{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009277224,0.0001691871,0.0001933552,0.00009099378,0.00006810825,0.00004484764,0.00008897571,0.00004784391,0.00015433],"category_scores_gemma":[0.00000333971,0.0001566088,0.00006320814,0.0002139755,0.00001407629,0.00006462468,0.00001348405,0.0001567854,0.00006515324],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007732034,"about_ca_system_score_gemma":0.00000448757,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001139642,"about_ca_topic_score_gemma":0.000488704,"domain_scores_codex":[0.9991827,0.00001754249,0.0002098396,0.0001551924,0.0001203582,0.0003143631],"domain_scores_gemma":[0.9997603,0.00003356875,0.00002051199,0.0001276189,0.00001493002,0.00004312861],"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":[0.000009682839,0.00002032046,0.0001166722,0.00001603974,0.0000427532,0.00001132477,0.0000164253,0.9756995,0.0015071,0.0002765781,0.000271312,0.02201228],"study_design_scores_gemma":[0.001189232,0.00001370363,0.003757362,0.00001280056,0.00004427463,0.000002687157,0.00001036458,0.9741179,0.00001501888,0.000006395942,0.02062405,0.0002061597],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7010674,0.001961872,0.2868595,0.0001693489,0.0005309073,0.0003207645,0.000004359338,0.002810632,0.006275192],"genre_scores_gemma":[0.9954065,0.00001305918,0.001379695,0.00003076807,0.00008660711,0.000009440629,0.00001327163,0.00003926235,0.00302139],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2943391,"threshold_uncertainty_score":0.6386321,"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."}}