Traffic Prediction With Missing Data: A Multi-Task Learning Approach
Traffic speed prediction based on real-world traffic data is a classical problem in intelligent transportation systems (ITS). Most existing traffic speed prediction models are proposed based on the hypothesis that traffic data are complete or have rare missing values. However, such data collected in...
Gespeichert in:
| Veröffentlicht in: | IEEE transactions on intelligent transportation systems Jg. 24; H. 4; S. 1 - 14 |
|---|---|
| Hauptverfasser: | , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
New York
IEEE
01.04.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Schlagworte: | |
| ISSN: | 1524-9050, 1558-0016 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | Traffic speed prediction based on real-world traffic data is a classical problem in intelligent transportation systems (ITS). Most existing traffic speed prediction models are proposed based on the hypothesis that traffic data are complete or have rare missing values. However, such data collected in real-world scenarios are often incomplete due to various human and natural factors. Although this problem can be solved by first estimating the missing values with an imputation model and then applying a prediction model, the former potentially breaks critical latent features and further leads to the error accumulation issues. To tackle this problem, we propose a graph-based spatio-temporal autoencoder that follows an encoder-decoder structure for spatio-temporal traffic speed prediction with missing values. Specifically, we regard the imputation and prediction as two parallel tasks and train them sequentially to eliminate the negative impact of imputation on raw data for prediction and accelerate the model training process. Furthermore, we utilize graph convolutional layers with a self-adaptive adjacency matrix for spatial dependencies modeling and apply gated recurrent units for temporal learning. To evaluate the proposed model, we conduct comprehensive case studies on two real-world traffic datasets with two different missing patterns and a wide and practical missing rate range from 20% to 80%. Experimental results demonstrate that the model consistently outperforms the state-of-the-art traffic prediction with missing values methods and achieves steady performance in the investigated missing scenarios and prediction horizons. |
|---|---|
| AbstractList | Traffic speed prediction based on real-world traffic data is a classical problem in intelligent transportation systems (ITS). Most existing traffic speed prediction models are proposed based on the hypothesis that traffic data are complete or have rare missing values. However, such data collected in real-world scenarios are often incomplete due to various human and natural factors. Although this problem can be solved by first estimating the missing values with an imputation model and then applying a prediction model, the former potentially breaks critical latent features and further leads to the error accumulation issues. To tackle this problem, we propose a graph-based spatio-temporal autoencoder that follows an encoder-decoder structure for spatio-temporal traffic speed prediction with missing values. Specifically, we regard the imputation and prediction as two parallel tasks and train them sequentially to eliminate the negative impact of imputation on raw data for