{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T06:25:13Z","timestamp":1783059913671,"version":"3.54.6"},"reference-count":45,"publisher":"Elsevier BV","issue":"1","license":[{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"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":["41471333"],"award-info":[{"award-number":["41471333"]}],"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":["42201500"],"award-info":[{"award-number":["42201500"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Information Processing &amp; Management"],"published-print":{"date-parts":[[2027,1]]},"DOI":"10.1016\/j.ipm.2026.104974","type":"journal-article","created":{"date-parts":[[2026,6,14]],"date-time":"2026-06-14T15:57:29Z","timestamp":1781452649000},"page":"104974","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"PA","title":["EVOLVE: A Continual Learning Framework for Evolving Traffic Networks"],"prefix":"10.1016","volume":"64","author":[{"given":"Shiyu","family":"Yang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hengyu","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-3860-2199","authenticated-orcid":false,"given":"Chuxi","family":"Fan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qunyong","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mengmeng","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiyuan","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.ipm.2026.104974_b1","doi-asserted-by":"crossref","DOI":"10.1016\/j.future.2026.108559","article-title":"Exploiting attention-driven weather-aware multimodal spatio-temporal fusion for urban traffic flow prediction","author":"Ali","year":"2026","journal-title":"Future Generation Computer Systems"},{"key":"10.1016\/j.ipm.2026.104974_b2","doi-asserted-by":"crossref","DOI":"10.1016\/j.chaos.2025.116898","article-title":"Dynamic multi-graph spatio-temporal learning for citywide traffic flow prediction in transportation systems","volume":"199","author":"Ali","year":"2025","journal-title":"Chaos, Solitons & Fractals"},{"key":"10.1016\/j.ipm.2026.104974_b3","doi-asserted-by":"crossref","DOI":"10.1016\/j.cosrev.2025.100886","article-title":"Advanced computational models for urban traffic flow prediction: A comprehensive review and future directions","volume":"60","author":"Ali","year":"2026","journal-title":"Computer Science Review"},{"key":"10.1016\/j.ipm.2026.104974_b4","doi-asserted-by":"crossref","first-page":"852","DOI":"10.1016\/j.ins.2021.08.042","article-title":"Exploiting dynamic spatio-temporal correlations for citywide traffic flow prediction using attention based neural networks","volume":"577","author":"Ali","year":"2021","journal-title":"Information Sciences"},{"key":"10.1016\/j.ipm.2026.104974_b5","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1016\/j.neunet.2021.10.021","article-title":"Exploiting dynamic spatio-temporal graph convolutional neural networks for citywide traffic flows prediction","volume":"145","author":"Ali","year":"2022","journal-title":"Neural Networks"},{"key":"10.1016\/j.ipm.2026.104974_b6","series-title":"Advances in neural information processing systems","first-page":"17804","article-title":"Adaptive graph convolutional recurrent network for traffic forecasting","author":"Bai","year":"2020"},{"key":"10.1016\/j.ipm.2026.104974_b7","doi-asserted-by":"crossref","DOI":"10.3389\/frai.2022.824655","article-title":"Catastrophic forgetting in deep graph networks: A graph classification benchmark","volume":"5","author":"Carta","year":"2022","journal-title":"Frontiers in Artificial Intelligence"},{"key":"10.1016\/j.ipm.2026.104974_b8","unstructured":"Chen, W., & Liang, Y. (2025). Expand and Compress: Exploring Tuning Principles for Continual Spatio-Temporal Graph Forecasting. In The thirteenth international conference on learning representations."