{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T16:07:01Z","timestamp":1783008421669,"version":"3.54.5"},"reference-count":64,"publisher":"Springer Science and Business Media LLC","issue":"24","license":[{"start":{"date-parts":[[2023,11,28]],"date-time":"2023-11-28T00:00:00Z","timestamp":1701129600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,28]],"date-time":"2023-11-28T00:00:00Z","timestamp":1701129600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62162012"],"award-info":[{"award-number":["62162012"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62173278"],"award-info":[{"award-number":["62173278"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62072061"],"award-info":[{"award-number":["62072061"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2023,12]]},"DOI":"10.1007\/s10489-023-05053-x","type":"journal-article","created":{"date-parts":[[2023,11,28]],"date-time":"2023-11-28T08:02:18Z","timestamp":1701158538000},"page":"30843-30864","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Spatial-temporal graph neural network based on gated convolution and topological attention for traffic flow prediction"],"prefix":"10.1007","volume":"53","author":[{"given":"Dewei","family":"Bai","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0151-9643","authenticated-orcid":false,"given":"Dawen","family":"Xia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dan","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yantao","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huaqing","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,11,28]]},"reference":[{"key":"5053_CR1","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijpe.2020.107868","volume":"231","author":"S Kaffash","year":"2021","unstructured":"Kaffash S, Nguyen AT, Zhu J (2021) Big data algorithms and applications in intelligent transportation system: A review and bibliometric analysis. Int J Prod Econ 231:107868","journal-title":"Int J Prod Econ"},{"issue":"1","key":"5053_CR2","doi-asserted-by":"crossref","first-page":"683","DOI":"10.1038\/s41598-018-36361-9","volume":"9","author":"CJ McGowan","year":"2019","unstructured":"McGowan CJ, Biggerstaff M, Johansson M, Apfeldorf KM, Ben-Nun M, Brooks L, Convertino M, Erraguntla M, Farrow DC, Freeze J et al (2019) Collaborative efforts to forecast seasonal influenza in the united states, 2015\u20132016. Sci Rep 9(1):683","journal-title":"Sci Rep"},{"key":"5053_CR3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.renene.2020.02.117","volume":"154","author":"R Jamil","year":"2020","unstructured":"Jamil R (2020) Hydroelectricity consumption forecast for pakistan using ARIMA modeling and supply-demand analysis for the year 2030. Renew Energy 154:1\u201310","journal-title":"Renew Energy"},{"key":"5053_CR4","doi-asserted-by":"crossref","DOI":"10.1016\/j.comnet.2020.107530","volume":"181","author":"A Boukerche","year":"2020","unstructured":"Boukerche A, Wang J (2020) Machine learning-based traffic prediction models for intelligent transportation systems. Comput Netw 181:107530","journal-title":"Comput Netw"},{"issue":"08","key":"5053_CR5","doi-asserted-by":"crossref","first-page":"3681","DOI":"10.1109\/TKDE.2020.3025580","volume":"34","author":"S Wang","year":"2022","unstructured":"Wang S, Cao J, Philip SY (2022) Deep learning for spatio-temporal data mining: A survey. IEEE Trans Knowl Data Eng 34(08):3681\u20133700","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"5053_CR6","doi-asserted-by":"crossref","unstructured":"Assaf AG, Li G, Song H, Tsionas MG (2019) Modeling and forecasting regional tourism demand using the bayesian global vector autoregressive (BGVAR) model. J Travel Res 58(3):383\u2013397","DOI":"10.1177\/0047287518759226"},{"key":"5053_CR7","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1016\/j.neucom.2015.12.013","volume":"179","author":"D