prediction and accelerate the model training process. Furthermore, we utilize graph convolutional layers with a self-adaptive adjacency matrix for spatial dependencies modeling and apply gated recurrent units for temporal learning. To evaluate the proposed model, we conduct comprehensive case studies on two real-world traffic datasets with two different missing patterns and a wide and practical missing rate range from 20% to 80%. Experimental results demonstrate that the model consistently outperforms the state-of-the-art traffic prediction with missing values methods and achieves steady performance in the investigated missing scenarios and prediction horizons. |
| Author | Zhang, Shiyao Wang, Ao Yu, James J. Q. Song, Xiaozhuang Ye, Yongchao |
| Author_xml | – sequence: 1 givenname: Ao surname: Wang fullname: Wang, Ao organization: Department of Computer Science and Engineering, Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation, Southern University of Science and Technology, Shenzhen, China – sequence: 2 givenname: Yongchao orcidid: 0000-0001-9782-218X surname: Ye fullname: Ye, Yongchao organization: Department of Computer Science and Engineering, Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation, Southern University of Science and Technology, Shenzhen, China – sequence: 3 givenname: Xiaozhuang orcidid: 0000-0002-7861-8957 surname: Song fullname: Song, Xiaozhuang organization: Department of Computer Science and Engineering, Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation, Southern University of Science and Technology, Shenzhen, China – sequence: 4 givenname: Shiyao orcidid: 0000-0002-0004-1801 surname: Zhang fullname: Zhang, Shiyao organization: Research Institute for Trustworthy Autonomous Systems, Southern University of Science and Technology, Shenzhen, China – sequence: 5 givenname: James J. Q. orcidid: 0000-0002-6392-6711 surname: Yu fullname: Yu, James J. Q. organization: Department of Computer Science and Engineering, Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation, Southern University of Science and Technology, Shenzhen, China |
| BookMark | eNp9kE1PAjEQhhuDiYD-ABMPTTwvttPudtcbAT9IIJq4xmNTSitF3MW2HPz3dgMH48HTfL3PTOYdoF7TNgahS0pGlJLqpp7VLyMgACMGjJUVOUF9mudlRggtel0OPKtITs7QIIRN6vKc0j6a1l5Z6zR-9mbldHRtg99cXOOFC8E173iqorrFY7zYb6PLahU-8Nwo33Sz8W7nW6XX5-jUqm0wF8c4RK_3d_XkMZs_Pcwm43mmoeIx42CXlulSc11SznVhgJhSWFFwWgi2FEwvV9YKZhUIMJabVKaxLRNlKsOG6PqwN5392psQ5abd-yadlCAqoAyAFUklDirt2xC8sVK7qLrPolduKymRnWWys0x2lsmjZYmkf8idd5_Kf__LXB0YZ4z5pScUCCPsB6QZeM4 |
| CODEN | ITISFG |
| CitedBy_id | crossref_primary_10_1016_j_ecoinf_2025_103283 crossref_primary_10_1016_j_inffus_2023_102078 crossref_primary_10_3390_fi16060193 crossref_primary_10_1007_s41060_024_00604_y crossref_primary_10_1007_s10489_024_05314_3 crossref_primary_10_1049_itr2_70069 crossref_primary_10_1145_3743141 crossref_primary_10_1007_s11831_025_10336_2 crossref_primary_10_1631_FITEE_2300873 crossref_primary_10_1109_JIOT_2024_3524030 crossref_primary_10_1109_JIOT_2024_3476498 crossref_primary_10_3390_app14198847 crossref_primary_10_3390_ijgi14080286 crossref_primary_10_1016_j_chaos_2024_115437 crossref_primary_10_1016_j_chaos_2024_114965 crossref_primary_10_1016_j_neucom_2024_128441 crossref_primary_10_1109_TITS_2025_3564578 crossref_primary_10_1007_s10489_024_05291_7 crossref_primary_10_1109_TITS_2024_3478816 crossref_primary_10_1007_s40747_024_01768_7 