Attention-based spatial–temporal adaptive dual-graph convolutional network for traffic flow forecasting

Accurate traffic flow forecasting (TFF) is a prerequisite for urban traffic control and guidance, which has become the key to avoiding traffic congestion and improving traffic management in intelligent transportation systems. To precisely characterize the spatial structure of road networks and disco...

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Vydáno v:	Neural computing & applications Ročník 35; číslo 23; s. 17217 - 17231
Hlavní autoři:	Xia, Dawen, Shen, Bingqi, Geng, Jian, Hu, Yang, Li, Yantao, Li, Huaqing
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London Springer London 01.08.2023 Springer Nature B.V
Témata:	Algorithms Artificial Intelligence Artificial neural networks Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Convolution Data Mining and Knowledge Discovery Forecasting Graphs Image Processing and Computer Vision Intelligent transportation systems Mathematical models Modules Nodes Original Article Probability and Statistics in Computer Science Roads & highways Similarity Traffic congestion Traffic control Traffic flow Traffic information Traffic management Transportation networks Big data analytics Traffic flow forecasting Graph convolutional network Adaptive graph modeling FastDTW
ISSN:	0941-0643, 1433-3058
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Abstract	Accurate traffic flow forecasting (TFF) is a prerequisite for urban traffic control and guidance, which has become the key to avoiding traffic congestion and improving traffic management in intelligent transportation systems. To precisely characterize the spatial structure of road networks and discover temporal and spatial characteristics, we propose an attention-based spatial–temporal adaptive dual-graph convolutional network (ASTA-DGCN) for TFF in this paper. Specifically, we employ a spatial–temporal attention module to explore the hidden temporal correlation information of traffic data and the implicit influence of weights among road network nodes and to further capture the dynamic influence of different spatial–temporal positions on the current spatial–temporal position. Then, we utilize an adaptive graph modeling module to automatically extract the one-way relationship between variables and integrate external knowledge into the module. The FastDTW algorithm is exploited to measure the similarity of road network nodes, and the non-Euclidean pairwise association between regions is encoded into graphs to discover the hidden temporal pattern similarity effectively. Furthermore, temporal and spatial correlations are explicitly modeled using dual-graph convolution and sequential convolution based on the obtained graphs to mine the spatial–temporal patterns in dynamic traffic flow, and the final prediction result is produced based on the weighted fusion of the output values of the recent, daily, and weekly components. Finally, the ASTA-DGCN algorithm is successfully applied to TFF on two real-world traffic datasets. The experimental results indicate that our ASTA-DGCN algorithm outperforms ARIMA, VAR, FNN, GCN, GAT, GWNet, STGCN, ASTGCN, and STSGCN.
AbstractList	Accurate traffic flow forecasting (TFF) is a prerequisite for urban traffic control and guidance, which has become the key to avoiding traffic congestion and improving traffic management in intelligent transportation systems. To precisely characterize the spatial structure of road networks and discover temporal and spatial characteristics, we propose an attention-based spatial–temporal adaptive dual-graph convolutional network (ASTA-DGCN) for TFF in this paper. Specifically, we employ a spatial–temporal attention module to explore the hidden temporal correlation information of traffic data and the implicit influence of weights among road network nodes and to further capture the dynamic influence of different spatial–temporal positions on the current spatial–temporal position. Then, we utilize an adaptive graph modeling module to automatically extract the one-way relationship between variables and integrate external knowledge into the module. The FastDTW algorithm is exploited to measure the similarity of road network nodes, and the non-Euclidean pairwise association between regions is encoded into graphs to discover the hidden temporal pattern similarity effectively. Furthermore, temporal and spatial correlations are explicitly modeled using dual-graph convolution and sequential convolution based on the obtained graphs to mine the spatial–temporal patterns in dynamic traffic flow, and the final prediction result is produced based on the weighted fusion of the output values of the recent, daily, and weekly components. Finally, the ASTA-DGCN algorithm is successfully applied to TFF on two real-world traffic datasets. The experimental results indicate that our ASTA-DGCN algorithm outperforms ARIMA, VAR, FNN, GCN, GAT, GWNet, STGCN, ASTGCN, and STSGCN.
