{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T16:50:28Z","timestamp":1770742228963,"version":"3.49.0"},"reference-count":48,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2022,10,1]],"date-time":"2022-10-01T00:00:00Z","timestamp":1664582400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2022,10,1]],"date-time":"2022-10-01T00:00:00Z","timestamp":1664582400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2022,10,1]],"date-time":"2022-10-01T00:00:00Z","timestamp":1664582400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2022,10,1]],"date-time":"2022-10-01T00:00:00Z","timestamp":1664582400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2022,10,1]],"date-time":"2022-10-01T00:00:00Z","timestamp":1664582400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2022,10,1]],"date-time":"2022-10-01T00:00:00Z","timestamp":1664582400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,10,1]],"date-time":"2022-10-01T00:00:00Z","timestamp":1664582400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Information Sciences"],"published-print":{"date-parts":[[2022,10]]},"DOI":"10.1016\/j.ins.2022.09.053","type":"journal-article","created":{"date-parts":[[2022,9,29]],"date-time":"2022-09-29T23:12:59Z","timestamp":1664493179000},"page":"19-34","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":16,"special_numbering":"C","title":["SPRNN: A spatial\u2013temporal recurrent neural network for crowd flow prediction"],"prefix":"10.1016","volume":"614","author":[{"given":"Gaozhong","family":"Tang","sequence":"first","affiliation":[]},{"given":"Bo","family":"Li","sequence":"additional","affiliation":[]},{"given":"Hong-Ning","family":"Dai","sequence":"additional","affiliation":[]},{"given":"Xi","family":"Zheng","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"2","key":"10.1016\/j.ins.2022.09.053_b0005","doi-asserted-by":"crossref","first-page":"865","DOI":"10.1109\/TKDE.2020.2985952","article-title":"Online spatio-temporal crowd flow distribution prediction for complex metro system","volume":"34","author":"Gong","year":"2022","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"10.1016\/j.ins.2022.09.053_b0010","doi-asserted-by":"crossref","first-page":"626","DOI":"10.1016\/j.ins.2022.07.054","article-title":"Hyper-clustering enhanced spatio-temporal deep learning for traffic and demand prediction in bike-sharing systems","volume":"612","author":"Zhao","year":"2022","journal-title":"Information Sciences"},{"issue":"1","key":"10.1016\/j.ins.2022.09.053_b0015","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1007\/s11263-020-01365-4","article-title":"Pixel-Wise Crowd Understanding via Synthetic Data","volume":"129","author":"Wang","year":"2021","journal-title":"International Journal of Computer Vision"},{"issue":"6","key":"10.1016\/j.ins.2022.09.053_b0020","doi-asserted-by":"crossref","first-page":"4675","DOI":"10.1109\/TCYB.2020.3033428","article-title":"Density-aware curriculum learning for crowd counting","volume":"52","author":"Wang","year":"2022","journal-title":"IEEE Transactions on Cybernetics"},{"key":"10.1016\/j.ins.2022.09.053_b0025","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.trc.2019.12.007","article-title":"Short-term traffic state prediction from latent structures: Accuracy vs. efficiency","volume":"111","author":"Li","year":"2020","journal-title":"Transportation Research Part C: Emerging Technologies"},{"key":"10.1016\/j.ins.2022.09.053_b0030","doi-asserted-by":"crossref","unstructured":"J. Zhang, Y. Zheng, D. Qi, Deep spatio-temporal residual networks for citywide crowd flows prediction, in: Thirty-First AAAI Conference on Artificial Intelligence, 2017.","DOI":"10.1609\/aaai.v31i1.10735"},{"issue":"1","key":"10.1016\/j.ins.2022.09.053_b0035","doi-asserted-by":"crossref","first-page":"116","DOI":"10.3141\/2024-14","article-title":"Adaptive seasonal time series models for forecasting short-term traffic flow","volume":"2024","author":"Shekhar","year":"2007","journal-title":"Transportation Research Record"},{"key":"10.1016\/j.ins.2022.09.053_b0040","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.trc.2014.02.006","article-title":"Adaptive kalman filter approach for stochastic short-term traffic flow rate prediction and uncertainty