Xia","year":"2016","unstructured":"Xia D, Wang B, Li H, Li Y, Zhang Z (2016) A distributed spatial-temporal weighted model on mapreduce for short-term traffic flow forecasting. Neurocomputing 179:246\u2013263","journal-title":"Neurocomputing"},{"key":"5053_CR8","doi-asserted-by":"crossref","DOI":"10.1016\/j.physa.2019.03.007","volume":"534","author":"J Tang","year":"2019","unstructured":"Tang J, Chen X, Hu Z, Zong F, Han C, Li L (2019) Traffic flow prediction based on combination of support vector machine and data denoising schemes. Phys A Stat Mech Appl 534:120642","journal-title":"Phys A Stat Mech Appl"},{"issue":"5","key":"5053_CR9","first-page":"1191","volume":"42","author":"X Liu","year":"2020","unstructured":"Liu X, Zhu X, Li M, Wang L, Zhu E, Liu T, Kloft M, Shen D, Yin J, Gao W (2020) Multiple kernel $$k$$-means with incomplete kernels. IEEE Trans Patt Anal Mach Intell 42(5):1191\u20131204","journal-title":"IEEE Trans Patt Anal Mach Intell"},{"key":"5053_CR10","volume":"123","author":"X Yu","year":"2022","unstructured":"Yu X, Ye X, Zhang S (2022) Floating pollutant image target extraction algorithm based on immune extremum region. Digit Sig Process 123:103442","journal-title":"Digit Sig Process"},{"key":"5053_CR11","doi-asserted-by":"crossref","DOI":"10.1016\/j.infrared.2022.104400","volume":"127","author":"Z Zhou","year":"2022","unstructured":"Zhou Z, Zhang B, Yu X (2022) Immune coordination deep network for hand heat trace extraction. Infrared Phys Technol 127:104400","journal-title":"Infrared Phys Technol"},{"key":"5053_CR12","doi-asserted-by":"crossref","DOI":"10.1016\/j.physd.2019.132306","volume":"404","author":"A Sherstinsky","year":"2020","unstructured":"Sherstinsky A (2020) Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network. Physica D: Nonlinear Phenomena 404:132306","journal-title":"Physica D: Nonlinear Phenomena"},{"key":"5053_CR13","doi-asserted-by":"crossref","unstructured":"Cho K, Van Merri\u00ebnboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y (2014) Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: Proc. of EMNLP, pp 1724\u20131734","DOI":"10.3115\/v1\/D14-1179"},{"key":"5053_CR14","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.trc.2015.03.014","volume":"54","author":"X Ma","year":"2015","unstructured":"Ma X, Tao Z, Wang Y, Yu H, Wang Y (2015) Long short-term memory neural network for traffic speed prediction using remote microwave sensor data. Transport Res Part C Emerg Tech 54:187\u2013197","journal-title":"Transport Res Part C Emerg Tech"},{"key":"5053_CR15","first-page":"3104","volume":"27","author":"I Sutskever","year":"2014","unstructured":"Sutskever I, Vinyals O, Le VQ (2014) Sequence to sequence learning with neural networks. Adv Neural Inf Process Syst 27:3104\u20133112","journal-title":"Adv Neural Inf Process Syst"},{"issue":"10","key":"5053_CR16","doi-asserted-by":"crossref","first-page":"1475","DOI":"10.1049\/iet-its.2018.5511","volume":"13","author":"D Yang","year":"2019","unstructured":"Yang D, Chen K, Yang M, Zhao X (2019) Urban rail transit passenger flow forecast based on LSTM with enhanced long-term features. IET Intell Transp Syst 13(10):1475\u20131482","journal-title":"IET Intell Transp Syst"},{"key":"5053_CR17","doi-asserted-by":"crossref","unstructured":"Xia D, Zhang M, Yan X, Bai Y, Zheng Y, Li Y, Li H (2021) A distributed WND-LSTM model on mapreduce for short-term traffic flow prediction. Neural Comput & Applic 33(7):2393\u20132410","DOI":"10.1007\/s00521-020-05076-2"},{"issue":"8","key":"5053_CR18","doi-asserted-by":"crossref","first-page":"3219","DOI":"10.1109\/TITS.2019.2924971","volume":"21","author":"K-F Chu","year":"2019","unstructured":"Chu K-F, Lam AY, Li VO (2019) Deep multi-scale convolutional LSTM network for travel demand and origin-destination predictions. IEEE Trans Intell Transp Syst 