},{"key":"10.1016\/j.ipm.2026.104974_b9","series-title":"2024 IEEE 27th international conference on intelligent transportation systems","first-page":"837","article-title":"Continual learning for adaptable car-following in dynamic traffic environments","author":"Chen","year":"2024"},{"key":"10.1016\/j.ipm.2026.104974_b10","series-title":"Proceedings of the thirtieth international joint conference on artificial intelligence, IJCAI-21","first-page":"3620","article-title":"TrafficStream: A streaming traffic flow forecasting framework based on graph neural networks and continual learning","author":"Chen","year":"2021"},{"key":"10.1016\/j.ipm.2026.104974_b11","series-title":"2017 IEEE 60th international midwest symposium on circuits and systems","first-page":"1597","article-title":"Gate-variants of gated recurrent unit (GRU) neural networks","author":"Dey","year":"2017"},{"key":"10.1016\/j.ipm.2026.104974_b12","series-title":"Proceedings of the AAAI conference on artificial intelligence","first-page":"922","article-title":"Attention based spatial-temporal graph convolutional networks for traffic flow forecasting","author":"Guo","year":"2019"},{"issue":"11","key":"10.1016\/j.ipm.2026.104974_b13","doi-asserted-by":"crossref","first-page":"E2496","DOI":"10.1073\/pnas.1717042115","article-title":"Note on the quadratic penalties in elastic weight consolidation","volume":"115","author":"Husz\u00e1r","year":"2018","journal-title":"Proceedings of the National Academy of Sciences"},{"key":"10.1016\/j.ipm.2026.104974_b14","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"},{"key":"10.1016\/j.ipm.2026.104974_b15","doi-asserted-by":"crossref","unstructured":"Jiang, R., Wang, Z., Yong, J., Jeph, P., Chen, Q., Kobayashi, Y., Song, X., Fukushima, S., & Suzumura, T. (2023). Spatio-temporal meta-graph learning for traffic forecasting. 37, In Proceedings of the AAAI conference on artificial intelligence (pp. 8078\u20138086).","DOI":"10.1609\/aaai.v37i7.25976"},{"issue":"2","key":"10.1016\/j.ipm.2026.104974_b16","doi-asserted-by":"crossref","first-page":"265","DOI":"10.14778\/3705829.3705844","article-title":"TEAM: Topological evolution-aware framework for traffic forecasting","volume":"18","author":"Kieu","year":"2024","journal-title":"Proceedings of the VLDB Endowment"},{"key":"10.1016\/j.ipm.2026.104974_b17","series-title":"Proceedings of the AAAI conference on artificial intelligence","first-page":"8627","article-title":"Spatio-temporal pivotal graph neural networks for traffic flow forecasting","volume":"38","author":"Kong","year":"2024"},{"key":"10.1016\/j.ipm.2026.104974_b18","article-title":"Spatial context-enhanced temporal knowledge graph reasoning","volume":"62","author":"Li","year":"2025","journal-title":"Information Processing & Management"},{"issue":"1","key":"10.1016\/j.ipm.2026.104974_b19","first-page":"1","article-title":"Dynamic graph convolutional recurrent network for traffic prediction: Benchmark and solution","volume":"17","author":"Li","year":"2023","journal-title":"ACM Transactions on Knowledge Discovery from Data"},{"key":"10.1016\/j.ipm.2026.104974_b20","doi-asserted-by":"crossref","unstructured":"Li, Z., Liang, L., Deliu, N., & Huang, C. (2024). UrbanGPT: Spatio-Temporal Large Language Models. In Proceedings of the 30th ACM SIGKDD conference on knowledge discovery and data mining (pp. 5351\u20135362).","DOI":"10.1145\/3637528.3671578"},{"key":"10.1016\/j.ipm.2026.104974_b21","unstructured":"Li, Y., Yu, R., Shahabi, C., & Liu, Y. (2018). Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. In International conference on learning representations."