{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T16:32:47Z","timestamp":1771518767240,"version":"3.50.1"},"reference-count":54,"publisher":"Association for Computing Machinery (ACM)","issue":"5","license":[{"start":{"date-parts":[[2023,2,27]],"date-time":"2023-02-27T00:00:00Z","timestamp":1677456000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"National Key Research and Development Plan Key Special Projects","award":["2018YFB2100303"],"award-info":[{"award-number":["2018YFB2100303"]}]},{"name":"Shandong Province colleges and universities youth innovation technology plan innovation team project","award":["2020KJN011"],"award-info":[{"award-number":["2020KJN011"]}]},{"DOI":"10.13039\/501100007129","name":"Shandong Provincial Natural Science Foundation","doi-asserted-by":"crossref","award":["ZR2020MF060"],"award-info":[{"award-number":["ZR2020MF060"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Program for Innovative Postdoctoral Talents in Shandong Province","award":["40618030001"],"award-info":[{"award-number":["40618030001"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61802216"],"award-info":[{"award-number":["61802216"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100002858","name":"Postdoctoral Science Foundation of China","doi-asserted-by":"crossref","award":["2018M642613"],"award-info":[{"award-number":["2018M642613"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Knowl. Discov. Data"],"published-print":{"date-parts":[[2023,10,31]]},"abstract":"<jats:p>Traffic flow prediction has always been the focus of research in the field of Intelligent Transportation Systems, which is conducive to the more reasonable allocation of basic transportation resources and formulation of transportation policies. The spread of COVID-19 has seriously affected the normal order in the transportation sector. With the increase in the number of infected people and the government's anti-epidemic policy, human outgoing activities have gradually decreased, resulting in increasingly obvious discreteness and irregularities in traffic flow data. This article proposes a deep-space time traffic flow prediction model based on discrete wavelet transform (DSTM-DWT) to overcome the highly discrete and irregular nature of the new crown epidemic. First, DSTM-DWT decomposes traffic flow into discrete attributes, such as flow trend, discrete amplitude, and discrete baseline. Second, we design the spatial relationship of the transportation network as a graph and integrate the new crown pneumonia epidemic data into the characteristics of each transportation node. Then, we use the graph convolutional network to calculate the spatial correlation of each node, and the temporal convolutional network to calculate the temporal correlation of the data. In order to solve the problem of high discreteness of traffic flow data during the epidemic, this article proposes a graph memory network (GMN), which is used to convert discrete magnitudes separated by discrete wavelet transform into high-dimensional discrete features. Finally, use DWT to segment the predicted traffic data, and then perform the inverse discrete wavelet transform between the newly segmented traffic trend and discrete baseline and the discrete model predicted by GMN to obtain the final traffic flow prediction result. In simulation experiments, this work was compared with the existing advanced baselines to verify the superiority of DSTM-DWT.<\/jats:p>","DOI":"10.1145\/3564753","type":"journal-article","created":{"date-parts":[[2022,9,27]],"date-time":"2022-09-27T12:04:26Z","timestamp":1664280266000},"page":"1-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":19,"title":["Traffic Flow Forecasting in the COVID-19: A Deep Spatial-temporal Model Based on Discrete Wavelet Transformation"],"prefix":"10.1145","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6421-0128","authenticated-orcid":false,"given":"Haoran","family":"Li","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, Qingdao University, Shandong, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3071-160X","authenticated-orcid":false,"given":"Zhiqiang","family":"Lv","sequence":"additional","affiliation":[{"name":"Institute of Ubiquitous Networks and Urban Computing, Qingdao University, Shandong, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8756-7007","authenticated-orcid":false,"given":"Jianbo","family":"Li","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Qingdao University, Shandong, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4246-7544","authenticated-orcid":false,"given":"Zhihao","family":"Xu","sequence":"additional","affiliation":[{"name":"Institute of Ubiquitous Networks and Urban Computing, Qingdao University, Shandong, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9822-6782","authenticated-orcid":false,"given":"Yue","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Ubiquitous Networks and Urban Computing, Qingdao University, Shandong, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7777-3429","authenticated-orcid":false,"given":"Haokai","family":"Sun","sequence":"additional","affiliation":[{"name":"College of Computer Science &amp; Technology, Qingdao University, Shandong, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1824-8253","authenticated-orcid":false,"given":"Zhaoyu","family":"Sheng","sequence":"additional","affiliation":[{"name":"College of Computer Science &amp; Technology, Qingdao