16.7.2.5.9 Traffic Flow Prediction, Forecast

Chapter Contents (Back)
Flow Prediction. Traffic Flow. Prediction. Traffic Prediction.

Quek, C., Pasquier, M., Lim, B.L.B.,
POP-TRAFFIC: A Novel Fuzzy Neural Approach to Road Traffic Analysis and Prediction,
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IEEE DOI 0606
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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
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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
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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
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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
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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
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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
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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
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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., Duan, Y., Kang, W., Li, Z., Wang, F.,
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

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

Jerath, K., Ray, A., Brennan, S., Gayah, V.V.,
Dynamic Prediction of Vehicle Cluster Distribution in Mixed Traffic: A Statistical Mechanics-Inspired Method,
ITS(16), No. 5, October 2015, pp. 2424-2434.
IEEE DOI 1511
adaptive control 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., 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.[Guohua],
Short-term traffic forecasting using self-adjusting k-nearest neighbours,
IET-ITS(12), No. 1, February 2018, pp. 41-48.
DOI Link 1801
BibRef

Ding, C., Duan, J., Zhang, Y., Wu, X., Yu, G.,
Using an ARIMA-GARCH Modeling Approach to Improve Subway Short-Term Ridership Forecasting Accounting for Dynamic Volatility,
ITS(19), No. 4, April 2018, pp. 1054-1064.
IEEE DOI 1804
Analytical models, Forecasting, Mathematical model, Predictive models, Public transportation, Reliability, prediction interval BibRef

Cheng, S.[Shifen], Lu, F.[Feng], Peng, P.[Peng], Wu, S.[Sheng],
A Spatiotemporal Multi-View-Based Learning Method for Short-Term Traffic Forecasting,
IJGI(7), No. 6, 2018, pp. xx-yy.
DOI Link 1806
BibRef

Wang, X.X.[Xiang-Xue], Xu, L.H.[Lun-Hui],
Wavelet-based short-term forecasting with improved threshold recognition for urban expressway traffic conditions,
IET-ITS(12), No. 6, August 2018, pp. 463-473.
DOI Link 1807
BibRef

Zhou, T.[Teng], Jiang, D.[Dazhi], Lin, Z.[Zhizhe], Han, G.[Guoqiang], Xu, X.[Xuemiao], 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

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., Lin, Y., Li, S., Chen, Z., Wan, H.,
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

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.(.[Xiqun (Michael)], Zhang, S.[Shuaichao], 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
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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
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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
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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
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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
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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
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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
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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
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Tong, X.H.[Xiao-Hua], Wang, R.[Runjie], Shi, W.Z.[Wen-Zhong], Li, Z.[Zhiyuan],
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
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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
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Guo, K., Hu, Y., Qian, Z., Liu, H., Zhang, K., Sun, Y., Gao, J., Yin, B.,
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

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, C.[Chen], Liu, Z.[Ziye], 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.[Yixuan], 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.[Yanyun], Wang, X.[Xiang], Zheng, J.[Jianying], E, W.J.[Wen-Juan], Zhao, P.[Po], Meng, S.[Shiwei],
Deep tree neural network for multiple-time-step prediction of short-term speed and confidence estimation,
IET-ITS(15), No. 3, 2021, pp. 446-462.
DOI Link 2106
BibRef

Tian, C.[Chenyu], Chan, W.K.(.[Wai Kin (Victor)],
Spatial-temporal attention wavenet: A deep learning framework for traffic prediction considering spatial-temporal dependencies,
IET-ITS(15), No. 4, 2021, pp. 549-561.
DOI Link 2106
BibRef

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

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

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

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

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

Tian, Z.[Zhongda],
Approach for Short-Term Traffic Flow Prediction Based on Empirical Mode Decomposition and Combination Model Fusion,
ITS(22), No. 9, September 2021, pp. 5566-5576.
IEEE DOI 2109
Predictive models, Roads, Training, Empirical mode decomposition, Optimization, Prediction algorithms, Biological system modeling, improved fruit fly optimization algorithm BibRef

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

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


Mallick, T.[Tanwi], Balaprakash, P.[Prasanna], Rask, E.[Eric], Macfarlane, J.[Jane],
Transfer Learning with Graph Neural Networks for Short-Term Highway Traffic Forecasting,
ICPR21(10367-10374)
IEEE DOI 2105
Road transportation, Training, Recurrent neural networks, Transfer learning, Predictive models, Traffic control, Pattern recognition BibRef

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

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

Li, Q., Wang, H.,
Elman short-term traffic flow prediction model based on association rules,
CVIDL20(673-678)
IEEE DOI 2102
backpropagation, data mining, recurrent neural nets, road traffic, time series, traffic engineering computing, time series factors, Elman neural network BibRef

Liu, D., Hui, S., Li, L., Liu, Z., Zhang, Z.,
A Method For Short-Term Traffic Flow Forecasting Based On GCN-LSTM,
CVIDL20(364-368)
IEEE DOI 2102
convolutional neural nets, data reduction, intelligent transportation systems, LSTM BibRef

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

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

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

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

Chapter on Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities continues in
Traffic Flow Analysis Using Phone Signals, Cell Data, Wi-Fi data .


Last update:Sep 19, 2021 at 21:11:01