Graph Neural Network for Traffic Forecasting: The Research Progress

Traffic forecasting has been regarded as the basis for many intelligent transportation system (ITS) applications, including but not limited to trip planning, road traffic control, and vehicle routing. Various forecasting methods have been proposed in the literature, including statistical models, sha...

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Published inISPRS international journal of geo-information Vol. 12; no. 3; p. 100
Main Authors Jiang, Weiwei, Luo, Jiayun, He, Miao, Gu, Weixi
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.03.2023
Subjects
Artificial intelligence
Computer science
data collection
Datasets
Decision trees
Deep learning
Forecasting
graph attention network
graph convolutional network
graph neural network
Graph neural networks
Intelligent transportation systems
Internet of Things
Literature reviews
Machine learning
Mathematical models
Neural networks
Route planning
Source code
Statistical analysis
Statistical models
Surveys
Time series
traffic
Traffic control
Traffic flow
traffic forecasting
Traffic planning
Transportation networks
Trends
Variables
Vehicle routing
Online AccessGet full text
ISSN2220-9964
2220-9964
DOI10.3390/ijgi12030100

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Abstract Traffic forecasting has been regarded as the basis for many intelligent transportation system (ITS) applications, including but not limited to trip planning, road traffic control, and vehicle routing. Various forecasting methods have been proposed in the literature, including statistical models, shallow machine learning models, and deep learning models. Recently, graph neural networks (GNNs) have emerged as state-of-the-art traffic forecasting solutions because they are well suited for traffic systems with graph structures. This survey aims to introduce the research progress on graph neural networks for traffic forecasting and the research trends observed from the most recent studies. Furthermore, this survey summarizes the latest open-source datasets and code resources for sharing with the research community. Finally, research challenges and opportunities are proposed to inspire follow-up research.
AbstractList Traffic forecasting has been regarded as the basis for many intelligent transportation system (ITS) applications, including but not limited to trip planning, road traffic control, and vehicle routing. Various forecasting methods have been proposed in the literature, including statistical models, shallow machine learning models, and deep learning models. Recently, graph neural networks (GNNs) have emerged as state-of-the-art traffic forecasting solutions because they are well suited for traffic systems with graph structures. This survey aims to introduce the research progress on graph neural networks for traffic forecasting and the research trends observed from the most recent studies. Furthermore, this survey summarizes the latest open-source datasets and code resources for sharing with the research community. Finally, research challenges and opportunities are proposed to inspire follow-up research.
Author Gu, Weixi
Jiang, Weiwei
Luo, Jiayun
He, Miao
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Cites_doi 10.1016/j.dcan.2021.09.007
10.1109/TKDE.2022.3187690
10.1109/TITS.2022.3202089
10.1016/j.knosys.2022.109028
10.3390/electronics11101613
10.1109/JIOT.2022.3142070
10.1145/3534678.3539281
10.1016/j.physa.2022.128075
10.3141/2460-08
10.1109/TITS.2022.3157056
10.1109/TITS.2022.3208943
10.1109/TITS.2022.3215326
10.3390/electronics11162620
10.1016/j.comcom.2022.08.008
10.3389/ffutr.2021.693708
10.1016/j.physa.2022.127762
10.1145/3511808.3557243
10.24963/ijcai.2022/328
10.1016/j.physa.2022.127789
10.1109/TITS.2020.3043250
10.1109/TITS.2022.3178136
10.1016/j.dsm.2021.05.001
10.1145/3534678.3539396
10.3390/s22124485
10.1080/01441647.2018.1442887
10.1016/j.trc.2022.103820
10.1002/int.22855
10.1155/2022/7682274
10.1109/IJCNN55064.2022.9892326
10.1109/TITS.2009.2021448
10.1016/j.compenvurbsys.2022.101776
10.1109/JIOT.2022.3196461
10.1155/2022/2723101
10.1016/j.ins.2022.04.045
10.1016/j.eswa.2022.117511
10.1109/ITSC55140.2022.9922512
10.1093/comjnl/bxac086
10.1007/s00521-022-07235-z
10.1016/j.knosys.2018.10.037
10.1109/TITS.2022.3140229
10.1109/JSEN.2022.3176016
10.1007/s10489-022-03224-w
10.1109/TITS.2022.3171451
10.1016/j.dsm.2022.05.002
10.1016/j.ins.2022.07.008
10.1177/03611981221112673
10.1016/j.ins.2022.02.031
10.1016/j.inffus.2022.09.028
10.3390/s21030706
10.24963/ijcai.2019/264
10.3390/rs14020303
10.1016/j.knosys.2022.108990
10.3390/ijgi9010058
10.1002/itl2.297
10.1089/big.2021.0251
10.1007/s11280-022-01045-y
10.1007/s10707-022-00466-1
10.1109/TITS.2022.3173689
10.1109/IJCNN55064.2022.9892031
10.1109/TKDE.2022.3179646
10.3390/ijgi11070381
10.1109/IJCNN55064.2022.9892016
10.1016/j.neucom.2022.09.010
10.3390/su141912397
10.1016/j.trc.2019.08.010
10.1155/2022/1358535
10.1609/aaai.v33i01.3301922
10.1109/TKDE.2020.2981333
10.1109/ACCESS.2021.3049556
10.1109/ACCESS.2022.3204036
10.1109/MITS.2018.2806634
10.1155/2022/5221362
10.1155/2022/2348375
10.1109/ISCC55528.2022.9912866
10.1145/3511808.3557705
10.1109/TITS.2022.3179391
10.1016/j.knosys.2022.109985
10.3390/app12052688
10.3390/asi5010023
10.1109/TCYB.2021.3117705
10.1016/j.trc.2021.103063
10.1109/TITS.2013.2247040
10.1109/TITS.2022.3146899
10.24963/ijcai.2022/545
10.1007/s10489-021-03128-1
10.1109/ACCESS.2022.3195353
10.1109/JSEN.2022.3152808
10.14778/3551793.3551827
10.1109/TITS.2022.3155753
10.1016/j.patrec.2022.03.005
10.1109/TNSE.2022.3152983
10.1145/3534678.3539397
10.3233/IDA-183832
10.1155/2022/2811961
10.3390/app12062842
10.1109/CSCWD54268.2022.9776256
