{"id":"W4367835987","doi":"10.1155/2023/9524966","title":"Short-Term Passenger Flow Forecasting for Rail Transit considering Chaos Theory and Improved EMD-PSO-LSTM-Combined Optimization","year":2023,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Particle swarm optimization; Hilbert–Huang transform; Mean squared error; Inertia; Algorithm; Computer science; Nonlinear system; Mathematics; Control theory (sociology); Mathematical optimization; Artificial intelligence; Statistics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000421723,0.0006792788,0.0005848235,0.0004596626,0.0002664012,0.0005626045,0.0006119786,0.0005773085,0.0006107335],"category_scores_gemma":[0.001031447,0.0003526144,0.0007847086,0.0004777074,0.0002120935,0.0009048764,0.0004956765,0.000664052,0.0001491109],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004064134,"about_ca_system_score_gemma":0.0007228412,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01199224,"about_ca_topic_score_gemma":0.007117111,"domain_scores_codex":[0.9997868,0.00003284268,0.00002038591,0.00006278225,0.00007186717,0.00002530529],"domain_scores_gemma":[0.9998146,0.00007120903,0.00002593938,0.00001189139,0.00006751611,0.000008894869],"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":[0.00005795699,0.00003529175,0.002503852,0.0000831112,0.00006083178,0.00007488184,0.00006546664,0.898758,0.005039539,0.001983029,0.0007694645,0.09056858],"study_design_scores_gemma":[0.000001618836,0.000008114894,0.0002530973,0.00000162249,0.000003806048,0.000005509481,0.000003093587,0.9990526,0.0003194715,0.0002207929,0.0001278172,0.00000254433],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05958189,0.000560065,0.9373647,0.0002048914,0.00007204714,0.00002553883,0.00008459939,0.000441413,0.001664827],"genre_scores_gemma":[0.9050103,0.0004537011,0.09115224,0.00007613946,0.00006606631,0.00008943138,0.0002758111,0.00005839987,0.002818093],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01199224,"threshold_uncertainty_score":0.0238449,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01279667886516696,"score_gpt":0.228796085528216,"score_spread":0.215999406663049,"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."}}