21(8):3219\u20133232","journal-title":"IEEE Trans Intell Transp Syst"},{"issue":"2","key":"5053_CR19","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1109\/72.279181","volume":"5","author":"Y Bengio","year":"1994","unstructured":"Bengio Y, Simard P, Frasconi P (1994) Learning long-term dependencies with gradient descent is difficult. IEEE Trans Neural Netw 5(2):157\u2013166","journal-title":"IEEE Trans Neural Netw"},{"key":"5053_CR20","unstructured":"Bai S, Kolter JZ, Koltun V (2018) Convolutional sequence modeling revisited. In: Proc. of ICLR, pp 1\u201320"},{"key":"5053_CR21","doi-asserted-by":"crossref","unstructured":"Lea C, Flynn MD, Vidal R, Reiter A, Hager GD (2017) Temporal convolutional networks for action segmentation and detection. In: Proc. of CVPR, pp 156\u2013165","DOI":"10.1109\/CVPR.2017.113"},{"key":"5053_CR22","doi-asserted-by":"crossref","unstructured":"Yu F, Koltun V, Funkhouser TA (2017) Dilated residual networks. In: Proc. of CVPR, pp 636\u2013644","DOI":"10.1109\/CVPR.2017.75"},{"key":"5053_CR23","doi-asserted-by":"crossref","first-page":"5668","DOI":"10.1609\/aaai.v33i01.33015668","volume":"33","author":"H Yao","year":"2019","unstructured":"Yao H, Tang X, Wei H, Zheng G, Li Z (2019) Revisiting spatial-temporal similarity: A deep learning framework for traffic prediction. Proc. of AAAI 33:5668\u20135675","journal-title":"Proc. of AAAI"},{"issue":"8","key":"5053_CR24","doi-asserted-by":"crossref","first-page":"7751","DOI":"10.1109\/JIOT.2020.2991401","volume":"7","author":"Y Liu","year":"2020","unstructured":"Liu Y, Yu JJQ, Kang J, Niyato D, Zhang S (2020) Privacy-preserving traffic flow prediction: A federated learning approach. IEEE Internet Things J 7(8):7751\u20137763","journal-title":"IEEE Internet Things J"},{"key":"5053_CR25","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.aiopen.2021.01.001","volume":"1","author":"J Zhou","year":"2020","unstructured":"Zhou J, Cui G, Hu S, Zhang Z, Yang C, Liu Z, Wang L, Li C, Sun M (2020) Graph neural networks: A review of methods and applications. AI Open 1:57\u201381","journal-title":"AI Open"},{"issue":"9","key":"5053_CR26","doi-asserted-by":"crossref","first-page":"1645","DOI":"10.1109\/TKDE.2018.2866809","volume":"31","author":"Z Jiang","year":"2018","unstructured":"Jiang Z (2018) A survey on spatial prediction methods. IEEE Trans Knowl Data Eng 31(9):1645\u20131664","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"5053_CR27","unstructured":"Veli\u010dkovi\u0107 P, Cucurull G, Casanova A, Romero A, Li\u00f2 P, Bengio Y (2018) Graph attention networks. In: Proc. of ICLR, pp 1\u201312"},{"key":"5053_CR28","unstructured":"Anava O, Hazan E, Zeevi A (2015) Online time series prediction with missing data. In: Proc. of ICML, pp 2191\u20132199"},{"issue":"4","key":"5053_CR29","doi-asserted-by":"crossref","first-page":"1624","DOI":"10.1109\/TITS.2011.2158001","volume":"12","author":"J Zhang","year":"2011","unstructured":"Zhang J, Wang F-Y, Wang K, Lin W-H, Xu X, Chen C (2011) Data-driven intelligent transportation systems: A survey. IEEE Trans Intell Transport Syst 12(4):1624\u20131639","journal-title":"IEEE Trans Intell Transport Syst"},{"issue":"3","key":"5053_CR30","doi-asserted-by":"crossref","first-page":"1755","DOI":"10.1109\/TITS.2020.3026025","volume":"23","author":"J Liu","year":"2020","unstructured":"Liu J, Ong GP, Chen X (2020) Graphsage-based traffic speed forecasting for segment network with sparse data. IEEE Trans Intell Transport Syst 23(3):1755\u20131766","journal-title":"IEEE Trans Intell Transport Syst"},{"issue":"6","key":"5053_CR31","doi-asserted-by":"crossref","first-page":"1079","DOI":"10.1109\/TKDE.2019.2898831","volume":"32","author":"Y Li","year":"2019","unstructured":"Li Y, Zheng Y (2019) Citywide bike usage prediction in a bike-sharing system. IEEE Trans Knowl Data Eng 