10.3390/app11104423
10.1016/j.future.2022.09.018
10.1016/j.knosys.2022.108199
10.1016/j.eswa.2022.118475
10.1109/TNSE.2021.3137381
10.1080/09540091.2022.2061915
10.1109/DDCLS.2018.8516114
10.1109/MITS.2021.3098627
10.1016/j.comnet.2020.107530
10.1016/j.physa.2021.126736
10.1109/TITS.2022.3148105
10.3390/su13158577
10.1109/TITS.2021.3113705
10.1016/j.ins.2022.07.125
10.1109/IJCNN55064.2022.9892453
10.1016/j.eswa.2022.117921
10.3390/ijgi11020088
10.3390/asi5060121
10.1609/aaai.v31i1.10735
10.1016/j.trc.2021.103466
10.1002/itl2.403
10.1109/TITS.2022.3196466
10.1137/1.9781611974973.87
10.1109/TITS.2021.3054840
10.1016/j.engappai.2022.105179
10.1109/TNNLS.2022.3186103
10.1109/TNNLS.2020.2978386
10.1016/j.trc.2022.103731
10.1007/s10707-022-00467-0
10.1002/itl2.322
10.1109/IJCNN55064.2022.9892191
10.1109/TITS.2022.3185503
10.1016/j.trc.2014.01.005
10.1007/s13042-022-01689-2
10.1109/ICASSP43922.2022.9746497
10.1007/s11280-021-00995-z
10.1016/j.eswa.2022.116585
10.1109/TBDATA.2022.3156366
10.1145/3511808.3557540
10.1016/j.dsp.2022.103419
10.1007/s10489-022-04218-4
10.1109/ITSC45102.2020.9294236
10.1109/WCNC51071.2022.9771883
10.1145/3511808.3557432
10.1109/TITS.2022.3148358
10.1016/j.dsm.2021.07.002
10.1007/s10489-021-02377-4
10.1016/j.aej.2020.09.038
10.1109/TITS.2022.3148116
10.26599/TST.2018.9010033
10.24963/ijcai.2018/505
10.3390/asi4030043
10.3390/ijgi11020085
10.1007/s00521-022-07285-3
10.1016/j.trc.2022.103984
10.1109/TITS.2021.3083957
10.1109/ICDE53745.2022.00136
10.1049/itr2.12254
10.3390/app12062890
10.1109/TITS.2022.3167019
10.1080/21680566.2022.2116125
10.1109/TITS.2022.3168865
10.1016/j.ins.2022.06.080
10.1016/j.dsm.2022.04.001
10.1002/eng2.12178
10.1016/j.eswa.2022.117163
10.1109/JIOT.2019.2946693
10.1609/aaai.v35i12.17325
10.1007/s10668-022-02585-z
10.1145/3534678.3539093
10.3390/electronics11152432
10.1016/j.physa.2022.127959
10.1109/TVT.2022.3209242
10.1109/IJCNN55064.2022.9892616
10.3390/electronics11142230
10.1109/TITS.2021.3136161
10.1016/j.eswa.2021.115537
10.1007/s10489-021-03022-w
10.3390/math10101754
10.1109/TITS.2022.3201879
10.1109/ACCESS.2021.3071174
10.1002/itl2.314
10.1016/j.ins.2022.05.127
10.1016/j.dsm.2022.07.002
10.3390/a15120447
10.1145/3485447.3511990
10.1016/j.dsm.2021.02.002
10.1002/itl2.383
10.1109/TITS.2022.3168879
10.1177/0361198119849059
10.1016/j.asoc.2022.108977
10.1080/15472450.2019.1582950
10.3390/electronics11193012
10.1007/s00521-022-07380-5
10.1080/0144164042000195072
10.1109/ICDE53745.2022.00058
10.1049/itr2.12296
10.1109/JIOT.2019.2940412
10.1155/2022/1217588
10.1007/s11063-022-11036-9
10.1007/s10489-022-03966-7
10.1109/TITS.2022.3157129
10.1109/TVT.2022.3178094
10.1016/j.ins.2022.04.024
10.3390/app12147010
10.1016/j.trc.2022.103659
10.1016/j.comnet.2020.107484
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References ref_139
ref_91
Lee (ref_50) 2021; 9
Huang (ref_84) 2022; 594
Ye (ref_107) 2022; 8
Sun (ref_74) 2022; 9
Gutmann (ref_209) 2021; 2
Su (ref_129) 2022; 156
ref_95
ref_134
Wang (ref_9) 2019; 23
Jiang (ref_40) 2022; 201
Zheng (ref_116) 2022; 9
ref_126
ref_125
ref_120
Xiao (ref_10) 2019; 164
ref_123
Liang (ref_137) 2022; 140
Xu (ref_215) 2021; 1
Ke (ref_206) 2021; 127
Zhao (ref_106) 2022; 204
ref_159
ref_71
ref_158
Long (ref_13) 2023; 72
Chen (ref_97) 2022; 23
Jiang (ref_30) 2021; 184
ref_78
ref_154
Lana (ref_16) 2018; 10
ref_157
ref_156
Huang (ref_85) 2022; 601
Vlahogianni (ref_7) 2014; 43
ref_160
Wang (ref_149) 2022; 13
ref_82
Wang (ref_153) 2022; 604
ref_81
Feng (ref_147) 2022; 606
ref_80
Xu (ref_99) 2022; 23
Li (ref_43) 2023; 147
ref_89
ref_88
Hu (ref_117) 2022; 22
ref_141
Santhosh (ref_31) 2020; 2
ref_144
ref_146
Zhang (ref_118) 2022; 250
Wang (ref_105) 2022; 607
Liao (ref_168) 2022; 52
Diao (ref_93) 2022; 24
Bao (ref_127) 2022; 210
ref_214
ref_213
ref_217
ref_219
ref_210
ref_212
Ni (ref_164) 2022; 52
Shankarnarayan (ref_33) 2021; 3
Khaled (ref_170) 2022; 249
Jiang (ref_28) 2018; 24
ref_203
Djenouri (ref_75) 2023; 139
ref_202
ref_205
ref_204
ref_207
ref_208
Jiang (ref_53) 2022; 34
Chen (ref_171) 2022; 194
Vlahogianni (ref_8) 2004; 24
ref_201
ref_200
Wang (ref_173) 2022; 590
Jiang (ref_36) 2022; 5
Jin (ref_72) 2022; 510
Lippi (ref_20) 2013; 14
He (ref_15) 2019; 7
Manibardo (ref_49) 2021; 23
Zhang (ref_98) 2022; 139
Xiao (ref_224) 2021; 14
Liao (ref_103) 2022; 52
Li (ref_148) 2022; 2022
Zheng (ref_73) 2022; 10
ref_119
ref_230
Jiang (ref_109) 2022; 2022
ref_111
ref_232
ref_110
ref_231
Ye (ref_44) 2022; 23
Shao (ref_185) 2022; 15
ref_234
Wang (ref_155) 2022; 23
ref_233
Wu (ref_57) 2020; 32
Sun (ref_151) 2022; 124
Lee (ref_114) 2022; 134
Zhang (ref_169) 2022; 608
ref_104
ref_227
Serrano (ref_221) 2023; 91
ref_226
ref_108
ref_229
ref_228
Xu (ref_87) 2022; 10
Wang (ref_4) 2021; 2
Xiao (ref_6) 2019; 6
Jin (ref_86) 2022; 23
James (ref_130) 2022; 23
ref_220
ref_223
ref_222
Geng (ref_131) 2022; 606
Liu (ref_94) 2022; 23
Duan (ref_124) 2022; 71
Zhang (ref_162) 2022; 34
Ge (ref_79) 2022; 2022
Li (ref_113) 2022; 13
Li (ref_140) 2022; 53
ref_12
Feng (ref_101) 2022; 2022
ref_17
Huang (ref_138) 2022; 23
Zhang (ref_161) 2022; 34
Xiu (ref_76) 2022; 24
Zhang (ref_135) 2022; 114
ref_24
ref_22
Sun (ref_25) 2021; 9
Zhang (ref_58) 2020; 34
Boukerche (ref_48) 2020; 182
Lu (ref_142) 2022; 13
ref_27
Wang (ref_167) 2022; 603
ref_26
Zhao (ref_102) 2022; 2022
Zhuang (ref_39) 2022; 5
Kong (ref_122) 2022; 23
Hou (ref_218) 2021; 60
Jiang (ref_23) 2022; 5
Liu (ref_225) 2022; 25
Zhan (ref_41) 2020; 24
Zhang (ref_128) 2022; 34
Yin (ref_51) 2021; 23
Sousa (ref_37) 2022; 5
Huang (ref_152) 2022; 609
Chen (ref_92) 2022; 143
Zhao (ref_34) 2022; 5
Shang (ref_90) 2022; 123
Gao (ref_216) 2022; 10
Ermagun (ref_11) 2018; 38
Cao (ref_96) 2022; 610
Ta (ref_100) 2022; 242
ref_172
