{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T16:58:44Z","timestamp":1780937924009,"version":"3.54.1"},"reference-count":56,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["T2322023"],"award-info":[{"award-number":["T2322023"]}],"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":["62141604"],"award-info":[{"award-number":["62141604"]}],"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":["92467206"],"award-info":[{"award-number":["92467206"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Expert Systems with Applications"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1016\/j.eswa.2026.132096","type":"journal-article","created":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T15:46:52Z","timestamp":1775317612000},"page":"132096","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["A competitive and cooperative spatio-temporal framework for urban traffic flow forecasting"],"prefix":"10.1016","volume":"321","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-8181-0614","authenticated-orcid":false,"given":"Haocheng","family":"Yu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4328-6339","authenticated-orcid":false,"given":"Kexin","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0037-2635","authenticated-orcid":false,"given":"Zhenqian","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6549-9760","authenticated-orcid":false,"given":"Shaolin","family":"Tan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0275-8387","authenticated-orcid":false,"given":"Jinhu","family":"L\u00fc","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.eswa.2026.132096_bib0001","series-title":"In Advances in Neural Information Processing Systems (NeurIPS)","first-page":"17804","article-title":"Adaptive graph convolutional recurrent network for traffic forecasting","author":"Bai","year":"2020"},{"key":"10.1016\/j.eswa.2026.132096_bib0002","series-title":"An empirical evaluation of generic convolutional and recurrent networks for sequence modeling","author":"Bai","year":"2018"},{"key":"10.1016\/j.eswa.2026.132096_bib0003","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2023.106044","article-title":"Spatial-temporal complex graph convolution network for traffic flow prediction","volume":"121","author":"Bao","year":"2023","journal-title":"Engineering Applications of Artificial Intelligence"},{"key":"10.1016\/j.eswa.2026.132096_bib0004","doi-asserted-by":"crossref","first-page":"2763","DOI":"10.1007\/s10489-021-02587-w","article-title":"Spatial-temporal graph neural network for traffic forecasting: An overview and open research issues","volume":"52","author":"Bui","year":"2022","journal-title":"Applied Intelligence"},{"key":"10.1016\/j.eswa.2026.132096_bib0005","series-title":"Proceedings of the 38th International Conference on Machine Learning","first-page":"1684","article-title":"Z-GCNETS: Time zigzags at graph convolutional networks for time-series forecasting","volume":"139","author":"Chen","year":"2021"},{"key":"10.1016\/j.eswa.2026.132096_bib0006","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2025.130036","article-title":"Short-term traffic flow prediction based on deep learning approach with inter-day and intra-day strategies","volume":"299","author":"Chiang","year":"2026","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2026.132096_bib0007","doi-asserted-by":"crossref","first-page":"6367","DOI":"10.1609\/aaai.v36i6.20587","article-title":"Graph neural controlled differential equations for traffic forecasting","volume":"36","author":"Choi","year":"2022","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"10.1016\/j.eswa.2026.132096_bib0008","series-title":"Proceedings of the 34th International Conference on Machine Learning","first-page":"933","article-title":"Language modeling with gated convolutional networks","volume":"70","author":"Dauphin","year":"2017"},{"key":"10.1016\/j.eswa.2026.132096_bib0009","series-title":"R-GCN: The R could stand for random","author":"Degraeve","year":"2022"},{"key":"10.1016\/j.eswa.2026.132096_bib0010","series-title":"Proceedings of the IEEE International Conference on Data Mining (ICDM),","first-page":"929","article-title":"Signed graph convolutional networks","author":"Derr","year":"2018"},{"key":"10.1016\/j.eswa.2026.132096_bib0011","series-title":"Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","first-page":"364","article-title":"Spatial-temporal graph ODE networks for traffic-flow forecasting","author":"Fang","year":"2021"},{"key":"10.1016\/j.eswa.2026.132096_bib0012","series-title":"Proceedings of the 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC)","first-page":"324","article-title":"Using LSTM and GRU neural network methods for traffic-flow