{"id":"W4414601380","doi":"10.1007/s10586-025-05346-5","title":"Improving traffic flow forecasting with variational mode decomposition and deep learning-based prediction models","year":2025,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Mode (computer interface); Traffic flow (computer networking); Convolutional neural network; Reliability (semiconductor); Nonlinear system; Decomposition; Artificial neural network; Deep learning","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006540352,0.0006969417,0.000924445,0.0005137322,0.0002817309,0.0006039517,0.001065889,0.0006077857,0.001335111],"category_scores_gemma":[0.002510851,0.0004653811,0.0005245346,0.0007773031,0.0002996483,0.001383981,0.0006001384,0.001501861,0.0003177456],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006653434,"about_ca_system_score_gemma":0.0009860285,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0221657,"about_ca_topic_score_gemma":0.01885357,"domain_scores_codex":[0.9998088,0.00005270159,0.000007830419,0.00005079677,0.00004786249,0.00003190607],"domain_scores_gemma":[0.9993647,0.0003169734,0.00004259805,0.00008027309,0.0001615714,0.00003391895],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007211245,0.00007511356,0.0009780189,0.00001884371,0.00003904176,0.00001355359,0.00002000732,0.9332572,0.002234579,0.003993177,0.001657154,0.05764126],"study_design_scores_gemma":[4.46115e-7,9.788045e-7,0.00002059146,2.52909e-7,4.87547e-7,2.998738e-7,4.563464e-7,0.9995111,0.0000591092,0.0003896705,0.000016125,4.085287e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06109767,0.0002253252,0.9362173,0.0002504142,0.0001033625,0.00001642815,0.0001425243,0.0006974538,0.001249589],"genre_scores_gemma":[0.861704,0.0002174329,0.13501,0.00009552739,0.00008236209,0.00004322761,0.0004776747,0.0001337217,0.002236064],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0221657,"threshold_uncertainty_score":0.0440734,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006479533948218917,"score_gpt":0.2130688049807407,"score_spread":0.2065892710325217,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}