},{"key":"10.1016\/j.ipm.2026.104974_b22","doi-asserted-by":"crossref","first-page":"75354","DOI":"10.52202\/075280-3293","article-title":"Largest: A benchmark dataset for large-scale traffic forecasting","volume":"36","author":"Liu","year":"2023","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.ipm.2026.104974_b23","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2024.123884","article-title":"An efficient spatial-temporal transformer with temporal aggregation and spatial memory for traffic forecasting","volume":"250","author":"Liu","year":"2024","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.ipm.2026.104974_b24","series-title":"STG4traffic: A survey and benchmark of spatial-temporal graph neural networks for traffic prediction","author":"Luo","year":"2023"},{"key":"10.1016\/j.ipm.2026.104974_b25","series-title":"2024 IEEE 40th international conference on data engineering","first-page":"1050","article-title":"A unified replay-based continuous learning framework for spatio-temporal prediction on streaming data","author":"Miao","year":"2024"},{"key":"10.1016\/j.ipm.2026.104974_b26","article-title":"Mvstt: A multiview spatial-temporal transformer network for traffic-flow forecasting","author":"Pu","year":"2022","journal-title":"IEEE Transactions on Cybernetics"},{"key":"10.1016\/j.ipm.2026.104974_b27","article-title":"Secure fuzzy-based H\u221e consensus control for nonlinear multi-agent systems under quantized sampled-data mechanism and its applications","volume":"162","author":"Samy","year":"2025","journal-title":"Engineering Applications of Artificial Intelligence"},{"key":"10.1016\/j.ipm.2026.104974_b28","series-title":"Advances in neural information processing systems","first-page":"802","article-title":"Convolutional LSTM network: A machine learning approach for precipitation nowcasting","author":"SHI","year":"2015"},{"key":"10.1016\/j.ipm.2026.104974_b29","article-title":"Continual learning: Applications and the road forward","author":"Verwimp","year":"2024","journal-title":"Transactions on Machine Learning Research","ISSN":"https:\/\/id.crossref.org\/issn\/2835-8856","issn-type":"print"},{"issue":"7","key":"10.1016\/j.ipm.2026.104974_b30","doi-asserted-by":"crossref","first-page":"7190","DOI":"10.1109\/TITS.2023.3263904","article-title":"Knowledge expansion and consolidation for continual traffic prediction with expanding graphs","volume":"24","author":"Wang","year":"2023","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"key":"10.1016\/j.ipm.2026.104974_b31","doi-asserted-by":"crossref","unstructured":"Wang, B., Zhang, Y., Wang, X., Wang, P., Zhou, Z., Bai, L., & Wang, Y. (2023). Pattern expansion and consolidation on evolving graphs for continual traffic prediction. In Proceedings of the 29th ACM SIGKDD conference on knowledge discovery and data mining (pp. 2223\u20132232).","DOI":"10.1145\/3580305.3599463"},{"key":"10.1016\/j.ipm.2026.104974_b32","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2023.109670","article-title":"A decomposition dynamic graph convolutional recurrent network for traffic forecasting","volume":"142","author":"Weng","year":"2023","journal-title":"Pattern Recognition"},{"key":"10.1016\/j.ipm.2026.104974_b33","series-title":"Proceedings of the ACM SIGKDD international conference on knowledge discovery & spec- tral temporal graph neural network for multivariate time- series forecasting","first-page":"753","article-title":"Connecting the dots: multivariate time series forecasting with graph neural networks","author":"Wu","year":"2020"},{"key":"10.1016\/j.ipm.2026.104974_b34","series-title":"Proceedings of international joint conference on artificial intelligence","first-page":"1907","article-title":"Graph WaveNet for deep spatial-temporal graph modeling","author":"Wu","year":"2019"},{"issue":"1","key":"10.1016\/j.ipm.2026.104974_b35","doi-asserted-by":"crossref","DOI":"10.1016\/j.ipm.2024.103942","article-title":"Spatial and temporal twin-guided pattern recurrent graph network for implementing reasoning of spatiotemporal knowledge graph","volume":"62","author":"Xu","year":"2025","journal-title":"Information Processing & Management"},{"issue":"12","key":"10.1016\/j.ipm.2026.104974_b36","doi-asserted-by":"crossref","first-page":"7805","DOI":"10.1109\/TKDE.2024.3447123","article-title":"Continual learning for smart city: A survey","volume":"36","author":"Yang","year":"2024","journal-title":"IEEE Transactions on Knowledge and Data