baldi, S.[Simone],
Yu, W.W.[Wen-Wu],
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, 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
Hu, Y.L.[Yong-Li],
Peng, T.[Ting],
Guo, K.[Kan],
Sun, Y.F.[Yan-Feng],
Gao, J.B.[Jun-Bin],
Yin, B.C.[Bao-Cai],
Graph transformer based dynamic multiple graph convolution networks
for traffic flow forecasting,
IET-ITS(17), No. 9, 2023, pp. 1835-1845.
DOI Link
2310
intelligent transportation systems, traffic information systems
BibRef
Guo, K.[Kan],
Tian, D.X.[Da-Xin],
Hu, Y.L.[Yong-Li],
Sun, Y.F.[Yan-Feng],
Qian, Z.S.[Zhen Sean],
Zhou, J.[Jianshan],
Gao, J.B.[Jun-Bin],
Yin, B.C.[Bao-Cai],
Contrastive learning for traffic flow forecasting based on multi
graph convolution network,
IET-ITS(18), No. 2, 2024, pp. 290-301.
DOI Link
2402
intelligent transportation systems, traffic information systems
BibRef
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
Wei, X.[Xiulan],
Zhang, Y.[Yong],
Wang, S.F.[Shao-Fan],
Zhao, X.[Xia],
Hu, Y.L.[Yong-Li],
Yin, B.C.[Bao-Cai],
Self-Attention Graph Convolution Imputation Network for
Spatio-Temporal Traffic Data,
ITS(25), No. 12, December 2024, pp. 19549-19562.
IEEE DOI
2412
Imputation, Convolution, Data models, Time series analysis,
Interpolation, Deep learning, Correlation, Learning systems,
diffusion graph convolution
BibRef
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.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
Yin, X.Y.[Xue-Yan],
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.H.[Jing-Hui],
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.B.[Jing-Bo],
Zhuang, D.Y.[Ding-Yi],
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
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.L.[Ming-Long],
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
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.Y.[Tong-Yu],
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
Guo, C.Y.[Can-Yang],
Hwang, F.J.[Feng-Jang],
Chen, C.H.[Chi-Hua],
Chang, C.C.[Ching-Chun],
Chang, C.C.[Chin-Chen],
Dynamic Spatiotemporal Straight-Flow Network for Efficient Learning
and Accurate Forecasting in Traffic,
ITS(25), No. 11, November 2024, pp. 18899-18912.
IEEE DOI
2411
Spatiotemporal phenomena, Accuracy, Forecasting, Vehicle dynamics,
Correlation, Aerodynamics, Data mining, Training, Predictive models,
heterogeneous dependencies
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.L.[Jin-Lei],
Wang, M.[Mudan],
Huang, J.C.[Jin-Cai],
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.S.[Zhi-Song],
SASTGCN: A Self-Adaptive Spatio-Temporal Graph Convolutional Network
for Traffic Prediction,
IJGI(12), No. 8, 2023, pp. 346.
DOI Link
2309
BibRef
Chen, J.[Jing],
Xu, M.Q.[Meng-Qi],
Xu, W.Q.[Wen-Qiang],
Li, D.P.[Da-Ping],
Peng, W.M.[Wei-Min],
Xu, H.T.[Hai-Tao],
A Flow Feedback Traffic Prediction Based on Visual Quantified
Features,
ITS(24), No. 9, September 2023, pp. 10067-10075.
IEEE DOI
2310
BibRef
Liu, T.[Tao],
Jiang, A.[Aimin],
Zhou, J.[Jia],
Li, M.[Min],
Kwan, H.K.[Hon Keung],
GraphSAGE-Based Dynamic Spatial-Temporal Graph Convolutional Network
for Traffic Prediction,
ITS(24), No. 10, October 2023, pp. 11210-11224.
IEEE DOI
2310
BibRef
Miao, M.[Meng],
Kang, M.Y.[Ming-Yu],
Qian, X.[Xusheng],
Chen, D.[Duxin],
Wu, W.J.[Wei-Jiang],
Yu, W.W.[Wen-Wu],
Improving traffic time-series predictability by imputing continuous
non-random missing data,
IET-ITS(17), No. 10, 2023, pp. 1925-1934.
DOI Link
2310
artificial intelligence, big data,
intelligent transportation systems, prediction theory
BibRef
Gou, Z.[Zhumei],
Shen, Y.G.[Yong-Gang],
Chen, S.[Shuifu],
Lanczos method for spatio-temporal graph convolutional networks to
forecast expressway flow,
IET-ITS(17), No. 10, 2023, pp. 1979-1991.
DOI Link
2310
big data, convolutional neural nets,
intelligent transportation systems, management and control,
traffic modelling
BibRef
Zhang, Q.Y.[Qing-Yong],
Zhou, L.F.[Ling-Feng],
Su, Y.X.[Yi-Xin],
Xia, H.[Huiwen],
Xu, B.[Bingrong],
Gated Recurrent Unit Embedded with Dual Spatial Convolution for
Long-Term Traffic Flow Prediction,
IJGI(12), No. 9, 2023, pp. 366.
