{"id":"W4392725951","doi":"10.1155/2024/7985408","title":"Attention Mechanism with Spatial‐Temporal Joint Deep Learning Model for the Forecasting of Short‐Term Passenger Flow Distribution at the Railway Station","year":2024,"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":"Fundamental Research Funds for the Central Universities; China Railway","keywords":"Joint (building); Term (time); Transport engineering; Mechanism (biology); Computer science; Flow (mathematics); Operations research; Simulation; Engineering; Civil engineering","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":[0.0004250884,0.0007965846,0.0005165922,0.0004833224,0.0002494855,0.000591844,0.001117107,0.0008505385,0.001748487],"category_scores_gemma":[0.0009209627,0.0003582906,0.000719808,0.0006591927,0.0002745358,0.0009827894,0.0006148319,0.001494084,0.0003512251],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009367673,"about_ca_system_score_gemma":0.001266072,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03030399,"about_ca_topic_score_gemma":0.0222683,"domain_scores_codex":[0.9998575,0.00001793822,0.000009443173,0.00005182316,0.00002647015,0.00003685282],"domain_scores_gemma":[0.9998252,0.00005858082,0.00002610785,0.00001206301,0.00006528971,0.00001276404],"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":[0.0001076,0.00007462817,0.002697173,0.00004150892,0.00005351364,0.00006543822,0.0000373151,0.942307,0.00242427,0.002140008,0.001878665,0.04817285],"study_design_scores_gemma":[0.00000134648,0.000004242545,0.0001402248,0.000001272145,0.000003537607,0.000001794332,0.000001377633,0.9993308,0.0001340569,0.0003325994,0.00004744134,0.000001320336],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3287096,0.002063475,0.6576636,0.001425884,0.0002894671,0.00005716114,0.001146394,0.002695961,0.005948505],"genre_scores_gemma":[0.9783918,0.0003546054,0.01641949,0.0001366939,0.00004573959,0.00005172713,0.0006366434,0.00003462167,0.003928674],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03030399,"threshold_uncertainty_score":0.06025517,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01408110639325387,"score_gpt":0.2227557527485832,"score_spread":0.2086746463553293,"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."}}