quantification","volume":"43","author":"Guo","year":"2014","journal-title":"Transportation Research Part C: Emerging Technologies"},{"key":"10.1016\/j.ins.2022.09.053_b0045","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.trc.2014.02.005","article-title":"New bayesian combination method for short-term traffic flow forecasting","volume":"43","author":"Wang","year":"2014","journal-title":"Transportation Research Part C: Emerging Technologies"},{"key":"10.1016\/j.ins.2022.09.053_b0050","series-title":"Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","first-page":"1","article-title":"Dnn-based prediction model for spatio-temporal data","author":"Zhang","year":"2016"},{"key":"10.1016\/j.ins.2022.09.053_b0055","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.trc.2019.09.008","article-title":"An effective spatial-temporal attention based neural network for traffic flow prediction","volume":"108","author":"Do","year":"2019","journal-title":"Transportation research part C: emerging technologies"},{"key":"10.1016\/j.ins.2022.09.053_b0060","doi-asserted-by":"crossref","unstructured":"B. Yu, H. Yin, Z. Zhu, Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting, in: Twenty-Seventh International Joint Conference on Artificial Intelligence IJCAI-18, 2018.","DOI":"10.24963\/ijcai.2018\/505"},{"key":"10.1016\/j.ins.2022.09.053_b0065","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1016\/j.neucom.2020.04.124","article-title":"Deep multi-view residual attention network for crowd flows prediction","volume":"404","author":"Yuan","year":"2020","journal-title":"Neurocomputing"},{"key":"10.1016\/j.ins.2022.09.053_b0070","doi-asserted-by":"crossref","unstructured":"H. Sak, A. Senior, F. Beaufays, Long short-term memory recurrent neural network architectures for large scale acoustic modeling, computer science (2014).","DOI":"10.21437\/Interspeech.2014-80"},{"key":"10.1016\/j.ins.2022.09.053_b0075","unstructured":"J. Chung, C. Gulcehre, K. Cho, Y. Bengio, Empirical evaluation of gated recurrent neural networks on sequence modeling, in: NIPS 2014 Workshop on Deep Learning, December 2014, 2014."},{"key":"10.1016\/j.ins.2022.09.053_b0080","doi-asserted-by":"crossref","unstructured":"Z. Lin, J. Feng, Z. Lu, Y. Li, D. Jin, Deepstn+: Context-aware spatial-temporal neural network for crowd flow prediction in metropolis, in: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33, 2019, pp. 1020\u20131027.","DOI":"10.1609\/aaai.v33i01.33011020"},{"issue":"2","key":"10.1016\/j.ins.2022.09.053_b0085","doi-asserted-by":"crossref","first-page":"338","DOI":"10.1007\/s11390-020-9970-y","article-title":"Exploiting multiple correlations among urban regions for crowd flow prediction","volume":"35","author":"Zhou","year":"2020","journal-title":"Journal of Computer Science and Technology"},{"key":"10.1016\/j.ins.2022.09.053_b0090","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1016\/j.ins.2021.02.036","article-title":"A novel prediction model for the inbound passenger flow of urban rail transit","volume":"566","author":"Yang","year":"2021","journal-title":"Information Sciences"},{"key":"10.1016\/j.ins.2022.09.053_b0095","series-title":"in: Proceedings of the International Joint Conference on Artificial Intelligence","first-page":"1981","article-title":"Stg2seq: Spatial-temporal graph to sequence model for multi-step passenger demand forecasting","author":"Bai","year":"2019"},{"key":"10.1016\/j.ins.2022.09.053_b0100","doi-asserted-by":"crossref","unstructured":"X. Yuan, J. Han, X. Wang, Y. He, W. Xu, K. Zhang, A novel learning approach for citywide crowd flow prediction, in: 2019 Computing, Communications and IoT Applications (ComComAp), 2019, pp. 341\u2013346. doi:10.1109\/ComComAp46287.2019.9018793.","DOI":"10.1109\/ComComAp46287.2019.9018793"},{"key":"10.1016\/j.ins.2022.09.053_b0105","doi-asserted-by":"crossref","unstructured":"S. Guo, Y. Lin, N. Feng, C. Song, H. Wan, Attention based spatial-temporal graph convolutional networks for traffic flow forecasting, in: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33, 2019, pp. 922\u2013929.","DOI":"10.1609\/aaai.v33i01.3301922"},{"key":"10.1016\/j.ins.2022.09.053_b0110","article-title":"T-gcn: A temporal graph convolutional network for traffic