Visualization of the true and predicted values of the STIL-TA model on the PEMS-BAY dataset (Horizon 12).
<p>Visualization of the true and predicted values of the STIL-TA model on the PEMS-BAY dataset (Horizon 12).</p>
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2025
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| _version_ | 1852017312584957952 |
|---|---|
| author | Linlong Chen (22120395) |
| author2 | Linbiao Chen (22120398) Hongyan Wang (41596) Jian Zhao (219525) |
| author2_role | author author author |
| author_facet | Linlong Chen (22120395) Linbiao Chen (22120398) Hongyan Wang (41596) Jian Zhao (219525) |
| author_role | author |
| dc.creator.none.fl_str_mv | Linlong Chen (22120395) Linbiao Chen (22120398) Hongyan Wang (41596) Jian Zhao (219525) |
| dc.date.none.fl_str_mv | 2025-08-25T17:31:36Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0331095.g010 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/Visualization_of_the_true_and_predicted_values_of_the_STIL-TA_model_on_the_PEMS-BAY_dataset_Horizon_12_/29981676 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Medicine Biotechnology Ecology Infectious Diseases Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified two key components hour fluctuations ). g ., rush g ., graph achieving significant improvements spatiotemporal interactive learning dynamic traffic patterns decoupled spatiotemporal learning term trend awareness model effectively enhances dynamic temporal dependencies traffic flow predictions term prediction accuracy traffic flow spatiotemporal characteristics new model head trend temporal multi temporal correlations synchronize interactions road networks processed separately numerical sequence limited long jointly modeling information loss improving long four real experimental results existing models critical role conquer strategy aware self |
| dc.title.none.fl_str_mv | Visualization of the true and predicted values of the STIL-TA model on the PEMS-BAY dataset (Horizon 12). |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <p>Visualization of the true and predicted values of the STIL-TA model on the PEMS-BAY dataset (Horizon 12).</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_3bc83ffb2b57c94dfeaaa7d1c1ac9c19 |
| identifier_str_mv | 10.1371/journal.pone.0331095.g010 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/29981676 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Visualization of the true and predicted values of the STIL-TA model on the PEMS-BAY dataset (Horizon 12).Linlong Chen (22120395)Linbiao Chen (22120398)Hongyan Wang (41596)Jian Zhao (219525)MedicineBiotechnologyEcologyInfectious DiseasesBiological Sciences not elsewhere classifiedMathematical Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedtwo key componentshour fluctuations ).g ., rushg ., graphachieving significant improvementsspatiotemporal interactive learningdynamic traffic patternsdecoupled spatiotemporal learningterm trend awarenessmodel effectively enhancesdynamic temporal dependenciestraffic flow predictionsterm prediction accuracytraffic flowspatiotemporal characteristicsnew modelhead trendtemporal multitemporal correlationssynchronize interactionsroad networksprocessed separatelynumerical sequencelimited longjointly modelinginformation lossimproving longfour realexperimental resultsexisting modelscritical roleconquer strategyaware self<p>Visualization of the true and predicted values of the STIL-TA model on the PEMS-BAY dataset (Horizon 12).</p>2025-08-25T17:31:36ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0331095.g010https://figshare.com/articles/figure/Visualization_of_the_true_and_predicted_values_of_the_STIL-TA_model_on_the_PEMS-BAY_dataset_Horizon_12_/29981676CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/299816762025-08-25T17:31:36Z |
| spellingShingle | Visualization of the true and predicted values of the STIL-TA model on the PEMS-BAY dataset (Horizon 12). Linlong Chen (22120395) Medicine Biotechnology Ecology Infectious Diseases Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified two key components hour fluctuations ). g ., rush g ., graph achieving significant improvements spatiotemporal interactive learning dynamic traffic patterns decoupled spatiotemporal learning term trend awareness model effectively enhances dynamic temporal dependencies traffic flow predictions term prediction accuracy traffic flow spatiotemporal characteristics new model head trend temporal multi temporal correlations synchronize interactions road networks processed separately numerical sequence limited long jointly modeling information loss improving long four real experimental results existing models critical role conquer strategy aware self |
| status_str | publishedVersion |
| title | Visualization of the true and predicted values of the STIL-TA model on the PEMS-BAY dataset (Horizon 12). |
| title_full | Visualization of the true and predicted values of the STIL-TA model on the PEMS-BAY dataset (Horizon 12). |
| title_fullStr | Visualization of the true and predicted values of the STIL-TA model on the PEMS-BAY dataset (Horizon 12). |
| title_full_unstemmed | Visualization of the true and predicted values of the STIL-TA model on the PEMS-BAY dataset (Horizon 12). |
| title_short | Visualization of the true and predicted values of the STIL-TA model on the PEMS-BAY dataset (Horizon 12). |
| title_sort | Visualization of the true and predicted values of the STIL-TA model on the PEMS-BAY dataset (Horizon 12). |
| topic | Medicine Biotechnology Ecology Infectious Diseases Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified two key components hour fluctuations ). g ., rush g ., graph achieving significant improvements spatiotemporal interactive learning dynamic traffic patterns decoupled spatiotemporal learning term trend awareness model effectively enhances dynamic temporal dependencies traffic flow predictions term prediction accuracy traffic flow spatiotemporal characteristics new model head trend temporal multi temporal correlations synchronize interactions road networks processed separately numerical sequence limited long jointly modeling information loss improving long four real experimental results existing models critical role conquer strategy aware self |