Author	Li, Yantao Shen, Bingqi Geng, Jian Xia, Dawen Li, Huaqing Hu, Yang
Author_xml	– sequence: 1 givenname: Dawen surname: Xia fullname: Xia, Dawen organization: College of Data Science and Information Engineering, Guizhou Minzu University – sequence: 2 givenname: Bingqi surname: Shen fullname: Shen, Bingqi organization: College of Data Science and Information Engineering, Guizhou Minzu University – sequence: 3 givenname: Jian surname: Geng fullname: Geng, Jian organization: College of Data Science and Information Engineering, Guizhou Minzu University – sequence: 4 givenname: Yang surname: Hu fullname: Hu, Yang organization: Department of Automotive Engineering, Guizhou Traffic Technician and Transportation College – sequence: 5 givenname: Yantao surname: Li fullname: Li, Yantao organization: College of Computer Science, Chongqing University – sequence: 6 givenname: Huaqing orcidid: 0000-0001-6310-8965 surname: Li fullname: Li, Huaqing email: huaqingli@swu.edu.cn organization: College of Electronic and Information Engineering, Southwest University
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Cites_doi	10.1007/s00521-021-06409-5 10.1007/s12652-020-02182-w 10.1016/j.trc.2019.03.022 10.1016/j.ijpe.2020.107868 10.1007/s00521-018-3578-y 10.1007/s00521-018-3850-1 10.1109/TITS.2020.2987909 10.1049/iet-its.2017.0313 10.1016/j.trc.2018.03.001 10.1007/s11042-022-12039-3 10.1016/j.trc.2019.12.022 10.3390/math10224279 10.3141/1748-12 10.1016/j.artint.2018.03.002 10.1109/TITS.2019.2935152 10.1016/j.physa.2019.03.007 10.1016/j.asoc.2019.02.006 10.1007/s00521-020-05076-2 10.1109/TITS.2019.2939290 10.1016/j.neucom.2020.11.038 10.1016/j.ins.2020.06.026 10.1080/15389588.2022.2130279 10.1049/iet-its.2016.0208 10.1016/j.ins.2022.06.090 10.1016/j.trc.2017.02.024 10.1016/j.neucom.2015.12.013 10.1109/TITS.2019.2906365 10.1016/j.eswa.2022.119161 10.1109/TITS.2021.3067603 10.1007/s40534-019-0193-2 10.1007/s11063-019-09994-8 10.1109/TITS.2021.3052796 10.1016/j.pmcj.2018.07.004 10.1007/s00521-020-05115-y 10.1049/iet-its.2016.0263 10.1145/3292500.3330884 10.1609/aaai.v34i01.5438 10.24963/ijcai.2018/505 10.24963/ijcai.2019/264 10.1007/978-981-10-6893-5_24 10.1609/aaai.v33i01.3301922 10.1109/INNOVATIONS.2016.7880022
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References	Lin, Lin, Gu (CR8) 2022; 608 Zhao, Song, Zhang, Liu, Wang, Lin, Deng, Li (CR22) 2019; 21 Zhang, Zheng, Qi, Li, Yi, Li (CR18) 2018; 259 Cheng, Lu, Zhou, Zhang, Zhang (CR7) 2021; 23 Shah, Muhammad, Ali, Ahmed, Almazah, Al-Rezami (CR6) 2022; 10 Chen, Zou, Li, Li, Yang, Chen (CR12) 2021; 542 CR14 Gu, Lu, Xu, Qin, Shao, Zhang (CR16) 2019; 21 CR31 AlKheder, Alkhamees, Almutairi, Alkhedher (CR10) 2021; 33 Liu, Guo, Cao, Wei, Huang (CR36) 2018; 30 Luo, Huang, Cao, Lu, Huang, Guo, Wei (CR32) 2019; 50 Chen, Petty, Skabardonis, Varaiya, Jia (CR45) 2001; 1748 Angayarkanni, Sivakumar, Ramana Rao (CR27) 2021; 12 Xu, Peng, Zeng, Zhou, Tian, Peng (CR13) 2019; 77 Sa, Yv, Sadiq (CR33) 2022; 17 Zou, Xia (CR34) 2019; 31 Xia, Wang, Li, Li, Zhang (CR28) 2016; 179 Taguchi, Yoshimura (CR9) 2021; 23 Xia, Zhang, Yan, Bai, Zheng, Li, Li (CR37) 2021; 33 Polson, Sokolov (CR42) 2017; 79 Nagy, Simon (CR2) 2018; 50 Tian (CR11) 2020; 22 Kaffash, Nguyen, Zhu (CR1) 2021; 231 Díaz, Macià, Valero, Boubeta-Puig, Cuartero (CR4) 2020; 32 Sutskever, Vinyals, Le (CR46) 2014; 27 CR48 Xia, Yang, Jian, Hu, Li (CR44) 2022; 81 CR47 CR24 Sun, Cheng, Goswami, Bai (CR35) 2018; 12 Tang, Chen, Hu, Zong, Han, Li (CR5) 2019; 534 Zhang, Kabuka (CR15) 2018; 12 CR23 Guo, Lin, Li, Chen, Wan (CR40) 2019; 20 Dai, Fu, Zhao, Zhang, Lin, Wang, Li (CR41) 2019; 103 CR21 Nassiri, Mohammadpour, Dahaghin (CR25) 2023; 24 Ma, Zhu, Song, Wang (CR29) 2023; 213 Xia, Yang, Jiang, Hu, Li, Li, Wang (CR38) 2022; 34 CR43 Yin, Wu, Wei, Shen, Qi, Yin (CR19) 2021; 428 CR20 Ma, Antoniou, Toledo (CR30) 2020; 111 Emami, Sarvi, Asadi Bagloee (CR26) 2019; 27 Tedjopurnomo, Bao, Zheng, Choudhury, Qin (CR3) 2020; 34 Zhao, Chen, Wu, Chen, Liu (CR39) 2017; 11 Wu, Tan, Qin, Ran, Jiang (CR17) 2018; 90 S Guo (8582_CR40) 2019; 20 AM Nagy (8582_CR2) 2018; 50 A Sa (8582_CR33) 2022; 17 Z Liu (8582_CR36) 2018; 30 A Emami (8582_CR26) 2019; 27 X Dai (8582_CR41) 2019; 103 B Sun (8582_CR35) 2018; 12 W Xu (8582_CR13) 2019; 77 I Sutskever (8582_CR46) 2014; 27 S Angayarkanni (8582_CR27) 2021; 12 S Taguchi (8582_CR9) 2021; 23 S Kaffash (8582_CR1) 2021; 231 G Díaz (8582_CR4) 2020; 32 8582_CR20 8582_CR21 8582_CR43 Z Cheng (8582_CR7) 2021; 23 8582_CR23 D Xia (8582_CR44) 2022; 81 8582_CR24 8582_CR47 8582_CR48 D Xia (8582_CR37) 2021; 33 C Chen (8582_CR45) 2001; 1748 C Luo (8582_CR32) 2019; 50 D Xia (8582_CR38) 2022; 34 Y Gu (8582_CR16) 2019; 21 G Lin (8582_CR8) 2022; 608 L Zhao (8582_CR22) 2019; 21 D Xia (8582_CR28) 2016; 179 H Nassiri (8582_CR25) 2023; 24 W Zou (8582_CR34) 2019; 31 X Yin (8582_CR19) 2021; 428 D Ma (8582_CR29) 2023; 213 NG Polson (8582_CR42) 2017; 79 DA Tedjopurnomo (8582_CR3) 2020; 34 S AlKheder (8582_CR10) 2021; 33 Z Tian (8582_CR11) 2020; 22 8582_CR31 8582_CR14 Y Chen (8582_CR12) 2021; 542 J Zhang (8582_CR18) 2018; 259 D Zhang (8582_CR15) 2018; 12 J Tang (8582_CR5) 2019; 534 Y Wu (8582_CR17) 2018; 90 I Shah (8582_CR6) 2022; 10 T Ma (8582_CR30) 2020; 111 Z Zhao (8582_CR39) 2017; 11
References_xml	– volume: 34 start-page: 1557 issue: 2 year: 2022 end-page: 1575 ident: CR38 article-title: A parallel NAW-DBLSTM algorithm on Spark for traffic flow forecasting publication-title: Neural Comput Appl doi: 10.1007/s00521-021-06409-5 – ident: CR43 – volume: 12 start-page: 1293 year: 2021 end-page: 1304 ident: CR27 article-title: Hybrid grey wolf: bald eagle search optimized support vector regression for traffic flow forecasting publication-title: J Ambient Intell Humaniz Comput doi: 10.1007/s12652-020-02182-w – ident: CR47 – volume: 103 start-page: 142 year: 2019 end-page: 157 ident: CR41 article-title: Deeptrend 2.0: a light-weighted multi-scale traffic prediction model using detrending publication-title: Transport Res Part C Emerg Technol doi: 10.1016/j.trc.2019.03.022 – volume: 231 year: 2021 ident: CR1 article-title: Big data algorithms and applications in intelligent transportation system: a review and bibliometric analysis publication-title: Int J Prod Econ doi: 10.1016/j.ijpe.2020.107868 – volume: 31 start-page: 7401 issue: 11 year: 2019 end-page: 7414 ident: CR34 article-title: Back propagation bidirectional extreme learning machine for traffic flow time series prediction publication-title: Neural Comput Appl doi: 10.1007/s00521-018-3578-y – ident: CR14 – volume: 32 start-page: 405 issue: 2 year: 2020 end-page: 426 ident: CR4 article-title: An intelligent transportation system to control air pollution and road traffic in cities integrating CEP and colored Petri Nets publication-title: Neural Comput Appl doi: 10.1007/s00521-018-3850-1 – volume: 22 start-page: 5566 issue: 9 year: 2020 end-page: 5576 ident: CR11 article-title: Approach for short-term traffic flow prediction based on empirical mode decomposition and