{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T03:06:37Z","timestamp":1781060797556,"version":"3.54.1"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2021,1,30]],"date-time":"2021-01-30T00:00:00Z","timestamp":1611964800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,30]],"date-time":"2021-01-30T00:00:00Z","timestamp":1611964800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"name":"the Fundamental Research Funds for the Central Universities","award":["NZ2020014"],"award-info":[{"award-number":["NZ2020014"]}]},{"name":"CCF-Tencent Open Research Fund"},{"DOI":"10.13039\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"crossref","award":["2018YFB1003900"],"award-info":[{"award-number":["2018YFB1003900"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2022,4]]},"DOI":"10.1007\/s11042-020-10492-6","type":"journal-article","created":{"date-parts":[[2021,1,30]],"date-time":"2021-01-30T16:03:10Z","timestamp":1612022590000},"page":"12029-12045","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Deep learning based origin-destination prediction via contextual information fusion"],"prefix":"10.1007","volume":"81","author":[{"given":"Hao","family":"Miao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Fei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Senzhang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Danyan","family":"Wen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,1,30]]},"reference":[{"issue":"2","key":"10492_CR1","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1287\/trsc.36.2.184.563","volume":"36","author":"K Ashok","year":"2002","unstructured":"Ashok K, Ben-Akiva ME (2002) Estimation and prediction of time-dependent origin-destination flows with a stochastic mapping to path flows and link flows. Trans Sci 36(2):184\u2013198","journal-title":"Trans Sci"},{"issue":"1","key":"10492_CR2","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1287\/opre.1030.0071","volume":"52","author":"M Bierlaire","year":"2004","unstructured":"Bierlaire M, Crittin F (2004) An efficient algorithm for real-time estimation and prediction of dynamic od tables. Oper Res 52(1):116\u2013127","journal-title":"Oper Res"},{"issue":"1","key":"10492_CR3","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1177\/0361198106196500103","volume":"1965","author":"M Cetin","year":"2006","unstructured":"Cetin M, Comert G (2006) Short-term traffic flow prediction with regime switching models. Transp Res Rec 1965(1):23\u201331","journal-title":"Transp Res Rec"},{"issue":"8","key":"10492_CR4","first-page":"1177","volume":"24","author":"Y Chen","year":"2009","unstructured":"Chen Y, Xiao D (2009) Traffic network flow forecasting based on switching model. Control and Decision 24(8):1177\u20131180","journal-title":"Control and Decision"},{"key":"10492_CR5","doi-asserted-by":"crossref","unstructured":"Chu KF, Lam AY, Li VO (2019) Deep multi-scale convolutional lstm network for travel demand and origin-destination predictions. IEEE Transactions on Intelligent Transportation Systems","DOI":"10.1109\/TITS.2019.2924971"},{"key":"10492_CR6","doi-asserted-by":"crossref","unstructured":"Diao Z, Wang X, Zhang D, Liu Y, Xie K, He S (2019) Dynamic spatial-temporal graph convolutional neural networks for traffic forecasting. In: Proceedings of the AAAI conference on artificial intelligence, vol 33, pp 890\u2013897","DOI":"10.1609\/aaai.v33i01.3301890"},{"issue":"3","key":"10492_CR7","doi-asserted-by":"publisher","first-page":"972","DOI":"10.1109\/TITS.2019.2900481","volume":"21","author":"B Du","year":"2020","unstructured":"Du B, Peng H, Wang S, Bhuiyan MZA, Wang L, Gong Q, Liu L, Li J (2020) Deep irregular convolutional residual lstm for urban traffic passenger flows prediction. IEEE Trans Intell Transp Syst 21(3):972\u2013985. https:\/\/doi.org\/10.1109\/TITS.2019.2900481","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"10492_CR8","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"issue":"8","key":"10492_CR9","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735\u20131780","journal-title":"Neural Comput"},{"key":"10492_CR10","unstructured":"Ioffe S, Szegedy C (2015) Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv:150203167"},{"key":"10492_CR11","unstructured":"Kipf TN, Welling M (2016) Semi-supervised classification with graph convolutional networks. arXiv:160902907"},{"key":"10492_CR12","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097\u20131105"},{"key":"10492_CR13","doi-asserted-by":"crossref","unstructured":"Lee S, Fambro D (1999) Application of subset autoregressive integrated moving average model for short-term freeway traffic volume forecasting. Transportation Research Record: Journal of the Transportation Research Board 1678","DOI":"10.3141\/1678-22"},{"key":"10492_CR14","unstructured":"Li Y, Yu R, Shahabi C, Liu Y (2017) Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. arXiv:170701926"},{"key":"10492_CR15","doi-asserted-by":"crossref","unstructured":"Lin Z, Feng J, Lu Z, Li Y, Jin D (2019) Deepstn+: Context-aware spatial-temporal neural network for crowd flow prediction in metropolis. In: Proceedings of the AAAI conference on artificial intelligence, vol 33, pp 1020\u20131027","DOI":"10.1609\/aaai.v33i01.33011020"},{"issue":"10","key":"10492_CR16","doi-asserted-by":"publisher","first-page":"3875","DOI":"10.1109\/TITS.2019.2915525","volume":"20","author":"L Liu","year":"2019","unstructured":"Liu L, Qiu Z, Li G, Wang Q, Ouyang W, Lin L (2019) Contextualized spatial\u2013temporal network for taxi origin-destination demand prediction. IEEE Trans Intell Transp Syst 20(10):3875\u20133887","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"10492_CR17","doi-asserted-by":"crossref","unstructured":"Ren