DOI Link
2310
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
Xue, R.[Rui],
Zhao, S.J.[Sheng-Jie],
Han, F.X.[Feng-Xia],
An Embedding-Driven Multi-Hop Spatio-Temporal Attention Network for
Traffic Prediction,
ITS(24), No. 11, November 2023, pp. 13192-13207.
IEEE DOI
2311
BibRef
Wang, L.H.[Li-Hua],
Zhang, F.Q.[Feng-Qi],
Cui, Y.H.[Ya-Hui],
Coskun, S.[Serdar],
Tang, X.L.[Xiao-Lin],
Yang, Y.[Yalian],
Hu, X.S.[Xiao-Song],
Stochastic Velocity Prediction for Connected Vehicles Considering V2V
Communication Interruption,
ITS(24), No. 11, November 2023, pp. 11654-11667.
IEEE DOI
2311
BibRef
Djenouri, Y.[Youcef],
Belhadi, A.[Asma],
Djenouri, D.[Djamel],
Srivastava, G.[Gautam],
Lin, J.C.W.[Jerry Chun-Wei],
A Secure Intelligent System for Internet of Vehicles: Case Study on
Traffic Forecasting,
ITS(24), No. 11, November 2023, pp. 13218-13227.
IEEE DOI
2311
BibRef
Chang, S.Y.[Shih Yu],
Wu, H.C.[Hsiao-Chun],
Kao, Y.C.[Yi-Chih],
Tensor Extended Kalman Filter and its Application to Traffic
Prediction,
ITS(24), No. 12, December 2023, pp. 13813-13829.
IEEE DOI
2312
BibRef
Wang, C.[Chen],
Zuo, K.[Kaizhong],
Zhang, S.[Shaokun],
Lei, H.[Hanwen],
Hu, P.[Peng],
Shen, Z.Y.[Zhang-Yi],
Wang, R.[Rui],
Zhao, P.[Peize],
PFNet: Large-Scale Traffic Forecasting With Progressive
Spatio-Temporal Fusion,
ITS(24), No. 12, December 2023, pp. 14580-14597.
IEEE DOI
2312
BibRef
Zhao, J.[Jie],
Chen, C.[Chao],
Liao, C.W.[Cheng-Wu],
Huang, H.Y.[Hong-Yu],
Ma, J.[Jie],
Pu, H.Y.[Hua-Yan],
Luo, J.[Jun],
Zhu, T.[Tao],
Wang, S.[Shilong],
2F-TP: Learning Flexible Spatiotemporal Dependency for Flexible
Traffic Prediction,
ITS(24), No. 12, December 2023, pp. 15379-15391.
IEEE DOI
2312
BibRef
Qi, Y.X.[Yu-Xin],
Wu, J.[Jun],
Bashir, A.K.[Ali Kashif],
Lin, X.[Xi],
Yang, W.[Wu],
Alshehri, M.D.[Mohammad Dahman],
Privacy-Preserving Cross-Area Traffic Forecasting in ITS: A
Transferable Spatial-Temporal Graph Neural Network Approach,
ITS(24), No. 12, December 2023, pp. 15499-15512.
IEEE DOI
2312
BibRef
Ali, F.[Faryal],
Khan, Z.H.[Zawar Hussain],
Khattak, K.S.[Khurram Shehzad],
Gulliver, T.A.[Thomas Aaron],
The effect of visibility on road traffic during foggy weather
conditions,
IET-ITS(18), No. 1, 2024, pp. 47-57.
DOI Link
2401
intelligent transportation systems, management and control,
road safety, road traffic, simulation, traffic modelling
BibRef
Ye, W.[Wei],
Kuang, H.X.[Hao-Xuan],
Li, J.[Jun],
Lai, X.J.[Xin-Jun],
Qu, H.[Haohao],
A parking occupancy prediction method incorporating time series
decomposition and temporal pattern attention mechanism,
IET-ITS(18), No. 1, 2024, pp. 58-71.
DOI Link
2401
intelligent transportation systems,
learning (artificial intelligence), time series
BibRef
Wang, Q.[Qing],
Liu, W.P.[Wei-Ping],
Wang, X.M.[Xiu-Mei],
Chen, X.H.[Xing-Hong],
Chen, G.N.[Guan-Nan],
Wu, Q.X.[Qing-Xiang],
GMHANN: A Novel Traffic Flow Prediction Method for Transportation
Management Based on Spatial-Temporal Graph Modeling,
ITS(25), No. 1, January 2024, pp. 386-401.
IEEE DOI
2402
Predictive models, Roads, Data models, Transportation,
Feature extraction, Complexity theory, Correlation, AGRU
BibRef
Hu, H.X.[He-Xuan],
Hu, Q.[Qiang],
Tan, G.P.[Guo-Ping],
Zhang, Y.[Ye],
Lin, Z.Z.[Zhen-Zhou],
A Multi-Layer Model Based on Transformer and Deep Learning for
Traffic Flow Prediction,
ITS(25), No. 1, January 2024, pp. 443-451.