University, Shandong, China"}]}],"member":"320","published-online":{"date-parts":[[2023,2,27]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.scitotenv.2021.145545"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.datak.2021.101912"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.annals.2021.103182"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)NH.1527-6996.0000409"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ssci.2020.104938"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/5536386"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.3390\/su12177216"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380186"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.aei.2022.101678"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5758"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2019.2958673"},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/MITS.2022.3162901"},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2991462"},{"key":"e_1_3_1_15_2","first-page":"3844","article-title":"Convolutional neural networks on graphs with fast localized spectral filtering","volume":"29","author":"Defferrard Micha\u00ebl","year":"2016","unstructured":"Micha\u00ebl Defferrard, Xavier Bresson, and Pierre Vandergheynst. 2016. Convolutional neural networks on graphs with fast localized spectral filtering. Advances in Neural Information Processing Systems 29, 1 (2016), 3844\u20133852.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1803.01271"},{"key":"e_1_3_1_17_2","doi-asserted-by":"publisher","DOI":"10.3390\/a13010020"},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2019.2935152"},{"key":"e_1_3_1_19_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.3301922"},{"key":"e_1_3_1_20_2","volume-title":"Proceedings of the 93rd Annual Meeting of Transportation Research Board","author":"Ni Ming","year":"2014","unstructured":"Ming Ni, Qing He, and Jing Gao. 2014. Using social media to predict traffic flow under special event conditions. In Proceedings of the 93rd Annual Meeting of Transportation Research Board."},{"key":"e_1_3_1_21_2","doi-asserted-by":"publisher","DOI":"10.1155\/2017\/6575947"},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_3_1_23_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2014.03.007"},{"key":"e_1_3_1_24_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2015.04.167"},{"key":"e_1_3_1_25_2","doi-asserted-by":"publisher","DOI":"10.1109\/IC3.2018.8530608"},{"key":"e_1_3_1_26_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jweia.2017.12.019"},{"key":"e_1_3_1_27_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11269-022-03255-5"},{"key":"e_1_3_1_28_2","doi-asserted-by":"publisher","DOI":"10.1175\/2009WCAS1014.1"},{"key":"e_1_3_1_29_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i17.17761"},{"key":"e_1_3_1_30_2","doi-asserted-by":"publisher","DOI":"10.1093\/comjnl\/bxab178"},{"key":"e_1_3_1_31_2","doi-asserted-by":"publisher","DOI":"10.1109\/OPTIM.2010.5510403"},{"key":"e_1_3_1_32_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3010786"},{"key":"e_1_3_1_33_2","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2019.2963722"},{"key":"e_1_3_1_34_2","doi-asserted-by":"publisher","DOI":"10.16182\/j.issn1004731x.joss.19-0565"},{"key":"e_1_3_1_35_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2008.05.052"},{"key":"e_1_3_1_36_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.tranpol.2021.05.005"},{"key":"e_1_3_1_37_2","doi-asserted-by":"publisher","DOI":"10.1145\/3419394.3423655"},{"key":"e_1_3_1_38_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/505"},{"key":"e_1_3_1_39_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33015668"},{"key":"e_1_3_1_40_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2941280"},{"key":"e_1_3_1_41_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11116-021-10182-8"},{"key":"e_1_3_1_42_2","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2012.2225049"},{"key":"e_1_3_1_43_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.apm.2013.10.002"},{"key":"e_1_3_1_44_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i5.16542"},{"key":"e_1_3_1_45_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2019.123913"},{"key":"e_1_3_1_46_2","doi-asserted-by":"publisher","DOI":"10.3390\/s17071501"},{"key":"e_1_3_1_47_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-021-97461-7"},{"key":"e_1_3_1_48_2","doi-asserted-by":"publisher","DOI":"10.1155\/2020\/1846926"},{"key":"e_1_3_1_49_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i01.5477"},{"key":"e_1_3_1_50_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-020-04954-0"},{"key":"e_1_3_1_51_2","doi-asserted-by":"publisher","DOI":"10.1038\/cdd.2010.63"},{"key":"e_1_3_1_52_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR.2018.8545246"},{"key":"e_1_3_1_53_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58580-8_39"},{"key":"e_1_3_1_54_2","doi-asserted-by":"publisher","DOI":"10.1145\/3477051"},{"key":"e_1_3_1_55_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2022.3176751"}],"container-title":["ACM Transactions on Knowledge Discovery from Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3564753","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3564753","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T17:51:22Z","timestamp":1750182682000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3564753"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,27]]},"references-count":54,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2023,10,31]]}},"alternative-id":["10.1145\/3564753"],"URL":"https:\/\/doi.org\/10.1145\/3564753","relation":{},"ISSN":["1556-4681","1556-472X"],"issn-type":[{"value":"1556-4681","type":"print"},{"value":"1556-472X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,27]]},"assertion":[{"value":"2021-12-29","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-09-21","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-02-27","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}