{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T05:48:53Z","timestamp":1783576133927,"version":"3.55.0"},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2022,3,31]],"date-time":"2022-03-31T00:00:00Z","timestamp":1648684800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,3,31]],"date-time":"2022-03-31T00:00:00Z","timestamp":1648684800000},"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":["61872258"],"award-info":[{"award-number":["61872258"]}],"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":["61772356"],"award-info":[{"award-number":["61772356"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["World Wide Web"],"published-print":{"date-parts":[[2023,5]]},"DOI":"10.1007\/s11280-022-01045-y","type":"journal-article","created":{"date-parts":[[2022,3,31]],"date-time":"2022-03-31T09:03:56Z","timestamp":1648717436000},"page":"849-865","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Learning to effectively model spatial-temporal heterogeneity for traffic flow forecasting"],"prefix":"10.1007","volume":"26","author":[{"given":"Minrui","family":"Xu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiyang","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fucheng","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jedi S.","family":"Shang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tai","family":"Chong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wanjun","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiajie","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,3,31]]},"reference":[{"key":"1045_CR1","unstructured":"Bai, L., Yao, L., Li, C., Wang, X., Wang, C.: Adaptive graph convolutional recurrent network for traffic forecasting. In: NeurIPS, pp. 1\u201312 (2020). https:\/\/proceedings.neurips.cc\/paper\/2020\/hash\/ce1aad92b939420fc17005e5461e6f48-Abstract.html"},{"issue":"3","key":"1045_CR2","doi-asserted-by":"publisher","first-page":"546","DOI":"10.1111\/j.1467-9892.2009.00643.x","volume":"31","author":"GEP Box","year":"2010","unstructured":"Box, G.E.P., Jenkins, G.M.: Time series analysis\u202f: forecasting and control. Journal of Time 31(3), 546\u2013549 (2010). https:\/\/doi.org\/10.1111\/j.1467-9892.2009.00643.x","journal-title":"Journal of Time"},{"issue":"2","key":"1045_CR3","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1080\/15472450902858368","volume":"13","author":"SR Chandra","year":"2009","unstructured":"Chandra, S.R., Al-Deek, H.: Predictions of freeway traffic speeds and volumes using vector autoregressive models. J. Intell. Transp. Syst. 13(2), 53\u201372 (2009). https:\/\/doi.org\/10.1080\/15472450902858368","journal-title":"J. Intell. Transp. Syst."},{"key":"1045_CR4","doi-asserted-by":"publisher","unstructured":"Chen, L., Shang, S., Guo, T.: Real-time route search by locations. In: AAAI, pp. 574\u2013581 (2020). https:\/\/doi.org\/10.1609\/aaai.v34i01.5396","DOI":"10.1609\/aaai.v34i01.5396"},{"key":"1045_CR5","doi-asserted-by":"publisher","unstructured":"Chen, L., Shang, S., Jensen, C.S., Yao, B., Zhang, Z., Shao, L.: Effective and efficient reuse of past travel behavior for route recommendation. In: KDD, pp. 488\u2013498 (2019). https:\/\/doi.org\/10.1145\/3292500.3330835","DOI":"10.1145\/3292500.3330835"},{"key":"1045_CR6","doi-asserted-by":"publisher","unstructured":"Chen, L., Shang, S., Yao, B., Li, J.: Pay your trip for traffic congestion: Dynamic pricing in traffic-aware road networks. In: AAAI, pp. 582\u2013589 (2020). https:\/\/doi.org\/10.1609\/aaai.v34i01.5397","DOI":"10.1609\/aaai.v34i01.5397"},{"key":"1045_CR7","doi-asserted-by":"publisher","unstructured":"Chen, H., Yin, H., Sun, X., Chen, T., Gabrys, B., Musial, K.: Multi-level graph convolutional networks for cross-platform anchor link prediction. In: KDD, pp. 1503\u20131511 (2020). https:\/\/doi.org\/10.1145\/3394486.3403201","DOI":"10.1145\/3394486.3403201"},{"issue":"12","key":"1045_CR8","doi-asserted-by":"publisher","first-page":"3701","DOI":"10.1109\/TKDE.2020.2975998","volume":"33","author":"Z Chen","year":"2021","unstructured":"Chen, Z., Yao, B., Wang, Z., Gao, X., Shang, S., Ma, S., Guo, M.: Flexible aggregate nearest neighbor queries and its keyword-aware variant on road networks. IEEE Trans. Knowl. Data Eng. 33(12), 3701\u20133715 (2021). https:\/\/doi.org\/10.1109\/TKDE.2020.2975998","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"1045_CR9","unstructured":"Dauphin, Y.N., Fan, A., Auli, M., Grangier, D.: Language modeling with gated convolutional networks. In: ICML. Proceedings of Machine Learning Research, vol. 70, pp. 933\u2013941 (2017). http:\/\/proceedings.mlr.press\/v70\/dauphin17a.html"},{"key":"1045_CR10","doi-asserted-by":"publisher","unstructured":"Guo, S., Lin, Y., Feng, N., Song, C., Wan, H.: Attention based spatial-temporal graph convolutional networks for traffic flow forecasting. In: AAAI, pp. 922\u2013929 (2019). https:\/\/doi.org\/10.1609\/aaai.v33i01.3301922","DOI":"10.1609\/aaai.v33i01.3301922"},{"key":"1045_CR11","doi-asserted-by":"publisher","unstructured":"Han, P., Li, Z., Liu, Y., Zhao, P., Li, J., Wang, H., Shang, S.: Contextualized point-of-interest recommendation. In: IJCAI, pp. 2484\u20132490 (2020). https:\/\/doi.org\/10.24963\/ijcai.2020\/344","DOI":"10.24963\/ijcai.2020\/344"},{"issue":"8","key":"1045_CR12","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.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997). https:\/\/doi.org\/10.1162\/neco.1997.9.8.1735","journal-title":"Neural Comput."},{"key":"1045_CR13","doi-asserted-by":"publisher","unstructured":"Hong, H., Lin, Y., Yang, X., Li, Z., Fu, K., Wang, Z., Qie, X., Ye, J.: Heteta: Heterogeneous information network embedding for estimating time of arrival. In: KDD, pp. 2444\u20132454 (2020). https:\/\/doi.org\/10.1145\/3394486.3403294","DOI":"10.1145\/3394486.3403294"},{"key":"1045_CR14","doi-asserted-by":"publisher","unstructured":"Hui, B., Yan, D., Chen, H., Ku, W.: Trajnet: A trajectory-based deep learning model for traffic prediction. In: KDD, pp. 716\u2013724 (2021). https:\/\/doi.org\/10.1145\/3447548.3467236","DOI":"10.1145\/3447548.3467236"},{"key":"1045_CR15","unstructured":"Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings, pp. 1\u201315 (2015). arxiv:1412.6980"},{"key":"1045_CR16","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: ICLR (Poster), pp. 1\u201314 (2017). https:\/\/openreview.net\/forum?id=SJU4ayYgl"},{"key":"1045_CR17","doi-asserted-by":"publisher","unstructured":"Li, K., Chen, L., Shang, S., Kalnis, P., Yao, B.: Traffic congestion alleviation over dynamic road networks: Continuous optimal route combination for trip query streams. In: IJCAI, pp. 3656\u20133662 (2021). https:\/\/doi.org\/10.24963\/ijcai.2021\/503","DOI":"10.24963\/ijcai.2021\/503"},{"key":"1045_CR18","doi-asserted-by":"publisher","unstructured":"Li, K., Chen, L., Shang, S.: Towards alleviating traffic congestion: Optimal route planning for massive-scale trips. In: IJCAI, pp. 3400\u20133406 (2020). https:\/\/doi.org\/10.24963\/ijcai.2020\/470","DOI":"10.24963\/ijcai.2020\/470"},{"key":"1045_CR19","doi-asserted-by":"publisher","unstructured":"Li, Y., Fu, K., Wang, Z., Shahabi, C., Ye, J., Liu, Y.: Multi-task representation learning for travel time estimation. In: KDD, pp. 1695\u20131704 (2018). https:\/\/doi.org\/10.1145\/3219819.3220033","DOI":"10.1145\/3219819.3220033"},{"key":"1045_CR20","doi-asserted-by":"crossref","unstructured":"Li, X., Shang, Y., Cao, Y., Li, Y., Tan, J., Liu, Y.: Type-aware anchor link prediction across heterogeneous networks based on graph attention network. In: AAAI, pp. 