{"id":"W578679485","doi":"","title":"Machine Learning for Optimal Traffic Corridor Control: Theoretical Framework and Early Results","year":2004,"lang":"en","type":"article","venue":"10th World Conference on Transport ResearchWorld Conference on Transport Research SocietyIstanbul Technical University","topic":"Traffic control and management","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Reinforcement learning; Control (management); Computer science; Metering mode; Traffic congestion; Intelligent transportation system; Grid; Transport engineering; Operations research; Engineering; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.00258163,0.001152741,0.001347722,0.0008123306,0.0005412938,0.002112555,0.001449884,0.002048837,0.003595464],"category_scores_gemma":[0.00821158,0.0006110429,0.001144277,0.00113677,0.002555693,0.002545862,0.001427855,0.00330252,0.0004498246],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002663028,"about_ca_system_score_gemma":0.001478543,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005871703,"about_ca_topic_score_gemma":0.002487192,"domain_scores_codex":[0.9990517,0.0004381438,0.00004036471,0.0001620015,0.0001949587,0.0001127998],"domain_scores_gemma":[0.9940294,0.005130119,0.0002068345,0.0001122537,0.0004235571,0.00009797014],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00005269047,0.00008382944,0.000717804,0.0003500113,0.0000674381,0.0000959902,0.0001414561,0.4155419,0.0003757989,0.5481243,0.002057484,0.03239128],"study_design_scores_gemma":[0.00001290902,0.00003594588,0.000157014,0.0000488019,0.000009465309,0.00001664325,0.00001786743,0.8045483,0.0001166107,0.1930748,0.001949869,0.00001171472],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01206372,0.01225553,0.9512227,0.002858585,0.0001758304,0.00004863366,0.00008211021,0.0001273867,0.02116559],"genre_scores_gemma":[0.8264696,0.01677695,0.1387079,0.0009284441,0.001239396,0.0004287403,0.0002206198,0.0001016778,0.01512664],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005871703,"threshold_uncertainty_score":0.01932174,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03795827373634487,"score_gpt":0.2750469299606548,"score_spread":0.23708865622431,"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."}}