combination model fusion publication-title: IEEE Trans Intell Transp Syst doi: 10.1109/TITS.2020.2987909 – volume: 12 start-page: 578 issue: 7 year: 2018 end-page: 585 ident: CR15 article-title: Combining weather condition data to predict traffic flow: a GRU-based deep learning approach publication-title: IET Intel Transport Syst doi: 10.1049/iet-its.2017.0313 – volume: 90 start-page: 166 year: 2018 end-page: 180 ident: CR17 article-title: A hybrid deep learning based traffic flow prediction method and its understanding publication-title: Transport Res Part C Emerg Technol doi: 10.1016/j.trc.2018.03.001 – volume: 81 start-page: 23589 year: 2022 end-page: 23614 ident: CR44 article-title: SW-BiLSTM: a Spark-based weighted BiLSTM model for traffic flow forecasting publication-title: Multimed Tools Appl doi: 10.1007/s11042-022-12039-3 – volume: 17 start-page: 0275104 issue: 9 year: 2022 ident: CR33 article-title: Traffic flow forecasting using natural selection based hybrid bald eagle search-grey wolf optimization algorithm publication-title: PLoS ONE – volume: 111 start-page: 352 year: 2020 end-page: 372 ident: CR30 article-title: Hybrid machine learning algorithm and statistical time series model for network-wide traffic forecast publication-title: Transport Res Part C Emerg Technol doi: 10.1016/j.trc.2019.12.022 – volume: 10 start-page: 4279 issue: 22 year: 2022 ident: CR6 article-title: Forecasting day-ahead traffic flow using functional time series approach publication-title: Mathematics doi: 10.3390/math10224279 – volume: 1748 start-page: 96 issue: 1 year: 2001 end-page: 102 ident: CR45 article-title: Freeway performance measurement system: mining loop detector data publication-title: Transport Res Rec doi: 10.3141/1748-12 – volume: 259 start-page: 147 year: 2018 end-page: 166 ident: CR18 article-title: Predicting citywide crowd flows using deep spatio-temporal residual networks publication-title: Artif Intell doi: 10.1016/j.artint.2018.03.002 – volume: 21 start-page: 3848 issue: 9 year: 2019 end-page: 3858 ident: CR22 article-title: T-GCN: a temporal graph convolutional network for traffic prediction publication-title: IEEE Trans Intell Transp Syst doi: 10.1109/TITS.2019.2935152 – volume: 534 year: 2019 ident: CR5 article-title: Traffic flow prediction based on combination of support vector machine and data denoising schemes publication-title: Physica A doi: 10.1016/j.physa.2019.03.007 – ident: CR23 – volume: 77 start-page: 605 year: 2019 end-page: 621 ident: CR13 article-title: Deep belief network-based AR model for nonlinear time series forecasting publication-title: Appl Soft Comput doi: 10.1016/j.asoc.2019.02.006 – ident: CR21 – volume: 33 start-page: 2393 issue: 7 year: 2021 end-page: 2410 ident: CR37 article-title: A distributed WND-LSTM model on MapReduce for short-term traffic flow prediction publication-title: Neural Comput Appl doi: 10.1007/s00521-020-05076-2 – volume: 21 start-page: 1332 issue: 3 year: 2019 end-page: 1342 ident: CR16 article-title: An improved bayesian combination model for short-term traffic prediction with deep learning publication-title: IEEE Trans Intell Transp Syst doi: 10.1109/TITS.2019.2939290 – volume: 428 start-page: 42 year: 2021 end-page: 53 ident: CR19 article-title: Multi-stage attention spatial-temporal graph networks for traffic prediction publication-title: Neurocomputing doi: 10.1016/j.neucom.2020.11.038 – ident: CR48 – volume: 542 start-page: 476 year: 2021 end-page: 491 ident: CR12 article-title: Multiple local 3D CNNs for region-based prediction in smart cities publication-title: Inf Sci doi: 10.1016/j.ins.2020.06.026 – volume: 24 start-page: 44 issue: 1 year: 2023 end-page: 49 ident: CR25 article-title: Forecasting time trends of fatal motor vehicle crashes in Iran using an ensemble learning algorithm publication-title: Traffic Inj Prev doi: 10.1080/15389588.2022.2130279 – volume: 11 start-page: 68 issue: 2 year: 2017 end-page: 75 ident: CR39 article-title: LSTM network: a deep learning approach for short-term traffic forecast publication-title: IET Intel Transport Syst doi: 10.1049/iet-its.2016.0208 – volume: 608 start-page: 517 year: 2022 end-page: 531 ident: CR8 article-title: Using support vector regression and k-nearest neighbors for short-term traffic flow prediction based on maximal information coefficient publication-title: Inf Sci doi: 10.1016/j.ins.2022.06.090 – volume: 79 start-page: 1 year: 2017 end-page: 17 ident: CR42 article-title: Deep learning for short-term traffic flow prediction publication-title: Transport Res Part C Emerg Technol doi: 10.1016/j.trc.2017.02.024 – volume: 27 start-page: 1 year: 2014 end-page: 9 ident: CR46 article-title: Sequence to sequence learning with neural networks publication-title: Adv Neural Inf Process Syst – volume: 30 start-page: 445 issue: 4 year: 2018 end-page: 456 ident: CR36 article-title: A hybrid short-term traffic flow forecasting method based on neural networks combined with k-nearest neighbor publication-title: Promet Traffic Transport – volume: 179 start-page: 246 year: 2016 end-page: 263 ident: CR28 article-title: A distributed spatial-temporal weighted model on MapReduce for short-term traffic flow forecasting publication-title: Neurocomputing doi: 10.1016/j.neucom.2015.12.013 – ident: CR31 – volume: 20 start-page: 3913 issue: 10 year: 2019 end-page: 3926 ident: CR40 article-title: Deep spatial-temporal 3D convolutional neural networks for traffic data forecasting publication-title: IEEE Trans Intell Transp Syst doi: 10.1109/TITS.2019.2906365 – volume: 213 year: 2023 ident: CR29 article-title: Traffic flow and speed forecasting through a bayesian deep multi-linear relationship network publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2022.119161 – volume: 23 start-page: 7233 issue: 7 year: 2021 end-page: 7243 ident: CR9 article-title: Online estimation and prediction of large-scale network traffic from sparse probe vehicle data publication-title: IEEE Trans Intell Transp Syst doi: 10.1109/TITS.2021.3067603 – volume: 27 start-page: 222 issue: 3 year: 2019 end-page: 232 ident: CR26 article-title: Using Kalman filter algorithm for short-term traffic flow prediction in a connected vehicle environment publication-title: J Mod Transport doi: 10.1007/s40534-019-0193-2 – volume: 50 start-page: 2305 issue: 3 year: 2019 end-page: 2322 ident: CR32 article-title: Short-term traffic flow prediction based on least square support vector machine with hybrid optimization algorithm publication-title: Neural Process Lett doi: 10.1007/s11063-019-09994-8 – volume: 34 start-page: 1544 year: 2020 end-page: 1561 ident: CR3 article-title: A survey on modern deep neural network for traffic prediction: trends, methods and challenges publication-title: IEEE Trans Knowl Data Eng – volume: 23 start-page: 5231 issue: 6 year: 2021 end-page: 5244 ident: CR7 article-title: Short-term traffic flow prediction: an integrated method of econometrics and hybrid deep learning publication-title: IEEE Trans