147\u2013155 (2020). https:\/\/aaai.org\/ojs\/index.php\/AAAI\/article\/view\/5345","DOI":"10.1609\/aaai.v34i01.5345"},{"key":"1045_CR21","doi-asserted-by":"crossref","unstructured":"Li, M., Tong, P., Li, M., Jin, Z., Huang, J., Hua, X.: Traffic flow prediction with vehicle trajectories. In: AAAI, pp. 294\u2013302 (2021). https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/16104","DOI":"10.1609\/aaai.v35i1.16104"},{"key":"1045_CR22","doi-asserted-by":"crossref","unstructured":"Li, R., Wang, S., Zhu, F., Huang, J.: Adaptive graph convolutional neural networks. In: AAAI, pp. 3546\u20133553 (2018). https:\/\/www.aaai.org\/ocs\/index.php\/AAAI\/AAAI18\/paper\/view\/16642","DOI":"10.1609\/aaai.v32i1.11691"},{"key":"1045_CR23","unstructured":"Li, Y., Yu, R., Shahabi, C., Liu, Y.: Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. In: ICLR (Poster) (2018). https:\/\/openreview.net\/forum?id=SJiHXGWAZ"},{"issue":"1","key":"1045_CR24","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1145\/3453724","volume":"16","author":"Y Li","year":"2022","unstructured":"Li, Y., Li, K., Chen, C., Zhou, X., Zeng, Z., Li, K.: Modeling temporal patterns with dilated convolutions for time-series forecasting. ACM Trans. Knowl. Discov. Data 16(1), 14\u201311422 (2022). https:\/\/doi.org\/10.1145\/3453724","journal-title":"ACM Trans. Knowl. Discov. Data"},{"key":"1045_CR25","doi-asserted-by":"publisher","unstructured":"Lin, Z., Feng, J., Lu, Z., Li, Y., Jin, D.: Deepstn+: Context-aware spatial-temporal neural network for crowd flow prediction in metropolis. In: AAAI, pp. 1020\u20131027 (2019). https:\/\/doi.org\/10.1609\/aaai.v33i01.33011020","DOI":"10.1609\/aaai.v33i01.33011020"},{"key":"1045_CR26","doi-asserted-by":"publisher","unstructured":"Lv, Z., Xu, J., Zheng, K., Yin, H., Zhao, P., Zhou, X.: LC-RNN: A deep learning model for traffic speed prediction. In: IJCAI, pp. 3470\u20133476 (2018). https:\/\/doi.org\/10.24963\/ijcai.2018\/482","DOI":"10.24963\/ijcai.2018\/482"},{"key":"1045_CR27","unstructured":"Niepert, M., Ahmed, M., Kutzkov, K.: Learning convolutional neural networks for graphsx. In: ICML. JMLR Workshop and Conference Proceedings, vol. 48, pp. 2014\u20132023 (2016). http:\/\/proceedings.mlr.press\/v48\/niepert16.html"},{"key":"1045_CR28","doi-asserted-by":"publisher","unstructured":"Pan, Z., Wang, Z., Wang, W., Yu, Y., Zhang, J., Zheng, Y.: Matrix factorization for spatio-temporal neural networks with applications to urban flow prediction. In: CIKM, pp. 2683\u20132691 (2019). https:\/\/doi.org\/10.1145\/3357384.3357832","DOI":"10.1145\/3357384.3357832"},{"key":"1045_CR29","doi-asserted-by":"publisher","unstructured":"Seo, Y., Defferrard, M., Vandergheynst, P., Bresson, X.: Structured sequence modeling with graph convolutional recurrent networks. In: ICONIP (1). Lecture Notes in Computer Science, vol. 11301, pp. 362\u2013373 (2018). https:\/\/doi.org\/10.1007\/978-3-030-04167-0_33","DOI":"10.1007\/978-3-030-04167-0_33"},{"key":"1045_CR30","doi-asserted-by":"publisher","unstructured":"Shang, S., Chen, L., Jensen, C.S., Wen, J., Kalnis, P.: Searching trajectories by regions of interest. In: ICDE, pp. 1741\u20131742 (2018). https:\/\/doi.org\/10.1109\/ICDE.2018.00228","DOI":"10.1109\/ICDE.2018.00228"},{"issue":"3","key":"1045_CR31","doi-asserted-by":"publisher","first-page":"449","DOI":"10.1007\/s00778-013-0331-0","volume":"23","author":"S Shang","year":"2014","unstructured":"Shang, S., Ding, R., Zheng, K., Jensen, C.S., Kalnis, P., Zhou, X.: Personalized trajectory matching in spatial networks. VLDB J. 23(3), 449\u2013468 (2014). https:\/\/doi.org\/10.1007\/s00778-013-0331-0","journal-title":"VLDB J."