prediction","author":"Zhao","year":"2019","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"key":"10.1016\/j.ins.2022.09.053_b0115","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1016\/j.ins.2020.07.049","article-title":"A spatiotemporal hierarchical attention mechanism-based model for multi-step station-level crowd flow prediction","volume":"544","author":"Zhou","year":"2021","journal-title":"Information Sciences"},{"key":"10.1016\/j.ins.2022.09.053_b0120","doi-asserted-by":"crossref","first-page":"548","DOI":"10.1016\/j.ins.2022.07.008","article-title":"Multi-mode dynamic residual graph convolution network for traffic flow prediction","volume":"609","author":"Huang","year":"2022","journal-title":"Information Sciences"},{"issue":"5","key":"10.1016\/j.ins.2022.09.053_b0125","doi-asserted-by":"crossref","first-page":"4695","DOI":"10.1109\/TITS.2021.3055207","article-title":"Spatio-temporal knowledge transfer for urban crowd flow prediction via deep attentive adaptation networks","volume":"23","author":"Wang","year":"2022","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"issue":"10","key":"10.1016\/j.ins.2022.09.053_b0130","doi-asserted-by":"crossref","first-page":"3927","DOI":"10.1109\/TITS.2019.2909904","article-title":"Deep and embedded learning approach for traffic flow prediction in urban informatics","volume":"20","author":"Zheng","year":"2019","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"key":"10.1016\/j.ins.2022.09.053_b0135","article-title":"Dynamic spatial-temporal representation learning for traffic flow prediction","author":"Liu","year":"2020","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"key":"10.1016\/j.ins.2022.09.053_b0140","series-title":"Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint International Conference on Web and Big Data","first-page":"111","article-title":"St-dcn: A spatial-temporal densely connected networks for crowd flow prediction","author":"Xu","year":"2019"},{"key":"10.1016\/j.ins.2022.09.053_b0145","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.ins.2022.09.053_b0150","series-title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence IJCAI-18","first-page":"2940","article-title":"Predcnn: Predictive learning with cascade convolutions","author":"Xu","year":"2018"},{"issue":"5","key":"10.1016\/j.ins.2022.09.053_b0155","doi-asserted-by":"crossref","first-page":"4723","DOI":"10.1609\/aaai.v35i5.16603","article-title":"Modeling heterogeneous relations across multiple modes for potential crowd flow prediction","volume":"35","author":"Zhou","year":"2021","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"issue":"6","key":"10.1016\/j.ins.2022.09.053_b0160","doi-asserted-by":"crossref","DOI":"10.1145\/3451394","article-title":"3dgcn: 3-dimensional dynamic graph convolutional network for citywide crowd flow prediction","volume":"15","author":"Xia","year":"2021","journal-title":"ACM Trans. Knowl. Discov. Data"},{"issue":"7","key":"10.1016\/j.ins.2022.09.053_b0165","doi-asserted-by":"crossref","first-page":"1501","DOI":"10.3390\/s17071501","article-title":"Spatiotemporal recurrent convolutional networks for traffic prediction in transportation networks","volume":"17","author":"Yu","year":"2017","journal-title":"Sensors"},{"issue":"6","key":"10.1016\/j.ins.2022.09.053_b0170","doi-asserted-by":"crossref","first-page":"3337","DOI":"10.1109\/TITS.2020.2983763","article-title":"Temporal multi-graph convolutional network for traffic flow prediction","volume":"22","author":"Lv","year":"2021","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"key":"10.1016\/j.ins.2022.09.053_b0175","series-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","first-page":"1","article-title":"Going deeper with convolutions","author":"Szegedy","year":"2015"},{"key":"10.1016\/j.ins.2022.09.053_b0180","doi-asserted-by":"crossref","unstructured":"K. He, X. Zhang, S. Ren, J. Sun, Delving deep into rectifiers: Surpassing human-level performance on imagenet classification, in: 2015 IEEE International Conference on Computer Vision (ICCV), 2015, pp. 1026\u20131034. doi:10.1109\/ICCV.2015.123.","DOI":"10.1109\/ICCV.2015.123"},{"key":"10.1016\/j.ins.2022.09.053_b0185","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1016\/j.patrec.2017.03.023","article-title":"Adam: a method for stochastic optimization","volume":"94","author":"Sharma","year":"2017","journal-title":"Pattern Recognition Letters"},{"issue":"4","key":"10.1016\/j.ins.2022.09.053_b0190","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1061\/(ASCE)0733-947X(1997)123:4(261)","article-title":"Traffic flow forecasting: comparison of modeling approaches","volume":"123","author":"Smith","year":"1997","journal-title":"Journal of transportation engineering"},{"issue":"6","key":"10.1016\/j.ins.2022.09.053_b0195","doi-asserted-by":"crossref","first-page":"1551","DOI":"10.2307\/2938278","article-title":"Estimation and hypothesis testing of cointegration vectors in gaussian vector autoregressive models","volume":"59","author":"Johansen","year":"1991","journal-title":"Econometrica"},{"key":"10.1016\/j.ins.2022.09.053_b0200","series-title":"Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining","first-page":"785","article-title":"Xgboost: A scalable tree boosting system","author":"Chen","year":"2016"},{"key":"10.1016\/j.ins.2022.09.053_b0205","unstructured":"X. Shi, Z. Chen, H. Wang, D.-Y. Yeung, W. kin Wong, W. chun Woo, Convolutional lstm network: a machine learning approach for precipitation nowcasting, in: NIPS\u201915 Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 1, Vol. 28, 2015, pp. 802\u2013810."},{"key":"10.1016\/j.ins.2022.09.053_b0210","series-title":"Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining","first-page":"364","article-title":"Spatial-temporal graph ode networks for traffic flow forecasting","author":"Fang","year":"2021"},{"key":"10.1016\/j.ins.2022.09.053_b0215","doi-asserted-by":"crossref","unstructured":"H. Yao, X. Tang, H. Wei, G. Zheng, Z. Li, Revisiting spatial-temporal similarity: A deep learning framework for traffic prediction, in: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33, 2019, pp. 5668\u20135675.","DOI":"10.1609\/aaai.v33i01.33015668"},{"key":"10.1016\/j.ins.2022.09.053_b0220","doi-asserted-by":"crossref","DOI":"10.1109\/TKDE.2021.3077056","article-title":"Deepcrowd: A deep model for large-scale citywide crowd density and flow prediction","author":"Jiang","year":"2021","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"issue":"45","key":"10.1016\/j.ins.2022.09.053_b0225","first-page":"1","article-title":"Deep spatial-temporal networks for crowd flows prediction by dilated convolutions and region-shifting attention mechanism","volume":"50","author":"Tian","year":"2020","journal-title":"Applied Intelligence"},{"key":"10.1016\/j.ins.2022.09.053_b0230","doi-asserted-by":"crossref","unstructured":"J. Choi, H. Choi, J. Hwang, N. Park, Graph neural controlled differential equations for traffic forecasting, in: AAAI, 2022.","DOI":"10.1609\/aaai.v36i6.20587"},{"key":"10.1016\/j.ins.2022.09.053_b0235","article-title":"Introduction to machine learning","author":"Alpaydin","year":"2020","journal-title":"MIT press"},{"key":"10.1016\/j.ins.2022.09.053_b0240","first-page":"879","article-title":"Predrnn: Recurrent neural networks for predictive learning using spatiotemporal lstms","author":"Wang","year":"2017","journal-title":"Advances in Neural Information Processing Systems"}],"container-title":["Information Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0020025522011057?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0020025522011057?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T05:44:08Z","timestamp":1758087848000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0020025522011057"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10]]},"references-count":48,"alternative-id":["S0020025522011057"],"URL":"https:\/\/doi.org\/10.1016\/j.ins.2022.09.053","relation":{},"ISSN":["0020-0255"],"issn-type":[{"value":"0020-0255","type":"print"}],"subject":[],"published":{"date-parts":[[2022,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"SPRNN: A spatial\u2013temporal recurrent neural network for crowd flow prediction","name":"articletitle","label":"Article Title"},{"value":"Information Sciences","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ins.2022.09.053","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2022 Elsevier Inc. All rights reserved.","name":"copyright","label":"Copyright"}]}}