crossref_primary_10_1016_j_neunet_2025_107963 crossref_primary_10_1016_j_trc_2025_105152 crossref_primary_10_1016_j_ins_2023_119972 crossref_primary_10_1007_s42421_024_00104_2 crossref_primary_10_1016_j_patcog_2025_112046 crossref_primary_10_1109_TII_2024_3396347 crossref_primary_10_1016_j_inffus_2025_103677 crossref_primary_10_1016_j_optlastec_2025_113647 crossref_primary_10_1109_TITS_2024_3447549 crossref_primary_10_1109_TMC_2025_3573373 crossref_primary_10_1038_s41598_025_02933_9 crossref_primary_10_1007_s10115_025_02505_3 crossref_primary_10_1007_s42421_025_00124_6 crossref_primary_10_1038_s41598_023_41902_y crossref_primary_10_1007_s10489_024_05970_5 crossref_primary_10_1109_ACCESS_2025_3574982 crossref_primary_10_1016_j_aap_2024_107830 crossref_primary_10_1371_journal_pone_0320567 crossref_primary_10_1016_j_asoc_2025_113656 crossref_primary_10_1287_trsc_2023_0326 crossref_primary_10_1080_15472450_2025_2526382 crossref_primary_10_1080_21680566_2025_2497941 crossref_primary_10_1016_j_knosys_2024_112578 crossref_primary_10_1016_j_neunet_2025_107298 crossref_primary_10_3390_technologies13070287 |
| Cites_doi | 10.1109/TITS.2020.3030546 10.24963/ijcai.2019/264 10.24963/ijcai.2019/429 10.48550/arXiv.1606.09375 10.1007/978-3-030-86362-3_20 10.1145/2996913.2997016 10.1109/ICDCS51616.2021.00073 10.24963/ijcai.2018/505 10.1145/1390156.1390294 10.1162/neco.1997.9.8.1735 10.1109/TITS.2019.2910560 10.1049/iet-its.2018.5114 10.3141/1678-22 10.1109/TPAMI.2021.3066551 10.1609/aaai.v35i10.17114 10.1145/3447548.3467401 10.1016/j.trc.2021.103372 10.1109/ACCESS.2019.2953888 10.1609/aaai.v33i01.3301922 10.1016/j.trc.2020.102671 10.1109/ACCESS.2019.2923663 10.1016/j.trc.2020.102870 10.1016/j.trc.2018.11.003 10.1109/ACCESS.2020.2999662 10.1016/j.trc.2014.02.006 10.1016/j.trc.2020.102674 10.1109/TITS.2019.2929020 10.1109/TITS.2018.2854968 10.1038/323533a0 10.1109/TNNLS.2020.2978386 10.1109/tits.2021.3069234 10.1016/j.trc.2012.12.007 10.1109/TITS.2004.837813 10.1609/aaai.v35i5.16575 |
| ContentType | Journal Article |
| Copyright | Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2023 |
| Copyright_xml | – notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2023 |
| DBID | 97E RIA RIE AAYXX CITATION 7SC 7SP 8FD FR3 JQ2 KR7 L7M L~C L~D |
| DOI | 10.1109/TITS.2022.3233890 |
| DatabaseName | IEEE All-Society Periodicals Package (ASPP) 2005–Present IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE/IET Electronic Library (IEL) (UW System Shared) CrossRef Computer and Information Systems Abstracts Electronics & Communications Abstracts Technology Research Database Engineering Research Database ProQuest Computer Science Collection Civil Engineering Abstracts Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional |
| DatabaseTitle | CrossRef Civil Engineering Abstracts Technology Research Database Computer and Information Systems Abstracts – Academic Electronics & Communications Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts Engineering Research Database Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Professional |
| DatabaseTitleList | Civil Engineering Abstracts |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 1558-0016 |
| EndPage | 14 |
| ExternalDocumentID | 10_1109_TITS_2022_3233890 10012030 |
| Genre | orig-research |