IEEE DOI
2402
Predictive models, Data models, Computational modeling,
Feature extraction, Transformers, Mathematical models,
multi-layer model
BibRef
Du, W.B.[Wen-Bo],
Chen, S.W.[Shen-Wen],
Li, Z.S.[Zhi-Shuai],
Cao, X.B.[Xian-Bin],
Lv, Y.S.[Yi-Sheng],
A Spatial-Temporal Approach for Multi-Airport Traffic Flow Prediction
Through Causality Graphs,
ITS(25), No. 1, January 2024, pp. 532-544.
IEEE DOI
2402
Airports, Atmospheric modeling, Feature extraction,
Predictive models, Adaptation models, Data mining, spatiotemporal analysis
BibRef
Nie, L.[Laisen],
Wang, X.J.[Xiao-Jie],
Zhao, Q.L.[Qing-Lin],
Shang, Z.G.[Zhi-Gang],
Feng, L.[Li],
Li, G.J.[Guo-Jun],
Digital Twin for Transportation Big Data: A Reinforcement
Learning-Based Network Traffic Prediction Approach,
ITS(25), No. 1, January 2024, pp. 896-906.
IEEE DOI
2402
COVID-19, Predictive models, Neural networks, Feature extraction,
Transportation, Generative adversarial networks, Digital twins,
generative adversarial networks
BibRef
Pu, B.[Bin],
Liu, J.S.[Jian-Song],
Kang, Y.[Yan],
Chen, J.G.[Jian-Guo],
Yu, P.S.[Philip S.],
MVSTT: A Multiview Spatial-Temporal Transformer Network for
Traffic-Flow Forecasting,
Cyber(54), No. 3, March 2024, pp. 1582-1595.
IEEE DOI Code:
WWW Link.
2402
Forecasting, Correlation, Convolution, Transformers, Roads, Predictive models,
Vehicle dynamics, Deep neural network, traffic-flow forecasting
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
Yao, S.[Shuilin],
Zhang, H.Z.[Hui-Zhen],
Wang, C.X.[Chen-Xi],
Zeng, D.[Dan],
Ye, M.[Ming],
GSTGAT: Gated spatiotemporal graph attention network for traffic
demand forecasting,
IET-ITS(18), No. 2, 2024, pp. 258-268.
DOI Link
2402
demand forecasting, traffic, traffic and demand managing
BibRef
Li, X.Y.[Xiao-Yu],
Gong, Y.S.[Yong-Shun],
Liu, W.[Wei],
Yin, Y.L.[Yi-Long],
Zheng, Y.[Yu],
Nie, L.Q.[Li-Qiang],
Dual-track spatio-temporal learning for urban flow prediction with
adaptive normalization,
AI(328), 2024, pp. 104065.
Elsevier DOI
2403
Urban flow prediction, Spatio-temporal learning,
Spatio-temporal normalization, Contrastive learning,
Regional and global correlations
BibRef
Qi, X.D.[Xu-Dong],
Yao, J.F.[Jun-Feng],
Wang, P.[Ping],
Shi, T.T.[Tong-Tong],
Zhang, Y.J.[Ya-Jie],
Zhao, X.M.[Xiang-Mo],
Combining Weather Factors to Predict Traffic Flow: A Spatial-Temporal
Fusion Graph Convolutional Network-Based Deep Learning Approach,
IET-ITS(18), No. 3, 2024, pp. 528-539.
DOI Link
2403
management and control, traffic and demand managing,
traffic information systems, traffic modelling
BibRef
Lu, S.[Shuai],
Chen, H.B.[Hai-Bo],
Teng, Y.L.[Yi-Long],
Multi-Scale Non-Local Spatio-Temporal Information Fusion Networks for
Multi-Step Traffic Flow Forecasting,
IJGI(13), No. 3, 2024, pp. 71.
DOI Link
2404
BibRef
Liu, L.J.[Li-Juan],
Wang, F.Z.[Feng-Zhi],
Liu, H.[Hang],
Zhu, S.Z.[Shun-Zhi],
Wang, Y.[Yan],
HD-Net: A hybrid dynamic spatio-temporal network for traffic flow
prediction,
IET-ITS(18), No. 4, 2024, pp. 672-690.
DOI Link
2404
management and control, traffic modelling,
transport modelling and microsimulation
BibRef
Shan, X.F.[Xiao-Feng],
Yu, W.J.[Wei-Jie],
Li, Z.B.[Zhi-Bin],
Wang, C.[Chishe],
Ren, Y.F.[Yi-Feng],
Zhang, J.J.[Jia-Jie],
Vehicle Trajectory-Based Traffic Volume Prediction on Urban Roads
With Fast-Communication License Plate Recognition Data,
ITS(25), No. 3, March 2024, pp. 2768-2778.