ref_56
ref_175
ref_55
Liu (ref_14) 2022; 9
ref_54
ref_177
Zheng (ref_83) 2022; 195
Dong (ref_163) 2022; 603
ref_176
ref_52
ref_179
ref_178
ref_180
ref_182
ref_59
ref_181
Liu (ref_3) 2022; 9
Zhao (ref_150) 2022; 34
Boukerche (ref_47) 2020; 181
Yang (ref_211) 2019; 107
ref_61
Tedjopurnomo (ref_46) 2020; 34
Lu (ref_132) 2022; 13
Ghosh (ref_19) 2009; 10
Dai (ref_70) 2022; 17
ref_67
ref_66
ref_65
ref_166
Wang (ref_174) 2022; 36
ref_64
ref_165
Rehman (ref_2) 2022; 22
ref_63
Wang (ref_69) 2022; 258
ref_62
Wang (ref_133) 2022; 23
Ling (ref_68) 2022; 17
Qin (ref_143) 2022; 37
Jiang (ref_45) 2022; 207
ref_195
ref_194
ref_197
ref_196
ref_32
ref_199
Luo (ref_121) 2022; 23
ref_198
Jiang (ref_38) 2022; 5
Jiang (ref_35) 2022; 5
Zhao (ref_112) 2022; 94
Jiang (ref_115) 2022; 23
ref_184
ref_183
ref_186
Xiao (ref_18) 2022; 23
ref_188
ref_42
ref_187
ref_189
ref_1
Jiang (ref_136) 2022; 9
ref_191
ref_190
Cao (ref_145) 2022; 2022
ref_193
Xu (ref_77) 2022; 2022
ref_192
Wang (ref_21) 2014; 2460
Sun (ref_60) 2022; 23
ref_5
Khan (ref_29) 2019; 2673
References_xml – volume: 8
  start-page: 343
  year: 2022
  ident: ref_107
  article-title: Attention-based spatio-temporal graph convolutional network considering external factors for multi-step traffic flow prediction
  publication-title: Digit. Commun. Netw.
  doi: 10.1016/j.dcan.2021.09.007
– ident: ref_165
  doi: 10.1109/TKDE.2022.3187690
– ident: ref_71
  doi: 10.1109/TITS.2022.3202089
– volume: 250
  start-page: 109028
  year: 2022
  ident: ref_118
  article-title: Dynamic graph convolutional networks based on spatiotemporal data embedding for traffic flow forecasting
  publication-title: Knowl.-Based Syst.
  doi: 10.1016/j.knosys.2022.109028
– ident: ref_123
  doi: 10.3390/electronics11101613
– volume: 9
  start-page: 13390
  year: 2022
  ident: ref_136
  article-title: Inter-Block Flow Prediction with Relation Graph Network for Cold-start on Bike-Sharing System
  publication-title: IEEE Internet Things J.
  doi: 10.1109/JIOT.2022.3142070
– ident: ref_191
  doi: 10.1145/3534678.3539281
– volume: 606
  start-page: 128075
  year: 2022
  ident: ref_147
  article-title: Multi-step ahead traffic speed prediction based on gated temporal graph convolution network
  publication-title: Phys. A Stat. Mech. Its Appl.
  doi: 10.1016/j.physa.2022.128075
– ident: ref_217
– volume: 2460
  start-page: 66
  year: 2014
  ident: ref_21
  article-title: Empirical mode decomposition–autoregressive integrated moving average: Hybrid short-term traffic speed prediction model
  publication-title: Transp. Res. Rec.
  doi: 10.3141/2460-08
– ident: ref_125
  doi: 10.1109/TITS.2022.3157056
– volume: 23
  start-page: 23680
  year: 2022
  ident: ref_60
  article-title: Dual Dynamic Spatial-Temporal Graph Convolution Network for Traffic Prediction
  publication-title: IEEE Trans. Intell. Transp. Syst.
  doi: 10.1109/TITS.2022.3208943
– ident: ref_67
  doi: 10.1109/TITS.2022.3215326
– ident: ref_119
  doi: 10.3390/electronics11162620
– volume: 194
  start-page: 446
  year: 2022
  ident: ref_171
  article-title: Traffic flow prediction using multi-view graph convolution and masked attention mechanism
  publication-title: Comput. Commun.
  doi: 10.1016/j.comcom.2022.08.008
– volume: 2
  start-page: 693708
  year: 2021
  ident: ref_209
  article-title: Truck parking occupancy prediction: Xgboost-LSTM model fusion
  publication-title: Front. Future Transp.
  doi: 10.3389/ffutr.2021.693708
– volume: 603
  start-page: 127762
  year: 2022
  ident: ref_167
  article-title: ST-MGAT: Spatio-temporal multi-head graph attention network for Traffic prediction
  publication-title: Phys. A Stat. Mech. Its Appl.
  doi: 10.1016/j.physa.2022.127762
– ident: ref_212
– ident: ref_177
  doi: 10.1145/3511808.3557243
– ident: ref_194
  doi: 10.24963/ijcai.2022/328
– volume: 13
  start-page: 1
  year: 2022
  ident: ref_113
  article-title: Crowd Flow Prediction for irregular Regions with Semantic Graph Attention Network
  publication-title: ACM Trans. Intell. Syst. Technol. (TIST)
– volume: 603
  start-page: 127789
  year: 2022
  ident: ref_163
  article-title: Spatiotemporal Graph Attention Network modeling for multi-step passenger demand prediction at multi-zone level
  publication-title: Phys. A Stat. Mech. Its Appl.
  doi: 10.1016/j.physa.2022.127789
– volume: 23
  start-page: 3904
  year: 2022
  ident: ref_44
  article-title: How to build a graph-based deep learning architecture in traffic domain: A survey
  publication-title: IEEE Trans. Intell. Transp. Syst.
  doi: 10.1109/TITS.2020.3043250
– volume: 23
  start-page: 20177
  year: 2022
  ident: ref_115
  article-title: Deep Graph Gaussian Processes for Short-Term Traffic Flow Forecasting From Spatiotemporal Data
  publication-title: IEEE Trans. Intell. Transp. Syst.
  doi: 10.1109/TITS.2022.3178136
– volume: 2
  start-page: 12
  year: 2021
  ident: ref_4
  article-title: Analyzing potential tourist behavior using PCA and modified affinity propagation clustering based on Baidu index: Taking Beijing city as an example
  publication-title: Data Sci. Manag.
  doi: 10.1016/j.dsm.2021.05.001
– ident: ref_193
  doi: 10.1145/3534678.3539396
– ident: ref_219
  doi: 10.3390/s22124485
– ident: ref_228
– ident: ref_196
– volume: 38
  start-page: 786
  year: 2018
  ident: ref_11
  article-title: Spatiotemporal traffic forecasting: Review and proposed directions
  publication-title: Transp. Rev.
  doi: 10.1080/01441647.2018.1442887
– volume: 143
  start-page: 103820
  year: 2022
  ident: ref_92
  article-title: A novel reinforced dynamic graph convolutional network model with data imputation for network-wide traffic flow prediction
  publication-title: Transp. Res. Part C Emerg. Technol.