},{"key":"1045_CR32","unstructured":"Shi, X., Gao, Z., Lausen, L., Wang, H., Yeung, D., Wong, W., Woo, W.: Deep learning for precipitation nowcasting: A benchmark and A new model. In: NIPS, pp. 5617\u20135627 (2017). https:\/\/proceedings.neurips.cc\/paper\/2017\/hash\/a6db4ed04f1621a119799fd3d7545d3d-Abstract.html"},{"key":"1045_CR33","unstructured":"Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Li\u00f2, P., Bengio, Y.: Graph attention networks. arxiv:1710.10903, 1\u201312 (2017)"},{"key":"1045_CR34","doi-asserted-by":"publisher","unstructured":"Wang, Z., Wang, J., Guo, Y., Gong, Z.: Zero-shot node classification with decomposed graph prototype network. In: KDD, pp. 1769\u20131779 (2021). https:\/\/doi.org\/10.1145\/3447548.3467230","DOI":"10.1145\/3447548.3467230"},{"key":"1045_CR35","doi-asserted-by":"publisher","unstructured":"Wang, F., Xu, J., Liu, C., Zhou, R., Zhao, P.: MTGCN: A multitask deep learning model for traffic flow prediction. In: DASFAA, vol. 12112, pp. 435\u2013451 (2020). https:\/\/doi.org\/10.1007\/978-3-030-59410-7_30","DOI":"10.1007\/978-3-030-59410-7_30"},{"issue":"3","key":"1045_CR36","doi-asserted-by":"publisher","first-page":"805","DOI":"10.1007\/s11280-021-00877-4","volume":"24","author":"F Wang","year":"2021","unstructured":"Wang, F., Xu, J., Liu, C., Zhou, R., Zhao, P.: On prediction of traffic flows in smart cities: a multitask deep learning based approach. World Wide Web 24(3), 805\u2013823 (2021). https:\/\/doi.org\/10.1007\/s11280-021-00877-4","journal-title":"World Wide Web"},{"issue":"6","key":"1045_CR37","doi-asserted-by":"publisher","first-page":"664","DOI":"10.1061\/(ASCE)0733-947X(2003)129:6(664)","volume":"129","author":"BM Williams","year":"2003","unstructured":"Williams, B.M., Hoel, L.A.: Modeling and forecasting vehicular traffic flow as a seasonal arima process: Theoretical basis and empirical results. Journal of Transportation Engineering 129(6), 664\u2013672 (2003). https:\/\/doi.org\/10.1061\/(ASCE)0733-947X(2003)129:6(664)","journal-title":"Journal of Transportation Engineering"},{"key":"1045_CR38","doi-asserted-by":"publisher","unstructured":"Wu, J., He, J., Xu, J.: Demo-net: Degree-specific graph neural networks for node and graph classification. In: KDD, pp. 406\u2013415 (2019). https:\/\/doi.org\/10.1145\/3292500.3330950","DOI":"10.1145\/3292500.3330950"},{"key":"1045_CR39","doi-asserted-by":"publisher","unstructured":"Wu, Z., Pan, S., Long, G., Jiang, J., Zhang, C.: Graph wavenet for deep spatial-temporal graph modeling. In: IJCAI, pp. 1907\u20131913 (2019). https:\/\/doi.org\/10.24963\/ijcai.2019\/264","DOI":"10.24963\/ijcai.2019\/264"},{"issue":"4","key":"1045_CR40","doi-asserted-by":"publisher","first-page":"276","DOI":"10.1109\/TITS.2004.837813","volume":"5","author":"C Wu","year":"2004","unstructured":"Wu, C., Ho, J., Lee, D.: Travel-time prediction with support vector regression. IEEE Trans. Intell. Transp. Syst. 5(4), 276\u2013281 (2004). https:\/\/doi.org\/10.1109\/TITS.2004.837813","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"1045_CR41","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10707-020-00422-x","volume":"1","author":"S Xu","year":"2020","unstructured":"Xu, S., Zhang, R., Cheng, W., Xu, J.: Mtlm: a multi-task learning model for travel time estimation. GeoInformatica 1, 1\u201317 (2020). https:\/\/doi.org\/10.1007\/s10707-020-00422-x","journal-title":"GeoInformatica"},{"issue":"2","key":"1045_CR42","doi-asserted-by":"publisher","first-page":"651","DOI":"10.1109\/TKDE.2019.2932984","volume":"33","author":"J Xu","year":"2021","unstructured":"Xu, J., Zhao, J., Zhou, R., Liu, C., Zhao, P., Zhao, L.: Predicting destinations by a deep learning based approach. IEEE Trans. Knowl. Data Eng. 33(2), 651\u2013666 (2021). https:\/\/doi.org\/10.1109\/TKDE.2019.2932984","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"1045_CR43","doi-asserted-by":"publisher","unstructured":"Yu, B., Yin, H., Zhu, Z.: Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. In: IJCAI, pp. 3634\u20133640 (2018). https:\/\/doi.org\/10.24963\/ijcai.2018\/505","DOI":"10.24963\/ijcai.2018\/505"},{"key":"1045_CR44","doi-asserted-by":"publisher","unstructured":"Yuan, C., Chakraborty, A.: Deep convolutional factor analyser for multivariate time series modeling. In: ICDM, pp. 1323\u20131328 (2016). https:\/\/doi.org\/10.1109\/ICDM.2016.0180","DOI":"10.1109\/ICDM.2016.0180"},{"key":"1045_CR45","doi-asserted-by":"publisher","unstructured":"Yuan, H., Li, G., Bao, Z., Feng, L.: Effective travel time estimation: When historical trajectories over road networks matter. In: SIGMOD Conference, pp. 2135\u20132149 (2020). https:\/\/doi.org\/10.1145\/3318464.3389771","DOI":"10.1145\/3318464.3389771"},{"key":"1045_CR46","doi-asserted-by":"crossref","unstructured":"Zhang, J., Zheng, Y., Qi, D.: Deep spatio-temporal residual networks for citywide crowd flows prediction. In: AAAI, pp. 1655\u20131661 (2017). http:\/\/aaai.org\/ocs\/index.php\/AAAI\/AAAI17\/paper\/view\/14501","DOI":"10.1609\/aaai.v31i1.10735"},{"issue":"9","key":"1045_CR47","doi-asserted-by":"publisher","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.: T-GCN: A temporal graph convolutional network for traffic prediction. IEEE Trans. Intell. Transp. Syst. 21(9), 3848\u20133858 (2020). https:\/\/doi.org\/10.1109\/TITS.2019.2935152","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"1045_CR48","doi-asserted-by":"crossref","unstructured":"Zheng, C., Fan, X., Wang, C., Qi, J.: GMAN: A graph multi-attention network for traffic prediction. In: AAAI, pp. 1234\u20131241 (2020). https:\/\/aaai.org\/ojs\/index.php\/AAAI\/article\/view\/5477","DOI":"10.1609\/aaai.v34i01.5477"},{"key":"1045_CR49","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1016\/j.trc.2014.02.009","volume":"43","author":"Z Zheng","year":"2014","unstructured":"Zheng, Z., Su, D.: Short-term traffic volume forecasting: A k-nearest neighbor approach enhanced by constrained linearly sewing principle component algorithm. Transportation Research Part C Emerging Technologies 43, 143\u2013157 (2014). https:\/\/doi.org\/10.1016\/j.trc.2014.02.009","journal-title":"Transportation Research Part C Emerging Technologies"},{"key":"1045_CR50","doi-asserted-by":"publisher","unstructured":"Zhou, F., Cao, C., Zhang, K., Trajcevski, G., Zhong, T., Geng, J.: Meta-gnn: On few-shot node classification in graph meta-learning. In: CIKM, pp. 2357\u20132360 (2019). https:\/\/doi.org\/10.1145\/3357384.3358106","DOI":"10.1145\/3357384.3358106"}],"container-title":["World Wide Web"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11280-022-01045-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11280-022-01045-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11280-022-01045-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,17]],"date-time":"2023-04-17T08:32:08Z","timestamp":1681720328000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11280-022-01045-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,31]]},"references-count":50,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2023,5]]}},"alternative-id":["1045"],"URL":"https:\/\/doi.org\/10.1007\/s11280-022-01045-y","relation":{},"ISSN":["1386-145X","1573-1413"],"issn-type":[{"value":"1386-145X","type":"print"},{"value":"1573-1413","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,31]]},"assertion":[{"value":"28 January 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 March 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 March 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 March 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"We declare that the named authors have no any conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}}]}}