IEEE DOI
2405
Trajectory, Predictive models, Solid modeling, Roads, Data models,
Recurrent neural networks, Real-time systems, Trajectory
BibRef
Kong, J.L.[Jian-Lei],
Fan, X.M.[Xiao-Meng],
Jin, X.[Xuebo],
Lin, S.[Sen],
Zuo, M.[Min],
A Variational Bayesian Inference-Based En-Decoder Framework for
Traffic Flow Prediction,
ITS(25), No. 3, March 2024, pp. 2966-2975.
IEEE DOI
2405
Bayes methods, Neural networks, Predictive models, Uncertainty,
Deep learning, Computational modeling, Time series analysis, encoder-decoder
BibRef
Liu, H.Z.[Hua-Zhong],
Zhang, Y.F.[Yun-Fan],
Ding, J.H.[Ji-Hong],
Zhang, H.[Hanning],
Yang, L.T.[Laurence T.],
Zhou, X.K.[Xiao-Kang],
Tensor-Train-Based Incremental High Order Dominant Z-Eigen
Decomposition for Multi-Modal Intelligent Transportation Prediction,
ITS(25), No. 3, March 2024, pp. 2534-2544.
IEEE DOI
2405
Tensors, Transportation, Markov processes, Mathematical models,
Computational modeling, Predictive models, Data models,
multi-modal ITS prediction
BibRef
Zhao, J.H.[Jun-Hui],
Xiong, X.C.[Xin-Cheng],
Zhang, Q.[Qingmiao],
Wang, D.M.[Dong-Ming],
Extended Multi-Component Gated Recurrent Graph Convolutional Network
for Traffic Flow Prediction,
ITS(25), No. 5, May 2024, pp. 4634-4644.
IEEE DOI
2405
Roads, Convolutional neural networks, Sensors, Predictive models,
Feature extraction, Correlation, Data models, graph convolutional network
BibRef
Song, X.X.[Xiao-Xiang],
Guo, Y.[Yan],
Li, N.[Ning],
Wang, H.[Hai],
Yu, W.[Weibo],
Online Matrix Factorization-Based Traffic Flow Prediction Empowered
by Edge Computing for the CAVs,
ITS(25), No. 5, May 2024, pp. 4049-4065.
IEEE DOI
2405
Predictive models, Edge computing, Real-time systems,
Computational modeling, Servers, Autonomous vehicles, online prediction
BibRef
Wu, Y.[Ying],
Ye, Y.C.[Yong-Chao],
Zeb, A.[Adnan],
Yu, J.J.Q.[James Jian-Qiao],
Wang, Z.[Zheng],
Adaptive Modeling of Uncertainties for Traffic Forecasting,
ITS(25), No. 5, May 2024, pp. 4427-4442.
IEEE DOI
2405
Predictive models, Uncertainty, Forecasting, Planning,
Adaptation models, Data models, Computational modeling,
quantile model
BibRef
Yang, H.Y.[Han-Yi],
Yu, W.[Wanxin],
Zhang, G.H.[Guo-Hui],
Du, L.[Lili],
Network-Wide Traffic Flow Dynamics Prediction Leveraging Macroscopic
Traffic Flow Model and Deep Neural Networks,
ITS(25), No. 5, May 2024, pp. 4443-4457.
IEEE DOI
2405
Predictive models, Roads, Hidden Markov models, Data models,
Boundary conditions, Traffic control, Deep learning,
graph theory
BibRef
Lv, Z.Q.[Zhi-Qiang],
Cheng, Z.[Zesheng],
Li, J.B.[Jian-Bo],
Xu, Z.H.[Zhi-Hao],
Yang, Z.[Zheng],
TreeCN: Time Series Prediction With the Tree Convolutional Network
for Traffic Prediction,
ITS(25), No. 5, May 2024, pp. 3751-3766.
IEEE DOI
2405
Correlation, Transportation, Time series analysis, Roads,
Convolutional neural networks, Task analysis, Predictive models,
hierarchical feature
BibRef
Ouyang, J.H.[Jin-Hui],
Yu, M.X.[Ming-Xia],
Yu, W.[Weiren],
Qin, Z.[Zheng],
Regan, A.C.[Amelia C.],
Wu, D.[Di],
TPGraph: A Spatial-Temporal Graph Learning Framework for Accurate
Traffic Prediction on Arterial Roads,
ITS(25), No. 5, May 2024, pp. 3911-3926.
IEEE DOI
2405
Roads, Feature extraction, Data mining,
Convolutional neural networks, Convolution, Transformers,
graph neural networks
BibRef
Bilotta, S.[Stefano],
Bonsignori, V.[Valerio],
Nesi, P.[Paolo],
High Precision Traffic Flow Reconstruction via Hybrid Method,
ITS(25), No. 5, May 2024, pp. 4066-4076.