  doi: 10.1016/j.trc.2022.103820
– volume: 37
  start-page: 6555
  year: 2022
  ident: ref_143
  article-title: Memory attention enhanced graph convolution long short-term memory network for traffic forecasting
  publication-title: Int. J. Intell. Syst.
  doi: 10.1002/int.22855
– volume: 2022
  start-page: 7682274
  year: 2022
  ident: ref_102
  article-title: An Attention Encoder-Decoder Dual Graph Convolutional Network with Time Series Correlation for Multi-Step Traffic Flow Prediction
  publication-title: J. Adv. Transp.
  doi: 10.1155/2022/7682274
– ident: ref_203
  doi: 10.1109/IJCNN55064.2022.9892326
– volume: 10
  start-page: 246
  year: 2009
  ident: ref_19
  article-title: Multivariate short-term traffic flow forecasting using time-series analysis
  publication-title: IEEE Trans. Intell. Transp. Syst.
  doi: 10.1109/TITS.2009.2021448
– ident: ref_234
– volume: 94
  start-page: 101776
  year: 2022
  ident: ref_112
  article-title: Coupling graph deep learning and spatial-temporal influence of built environment for short-term bus travel demand prediction
  publication-title: Comput. Environ. Urban Syst.
  doi: 10.1016/j.compenvurbsys.2022.101776
– volume: 9
  start-page: 25685
  year: 2022
  ident: ref_74
  article-title: HMIAN: A Hierarchical Mapping and Interactive Attention Data Fusion Network for Traffic Forecasting
  publication-title: IEEE Internet Things J.
  doi: 10.1109/JIOT.2022.3196461
– volume: 2022
  start-page: 2723101
  year: 2022
  ident: ref_79
  article-title: Traffic Flow Prediction Based on Multi-Spatiotemporal Attention Gated Graph Convolution Network
  publication-title: J. Adv. Transp.
  doi: 10.1155/2022/2723101
– volume: 606
  start-page: 126
  year: 2022
  ident: ref_131
  article-title: Graph correlated attention recurrent neural network for multivariate time series forecasting
  publication-title: Inf. Sci.
  doi: 10.1016/j.ins.2022.04.045
– volume: 204
  start-page: 117511
  year: 2022
  ident: ref_106
  article-title: Attention-based dynamic spatial-temporal graph convolutional networks for traffic speed forecasting
  publication-title: Expert Syst. Appl.
  doi: 10.1016/j.eswa.2022.117511
– volume: 13
  start-page: 1
  year: 2022
  ident: ref_149
  article-title: Multivariate Correlation-aware Spatio-temporal Graph Convolutional Networks for Multi-scale Traffic Prediction
  publication-title: ACM Trans. Intell. Syst. Technol. (TIST)
– ident: ref_200
– ident: ref_220
  doi: 10.1109/ITSC55140.2022.9922512
– ident: ref_154
  doi: 10.1093/comjnl/bxac086
– volume: 34
  start-page: 15457
  year: 2022
  ident: ref_128
  article-title: Forecasting traffic flow with spatial–temporal convolutional graph attention networks
  publication-title: Neural Comput. Appl.
  doi: 10.1007/s00521-022-07235-z
– volume: 164
  start-page: 213
  year: 2019
  ident: ref_10
  article-title: Short-term traffic volume prediction by ensemble learning in concept drifting environments
  publication-title: Knowl.-Based Syst.
  doi: 10.1016/j.knosys.2018.10.037
– volume: 24
  start-page: 904
  year: 2022
  ident: ref_93
  article-title: A Novel Spatial-Temporal Multi-Scale Alignment Graph Neural Network Security Model for Vehicles Prediction
  publication-title: IEEE Trans. Intell. Transp. Syst.
  doi: 10.1109/TITS.2022.3140229
– volume: 22
  start-page: 13116
  year: 2022
  ident: ref_117
  article-title: DSTGCN: Dynamic Spatial-Temporal Graph Convolutional Network for Traffic Prediction
  publication-title: IEEE Sens. J.
  doi: 10.1109/JSEN.2022.3176016
– ident: ref_179
– volume: 52
  start-page: 15026
  year: 2022
  ident: ref_164
  article-title: STGMN: A gated multi-graph convolutional network framework for traffic flow prediction
  publication-title: Appl. Intell.
  doi: 10.1007/s10489-022-03224-w
– volume: 23
  start-page: 17201
  year: 2022
  ident: ref_97
  article-title: AARGNN: An Attentive Attributed Recurrent Graph Neural Network for Traffic Flow Prediction Considering Multiple Dynamic Factors
  publication-title: IEEE Trans. Intell. Transp. Syst.
  doi: 10.1109/TITS.2022.3171451
– volume: 5
  start-page: 84
  year: 2022
  ident: ref_34
  article-title: New developments in wind energy forecasting with artificial intelligence and big data: A scientometric insight
  publication-title: Data Sci. Manag.
  doi: 10.1016/j.dsm.2022.05.002
– volume: 609
  start-page: 548
  year: 2022
  ident: ref_152
  article-title: Multi-mode dynamic residual graph convolution network for traffic flow prediction
  publication-title: Inf. Sci.
  doi: 10.1016/j.ins.2022.07.008
– ident: ref_158
  doi: 10.1177/03611981221112673
– volume: 594
  start-page: 286
  year: 2022
  ident: ref_84
  article-title: A dynamical spatial-temporal graph neural network for traffic demand prediction
  publication-title: Inf. Sci.
  doi: 10.1016/j.ins.2022.02.031
– volume: 91
  start-page: 1
  year: 2023
  ident: ref_221
  article-title: Combining heterogeneous data sources for spatio-temporal mobility demand forecasting
  publication-title: Inf. Fusion
  doi: 10.1016/j.inffus.2022.09.028
– ident: ref_5
  doi: 10.3390/s21030706
– ident: ref_214
  doi: 10.24963/ijcai.2019/264
– ident: ref_156
  doi: 10.3390/rs14020303
– volume: 249
  start-page: 108990
  year: 2022
  ident: ref_170
  article-title: TFGAN: Traffic forecasting using generative adversarial network with multi-graph convolutional network
  publication-title: Knowl.-Based Syst.
  doi: 10.1016/j.knosys.2022.108990
– ident: ref_1
  doi: 10.3390/ijgi9010058
– volume: 5
  start-page: e297
  year: 2022
  ident: ref_23
  article-title: TaxiBJ21: An open crowd flow dataset based on Beijing taxi GPS trajectories
  publication-title: Internet Technol. Lett.
  doi: 10.1002/itl2.297
– ident: ref_55
– ident: ref_134
  doi: 10.1089/big.2021.0251
– ident: ref_139
  doi: 10.1007/s11280-022-01045-y
– ident: ref_146
  doi: 10.1007/s10707-022-00466-1
– volume: 23
  start-page: 20681
  year: 2022
  ident: ref_138
  article-title: Learning Multiaspect Traffic Couplings by Multirelational Graph Attention Networks for Traffic Prediction
  publication-title: IEEE Trans. Intell. Transp. Syst.
  doi: 10.1109/TITS.2022.3173689
– ident: ref_202
  doi: 10.1109/IJCNN55064.2022.9892031
– ident: ref_88
  doi: 10.1109/TKDE.2022.3179646
– ident: ref_166
  doi: 10.3390/ijgi11070381
– ident: ref_205
  doi: 10.1109/IJCNN55064.2022.9892016
– volume: 510
  start-page: 79
  year: 2022
  ident: ref_72
  article-title: Deep multi-view graph-based network for citywide ride-hailing demand prediction
  publication-title: Neurocomputing
  doi: 10.1016/j.neucom.2022.09.010
– ident: ref_65
  doi: 10.3390/su141912397
– volume: 107
  start-page: 248
  year: 2019
  ident: ref_211
  article-title: A deep learning approach to real-time parking occupancy prediction in transportation networks incorporating multiple spatio-temporal data sources
  publication-title: Transp. Res. Part C Emerg. Technol.
