{"id":"W2148445809","doi":"10.1109/itsc.2006.1706753","title":"Highway Work Zone Dynamic Traffic Control Using Machine Learning","year":2006,"lang":"en","type":"article","venue":"","topic":"Traffic control and management","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Procter & Gamble (Canada)","funders":"Transport Canada","keywords":"Reinforcement learning; Work zone; Computer science; Work (physics); Routing (electronic design automation); Focus (optics); Control (management); Intelligent transportation system; Advanced Traffic Management System; Traffic congestion; Transport engineering; Engineering; Computer network; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0005224984,0.0003646824,0.0004087431,0.000331472,0.0002715347,0.0005853223,0.0004687553,0.0003368876,0.0008684832],"category_scores_gemma":[0.001014173,0.000141871,0.0002446654,0.0002761974,0.0004400486,0.0004273523,0.0003328834,0.0004170471,0.0001251229],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006954631,"about_ca_system_score_gemma":0.00074581,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008925368,"about_ca_topic_score_gemma":0.006034815,"domain_scores_codex":[0.9997718,0.00008734736,0.000008891198,0.00004786191,0.00004584037,0.00003819211],"domain_scores_gemma":[0.9994341,0.0002920962,0.00008467989,0.00004501967,0.0001252734,0.00001890974],"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":[0.00004027954,0.00005012186,0.000789856,0.0000184568,0.00001993906,0.00001380891,0.00001860191,0.9583344,0.001285516,0.001734627,0.0001955263,0.0374989],"study_design_scores_gemma":[0.000004633591,0.00002384894,0.0001663148,0.000001918116,0.000002644641,0.000003337071,0.000003598612,0.9985662,0.0004685394,0.0005597947,0.0001965372,0.000002579988],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.111919,0.0002456694,0.8815935,0.0001757708,0.0000337932,0.00006393019,0.00003803289,0.0008830806,0.005047157],"genre_scores_gemma":[0.9716089,0.00005688396,0.02725751,0.00002012334,0.00001150668,0.00004737547,0.00002789291,0.00001174689,0.0009580522],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008925368,"threshold_uncertainty_score":0.01774687,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00365195605321401,"score_gpt":0.1710554989247577,"score_spread":0.1674035428715437,"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."}}