IEEE DOI
2405
Roads, Sensors, Data models, Computational modeling,
Predictive models, Junctions, Pollution measurement,
machine learning PDE solution
BibRef
Feng, J.[Jian],
Du, C.[Cailing],
Mu, Q.[Qi],
Traffic Flow Prediction Based on Federated Learning and
Spatio-Temporal Graph Neural Networks,
IJGI(13), No. 6, 2024, pp. 210.
DOI Link
2406
BibRef
He, Y.L.[Ying-Long],
Mattas, K.[Konstantinos],
Makridis, M.A.[Michail A.],
Komnos, D.[Dimitrios],
Marin, A.L.[Andres L.],
Fontaras, G.[Georgios],
Ciuffo, B.[Biagio],
Introducing Hybrid Vehicle Dynamics in Microscopic Traffic Simulation,
ITS(25), No. 7, July 2024, pp. 7977-7986.
IEEE DOI
2407
Vehicle dynamics, Microscopy, Mathematical models, Ice,
Computational modeling, Hybrid electric vehicles, Vehicles,
charge sustaining (CS)
BibRef
Chang, M.M.[Meng-Meng],
Ding, Z.M.[Zhi-Ming],
Zhao, Z.[Zilin],
Cai, Z.[Zhi],
Heterogeneous Modular Traffic Prediction Based on Multilayer Graph
Convolutional Network,
ITS(25), No. 7, July 2024, pp. 7805-7817.
IEEE DOI
2407
Spatiotemporal phenomena, Correlation, Nonhomogeneous media,
Convolution, Traffic control, Feature extraction, heterogeneous links
BibRef
Liu, A.[Aoyu],
Zhang, Y.[Yaying],
Spatial-Temporal Dynamic Graph Convolutional Network With Interactive
Learning for Traffic Forecasting,
ITS(25), No. 7, July 2024, pp. 7645-7660.
IEEE DOI Code:
WWW Link.
2407
Correlation, Forecasting, Roads, Convolutional neural networks,
Traffic control, Adaptation models, traffic forecasting
BibRef
Shin, Y.[Yuyol],
Yoon, Y.[Yoonjin],
PGCN: Progressive Graph Convolutional Networks for Spatial-Temporal
Traffic Forecasting,
ITS(25), No. 7, July 2024, pp. 7633-7644.
IEEE DOI
2407
Forecasting, Convolution, Adaptation models, Feature extraction,
Correlation, Transportation, Predictive models, multivariate time-series
BibRef
Sun, J.[Jie],
Kim, J.[Jiwon],
Toward Data-Driven Simulation of Network-Wide Traffic: A Multi-Agent
Imitation Learning Approach Using Urban Vehicle Trajectory Data,
ITS(25), No. 7, July 2024, pp. 6645-6657.
IEEE DOI
2407
Trajectory, Predictive models, Traffic control, Load modeling,
Data models, Roads, Loading, Traffic simulation, MAGAIL
BibRef
Zou, G.J.[Guo-Jian],
Lai, Z.L.[Zi-Liang],
Wang, T.[Ting],
Liu, Z.S.[Zong-Shi],
Li, Y.[Ye],
MT-STNet: A Novel Multi-Task Spatiotemporal Network for Highway
Traffic Flow Prediction,
ITS(25), No. 7, July 2024, pp. 8221-8236.
IEEE DOI
2407
Spatiotemporal phenomena, Correlation, Predictive models,
Feature extraction, Hidden Markov models, Multitasking,
generative inference system
BibRef
Yu, Q.[Qian],
Ma, L.[Liang],
Lai, P.[Pei],
Guo, J.[Jin],
Dynamic spatial-temporal network for traffic forecasting based on
joint latent space representation,
IET-ITS(18), No. 8, 2024, pp. 1369-1384.
DOI Link
2408
intelligent transportation systems, traffic modeling,
management and control, neural nets
BibRef
Wei, S.Q.[Shu-Qing],
Feng, S.Y.[Si-Yuan],
Yang, H.[Hai],
Multi-View Spatial-Temporal Graph Convolutional Network for Traffic
Prediction,
ITS(25), No. 8, August 2024, pp. 9572-9586.
IEEE DOI
2408
Roads, Correlation, Public transportation,
Convolutional neural networks, Predictive models, Data models,
traffic prediction
BibRef
Cao, S.Q.[Shu-Qin],
Wu, L.[Libing],
Zhang, R.[Rui],
Wu, D.[Dan],
Cui, J.[Jianqun],
Chang, Y.[Yanan],
A Spatiotemporal Multiscale Graph Convolutional Network for Traffic
Flow Prediction,
ITS(25), No. 8, August 2024, pp. 8705-8718.
IEEE DOI
2408
Correlation, Roads, Spatiotemporal phenomena, Feature extraction,
Convolutional neural networks, Predictive models, cross-scale fusion
BibRef
Chauhan, N.S.[Nisha Singh],
Kumar, N.[Neetesh],
Eskandarian, A.[Azim],
A Novel Confined Attention Mechanism Driven Bi-GRU Model for Traffic
Flow Prediction,
ITS(25), No. 8, August 2024, pp. 9181-9191.