  doi: 10.1016/j.trc.2019.08.010
– volume: 2022
  start-page: 1358535
  year: 2022
  ident: ref_77
  article-title: Multi-Dimensional Attention Based Spatial-Temporal Networks for Traffic Forecasting
  publication-title: Wirel. Commun. Mob. Comput.
  doi: 10.1155/2022/1358535
– ident: ref_188
– ident: ref_24
  doi: 10.1609/aaai.v33i01.3301922
– volume: 34
  start-page: 249
  year: 2020
  ident: ref_58
  article-title: Deep learning on graphs: A survey
  publication-title: IEEE Trans. Knowl. Data Eng.
  doi: 10.1109/TKDE.2020.2981333
– volume: 9
  start-page: 8581
  year: 2021
  ident: ref_25
  article-title: Modeling global spatial–temporal graph attention network for traffic prediction
  publication-title: IEEE Access
  doi: 10.1109/ACCESS.2021.3049556
– volume: 10
  start-page: 94051
  year: 2022
  ident: ref_73
  article-title: GCN-GAN: Integrating Graph Convolutional Network and Generative Adversarial Network for Traffic Flow Prediction
  publication-title: IEEE Access
  doi: 10.1109/ACCESS.2022.3204036
– volume: 10
  start-page: 93
  year: 2018
  ident: ref_16
  article-title: Road traffic forecasting: Recent advances and new challenges
  publication-title: IEEE Intell. Transp. Syst. Mag.
  doi: 10.1109/MITS.2018.2806634
– volume: 2022
  start-page: 5221362
  year: 2022
  ident: ref_109
  article-title: Bi-GRCN: A Spatio-Temporal Traffic Flow Prediction Model Based on Graph Neural Network
  publication-title: J. Adv. Transp.
  doi: 10.1155/2022/5221362
– ident: ref_233
– ident: ref_63
  doi: 10.1155/2022/2348375
– ident: ref_178
  doi: 10.1109/ISCC55528.2022.9912866
– ident: ref_180
  doi: 10.1145/3511808.3557705
– ident: ref_159
  doi: 10.1109/TITS.2022.3179391
– volume: 258
  start-page: 109985
  year: 2022
  ident: ref_69
  article-title: STHGCN: A spatiotemporal prediction framework based on higher-order graph convolution networks
  publication-title: Knowl.-Based Syst.
  doi: 10.1016/j.knosys.2022.109985
– ident: ref_183
– ident: ref_144
  doi: 10.3390/app12052688
– ident: ref_52
  doi: 10.3390/asi5010023
– ident: ref_17
  doi: 10.1109/TCYB.2021.3117705
– volume: 127
  start-page: 103063
  year: 2021
  ident: ref_206
  article-title: Joint predictions of multi-modal ride-hailing demands: A deep multi-task multi-graph learning-based approach
  publication-title: Transp. Res. Part C Emerg. Technol.
  doi: 10.1016/j.trc.2021.103063
– volume: 14
  start-page: 871
  year: 2013
  ident: ref_20
  article-title: Short-term traffic flow forecasting: An experimental comparison of time-series analysis and supervised learning
  publication-title: IEEE Trans. Intell. Transp. Syst.
  doi: 10.1109/TITS.2013.2247040
– ident: ref_81
  doi: 10.1109/TITS.2022.3146899
– ident: ref_187
  doi: 10.24963/ijcai.2022/545
– volume: 52
  start-page: 12077
  year: 2022
  ident: ref_168
  article-title: Taxi demand forecasting based on the temporal multimodal information fusion graph neural network
  publication-title: Appl. Intell.
  doi: 10.1007/s10489-021-03128-1
– volume: 10
  start-page: 82384
  year: 2022
  ident: ref_216
  article-title: Short-Term Traffic Speed Forecasting Using a Deep Learning Method Based on Multitemporal Traffic Flow Volume
  publication-title: IEEE Access
  doi: 10.1109/ACCESS.2022.3195353
– volume: 24
  start-page: 100342
  year: 2022
  ident: ref_76
  article-title: Modelling traffic as multi-graph signals: Using domain knowledge to enhance the network-level passenger flow prediction in metro systems
  publication-title: J. Rail Transp. Plan. Manag.
– volume: 22
  start-page: 7352
  year: 2022
  ident: ref_2
  article-title: Towards resilient and secure cooperative behavior of intelligent transportation system using sensor technologies
  publication-title: IEEE Sens. J.
  doi: 10.1109/JSEN.2022.3152808
– volume: 15
  start-page: 2733
  year: 2022
  ident: ref_185
  article-title: Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting
  publication-title: Proc. VLDB Endow.
  doi: 10.14778/3551793.3551827
– volume: 23
  start-page: 18632
  year: 2022
  ident: ref_99
  article-title: Adaptive Spatiotemporal Dependence Learning for Multi-Mode Transportation Demand Prediction
  publication-title: IEEE Trans. Intell. Transp. Syst.
  doi: 10.1109/TITS.2022.3155753
– volume: 156
  start-page: 126
  year: 2022
  ident: ref_129
  article-title: GDFormer: A Graph Diffusing Attention based approach for Traffic Flow Prediction
  publication-title: Pattern Recognit. Lett.
  doi: 10.1016/j.patrec.2022.03.005
– volume: 9
  start-page: 1765
  year: 2022
  ident: ref_14
  article-title: Spatial-temporal conv-sequence learning with accident encoding for traffic flow prediction
  publication-title: IEEE Trans. Netw. Sci. Eng.
  doi: 10.1109/TNSE.2022.3152983
– ident: ref_190
  doi: 10.1145/3534678.3539397
– volume: 23
  start-page: 481
  year: 2019
  ident: ref_9
  article-title: Bayesian optimization of support vector machine for regression prediction of short-term traffic flow
  publication-title: Intell. Data Anal.
  doi: 10.3233/IDA-183832
– ident: ref_54
– volume: 2022
  start-page: 2811961
  year: 2022
  ident: ref_145
  article-title: MSASGCN: Multi-Head Self-Attention Spatiotemporal Graph Convolutional Network for Traffic Flow Forecasting
  publication-title: J. Adv. Transp.
  doi: 10.1155/2022/2811961
– ident: ref_172
  doi: 10.3390/app12062842
– ident: ref_210
– ident: ref_198
  doi: 10.1109/CSCWD54268.2022.9776256
– ident: ref_42
  doi: 10.3390/app11104423
– volume: 139
  start-page: 100
  year: 2023
  ident: ref_75
  article-title: Hybrid graph convolution neural network and branch-and-bound optimization for traffic flow forecasting
  publication-title: Future Gener. Comput. Syst.
  doi: 10.1016/j.future.2022.09.018
– volume: 242
  start-page: 108199
  year: 2022
  ident: ref_100
  article-title: Adaptive Spatio-temporal Graph Neural Network for traffic forecasting
  publication-title: Knowl.-Based Syst.
  doi: 10.1016/j.knosys.2022.108199
– volume: 210
  start-page: 118475
  year: 2022
  ident: ref_127
  article-title: Forecasting network-wide multi-step metro ridership with an attention-weighted multi-view graph to sequence learning approach
  publication-title: Expert Syst. Appl.