prediction","author":"Fu","year":"2016"},{"issue":"1","key":"10.1016\/j.eswa.2026.132096_bib0013","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1109\/TIV.2018.2886687","article-title":"A hybrid approach to side-slip angle estimation with recurrent neural networks and kinematic vehicle models","volume":"4","author":"Gr\u00e4ber","year":"2019","journal-title":"IEEE Transactions on Intelligent Vehicles"},{"issue":"01","key":"10.1016\/j.eswa.2026.132096_bib0014","doi-asserted-by":"crossref","first-page":"922","DOI":"10.1609\/aaai.v33i01.3301922","article-title":"Attention-based spatial-temporal graph convolutional networks for traffic-flow forecasting","volume":"33","author":"Guo","year":"2019","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"10.1016\/j.eswa.2026.132096_bib0015","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1061\/(ASCE)0733-947X(1995)121:3(249)","article-title":"Short-term prediction of traffic volume in urban arterials","volume":"121","author":"Hamed","year":"1995","journal-title":"Journal of Transportation Engineering"},{"key":"10.1016\/j.eswa.2026.132096_bib0016","series-title":"Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021,","first-page":"929","article-title":"RealFormer: Transformer likes residual attention","author":"He","year":"2021"},{"issue":"8","key":"10.1016\/j.eswa.2026.132096_bib0017","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Computation"},{"key":"10.1016\/j.eswa.2026.132096_bib0018","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2022.117921","article-title":"Graph neural network for traffic forecasting: A survey","volume":"207","author":"Jiang","year":"2022","journal-title":"Expert Systems with Applications"},{"issue":"2","key":"10.1016\/j.eswa.2026.132096_bib0019","doi-asserted-by":"crossref","first-page":"1188","DOI":"10.1109\/TVT.2018.2885366","article-title":"Image-to-image learning to predict traffic speeds by considering area-wide spatio-temporal dependencies","volume":"68","author":"Jo","year":"2019","journal-title":"IEEE Transactions on Vehicular Technology"},{"issue":"4","key":"10.1016\/j.eswa.2026.132096_bib0020","doi-asserted-by":"crossref","first-page":"714","DOI":"10.1109\/TIV.2020.3003889","article-title":"Real-time driver manoeuvre prediction using LSTM","volume":"5","author":"Khairdoost","year":"2020","journal-title":"IEEE Transactions on Intelligent Vehicles"},{"key":"10.1016\/j.eswa.2026.132096_bib0021","series-title":"Proceedings of the International Conference on Learning Representations (ICLR)","article-title":"Semi-supervised classification with graph convolutional networks","author":"Kipf","year":"2017"},{"key":"10.1016\/j.eswa.2026.132096_bib0022","series-title":"Proceedings of the 39th International Conference on Machine Learning","first-page":"11906","article-title":"DSTAGNN: Dynamic spatial-temporal aware graph neural network for traffic-flow forecasting","volume":"162","author":"Lan","year":"2022"},{"issue":"4","key":"10.1016\/j.eswa.2026.132096_bib0023","first-page":"31","article-title":"Dynamic graph convolutional recurrent network for traffic prediction: Benchmark and solution","volume":"15","author":"Li","year":"2021","journal-title":"ACM Transactions on Knowledge Discovery from Data"},{"key":"10.1016\/j.eswa.2026.132096_bib0024","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence,","first-page":"4189","article-title":"Spatial-temporal fusion graph neural networks for traffic-flow forecasting","author":"Li","year":"2021"},{"key":"10.1016\/j.eswa.2026.132096_bib0025","series-title":"Proceedings of the International Conference on Learning Representations (ICLR)","article-title":"Diffusion convolutional recurrent neural network: Data-driven traffic forecasting","author":"Li","year":"2018"},{"issue":"6","key":"10.1016\/j.eswa.2026.132096_bib0026","doi-asserted-by":"crossref","first-page":"7796","DOI":"10.1109\/TVT.2023.3239054","article-title":"Semantics-aware dynamic graph convolutional network for traffic flow forecasting","volume":"72","author":"Liang","year":"2023","journal-title":"IEEE Transactions on Vehicular Technology"},{"key":"10.1016\/j.eswa.2026.132096_bib0027","series-title":"New introduction to multiple time series analysis","author":"L\u00fctkepohl","year":"2005"},{"key":"10.1016\/j.eswa.2026.132096_bib0028","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2022.119161","article-title":"Traffic flow and speed forecasting through a bayesian deep multi-linear relationship network","volume":"213","author":"Ma","year":"2023","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2026.132096_bib0029","doi-asserted-by":"crossref","first-page":"405","DOI":"10.1146\/annurev-statistics-030718-104938","article-title":"Statistical aspects of wasserstein distances","volume":"6","author":"Panaretos","year":"2019","journal-title":"Annual Review of Statistics and