Intell Transp Syst doi: 10.1109/TITS.2021.3052796 – volume: 50 start-page: 148 year: 2018 end-page: 163 ident: CR2 article-title: Survey on traffic prediction in smart cities publication-title: Pervasive Mob Comput doi: 10.1016/j.pmcj.2018.07.004 – ident: CR24 – volume: 33 start-page: 1785 issue: 6 year: 2021 end-page: 1836 ident: CR10 article-title: Bayesian combined neural network for traffic volume short-term forecasting at adjacent intersections publication-title: Neural Comput Appl doi: 10.1007/s00521-020-05115-y – volume: 12 start-page: 41 issue: 1 year: 2018 end-page: 48 ident: CR35 article-title: Short-term traffic forecasting using self-adjusting k-nearest neighbours publication-title: IET Intel Transport Syst doi: 10.1049/iet-its.2016.0263 – ident: CR20 – volume: 32 start-page: 405 issue: 2 year: 2020 ident: 8582_CR4 publication-title: Neural Comput Appl doi: 10.1007/s00521-018-3850-1 – volume: 34 start-page: 1557 issue: 2 year: 2022 ident: 8582_CR38 publication-title: Neural Comput Appl doi: 10.1007/s00521-021-06409-5 – volume: 111 start-page: 352 year: 2020 ident: 8582_CR30 publication-title: Transport Res Part C Emerg Technol doi: 10.1016/j.trc.2019.12.022 – volume: 30 start-page: 445 issue: 4 year: 2018 ident: 8582_CR36 publication-title: Promet Traffic Transport – volume: 33 start-page: 1785 issue: 6 year: 2021 ident: 8582_CR10 publication-title: Neural Comput Appl doi: 10.1007/s00521-020-05115-y – volume: 542 start-page: 476 year: 2021 ident: 8582_CR12 publication-title: Inf Sci doi: 10.1016/j.ins.2020.06.026 – volume: 17 start-page: 0275104 issue: 9 year: 2022 ident: 8582_CR33 publication-title: PLoS ONE – volume: 11 start-page: 68 issue: 2 year: 2017 ident: 8582_CR39 publication-title: IET Intel Transport Syst doi: 10.1049/iet-its.2016.0208 – volume: 20 start-page: 3913 issue: 10 year: 2019 ident: 8582_CR40 publication-title: IEEE Trans Intell Transp Syst doi: 10.1109/TITS.2019.2906365 – volume: 103 start-page: 142 year: 2019 ident: 8582_CR41 publication-title: Transport Res Part C Emerg Technol doi: 10.1016/j.trc.2019.03.022 – volume: 22 start-page: 5566 issue: 9 year: 2020 ident: 8582_CR11 publication-title: IEEE Trans Intell Transp Syst doi: 10.1109/TITS.2020.2987909 – volume: 50 start-page: 148 year: 2018 ident: 8582_CR2 publication-title: Pervasive Mob Comput doi: 10.1016/j.pmcj.2018.07.004 – ident: 8582_CR48 doi: 10.1145/3292500.3330884 – volume: 23 start-page: 5231 issue: 6 year: 2021 ident: 8582_CR7 publication-title: IEEE Trans Intell Transp Syst doi: 10.1109/TITS.2021.3052796 – volume: 12 start-page: 578 issue: 7 year: 2018 ident: 8582_CR15 publication-title: IET Intel Transport Syst doi: 10.1049/iet-its.2017.0313 – volume: 428 start-page: 42 year: 2021 ident: 8582_CR19 publication-title: Neurocomputing doi: 10.1016/j.neucom.2020.11.038 – volume: 79 start-page: 1 year: 2017 ident: 8582_CR42 publication-title: Transport Res Part C Emerg Technol doi: 10.1016/j.trc.2017.02.024 – volume: 179 start-page: 246 year: 2016 ident: 8582_CR28 publication-title: Neurocomputing doi: 10.1016/j.neucom.2015.12.013 – ident: 8582_CR43 doi: 10.1609/aaai.v34i01.5438 – volume: 21 start-page: 1332 issue: 3 year: 2019 ident: 8582_CR16 publication-title: IEEE Trans Intell Transp Syst doi: 10.1109/TITS.2019.2939290 – volume: 213 year: 2023 ident: 8582_CR29 publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2022.119161 – ident: 8582_CR23 doi: 10.24963/ijcai.2018/505 – ident: 8582_CR20 – volume: 12 start-page: 41 issue: 1 year: 2018 