  doi: 10.1016/j.eswa.2022.118475
– volume: 9
  start-page: 834
  year: 2022
  ident: ref_3
  article-title: Exploiting Spatiotemporal Correlations of Arrive-Stay-Leave Behaviors for Private Car Flow Prediction
  publication-title: IEEE Trans. Netw. Sci. Eng.
  doi: 10.1109/TNSE.2021.3137381
– volume: 34
  start-page: 1252
  year: 2022
  ident: ref_150
  article-title: Multi-featured spatial-temporal and dynamic multi-graph convolutional network for metro passenger flow prediction
  publication-title: Connect. Sci.
  doi: 10.1080/09540091.2022.2061915
– ident: ref_207
  doi: 10.1109/DDCLS.2018.8516114
– volume: 14
  start-page: 129
  year: 2021
  ident: ref_224
  article-title: Exploring human mobility patterns and travel behavior: A focus on private cars
  publication-title: IEEE Intell. Transp. Syst. Mag.
  doi: 10.1109/MITS.2021.3098627
– volume: 181
  start-page: 107530
  year: 2020
  ident: ref_47
  article-title: Machine Learning-based traffic prediction models for Intelligent Transportation Systems
  publication-title: Comput. Netw.
  doi: 10.1016/j.comnet.2020.107530
– volume: 590
  start-page: 126736
  year: 2022
  ident: ref_173
  article-title: Traffic prediction based on auto spatiotemporal multi-graph adversarial neural network
  publication-title: Phys. A Stat. Mech. Its Appl.
  doi: 10.1016/j.physa.2021.126736
– volume: 23
  start-page: 16137
  year: 2022
  ident: ref_133
  article-title: Hierarchical traffic flow prediction based on spatial-temporal graph convolutional network
  publication-title: IEEE Trans. Intell. Transp. Syst.
  doi: 10.1109/TITS.2022.3148105
– ident: ref_208
  doi: 10.3390/su13158577
– volume: 23
  start-page: 12343
  year: 2022
  ident: ref_18
  article-title: Understanding urban area attractiveness based on private car trajectory data using a deep learning approach
  publication-title: IEEE Trans. Intell. Transp. Syst.
  doi: 10.1109/TITS.2021.3113705
– volume: 610
  start-page: 185
  year: 2022
  ident: ref_96
  article-title: A spatio-temporal sequence-to-sequence network for traffic flow prediction
  publication-title: Inf. Sci.
  doi: 10.1016/j.ins.2022.07.125
– ident: ref_201
  doi: 10.1109/IJCNN55064.2022.9892453
– volume: 207
  start-page: 117921
  year: 2022
  ident: ref_45
  article-title: Graph neural network for traffic forecasting: A survey
  publication-title: Expert Syst. Appl.
  doi: 10.1016/j.eswa.2022.117921
– ident: ref_108
  doi: 10.3390/ijgi11020088
– ident: ref_229
  doi: 10.3390/asi5060121
– ident: ref_22
  doi: 10.1609/aaai.v31i1.10735
– volume: 134
  start-page: 103466
  year: 2022
  ident: ref_114
  article-title: DDP-GCN: Multi-graph convolutional network for spatiotemporal traffic forecasting
  publication-title: Transp. Res. Part C Emerg. Technol.
  doi: 10.1016/j.trc.2021.103466
– ident: ref_232
– ident: ref_27
  doi: 10.1002/itl2.403
– ident: ref_80
  doi: 10.1109/TITS.2022.3196466
– ident: ref_12
  doi: 10.1137/1.9781611974973.87
– volume: 23
  start-page: 4927
  year: 2021
  ident: ref_51
  article-title: Deep learning on traffic prediction: Methods, analysis and future directions
  publication-title: IEEE Trans. Intell. Transp. Syst.
  doi: 10.1109/TITS.2021.3054840
– volume: 114
  start-page: 105179
  year: 2022
  ident: ref_135
  article-title: IGCRRN: Improved Graph Convolution Res-Recurrent Network for spatio-temporal dependence capturing and traffic flow prediction
  publication-title: Eng. Appl. Artif. Intell.
  doi: 10.1016/j.engappai.2022.105179
– ident: ref_56
– ident: ref_223
  doi: 10.1109/TNNLS.2022.3186103
– volume: 32
  start-page: 4
  year: 2020
  ident: ref_57
  article-title: A comprehensive survey on graph neural networks
  publication-title: IEEE Trans. Neural Netw. Learn. Syst.
  doi: 10.1109/TNNLS.2020.2978386
– volume: 140
  start-page: 103731
  year: 2022
  ident: ref_137
  article-title: Joint demand prediction for multimodal systems: A multi-task multi-relational spatiotemporal graph neural network approach
  publication-title: Transp. Res. Part C Emerg. Technol.
  doi: 10.1016/j.trc.2022.103731
– volume: 10
  start-page: 718
  year: 2022
  ident: ref_87
  article-title: A GATs-GAN framework for road traffic states forecasting
  publication-title: Transp. B Transp. Dyn.
– ident: ref_111
  doi: 10.1007/s10707-022-00467-0
– volume: 5
  start-page: e322
  year: 2022
  ident: ref_36
  article-title: Internet traffic matrix prediction with convolutional LSTM neural network
  publication-title: Internet Technol. Lett.
  doi: 10.1002/itl2.322
– ident: ref_195
  doi: 10.1109/IJCNN55064.2022.9892191
– volume: 34
  start-page: 1544
  year: 2020
  ident: ref_46
  article-title: A survey on modern deep neural network for traffic prediction: Trends, methods and challenges
  publication-title: IEEE Trans. Knowl. Data Eng.
– volume: 34
  start-page: 8996
  year: 2022
  ident: ref_162
  article-title: Spatial-temporal upsampling graph convolutional network for daily long-term traffic speed prediction
  publication-title: J. King Saud Univ.-Comput. Inf. Sci.
– ident: ref_160
  doi: 10.1109/TITS.2022.3185503
– volume: 43
  start-page: 3
  year: 2014
  ident: ref_7
  article-title: Short-term traffic forecasting: Where we are and where we’re going
  publication-title: Transp. Res. Part C Emerg. Technol.
  doi: 10.1016/j.trc.2014.01.005
– ident: ref_66
  doi: 10.1007/s13042-022-01689-2
– ident: ref_184
  doi: 10.1109/ICASSP43922.2022.9746497
– volume: 25
  start-page: 2515
  year: 2022
  ident: ref_225
  article-title: Foreseeing private car transfer between urban regions with multiple graph-based generative adversarial networks
  publication-title: World Wide Web
  doi: 10.1007/s11280-021-00995-z
– volume: 195
  start-page: 116585
  year: 2022
  ident: ref_83
  article-title: A dynamic spatial–temporal deep learning framework for traffic speed prediction on large-scale road networks
  publication-title: Expert Syst. Appl.
  doi: 10.1016/j.eswa.2022.116585
– volume: 9
  start-page: 241
  year: 2022
  ident: ref_116
  article-title: DSTAGCN: Dynamic Spatial-Temporal Adjacent Graph Convolutional Network for Traffic Forecasting
  publication-title: IEEE Trans. Big Data
  doi: 10.1109/TBDATA.2022.3156366
– volume: 36
  start-page: 100779
  year: 2022
  ident: ref_174
  article-title: TransGAT: A dynamic graph attention residual networks for traffic flow forecasting
  publication-title: Sustain. Comput. Inform. Syst.