its Application"},{"key":"10.1016\/j.eswa.2026.132096_bib0030","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2025.128244","article-title":"Long-term spatio-temporal graph attention network for traffic forecasting","volume":"288","author":"Remmouche","year":"2025","journal-title":"Expert Systems with Applications"},{"issue":"4","key":"10.1016\/j.eswa.2026.132096_bib0031","doi-asserted-by":"crossref","first-page":"414","DOI":"10.1109\/TIV.2018.2873901","article-title":"Intent prediction of pedestrians via motion trajectories using stacked recurrent neural networks","volume":"3","author":"Saleh","year":"2018","journal-title":"IEEE Transactions on Intelligent Vehicles"},{"issue":"15","key":"10.1016\/j.eswa.2026.132096_bib0032","doi-asserted-by":"crossref","DOI":"10.3390\/su151511893","article-title":"A graph neural network (GNN)-based approach for real-time estimation of traffic speed in sustainable smart cities","volume":"15","author":"Sharma","year":"2023","journal-title":"Sustainability"},{"key":"10.1016\/j.eswa.2026.132096_bib0033","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR),","first-page":"3693","article-title":"Dynamic edge-conditioned filters in convolutional neural networks on graphs","author":"Simonovsky","year":"2017"},{"issue":"7","key":"10.1016\/j.eswa.2026.132096_bib0034","doi-asserted-by":"crossref","first-page":"9421","DOI":"10.1109\/TITS.2025.3563631","article-title":"Correlated channeled spatio-temporal graph attention network model for traffic prediction","volume":"26","author":"Singh","year":"2025","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"key":"10.1016\/j.eswa.2026.132096_bib0035","series-title":"Spatial-temporal synchronous graph convolutional networks: A new framework for spatial-temporal network-data forecasting","first-page":"914","volume":"34","author":"Song","year":"2020"},{"key":"10.1016\/j.eswa.2026.132096_bib0036","series-title":"Advances in Neural Information Processing Systems (NeurIPS)","first-page":"3104","article-title":"Sequence to sequence learning with neural networks","author":"Sutskever","year":"2014"},{"key":"10.1016\/j.eswa.2026.132096_bib0037","series-title":"Proceedings of the International Conference on Learning Representations (ICLR)","article-title":"Composition-based multi-relational graph convolutional networks","author":"Vashishth","year":"2020"},{"key":"10.1016\/j.eswa.2026.132096_bib0038","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2024.125134","article-title":"TADGCN: A time-aware dynamic graph convolution network for long-term traffic flow prediction","volume":"258","author":"Wang","year":"2024","journal-title":"Expert Systems with Applications"},{"issue":"6","key":"10.1016\/j.eswa.2026.132096_bib0039","doi-asserted-by":"crossref","first-page":"8682","DOI":"10.1109\/TITS.2025.3552010","article-title":"A lightweight spatio-temporal neural network with sampling-based time-series decomposition for traffic forecasting","volume":"26","author":"Wang","year":"2025","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"issue":"2","key":"10.1016\/j.eswa.2026.132096_bib0040","doi-asserted-by":"crossref","first-page":"2220","DOI":"10.1109\/TVT.2024.3390997","article-title":"Adaptive spatio-temporal relation based transformer for traffic flow prediction","volume":"74","author":"Wang","year":"2025","journal-title":"IEEE Transactions on Vehicular Technology"},{"issue":"6","key":"10.1016\/j.eswa.2026.132096_bib0041","doi-asserted-by":"crossref","first-page":"664","DOI":"10.1061\/(ASCE)0733-947X(2003)129:6(664)","article-title":"Modeling and forecasting vehicular traffic flow as a seasonal ARIMA process: Theoretical basis and empirical results","volume":"129","author":"Williams","year":"2003","journal-title":"Journal of Transportation Engineering"},{"key":"10.1016\/j.eswa.2026.132096_bib0042","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.122381","article-title":"Dynamic spatial-temporal graph convolutional recurrent networks for traffic flow forecasting","volume":"240","author":"Xia","year":"2024","journal-title":"Expert Systems with Applications"},{"issue":"10","key":"10.1016\/j.eswa.2026.132096_bib0043","doi-asserted-by":"crossref","first-page":"17135","DOI":"10.1109\/TITS.2025.3570009","article-title":"Bike-sharing demand prediction based on dynamic time warping and spatio-temporal graph attention network","volume":"26","author":"Xiang","year":"2025","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"issue":"4","key":"10.1016\/j.eswa.2026.132096_bib0044","doi-asserted-by":"crossref","first-page":"3140","DOI":"10.1109\/TVT.2019.2899125","article-title":"Short-term traffic prediction for edge computing-enhanced autonomous and connected cars","volume":"68","author":"Yang","year":"2019","journal-title":"IEEE Transactions on