32(6):1079\u20131091","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"3","key":"5053_CR32","doi-asserted-by":"crossref","first-page":"468","DOI":"10.1109\/TKDE.2019.2891537","volume":"32","author":"J Zhang","year":"2019","unstructured":"Zhang J, Zheng Y, Sun J, Qi D (2019) Flow prediction in spatio-temporal networks based on multitask deep learning. IEEE Trans Knowl Data Eng 32(3):468\u2013478","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"1","key":"5053_CR33","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.csl.2014.09.005","volume":"30","author":"WD Mulder","year":"2015","unstructured":"Mulder WD, Bethard S, Moens MF (2015) A survey on the application of recurrent neural networks to statistical language modeling. Comp Speech Lang 30(1):61\u201398","journal-title":"Comp Speech Lang"},{"key":"5053_CR34","unstructured":"Dauphin YN, Fan A, Auli M, Grangier D (2017) Language modeling with gated convolutional networks. In: Proc. of ICML, pp 933\u2013941"},{"key":"5053_CR35","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1016\/j.neunet.2021.08.030","volume":"144","author":"F Landi","year":"2021","unstructured":"Landi F, Baraldi L, Cornia M, Cucchiara R (2021) Working memory connections for LSTM. Neural Networks 144:334\u2013341","journal-title":"Neural Networks"},{"issue":"13","key":"5053_CR36","doi-asserted-by":"crossref","first-page":"14773","DOI":"10.1007\/s10489-021-02770-z","volume":"52","author":"X Huang","year":"2022","unstructured":"Huang X, Ye Y, Wang C, Yang X, Xiong L (2022) A multi-mode traffic flow prediction method with clustering based attention convolution LSTM. Appl Intell 52(13):14773\u201314786","journal-title":"Appl Intell"},{"issue":"10","key":"5053_CR37","doi-asserted-by":"crossref","first-page":"6526","DOI":"10.1109\/TITS.2020.2993798","volume":"22","author":"N Davis","year":"2020","unstructured":"Davis N, Raina G, Jagannathan K (2020) Grids versus graphs: Partitioning space for improved taxi demand-supply forecasts. IEEE Trans Intell Transport Sys 22(10):6526\u20136535","journal-title":"IEEE Trans Intell Transport Sys"},{"issue":"8","key":"5053_CR38","doi-asserted-by":"crossref","first-page":"6277","DOI":"10.1007\/s10462-022-10151-2","volume":"55","author":"AA Yusuf","year":"2022","unstructured":"Yusuf AA, Chong F, Xianling M (2022) An analysis of graph convolutional networks and recent datasets for visual question answering. Artif Intell Rev 55(8):6277\u20136300","journal-title":"Artif Intell Rev"},{"key":"5053_CR39","doi-asserted-by":"crossref","unstructured":"Yu B, Yin H, Zhu Z (2018) Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. In: Proc. of IJCAI, pp 3634\u20133640","DOI":"10.24963\/ijcai.2018\/505"},{"key":"5053_CR40","unstructured":"Li Y, Yu R, Shahabi C, Liu Y (2018) Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. In: Proc. of ICLR, pp 1\u201316"},{"key":"5053_CR41","doi-asserted-by":"crossref","unstructured":"Wu Z, Pan S, Long G, Jiang J, Zhang C (2019) Graph wavenet for deep spatial-temporal graph modeling. In: Proc. of IJCAI, pp 1907\u20131913","DOI":"10.24963\/ijcai.2019\/264"},{"issue":"10","key":"5053_CR42","doi-asserted-by":"crossref","first-page":"1210","DOI":"10.1049\/iet-its.2019.0873","volume":"14","author":"J Zhang","year":"2020","unstructured":"Zhang J, Chen F, Guo Y, Li X (2020) Multi-graph convolutional network for short-term passenger flow forecasting in urban rail transit. IET Intell Trans Sys 14(10):1210\u20131217","journal-title":"IET Intell Trans Sys"},{"issue":"9","key":"5053_CR43","doi-asserted-by":"crossref","first-page":"3848","DOI":"10.1109\/TITS.2019.2935152","volume":"21","author":"L Zhao","year":"2020","unstructured":"Zhao L, Song Y, Zhang C, Liu Y, Wang P, Lin T, Deng M, Li H (2020) T-GCN: A temporal graph convolutional network for traffic prediction. IEEE Trans Intell Transport Syst 21(9):3848\u20133858","journal-title":"IEEE Trans