ident: 8582_CR35 publication-title: IET Intel Transport Syst doi: 10.1049/iet-its.2016.0263 – ident: 8582_CR21 doi: 10.24963/ijcai.2019/264 – volume: 50 start-page: 2305 issue: 3 year: 2019 ident: 8582_CR32 publication-title: Neural Process Lett doi: 10.1007/s11063-019-09994-8 – volume: 12 start-page: 1293 year: 2021 ident: 8582_CR27 publication-title: J Ambient Intell Humaniz Comput doi: 10.1007/s12652-020-02182-w – volume: 231 year: 2021 ident: 8582_CR1 publication-title: Int J Prod Econ doi: 10.1016/j.ijpe.2020.107868 – ident: 8582_CR14 doi: 10.1007/978-981-10-6893-5_24 – volume: 24 start-page: 44 issue: 1 year: 2023 ident: 8582_CR25 publication-title: Traffic Inj Prev doi: 10.1080/15389588.2022.2130279 – volume: 27 start-page: 222 issue: 3 year: 2019 ident: 8582_CR26 publication-title: J Mod Transport doi: 10.1007/s40534-019-0193-2 – ident: 8582_CR47 – volume: 10 start-page: 4279 issue: 22 year: 2022 ident: 8582_CR6 publication-title: Mathematics doi: 10.3390/math10224279 – ident: 8582_CR24 doi: 10.1609/aaai.v33i01.3301922 – volume: 90 start-page: 166 year: 2018 ident: 8582_CR17 publication-title: Transport Res Part C Emerg Technol doi: 10.1016/j.trc.2018.03.001 – volume: 33 start-page: 2393 issue: 7 year: 2021 ident: 8582_CR37 publication-title: Neural Comput Appl doi: 10.1007/s00521-020-05076-2 – volume: 608 start-page: 517 year: 2022 ident: 8582_CR8 publication-title: Inf Sci doi: 10.1016/j.ins.2022.06.090 – volume: 21 start-page: 3848 issue: 9 year: 2019 ident: 8582_CR22 publication-title: IEEE Trans Intell Transp Syst doi: 10.1109/TITS.2019.2935152 – volume: 1748 start-page: 96 issue: 1 year: 2001 ident: 8582_CR45 publication-title: Transport Res Rec doi: 10.3141/1748-12 – volume: 27 start-page: 1 year: 2014 ident: 8582_CR46 publication-title: Adv Neural Inf Process Syst – volume: 34 start-page: 1544 year: 2020 ident: 8582_CR3 publication-title: IEEE Trans Knowl Data Eng – volume: 534 year: 2019 ident: 8582_CR5 publication-title: Physica A doi: 10.1016/j.physa.2019.03.007 – volume: 81 start-page: 23589 year: 2022 ident: 8582_CR44 publication-title: Multimed Tools Appl doi: 10.1007/s11042-022-12039-3 – ident: 8582_CR31 doi: 10.1109/INNOVATIONS.2016.7880022 – volume: 31 start-page: 7401 issue: 11 year: 2019 ident: 8582_CR34 publication-title: Neural Comput Appl doi: 10.1007/s00521-018-3578-y – volume: 23 start-page: 7233 issue: 7 year: 2021 ident: 8582_CR9 publication-title: IEEE Trans Intell Transp Syst doi: 10.1109/TITS.2021.3067603 – volume: 77 start-page: 605 year: 2019 ident: 8582_CR13 publication-title: Appl Soft Comput doi: 10.1016/j.asoc.2019.02.006 – volume: 259 start-page: 147 year: 2018 ident: 8582_CR18 publication-title: Artif Intell doi: 10.1016/j.artint.2018.03.002
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SubjectTerms	Algorithms Artificial Intelligence Artificial neural networks Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Convolution Data Mining and Knowledge Discovery Forecasting Graphs Image Processing and Computer Vision Intelligent transportation systems Mathematical models Modules Nodes Original Article Probability and Statistics in Computer Science Roads & highways Similarity Traffic congestion Traffic control Traffic flow Traffic information Traffic management Transportation networks
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Title	Attention-based spatial–temporal adaptive dual-graph convolutional network for traffic flow forecasting
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