Engineering","ISSN":"https:\/\/id.crossref.org\/issn\/1041-4347","issn-type":"print"},{"key":"10.1016\/j.ipm.2026.104974_b37","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2025.131064","article-title":"MTEGCRN: Multi-scale temporal enhanced graph convolutional recurrent network for traffic prediction","author":"Yang","year":"2025","journal-title":"Neurocomputing"},{"key":"10.1016\/j.ipm.2026.104974_b38","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2025.112956","article-title":"Decoupled multi-spatio-temporal fusion graph convolutional recurrent network for traffic prediction","volume":"163","author":"Yang","year":"2026","journal-title":"Engineering Applications of Artificial Intelligence"},{"issue":"11","key":"10.1016\/j.ipm.2026.104974_b39","doi-asserted-by":"crossref","first-page":"15057","DOI":"10.1109\/JIOT.2025.3529761","article-title":"Temporal identity interaction dynamic graph convolutional network for traffic forecasting","volume":"12","author":"Yang","year":"2025","journal-title":"IEEE Internet of Things Journal"},{"key":"10.1016\/j.ipm.2026.104974_b40","series-title":"Proceedings of the international joint conference on artificial intelligence","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.ipm.2026.104974_b41","doi-asserted-by":"crossref","unstructured":"Yuan, Y., Ding, J., Feng, J., Jin, D., & Li, Y. (2024). Unist: A prompt-empowered universal model for urban spatio-temporal prediction. In Proceedings of the 30th ACM SIGKDD conference on knowledge discovery and data mining (pp. 4095\u20134106).","DOI":"10.1145\/3637528.3671662"},{"key":"10.1016\/j.ipm.2026.104974_b42","doi-asserted-by":"crossref","unstructured":"Zheng, C., Fan, X., Wang, C., & Qi, J. (2020). Gman: A graph multi-attention network for traffic prediction. vol. 34, In Proceedings of the AAAI conference on artificial intelligence (pp. 1234\u20131241).","DOI":"10.1609\/aaai.v34i01.5477"},{"key":"10.1016\/j.ipm.2026.104974_b43","doi-asserted-by":"crossref","unstructured":"Zhou, F., & Cao, C. (2021). Overcoming catastrophic forgetting in graph neural networks with experience replay. vol. 35, In Proceedings of the AAAI conference on artificial intelligence (pp. 4714\u20134722).","DOI":"10.1609\/aaai.v35i5.16602"},{"issue":"1","key":"10.1016\/j.ipm.2026.104974_b44","article-title":"MReDTrajRec: a multi-representation data-driven model for trajectory recovery under road network constraints","volume":"13","author":"Zhou","year":"2025","journal-title":"Transportmetrica B: Transport Dynamics"},{"issue":"9","key":"10.1016\/j.ipm.2026.104974_b45","doi-asserted-by":"crossref","first-page":"6486","DOI":"10.1109\/TPAMI.2024.3382294","article-title":"Towards understanding convergence and generalization of adamw","volume":"46","author":"Zhou","year":"2024","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"}],"container-title":["Information Processing &amp; Management"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0306457326003651?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0306457326003651?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T05:59:40Z","timestamp":1783058380000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0306457326003651"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2027,1]]},"references-count":45,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2027,1]]}},"alternative-id":["S0306457326003651"],"URL":"https:\/\/doi.org\/10.1016\/j.ipm.2026.104974","relation":{"is-supplemented-by":[{"id-type":"uri","id":"https:\/\/github.com\/OvOYu\/EVOLVE","asserted-by":"subject"}]},"ISSN":["0306-4573"],"issn-type":[{"value":"0306-4573","type":"print"}],"subject":[],"published":{"date-parts":[[2027,1]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"EVOLVE: A Continual Learning Framework for Evolving Traffic Networks","name":"articletitle","label":"Article Title"},{"value":"Information Processing & Management","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ipm.2026.104974","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"104974"}}