Intell Transport Syst"},{"issue":"4","key":"5053_CR44","doi-asserted-by":"crossref","first-page":"4300","DOI":"10.1007\/s10489-021-02648-0","volume":"52","author":"X Kong","year":"2022","unstructured":"Kong X, Zhang J, Wei X, Xing W, Lu W (2022) Adaptive spatial-temporal graph attention networks for traffic flow forecasting. Appl Intell 52(4):4300\u20134316","journal-title":"Appl Intell"},{"issue":"5","key":"5053_CR45","doi-asserted-by":"crossref","first-page":"4001","DOI":"10.1109\/JIOT.2021.3102238","volume":"9","author":"T Qi","year":"2022","unstructured":"Qi T, Li G, Chen L, Xue Y (2022) ADGCN: An asynchronous dilation graph convolutional network for traffic flow prediction. IEEE Internet Things J 9(5):4001\u20134014","journal-title":"IEEE Internet Things J"},{"issue":"4","key":"5053_CR46","doi-asserted-by":"crossref","first-page":"2802","DOI":"10.1109\/TII.2020.3009280","volume":"17","author":"F Zhou","year":"2021","unstructured":"Zhou F, Yang Q, Zhong T, Chen D, Zhang N (2021) Variational graph neural networks for road traffic prediction in intelligent transportation systems. IEEE Trans Ind Inf 17(4):2802\u20132812","journal-title":"IEEE Trans Ind Inf"},{"issue":"9","key":"5053_CR47","doi-asserted-by":"crossref","first-page":"6803","DOI":"10.1109\/JIOT.2021.3116241","volume":"9","author":"D Luo","year":"2022","unstructured":"Luo D, Zhao D, Ke Q, You X, Liu L, Ma H (2022) Spatiotemporal hashing multigraph convolutional network for service-level passenger flow forecasting in bus transit systems. IEEE Internet Things J 9(9):6803\u20136815","journal-title":"IEEE Internet Things J"},{"key":"5053_CR48","doi-asserted-by":"crossref","first-page":"548","DOI":"10.1016\/j.ins.2022.07.008","volume":"609","author":"X Huang","year":"2022","unstructured":"Huang X, Ye Y, Ding W, Yang X, Xiong L (2022) Multi-mode dynamic residual graph convolution network for traffic flow prediction. Inf Sci 609:548\u2013564","journal-title":"Inf Sci"},{"key":"5053_CR49","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/j.ins.2022.07.125","volume":"610","author":"S Cao","year":"2022","unstructured":"Cao S, Wu L, Wu J, Wu D, Li Q (2022) A spatio-temporal sequence-to-sequence network for traffic flow prediction. Inf Sci 610:185\u2013203","journal-title":"Inf Sci"},{"key":"5053_CR50","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.trc.2019.09.008","volume":"108","author":"LN Do","year":"2019","unstructured":"Do LN, Vu HL, Vo BQ, Liu Z, Phung D (2019) An effective spatial-temporal attention based neural network for traffic flow prediction. Transport Res Part C Emerg Technol 108:12\u201328","journal-title":"Transport Res Part C Emerg Technol"},{"key":"5053_CR51","doi-asserted-by":"crossref","first-page":"922","DOI":"10.1609\/aaai.v33i01.3301922","volume":"33","author":"S Guo","year":"2019","unstructured":"Guo S, Lin Y, Feng N, Song C, Wan H (2019) Attention based spatial-temporal graph convolutional networks for traffic flow forecasting. Proc. of AAAI 33:922\u2013929","journal-title":"Proc. of AAAI"},{"key":"5053_CR52","doi-asserted-by":"crossref","unstructured":"Pan Z, Liang Y, Wang W, Yu Y, Zheng Y, Zhang J (2019) Urban traffic prediction from spatio-temporal data using deep meta learning. In: Proc. of KDD, p 1720\u20131730","DOI":"10.1145\/3292500.3330884"},{"key":"5053_CR53","doi-asserted-by":"crossref","first-page":"1234","DOI":"10.1609\/aaai.v34i01.5477","volume":"34","author":"C Zheng","year":"2020","unstructured":"Zheng C, Fan X, Wang C, Qi J (2020) GMAN: A graph multi-attention network for traffic prediction. Proc. of AAAI 34:1234\u20131241","journal-title":"Proc. of AAAI"},{"key":"5053_CR54","doi-asserted-by":"crossref","first-page":"869","DOI":"10.1016\/j.ins.2022.05.127","volume":"607","author":"Y Wang","year":"2022","unstructured":"Wang Y, Jing C, Xu S, Guo T (2022) Attention based spatiotemporal graph