J, Xie Q (2017) Efficient od trip matrix prediction based on tensor decomposition. In: 2017 18Th IEEE international conference on mobile data management, MDM, IEEE, pp 180\u2013185","DOI":"10.1109\/MDM.2017.32"},{"key":"10492_CR18","doi-asserted-by":"crossref","unstructured":"Seo Y, Defferrard M, Vandergheynst P, Bresson X (2018) Structured sequence modeling with graph convolutional recurrent networks. In: International conference on neural information processing, Springer, pp 362\u2013373","DOI":"10.1007\/978-3-030-04167-0_33"},{"key":"10492_CR19","unstructured":"Shi X, Chen Z, Wang H, Yeung DY, Wong WK, Woo Wc (2015) Convolutional lstm network: A machine learning approach for precipitation nowcasting. In: Advances in neural information processing systems, pp 802\u2013810"},{"issue":"4","key":"10492_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3378889","volume":"6","author":"S Wang","year":"2020","unstructured":"Wang S, Cao J, Chen H, Peng H, Huang Z (2020) Seqst-gan: Seq2seq generative adversarial nets for multi-step urban crowd flow prediction. ACM Transactions on Spatial Algorithms and Systems (TSAS) 6(4):1\u201324","journal-title":"ACM Transactions on Spatial Algorithms and Systems (TSAS)"},{"key":"10492_CR21","doi-asserted-by":"publisher","unstructured":"Wang S, Cao J, Yu P (2020) Deep learning for spatio-temporal data mining: a survey. IEEE Trans Knowl Data Eng, pp 1\u201320. https:\/\/doi.org\/10.1109\/TKDE.2020.3025580","DOI":"10.1109\/TKDE.2020.3025580"},{"key":"10492_CR22","doi-asserted-by":"crossref","unstructured":"Wang S, Miao H, Chen H, Huang Z (2020) Multi-task adversarial spatial-temporal networks for crowd flow prediction. In: Proceedings of the 29th ACM international conference on information & knowledge management, pp 1555\u20131564","DOI":"10.1145\/3340531.3412054"},{"key":"10492_CR23","doi-asserted-by":"crossref","unstructured":"Wang Y, Yin H, Chen H, Wo T, Xu J, Zheng K (2019) Origin-destination matrix prediction via graph convolution: a new perspective of passenger demand modeling. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, pp 1227\u20131235","DOI":"10.1145\/3292500.3330877"},{"key":"10492_CR24","doi-asserted-by":"crossref","unstructured":"Williams B (2001) Multivariate vehicular traffic flow prediction: Evaluation of arimax modeling. Transportation Research Record: Journal of the Transportation Research Board 1776","DOI":"10.3141\/1776-25"},{"key":"10492_CR25","doi-asserted-by":"crossref","unstructured":"Yao H, Wu F, Ke J, Tang X, Jia Y, Lu S, Gong P, Ye J, Zhenhui L (2018) Deep multi-view spatial-temporal network for taxi demand prediction. In: The Thirty-Second AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v32i1.11836"},{"key":"10492_CR26","doi-asserted-by":"crossref","unstructured":"Yao H, Tang X, Wei H, Zheng G, Li Z (2019) Revisiting spatial-temporal similarity: a deep learning framework for traffic prediction. In: Proceedings of the AAAI conference on artificial intelligence, vol 33, pp 5668\u20135675","DOI":"10.1609\/aaai.v33i01.33015668"},{"key":"10492_CR27","doi-asserted-by":"crossref","unstructured":"Yu B, Yin H, Zhu Z (2017) Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv:170904875","DOI":"10.24963\/ijcai.2018\/505"},{"key":"10492_CR28","doi-asserted-by":"crossref","unstructured":"Zhang J, Zheng Y, Qi D (2017) Deep spatio-temporal residual networks for citywide crowd flows prediction. In: Proceedings of the Thirty-First AAAI conference on artificial intelligence, AAAI Press, pp 1655\u20131661","DOI":"10.1609\/aaai.v31i1.10735"},{"key":"10492_CR29","doi-asserted-by":"crossref","unstructured":"Zhang J, Zheng Y, Qi D (2017) Deep spatio-temporal residual networks for citywide crowd flows prediction. Proceedings of AAAI","DOI":"10.1609\/aaai.v31i1.10735"},{"key":"10492_CR30","doi-asserted-by":"publisher","first-page":"133452","DOI":"10.1109\/ACCESS.2019.2941177","volume":"7","author":"J Zhang","year":"2019","unstructured":"Zhang J, Chen F, Wang Z, Liu H (2019) Short-term origin-destination forecasting in urban rail transit based on attraction degree. IEEE Access 7:133452\u2013133462","journal-title":"IEEE Access"},{"key":"10492_CR31","doi-asserted-by":"crossref","unstructured":"Zhang J, Zheng Y, Sun J, Qi D (2019) Flow prediction in spatio-temporal networks based on multitask deep learning. IEEE Transactions on Knowledge and Data Engineering","DOI":"10.1109\/TKDE.2019.2891537"},{"key":"10492_CR32","doi-asserted-by":"publisher","unstructured":"Zhang Y, Wang S, Chen B, Cao J, Huang Z (2019) Trafficgan: Network-scale deep traffic prediction with generative adversarial nets. IEEE Trans Intell Transp Syst, pp 1\u201312. https:\/\/doi.org\/10.1109\/TITS.2019.2955794","DOI":"10.1109\/TITS.2019.2955794"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-020-10492-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-020-10492-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-020-10492-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,14]],"date-time":"2022-12-14T02:39:09Z","timestamp":1670985549000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-020-10492-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,30]]},"references-count":32,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2022,4]]}},"alternative-id":["10492"],"URL":"https:\/\/doi.org\/10.1007\/s11042-020-10492-6","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,30]]},"assertion":[{"value":"29 July 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 November 2020","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 December 2020","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 January 2021","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}