IEEE DOI
2408
Predictive models, Long short term memory, Feature extraction,
Data models, Roads, Forecasting, Data mining,
external features
BibRef
Mallick, T.[Tanwi],
Macfarlane, J.[Jane],
Balaprakash, P.[Prasanna],
Uncertainty Quantification for Traffic Forecasting Using
Deep-Ensemble-Based Spatiotemporal Graph Neural Networks,
ITS(25), No. 8, August 2024, pp. 9141-9152.
IEEE DOI Code:
WWW Link.
2408
BibRef
Wang, Q.Y.[Qing-Yi],
Wang, S.[Shenhao],
Zhuang, D.[Dingyi],
Koutsopoulos, H.[Haris],
Zhao, J.H.[Jin-Hua],
Uncertainty Quantification of Spatiotemporal Travel Demand With
Probabilistic Graph Neural Networks,
ITS(25), No. 8, August 2024, pp. 8770-8781.
IEEE DOI
2408
Uncertainty, Probabilistic logic, Deep learning,
Spatiotemporal phenomena, Data models, Predictive models,
travel demand prediction
BibRef
Kumar, K.N.[K. Naveen],
Roy, D.[Debaditya],
Suman, T.A.[Thakur Ashutosh],
Vishnu, C.[Chalavadi],
Mohan, C.K.[C. Krishna],
TSANet: Forecasting traffic congestion patterns from aerial videos
using graphs and transformers,
PR(155), 2024, pp. 110721.
Elsevier DOI
2408
Spatio-temporal graphs, Transformers, Sequence modelling, Sequence estimation
BibRef
Meese, C.[Collin],
Chen, H.[Hang],
Li, W.[Wanxin],
Lee, D.[Danielle],
Guo, H.[Hao],
Shen, C.C.[Chien-Chung],
Nejad, M.[Mark],
Adaptive Traffic Prediction at the ITS Edge With Online Models and
Blockchain-Based Federated Learning,
ITS(25), No. 9, September 2024, pp. 10725-10740.
IEEE DOI
2409
Predictive models, Data models, Blockchains, Training,
Computational modeling, Streams, Sensors, Blockchain, deep learning,
traffic prediction
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
Laña, I.[Ibai],
Olabarrieta, I.[Ignacio],
Ser, J.D.[Javier Del],
Measuring the Confidence of Single-Point Traffic Forecasting Models:
Techniques, Experimental Comparison, and Guidelines Toward Their
Actionability,
ITS(25), No. 9, September 2024, pp. 11180-11199.
IEEE DOI
2409
Uncertainty, Predictive models, Forecasting, Estimation,
Measurement uncertainty, Data models, Machine learning,
traffic forecasting
BibRef
Schrader, M.[Maxwell],
Bittle, J.[Joshua],
A Global Sensitivity Analysis of Traffic Microsimulation Input
Parameters on Performance Metrics,
ITS(25), No. 9, September 2024, pp. 11739-11752.
IEEE DOI
2409
Uncertainty, Calibration, Traffic control, Vehicles,
Sensitivity analysis, Optimization, Aggregates,
Sobal sensitivity analysis
BibRef
Liu, Q.X.[Qing-Xiang],
Sun, S.[Sheng],
Liu, M.[Min],
Wang, Y.W.[Yu-Wei],
Gao, B.[Bo],
Online Spatio-Temporal Correlation-Based Federated Learning for
Traffic Flow Forecasting,
ITS(25), No. 10, October 2024, pp. 13027-13039.
IEEE DOI
2410
Predictive models, Forecasting, Servers, Correlation, Data models,
Federated learning, Adaptation models, Federated learning,
traffic flow forecasting
BibRef
Gan, R.[Rui],
An, B.[Bocheng],
Li, L.H.[Lin-Heng],
Qu, X.[Xu],
Ran, B.[Bin],
A Freeway Traffic Flow Prediction Model Based on a Generalized
Dynamic Spatio-Temporal Graph Convolutional Network,
ITS(25), No. 10, October 2024, pp. 13682-13693.
IEEE DOI
2410
Predictive models, Data models, Roads, Feature extraction,
Long short term memory, Convolution, Detectors, Traffic prediction,
traffic big data
BibRef
Zhang, X.J.[Xiao-Jian],
Ke, Q.[Qian],
Zhao, X.[Xilei],
Travel Demand Forecasting: A Fair AI Approach,
ITS(25), No. 10, October 2024, pp. 14611-14627.
IEEE DOI
2410
Predictive models, Demand forecasting, Transportation, Sociology,
Correlation, Decision making, Deep learning, AI, fairness, forecasting,
travel demand
BibRef
Du, K.[Kejun],
Wang, S.[Shuling],
Lo, H.K.[Hong K.],
Traffic Parameters Estimation With Partial Vehicle Trajectories by
the Iterative Partial Backpropagation Maximum Likelihood Estimation
(IPB-MLE) Framework,
ITS(25), No. 10, October 2024, pp. 14855-14865.