| GrantInformation_xml | – fundername: Stable Support Plan Program of Shenzhen Natural Science Fund grantid: 20200925155105002 – fundername: General Program of Guangdong Basic and Applied Basic Research Foundation grantid: 2019A1515011032 – fundername: Guangdong Provincial Key Laboratory grantid: 2020B121201001 |
| GroupedDBID | -~X 0R~ 29I 4.4 5GY 5VS 6IK 97E AAJGR AARMG AASAJ AAWTH ABAZT ABQJQ ABVLG ACGFO ACGFS ACIWK ACNCT AENEX AGQYO AHBIQ AKJIK AKQYR ALMA_UNASSIGNED_HOLDINGS ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ CS3 DU5 EBS HZ~ IFIPE IPLJI JAVBF LAI M43 O9- OCL P2P PQQKQ RIA RIE RNS AAYXX AETIX AGSQL AIBXA CITATION EJD H~9 ZY4 7SC 7SP 8FD FR3 JQ2 KR7 L7M L~C L~D |
| ID | FETCH-LOGICAL-c294t-42fbf3c8c4c8144c6e20e87f7641673b73cbdff73fa272ef4ebdf7f7f8f3ce9e3 |
| IEDL.DBID | RIE |
| ISICitedReferencesCount | 52 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000989285000001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1524-9050 |
| IngestDate | Sun Nov 09 06:08:06 EST 2025 Tue Nov 18 22:11:51 EST 2025 Sat Nov 29 06:35:02 EST 2025 Wed Aug 27 02:21:51 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 4 |
| Language | English |
| License | https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html https://doi.org/10.15223/policy-029 https://doi.org/10.15223/policy-037 |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c294t-42fbf3c8c4c8144c6e20e87f7641673b73cbdff73fa272ef4ebdf7f7f8f3ce9e3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0001-9782-218X 0000-0002-6392-6711 0000-0002-7861-8957 0000-0002-0004-1801 0000-0002-8913-0885 |
| PQID | 2792132236 |
| PQPubID | 75735 |
| PageCount | 14 |
| ParticipantIDs | crossref_citationtrail_10_1109_TITS_2022_3233890 crossref_primary_10_1109_TITS_2022_3233890 ieee_primary_10012030 proquest_journals_2792132236 |
| PublicationCentury | 2000 |
| PublicationDate | 2023-04-01 |
| PublicationDateYYYYMMDD | 2023-04-01 |
| PublicationDate_xml | – month: 04 year: 2023 text: 2023-04-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | New York |
| PublicationPlace_xml | – name: New York |
| PublicationTitle | IEEE transactions on intelligent transportation systems |
| PublicationTitleAbbrev | TITS |
| PublicationYear | 2023 |
| Publisher | IEEE The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher_xml | – name: IEEE – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| References | ref12 ref14 ref52 Yoon (ref30) Luong (ref39) ref17 Bai (ref27); 33 ref16 ref19 ref18 Wu (ref48); 97 ref50 Bruna (ref26) 2014 ref46 Ruder (ref25) 2017 Chung (ref13) 2014 ref47 ref42 ref44 ref43 Zhu (ref15) 2021 Jiang (ref10) 2021 ref49 ref8 ref7 ref4 ref3 Li (ref9) ref6 Zaremba (ref11) 2014 ref5 ref40 Sutskever (ref41); 27 ref35 ref34 ref37 ref36 ref31 ref33 ref32 ref2 ref1 Chen (ref23) 2019; 98 Sener (ref38) Cao (ref29); 31 ref24 Che (ref21) 2016 ref20 ref22 Kingma (ref51) 2014 Kipf (ref45) Zheng (ref28) |
| References_xml | – ident: ref14 doi: 10.1109/TITS.2020.3030546 – ident: ref47 doi: 10.24963/ijcai.2019/264 – ident: ref31 doi: 10.24963/ijcai.2019/429 – volume: 33 start-page: 17804 volume-title: Proc. Adv. Neural Inf. Process. Syst. ident: ref27 article-title: Adaptive graph convolutional recurrent network for traffic forecasting – year: 2014 ident: ref11 article-title: Recurrent neural network regularization publication-title: arXiv:1409.2329 – start-page: 525 volume-title: Proc. Annu. Conf. Neural Inf. Process. Syst. ident: ref38 article-title: Multi-task learning as multi-objective optimization – ident: ref46 doi: 10.48550/arXiv.1606.09375 – ident: ref36 doi: 10.1007/978-3-030-86362-3_20 – ident: ref6 doi: 10.1145/2996913.2997016 – ident: ref17 doi: 10.1109/ICDCS51616.2021.00073 – ident: ref40 doi: 10.24963/ijcai.2018/505 – ident: ref43 doi: 10.1145/1390156.1390294 – start-page: 1 volume-title: Proc. Int. Conf. Learn. Represent. (ICLR) ident: ref9 article-title: Diffusion convolutional recurrent neural network: Data-driven traffic forecasting – volume: 97 start-page: 6861 volume-title: Proc. 36th Int. Conf. Mach. Learn. ident: ref48 article-title: Simplifying graph convolutional networks – year: 2021 ident: ref15 article-title: Networked time series prediction with incomplete data publication-title: arXiv:2110.02271 – ident: ref12 doi: 10.1162/neco.1997.9.8.1735 – ident: ref8 doi: 10.1109/TITS.2019.2910560 – ident: ref35 doi: 10.1049/iet-its.2018.5114 – ident: ref2 doi: 10.3141/1678-22 – ident: ref22 doi: 10.1109/TPAMI.2021.3066551 – ident: ref52 doi: 10.1609/aaai.v35i10.17114 – year: 2021 ident: ref10 article-title: Graph neural network for traffic forecasting: A survey publication-title: arXiv:2101.11174 – ident: ref42 doi: 10.1145/3447548.3467401 – ident: ref16 doi: 10.1016/j.trc.2021.103372 – year: 2016 ident: ref21 article-title: Recurrent neural networks for multivariate time series with missing values publication-title: arXiv:1606.01865 – ident: ref7 doi: 10.1109/ACCESS.2019.2953888 – ident: ref18 doi: 10.1609/aaai.v33i01.3301922 – ident: ref19 doi: 10.1016/j.trc.2020.102671 – ident: ref4 doi: 10.1109/ACCESS.2019.2923663 – ident: ref33 doi: 10.1016/j.trc.2020.102870 – volume: 98 start-page: 73 year: 2019 ident: ref23 article-title: A Bayesian tensor decomposition approach for spatiotemporal traffic data imputation publication-title: Transp. Res. C, Emerg. Technol. doi: 10.1016/j.trc.2018.11.003 – start-page: 5675 volume-title: Proc. 35th Int. Conf. Mach. Learn. (ICML) ident: ref30 article-title: GAIN: Missing data imputation using generative adversarial nets – ident: ref34 doi: 10.1109/ACCESS.2020.2999662 – start-page: 1 volume-title: Proc. 5th Int. Conf. Learn. Represent. (ICLR) ident: ref45 article-title: Semi-supervised classification with graph convolutional networks – year: 2017 ident: ref25 article-title: An overview of multi-task learning in deep neural networks publication-title: arXiv:1706.05098 – year: 2014 ident: ref13 article-title: Empirical evaluation of gated recurrent neural networks on sequence modeling publication-title: arXiv:1412.3555 – start-page: 1 volume-title: Proc. 4th Int. Conf. Learn. Represent. (ICLR) ident: ref39 article-title: Multi-task sequence to sequence learning – ident: ref3 doi: 10.1016/j.trc.2014.02.006 – year: 2014 ident: ref26 article-title: Spectral networks and locally connected networks on graphs publication-title: arXiv:1312.6203 – ident: ref24 doi: 10.1016/j.trc.2020.102674 – volume: 27 start-page: 3104 volume-title: Proc. Adv. Neural Inf. Process. Syst. ident: ref41 article-title: Sequence to sequence learning with neural networks – ident: ref1 doi: 10.1109/TITS.2019.2929020 – start-page: 1234 volume-title: Proc. 34th AAAI Conf. Artif. Intell., (AAAI) ident: ref28 article-title: GMAN: A graph multi-attention network for traffic prediction – ident: ref32 doi: 10.1109/TITS.2018.2854968 – ident: ref44 doi: 10.1038/323533a0 – ident: ref49 doi: 10.1109/TNNLS.2020.2978386 – ident: ref50 doi: 10.1109/tits.2021.3069234 – year: 2014 ident: ref51 article-title: Adam: A method for stochastic optimization publication-title: arXiv:1412.6980 – volume: 31 start-page: 6776 volume-title: Proc. Adv. Neural Inf. Process. Syst. ident: ref29 article-title: BRITS: Bidirectional recurrent imputation for time series – ident: ref20 doi: 10.1016/j.trc.2012.12.007 – ident: ref5 doi: 10.1109/TITS.2004.837813 – ident: ref37 doi: 10.1609/aaai.v35i5.16575 |