– ident: ref_176
  doi: 10.1145/3511808.3557540
– volume: 123
  start-page: 103419
  year: 2022
  ident: ref_90
  article-title: A new ensemble deep graph reinforcement learning network for spatio-temporal traffic volume forecasting in a freeway network
  publication-title: Digit. Signal Process.
  doi: 10.1016/j.dsp.2022.103419
– ident: ref_62
  doi: 10.1007/s10489-022-04218-4
– ident: ref_227
  doi: 10.1109/ITSC45102.2020.9294236
– ident: ref_189
  doi: 10.1109/WCNC51071.2022.9771883
– ident: ref_181
  doi: 10.1145/3511808.3557432
– volume: 23
  start-page: 16185
  year: 2022
  ident: ref_86
  article-title: A GAN-Based Short-Term Link Traffic Prediction Approach for Urban Road Networks Under a Parallel Learning Framework
  publication-title: IEEE Trans. Intell. Transp. Syst.
  doi: 10.1109/TITS.2022.3148358
– volume: 3
  start-page: 3
  year: 2021
  ident: ref_33
  article-title: Comparative study of three stochastic future weather forecast approaches: A case study
  publication-title: Data Sci. Manag.
  doi: 10.1016/j.dsm.2021.07.002
– volume: 2022
  start-page: 9815133
  year: 2022
  ident: ref_148
  article-title: Multigraph Aggregation Spatiotemporal Graph Convolution Network for Ride-Hailing Pick-Up Region Prediction
  publication-title: Wirel. Commun. Mob. Comput.
– volume: 53
  start-page: 1
  year: 2022
  ident: ref_140
  article-title: Long-term traffic forecasting based on adaptive graph cross strided convolution network
  publication-title: Appl. Intell.
  doi: 10.1007/s10489-021-02377-4
– ident: ref_231
– volume: 60
  start-page: 597
  year: 2021
  ident: ref_218
  article-title: The effect of the dataset on evaluating urban traffic prediction
  publication-title: Alex. Eng. J.
  doi: 10.1016/j.aej.2020.09.038
– volume: 23
  start-page: 16148
  year: 2022
  ident: ref_122
  article-title: Exploring Human Mobility for Multi-Pattern Passenger Prediction: A Graph Learning Framework
  publication-title: IEEE Trans. Intell. Transp. Syst.
  doi: 10.1109/TITS.2022.3148116
– volume: 24
  start-page: 52
  year: 2018
  ident: ref_28
  article-title: Geospatial data to images: A deep-learning framework for traffic forecasting
  publication-title: Tsinghua Sci. Technol.
  doi: 10.26599/TST.2018.9010033
– ident: ref_213
  doi: 10.24963/ijcai.2018/505
– ident: ref_32
  doi: 10.3390/asi4030043
– ident: ref_26
  doi: 10.3390/ijgi11020085
– volume: 34
  start-page: 16655
  year: 2022
  ident: ref_161
  article-title: Spatial-temporal dynamic semantic graph neural network
  publication-title: Neural Comput. Appl.
  doi: 10.1007/s00521-022-07285-3
– volume: 147
  start-page: 103984
  year: 2023
  ident: ref_43
  article-title: Improving short-term bike sharing demand forecast through an irregular convolutional neural network
  publication-title: Transp. Res. Part C Emerg. Technol.
  doi: 10.1016/j.trc.2022.103984
– volume: 23
  start-page: 6164
  year: 2021
  ident: ref_49
  article-title: Deep learning for road traffic forecasting: Does it make a difference?
  publication-title: IEEE Trans. Intell. Transp. Syst.
  doi: 10.1109/TITS.2021.3083957
– ident: ref_222
  doi: 10.1109/ICDE53745.2022.00136
– ident: ref_89
  doi: 10.1049/itr2.12254
– ident: ref_120
  doi: 10.3390/app12062890
– volume: 23
  start-page: 19201
  year: 2022
  ident: ref_121
  article-title: ESTNet: Embedded Spatial-Temporal Network for Modeling Traffic Flow Dynamics
  publication-title: IEEE Trans. Intell. Transp. Syst.
  doi: 10.1109/TITS.2022.3167019
– ident: ref_126
  doi: 10.1080/21680566.2022.2116125
– ident: ref_157
  doi: 10.1109/TITS.2022.3168865
– volume: 608
  start-page: 718
  year: 2022
  ident: ref_169
  article-title: TCP-BAST: A novel approach to traffic congestion prediction with bilateral alternation on spatiality and temporality
  publication-title: Inf. Sci.
  doi: 10.1016/j.ins.2022.06.080
– volume: 5
  start-page: 43
  year: 2022
  ident: ref_39
  article-title: A combined forecasting method for intermittent demand using the automotive aftermarket data
  publication-title: Data Sci. Manag.
  doi: 10.1016/j.dsm.2022.04.001
– volume: 2
  start-page: e12178
  year: 2020
  ident: ref_31
  article-title: Current advances and approaches in wind speed and wind power forecasting for improved renewable energy integration: A review
  publication-title: Eng. Rep.
  doi: 10.1002/eng2.12178
– volume: 201
  start-page: 117163
  year: 2022
  ident: ref_40
  article-title: Cellular traffic prediction with machine learning: A survey
  publication-title: Expert Syst. Appl.
  doi: 10.1016/j.eswa.2022.117163
– volume: 7
  start-page: 598
  year: 2019
  ident: ref_15
  article-title: Causalbg: Causal recurrent neural network for the blood glucose inference with IoT platform
  publication-title: IEEE Internet Things J.
  doi: 10.1109/JIOT.2019.2946693
– ident: ref_59
  doi: 10.1609/aaai.v35i12.17325
– ident: ref_110
  doi: 10.1007/s10668-022-02585-z
– ident: ref_199
  doi: 10.1145/3534678.3539093
– ident: ref_91
  doi: 10.3390/electronics11152432
– volume: 604
  start-page: 127959
  year: 2022
  ident: ref_153
  article-title: Multi-point short-term prediction of station passenger flow based on temporal multi-graph convolutional network
  publication-title: Phys. A Stat. Mech. Its Appl.
  doi: 10.1016/j.physa.2022.127959
– volume: 72
  start-page: 1515
  year: 2023
  ident: ref_13
  article-title: Unified Spatial-Temporal Neighbor Attention Network for Dynamic Traffic Prediction
  publication-title: IEEE Trans. Veh. Technol.
  doi: 10.1109/TVT.2022.3209242
– ident: ref_197
– ident: ref_204
  doi: 10.1109/IJCNN55064.2022.9892616
– ident: ref_141
  doi: 10.3390/electronics11142230
– volume: 17
  start-page: 1
  year: 2022
  ident: ref_68
  article-title: STHAN: Transportation Demand Forecasting with Compound Spatio-Temporal Relationships
  publication-title: ACM Trans. Knowl. Discov. Data (TKDD)
– volume: 23
  start-page: 15015
  year: 2022
  ident: ref_130
  article-title: Graph Construction for Traffic Prediction: A Data-Driven Approach
  publication-title: IEEE Trans. Intell. Transp. Syst.
  doi: 10.1109/TITS.2021.3136161
– volume: 184
  start-page: 115537
  year: 2021
  ident: ref_30
  article-title: Applications of deep learning in stock market prediction: Recent progress
  publication-title: Expert Syst. Appl.