IEEE DOI
2410
Trajectory, Maximum likelihood estimation, Convergence,
Backpropagation, Robustness, Real-time systems, Probes,
maximum likelihood estimation (MLE)
BibRef
Zhang, Y.D.[Yu-Dong],
Wang, P.[Pengkun],
Wang, B.[Binwu],
Wang, X.[Xu],
Zhao, Z.[Zhe],
Zhou, Z.Y.[Zheng-Yang],
Bai, L.[Lei],
Wang, Y.[Yang],
Adaptive and Interactive Multi-Level Spatio-Temporal Network for
Traffic Forecasting,
ITS(25), No. 10, October 2024, pp. 14070-14086.
IEEE DOI
2410
Forecasting, Roads, Correlation, Urban areas, Traffic control, Layout,
Data models, Spatio-temporal data mining, traffic forecasting,
urban computing
BibRef
Zhao, J.L.[Jian-Li],
Zhuo, F.T.[Fu-Tong],
Sun, Q.X.[Qiu-Xia],
Li, Q.[Qing],
Hua, Y.[Yiran],
Zhao, J.[Jianye],
DSFormer-LRTC: Dynamic Spatial Transformer for Traffic Forecasting
With Low-Rank Tensor Compression,
ITS(25), No. 11, November 2024, pp. 16323-16335.
IEEE DOI
2411
Tensors, Transformers, Predictive models, Computational modeling,
Forecasting, Correlation, Matrix decomposition,
tensor compression
BibRef
Zheng, X.[Xiao],
Bagloee, S.A.[Saeed Asadi],
Sarvi, M.[Majid],
TRECK: Long-Term Traffic Forecasting With Contrastive Representation
Learning,
ITS(25), No. 11, November 2024, pp. 16964-16977.
IEEE DOI
2411
Forecasting, Predictive models, Representation learning,
Contrastive learning, Data models, Task analysis, Casting,
prediction interval
BibRef
Zhu, W.G.[Wei-Guo],
Zhang, X.Y.[Xing-Yu],
Liu, C.[Caiyuan],
Sun, Y.Q.[Yong-Qi],
D³STN: Dynamic Delay Differential Equation Spatiotemporal Network for
Traffic Flow Forecasting,
ITS(25), No. 11, November 2024, pp. 18093-18106.
IEEE DOI
2411
Delays, Forecasting, Mathematical models, Correlation,
Spatiotemporal phenomena, Predictive models, Convolution,
traffic forecasting
BibRef
Zhang, C.[Chengyang],
Zhang, Y.[Yong],
Shao, Q.[Qitan],
Feng, J.T.[Jiang-Tao],
Li, B.[Bo],
Lv, Y.S.[Yi-Sheng],
Piao, X.[Xinglin],
Yin, B.C.[Bao-Cai],
BjTT: A Large-Scale Multimodal Dataset for Traffic Prediction,
ITS(25), No. 11, November 2024, pp. 18992-19003.
IEEE DOI Code:
WWW Link.
2411
Roads, Social networking (online), Transportation, Data collection,
Task analysis, Blogs, Meteorology, Traffic prediction, large-scale, new dataset
BibRef
Li, J.[Junyi],
Liao, C.[Chenlei],
Hu, S.[Simon],
Chen, X.[Xiqun],
Lee, D.H.[Der-Horng],
Physics-Guided Multi-Source Transfer Learning for Network-Scale
Traffic Flow Prediction,
ITS(25), No. 11, November 2024, pp. 17533-17546.
IEEE DOI
2411
Transfer learning, Adaptation models, Telecommunication traffic,
Task analysis, Predictive models, Feature extraction, Data models,
physics-guided machine learning
BibRef
Wu, Y.L.[Yi-Ling],
Zhao, Y.P.[Ying-Ping],
Zhang, X.F.[Xin-Feng],
Wang, Y.[Yaowei],
Spatial-Temporal Correlation Learning for Traffic Demand Prediction,
ITS(25), No. 11, November 2024, pp. 15745-15758.
IEEE DOI
2411
Correlation, Predictive models, Public transportation, Automobiles,
Attention mechanisms, Accuracy, Transformers, spatial-temporal mining
BibRef
Zhang, J.F.[Jun-Feng],
Xie, C.[Cheng],
Cai, H.M.[Hong-Ming],
Shen, W.M.[Wei-Ming],
Yang, R.[Rui],
Knowledge Distillation-Based Spatio-Temporal MLP Model for Real-Time
Traffic Flow Prediction,
ITS(25), No. 11, November 2024, pp. 18122-18135.
IEEE DOI Code:
WWW Link.
2411
Computational modeling, Spatiotemporal phenomena,
Predictive models, Accuracy, Real-time systems, Data models,
real-time traffic flow prediction
BibRef
Sattarzadeh, A.R.[Ali Reza],
Pathirana, P.N.[Pubudu N.],
Kutadinata, R.[Ronny],
Huynh, V.T.[Van Thanh],
Extracting long-term spatiotemporal characteristics of traffic flow
using attention-based convolutional transformer,
IET-ITS(18), No. 10, 2024, pp. 1797-1814.