| SSID | ssj0014511 |
| Score | 2.5992107 |
| Snippet | Traffic speed prediction based on real-world traffic data is a classical problem in intelligent transportation systems (ITS). Most existing traffic speed... |
| SourceID | proquest crossref ieee |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 1 |
| SubjectTerms | Coders Data mining Deep learning Encoders-Decoders Feature extraction Intelligent transportation systems Learning Missing data multi-task learning Multitasking Prediction models Predictive models Spatial dependencies spatio-temporal modeling Task analysis Traffic information Traffic models Traffic speed Traffic speed prediction Training |
| Title | Traffic Prediction With Missing Data: A Multi-Task Learning Approach |
| URI | https://ieeexplore.ieee.org/document/10012030 https://www.proquest.com/docview/2792132236 |
| Volume | 24 |
| WOSCitedRecordID | wos000989285000001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVIEE databaseName: IEEE Electronic Library (IEL) customDbUrl: eissn: 1558-0016 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0014511 issn: 1524-9050 databaseCode: RIE dateStart: 20000101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3PS8MwFA46POjBnxOnU3LwJHTLkq5pvQ116MExsOJuJU1fdCibbJ1_v3lpNgai4K3QBMr38uvry_c9Qi5zloeKMRWwAiU53MZCQV4EIjKaK1R2xsYVm5CDQTwaJUMvVndaGABwl8-ghY8ul19M9QJ_lbU7TuopLEPflDKqxFqrlAEabTlzVB4GCesuU5gdlrTTh_TJUkHOW4JbSobr79om5Kqq_FiK3f7S3_vnl-2TXX-QpL0q8gdkAyaHZGfNXvCI3NqNCB0i6HCG6RgMAX0Zl2_00aJtW9BbVapr2qNOhRukav5Ovd_qK-15s_E6ee7fpTf3ga-aEGiehKXF2-RG6FiHOrZsSUfAGcTSyMievaTIpdB5YYwURnHJwYQ2NMa-NrHtBQmIY1KbTCdwQijjRYcVmoHomlCAtnObmTgEEFGeWCrUIGwJY6a9pThWtvjIHLVgSYbIZ4h85pFvkKtVl8_KT-OvxnWEeq1hhXKDNJfByvyUm2fohIjUWkSnv3Q7I9tYLL66d9MktXK2gHOypb_K8Xx24UbTN7u_xc0 |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1dS8MwFA0yBfXBb3E6NQ8-Cd2yJOuHb8M5NtzGwIp7C216o0PZZOv8_SZpNgai4FuhCZRz83V6c85F6CYlKU8ISTySGUkO1bFIIM085itJE6PsDJUtNhEMBuFoFA2dWN1qYQDAXj6Dqnm0ufxsKhfmV1mtbqWeTDP0zQbnlBRyrVXSwFhtWXtUyr2INJZJzDqJanE3ftJkkNIqo5qUmRV4bRuydVV-LMZ2h2nv__PbDtCeO0riZhH7Q7QBkyO0u2YweIxaeisyHhF4ODMJGRME_DLO33Bf461b4FaSJ3e4ia0O14uT-Tt2jquvuOnsxk_Qc_shvu94rm6CJ2nEc424ShWToeQy1HxJ-kAJhIEKfH36ClgaMJlmSgVMJTSgoLgOjtKvVah7QQTsFJUm0wmcIUxoVieZJMAaijOQenYTFXIA5qeRJkNlRJYwCulMxU1tiw9hyQWJhEFeGOSFQ76MblddPgtHjb8anxio1xoWKJdRZRks4SbdXBgvREOumX_-S7drtN2J-z3R6w4eL9COKR1f3MKpoFI-W8Al2pJf-Xg-u7Ij6xsy-MkU |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Traffic+Prediction+With+Missing+Data%3A+A+Multi-Task+Learning+Approach&rft.jtitle=IEEE+transactions+on+intelligent+transportation+systems&rft.au=Wang%2C+Ao&rft.au=Ye%2C+Yongchao&rft.au=Song%2C+Xiaozhuang&rft.au=Zhang%2C+Shiyao&rft.date=2023-04-01&rft.pub=IEEE&rft.issn=1524-9050&rft.spage=1&rft.epage=14&rft_id=info:doi/10.1109%2FTITS.2022.3233890&rft.externalDocID=10012030 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1524-9050&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1524-9050&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1524-9050&client=summon |