attention networks for traffic flow forecasting. Inf Sci 607:869\u2013883","journal-title":"Inf Sci"},{"key":"5053_CR55","doi-asserted-by":"crossref","first-page":"15008","DOI":"10.1609\/aaai.v35i17.17761","volume":"35","author":"X Zhang","year":"2021","unstructured":"Zhang X, Huang C, Xu Y, Xia L, Dai P, Bo L, Zhang J, Zheng Y (2021) Traffic flow forecasting with spatial-temporal graph diffusion network. Proc. of AAAI 35:15008\u201315015","journal-title":"Proc. of AAAI"},{"issue":"2","key":"5053_CR56","first-page":"1","volume":"13","author":"B Lu","year":"2022","unstructured":"Lu B, Gan X, Jin H, Fu L, Wang X, Zhang H (2022) Make more connections: Urban traffic flow forecasting with spatiotemporal adaptive gated graph convolution network. ACM Trans Intell Syst Tech 13(2):1\u201325","journal-title":"ACM Trans Intell Syst Tech"},{"key":"5053_CR57","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2015) Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In: Proc. of ICCV, pp 1026\u20131034","DOI":"10.1109\/ICCV.2015.123"},{"key":"5053_CR58","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukhin I (2017) Attention is all you need. In: Proc. of NeurIPS, pp 5998\u20136008"},{"key":"5053_CR59","volume":"142","author":"S Liu","year":"2022","unstructured":"Liu S, Zhang X, Xu L, Ding F (2022) Expectation-maximization algorithm for bilinear systems by using the Rauch-Rung-Striebel smoother. Automatica 142:110365","journal-title":"Automatica"},{"key":"5053_CR60","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/j.inffus.2019.01.004","volume":"50","author":"K-W Chiang","year":"2019","unstructured":"Chiang K-W, Tsai G-J, Chang H, Joly C, Ei-Sheimy N (2019) Seamless navigation and mapping using an INS\/GNNS\/grid-based slam semi-tightly coupled integration scheme. Inf Fusion 50:181\u2013196","journal-title":"Inf Fusion"},{"issue":"9","key":"5053_CR61","doi-asserted-by":"crossref","first-page":"4256","DOI":"10.1109\/TVT.2010.2070850","volume":"59","author":"H Liu","year":"2010","unstructured":"Liu H, Nassar S, El-Sheimy N (2010) Two-filter smoothing for accurate INS\/GPS land-vehicle navigation in urban centers. IEEE Trans Veh Technol 59(9):4256\u20134267","journal-title":"IEEE Trans Veh Technol"},{"key":"5053_CR62","doi-asserted-by":"crossref","unstructured":"Seo T (2020) Calibration-free traffic state estimation method using single detector and connected vehicles with kalman filtering and RTS smoothing. In: Proc. of ITSC, pp 1\u20135","DOI":"10.1109\/ITSC45102.2020.9294229"},{"issue":"1","key":"5053_CR63","doi-asserted-by":"crossref","first-page":"96","DOI":"10.3141\/1748-12","volume":"1748","author":"C Chen","year":"2001","unstructured":"Chen C, Petty K, Skabardonis A, Varaiya P, Jia Z (2001) Freeway performance measurement system: Mining loop detector data. Transport Res Rec 1748(1):96\u2013102","journal-title":"Transport Res Rec"},{"key":"5053_CR64","unstructured":"Loshchilov I, Hutter F (2019) Decoupled weight decay regularization. In: Proc. of ICLR, pp 1\u20138"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-023-05053-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-023-05053-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-023-05053-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,28]],"date-time":"2023-12-28T06:29:19Z","timestamp":1703744959000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-023-05053-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,28]]},"references-count":64,"journal-issue":{"issue":"24","published-print":{"date-parts":[[2023,12]]}},"alternative-id":["5053"],"URL":"https:\/\/doi.org\/10.1007\/s10489-023-05053-x","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,28]]},"assertion":[{"value":"26 September 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 November 2023","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interest"}}]}}