DOI Link
2411
convolutional neural nets, data mining, feature extraction,
intelligent transportation systems, time series
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.[Yibin],
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
Xu, C.[Chen],
Wang, Q.[Qiang],
Zhang, W.Q.[Wen-Qi],
Sun, C.[Chen],
Spatiotemporal Ego-Graph Domain Adaptation for Traffic Prediction
With Data Missing,
ITS(25), No. 12, December 2024, pp. 20804-20819.
IEEE DOI
2412
Adaptation models, Predictive models, Data models, Tensors,
Spatiotemporal phenomena, Roads, Feature extraction,
domain adaptation
BibRef
Wang, Y.Q.[Yu-Qing],
Zhang, J.W.[Jun-Wei],
Ma, Z.[Zhuo],
Lu, N.[Ning],
Li, T.[Teng],
Ma, J.F.[Jian-Feng],
Location-Aware and Privacy-Preserving Data Cleaning for Intelligent
Transportation,
ITS(25), No. 12, December 2024, pp. 20405-20418.
IEEE DOI
2412
Cleaning, Data privacy, Accuracy, Reflective binary codes, Privacy,
Predictive models, Forecasting, Data cleaning, location-aware,
traffic forecasting
BibRef
Lim, J.[Junwoo],
Lee, J.[Juyeob],
An, C.[Chaehee],
Park, E.[Eunil],
Enhancing real-time traffic volume prediction: A two-step approach of
object detection and time series modelling,
IET-ITS(18), No. 12, 2024, pp. 2744-2758.
DOI Link
2501
artificial intelligence, object detection, real-time systems,
road traffic, time series, traffic management and control
BibRef
Dou, Z.[Zeping],
Guo, D.[Danhuai],
DPSTCN: Dynamic Pattern-Aware Spatio-Temporal Convolutional Networks
for Traffic Flow Forecasting,
IJGI(14), No. 1, 2025, pp. 10.
DOI Link
2501
BibRef
Zhang, D.K.[Ding-Kai],
Wang, P.F.[Peng-Fei],
Ding, L.[Lu],
Wang, X.L.[Xiao-Ling],
He, J.F.[Ji-Feng],
Spatio-Temporal Contrastive Learning-Based Adaptive Graph
Augmentation for Traffic Flow Prediction,
ITS(26), No. 1, January 2025, pp. 1304-1318.
IEEE DOI
2501
Adaptation models, Data models, Correlation, Predictive models,
Roads, Computational modeling, Accuracy, Traffic control,
graph structure learning
BibRef
Lin, M.W.[Ming-Wei],
Liu, J.Q.[Jia-Qi],
Chen, H.[Hong],
Xu, X.[Xiuqin],
Luo, X.[Xin],
Xu, Z.[Zeshui],
A 3D Convolution-Incorporated Dimension Preserved Decomposition Model
for Traffic Data Prediction,
ITS(26), No. 1, January 2025, pp. 673-690.
IEEE DOI
2501
Data models, Predictive models, Feature extraction, Roads,
Solid modeling, Data mining, Accuracy, Complexity theory, Tensors,
3D convolution
BibRef
Nie, T.[Tong],
Qin, G.[Guoyang],
Sun, L.J.[Li-Jun],
Ma, W.[Wei],
Mei, Y.[Yu],
Sun, J.[Jian],
Contextualizing MLP-Mixers Spatiotemporally for Urban Traffic Data
Forecast at Scale,
ITS(26), No. 1, January 2025, pp. 1241-1256.
IEEE DOI
2501
Spatiotemporal phenomena, Forecasting, Computational modeling,
Computer architecture, Sensors, Predictive models, Scalability,
deployed traffic applications
BibRef
Yingran, Z.[Zheng],
Chao, L.[Luo],
Rui, S.[Shao],
Enhancing Traffic Flow Forecasting With Delay Propagation: Adaptive
Graph Convolution Networks for Spatio-Temporal Data,
ITS(26), No. 1, January 2025, pp. 650-660.
IEEE DOI
2501
Delays, Convolution, Roads, Forecasting, Feature extraction,
Correlation, Adaptation models, Predictive models, Logic gates,
traffic flow delay propagation
BibRef
Hua, X.[Xin],
Liu, W.[Wei],
Spatial-Temporal Network Data-Driven Multi-Layer Traffic Knowledge
Graph Reconstruction for Dynamic Prediction,
ICRVC22(20-24)
IEEE DOI
2301
Knowledge engineering, Training, Correlation, Roads, Scalability,
Weather forecasting, Transportation, spatial-temporal, ST-KG, reconstruction
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, 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
BibRef
Sun, Y.W.[Yi-Wen],
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
Traffic Origin-Destination Analysis .