Vehicular Technology"},{"key":"10.1016\/j.eswa.2026.132096_bib0045","doi-asserted-by":"crossref","first-page":"4617","DOI":"10.1609\/aaai.v35i5.16591","article-title":"Coupled layer-wise graph convolution for transportation-demand prediction","volume":"35","author":"Ye","year":"2021","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"10.1016\/j.eswa.2026.132096_bib0046","series-title":"Advances in Neural Information Processing Systems (NeurIPS)","first-page":"1","article-title":"FourierGNN: Rethinking multivariate time-series forecasting from a pure graph perspective","volume":"36","author":"Yi","year":"2023"},{"key":"10.1016\/j.eswa.2026.132096_bib0047","series-title":"Proceedings of the International joint Conference on Artificial Intelligence (IJCAI),","first-page":"3634","article-title":"Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting","author":"Yu","year":"2018"},{"key":"10.1016\/j.eswa.2026.132096_bib0048","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.120724","article-title":"GSTC-UNet: A U-shaped multi-scaled spatiotemporal graph convolutional network with channel self-attention mechanism for traffic flow forecasting","volume":"232","author":"Yu","year":"2023","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2026.132096_bib0049","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/0166-218X(82)90033-6","article-title":"Signed graphs","volume":"4","author":"Zaslavsky","year":"1982","journal-title":"Discrete Applied Mathematics"},{"issue":"5","key":"10.1016\/j.eswa.2026.132096_bib0050","doi-asserted-by":"crossref","first-page":"3015","DOI":"10.1109\/TNSE.2021.3126830","article-title":"Graph neural network-driven traffic forecasting for the connected internet of vehicles","volume":"9","author":"Zhang","year":"2022","journal-title":"IEEE Transactions on Network Science and Engineering"},{"key":"10.1016\/j.eswa.2026.132096_bib0051","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2025.111208","article-title":"Adaptive lightweight temporal convolutional network with context-aware downsampling strategy for traffic flow prediction","volume":"156","author":"Zhang","year":"2025","journal-title":"Engineering Applications of Artificial Intelligence"},{"key":"10.1016\/j.eswa.2026.132096_bib0052","series-title":"Proceedings of the IEEE 29th International Conference on Parallel and Distributed Systems (ICPADS)","article-title":"SGRU: A high-performance structured gated recurrent unit for traffic-flow prediction","author":"Zhang","year":"2023"},{"key":"10.1016\/j.eswa.2026.132096_bib0053","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.122766","article-title":"Spatio-temporal Fourier enhanced heterogeneous graph learning for traffic forecasting","volume":"241","author":"Zhang","year":"2024","journal-title":"Expert Systems with Applications"},{"issue":"8","key":"10.1016\/j.eswa.2026.132096_bib0054","doi-asserted-by":"crossref","first-page":"12647","DOI":"10.1109\/TITS.2025.3563532","article-title":"Triple dynamic graph convolutional recurrent network for traffic prediction","volume":"26","author":"Zhang","year":"2025","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"issue":"1","key":"10.1016\/j.eswa.2026.132096_bib0055","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1109\/TVT.2019.2952605","article-title":"EnLSTM-WPEO: Short-term traffic flow prediction by ensemble LSTM, NNCT weight integration and population extremal optimization","volume":"69","author":"Zhao","year":"2020","journal-title":"IEEE Transactions on Vehicular Technology"},{"key":"10.1016\/j.eswa.2026.132096_bib0056","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2025.128961","article-title":"An interpretable and efficient multi-scale spatio-temporal neural network for traffic flow forecasting","volume":"296","author":"Zhao","year":"2026","journal-title":"Expert Systems with Applications"}],"container-title":["Expert Systems with Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417426010092?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417426010092?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T16:06:45Z","timestamp":1780934805000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0957417426010092"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":56,"alternative-id":["S0957417426010092"],"URL":"https:\/\/doi.org\/10.1016\/j.eswa.2026.132096","relation":{},"ISSN":["0957-4174"],"issn-type":[{"value":"0957-4174","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A competitive and cooperative spatio-temporal framework for urban traffic flow forecasting","name":"articletitle","label":"Article Title"},{"value":"Expert Systems with Applications","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.eswa.2026.132096","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"132096"}}