  doi: 10.1016/j.eswa.2021.115537
– volume: 52
  start-page: 16104
  year: 2022
  ident: ref_103
  article-title: An improved dynamic Chebyshev graph convolution network for traffic flow prediction with spatial-temporal attention
  publication-title: Appl. Intell.
  doi: 10.1007/s10489-021-03022-w
– ident: ref_95
  doi: 10.3390/math10101754
– volume: 13
  start-page: 1
  year: 2022
  ident: ref_142
  article-title: Make More Connections: Urban Traffic Flow Forecasting with Spatiotemporal Adaptive Gated Graph Convolution Network
  publication-title: ACM Trans. Intell. Syst. Technol. (TIST)
– ident: ref_82
  doi: 10.1109/TITS.2022.3201879
– volume: 9
  start-page: 54739
  year: 2021
  ident: ref_50
  article-title: Short-term traffic prediction with deep neural networks: A survey
  publication-title: IEEE Access
  doi: 10.1109/ACCESS.2021.3071174
– volume: 5
  start-page: e314
  year: 2022
  ident: ref_35
  article-title: Internet traffic prediction with deep neural networks
  publication-title: Internet Technol. Lett.
  doi: 10.1002/itl2.314
– volume: 607
  start-page: 869
  year: 2022
  ident: ref_105
  article-title: Attention based spatiotemporal graph attention networks for traffic flow forecasting
  publication-title: Inf. Sci.
  doi: 10.1016/j.ins.2022.05.127
– volume: 5
  start-page: 137
  year: 2022
  ident: ref_37
  article-title: Long-term forecasting of hourly retail customer flow on intermittent time series with multiple seasonality
  publication-title: Data Sci. Manag.
  doi: 10.1016/j.dsm.2022.07.002
– ident: ref_226
  doi: 10.3390/a15120447
– ident: ref_230
  doi: 10.1145/3485447.3511990
– ident: ref_192
– volume: 1
  start-page: 32
  year: 2021
  ident: ref_215
  article-title: Data science: Connotation, methods, technologies, and development
  publication-title: Data Sci. Manag.
  doi: 10.1016/j.dsm.2021.02.002
– volume: 5
  start-page: e383
  year: 2022
  ident: ref_38
  article-title: Deep learning based short-term load forecasting incorporating calendar and weather information
  publication-title: Internet Technol. Lett.
  doi: 10.1002/itl2.383
– volume: 23
  start-page: 18557
  year: 2022
  ident: ref_155
  article-title: Multitask Hypergraph Convolutional Networks: A Heterogeneous Traffic Prediction Framework
  publication-title: IEEE Trans. Intell. Transp. Syst.
  doi: 10.1109/TITS.2022.3168879
– volume: 2673
  start-page: 489
  year: 2019
  ident: ref_29
  article-title: Development and evaluation of recurrent neural network-based models for hourly traffic volume and annual average daily traffic prediction
  publication-title: Transp. Res. Rec.
  doi: 10.1177/0361198119849059
– volume: 124
  start-page: 108977
  year: 2022
  ident: ref_151
  article-title: Multi-fold Correlation Attention Network for Predicting Traffic Speeds with Heterogeneous Frequency
  publication-title: Appl. Soft Comput.
  doi: 10.1016/j.asoc.2022.108977
– volume: 24
  start-page: 125
  year: 2020
  ident: ref_41
  article-title: Multi-step-ahead traffic speed forecasting using multi-output gradient boosting regression tree
  publication-title: J. Intell. Transp. Syst.
  doi: 10.1080/15472450.2019.1582950
– ident: ref_78
  doi: 10.3390/electronics11193012
– ident: ref_186
– volume: 34
  start-page: 15369
  year: 2022
  ident: ref_53
  article-title: Bike sharing usage prediction with deep learning: A survey
  publication-title: Neural Comput. Appl.
  doi: 10.1007/s00521-022-07380-5
– volume: 24
  start-page: 533
  year: 2004
  ident: ref_8
  article-title: Short-term traffic forecasting: Overview of objectives and methods
  publication-title: Transp. Rev.
  doi: 10.1080/0144164042000195072
– volume: 13
  start-page: 1
  year: 2022
  ident: ref_132
  article-title: Graph Sequence Neural Network with an Attention Mechanism for Traffic Speed Prediction
  publication-title: ACM Trans. Intell. Syst. Technol. (TIST)
– ident: ref_182
  doi: 10.1109/ICDE53745.2022.00058
– ident: ref_61
  doi: 10.1049/itr2.12296
– volume: 6
  start-page: 10652
  year: 2019
  ident: ref_6
  article-title: Toward accurate vehicle state estimation under non-Gaussian noises
  publication-title: IEEE Internet Things J.
  doi: 10.1109/JIOT.2019.2940412
– volume: 2022
  start-page: 1217588
  year: 2022
  ident: ref_101
  article-title: AGCN-T: A Traffic Flow Prediction Model for Spatial-Temporal Network Dynamics
  publication-title: J. Adv. Transp.
  doi: 10.1155/2022/1217588
– ident: ref_64
  doi: 10.1007/s11063-022-11036-9
– volume: 17
  start-page: 1
  year: 2022
  ident: ref_70
  article-title: Dynamic Multi-View Graph Neural Networks for Citywide Traffic Inference
  publication-title: ACM Trans. Knowl. Discov. Data (TKDD)
– ident: ref_175
  doi: 10.1007/s10489-022-03966-7
– volume: 23
  start-page: 19064
  year: 2022
  ident: ref_94
  article-title: A Universal Framework of Spatiotemporal Bias Block for Long-Term Traffic Forecasting
  publication-title: IEEE Trans. Intell. Transp. Syst.
  doi: 10.1109/TITS.2022.3157129
– volume: 71
  start-page: 9250
  year: 2022
  ident: ref_124
  article-title: Fdsa-STG: Fully Dynamic Self-Attention Spatio-Temporal Graph Networks for Intelligent Traffic Flow Prediction
  publication-title: IEEE Trans. Veh. Technol.
  doi: 10.1109/TVT.2022.3178094
– volume: 601
  start-page: 129
  year: 2022
  ident: ref_85
  article-title: A GAN framework-based dynamic multi-graph convolutional network for origin–destination-based ride-hailing demand prediction
  publication-title: Inf. Sci.
  doi: 10.1016/j.ins.2022.04.024
– ident: ref_104
  doi: 10.3390/app12147010
– volume: 139
  start-page: 103659
  year: 2022
  ident: ref_98
  article-title: AdapGL: An adaptive graph learning algorithm for traffic prediction based on spatiotemporal neural networks
  publication-title: Transp. Res. Part C Emerg. Technol.
  doi: 10.1016/j.trc.2022.103659
– volume: 182
  start-page: 107484
  year: 2020
  ident: ref_48
  article-title: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems
  publication-title: Comput. Netw.
  doi: 10.1016/j.comnet.2020.107484
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Snippet Traffic forecasting has been regarded as the basis for many intelligent transportation system (ITS) applications, including but not limited to trip planning,...
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SubjectTerms Artificial intelligence
Computer science
data collection
Datasets
Decision trees
Deep learning
Forecasting
graph attention network
graph convolutional network
graph neural network
Graph neural networks
Intelligent transportation systems
Internet of Things
Literature reviews
Machine learning
Mathematical models
Neural networks
Route planning
Source code
Statistical analysis
Statistical models
Surveys
Time series
traffic
Traffic control
Traffic flow
traffic forecasting
Traffic planning
Transportation networks
Trends
Variables
Vehicle routing
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Title Graph Neural Network for Traffic Forecasting: The Research Progress
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