{"id":"W3067407344","doi":"10.1016/j.aap.2020.105713","title":"Self-learning adaptive traffic signal control for real-time safety optimization","year":2020,"lang":"en","type":"article","venue":"Accident Analysis & Prevention","topic":"Traffic control and management","field":"Engineering","cited_by":85,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Traffic signal; Poison control; Computer science; Occupational safety and health; Injury prevention; Control (management); SIGNAL (programming language); Transport engineering; Human factors and ergonomics; Engineering; Real-time computing; Computer security; Simulation; Medical emergency; Artificial intelligence; Medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002834256,0.000185018,0.0003730376,0.0001775287,0.0001070302,0.00006623579,0.0001342972,0.00006772645,0.0004346419],"category_scores_gemma":[0.00001905555,0.0001998025,0.0004637362,0.0005748969,0.000005835792,0.0002406516,0.00001932466,0.00009612461,0.00003996788],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009547922,"about_ca_system_score_gemma":0.00001143559,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004854472,"about_ca_topic_score_gemma":0.00007235721,"domain_scores_codex":[0.9987645,0.00008842359,0.0004211105,0.0002891395,0.00020268,0.0002341939],"domain_scores_gemma":[0.9995182,0.00007877304,0.0001181118,0.0001142544,0.00007454585,0.00009614309],"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":[0.00010333,0.00003171622,0.0002431098,0.000008566587,0.002613703,0.000001193225,0.0002216533,0.9816206,0.0001652716,0.00008615263,0.0002514971,0.01465323],"study_design_scores_gemma":[0.001389118,0.0001342538,0.002061469,0.000008583266,0.003810703,1.547337e-7,0.00009173003,0.9913176,0.0000165421,0.00001220029,0.0009569633,0.0002007103],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01088624,0.0001423144,0.9864126,0.00022896,0.00004466402,0.0007378274,0.000001269849,0.0009001251,0.0006459259],"genre_scores_gemma":[0.9913624,0.0001877452,0.007696431,0.00003000621,0.0001426302,0.00009998146,0.0002167702,0.00002959569,0.000234427],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9804761,"threshold_uncertainty_score":0.8147709,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006339414410748187,"score_gpt":0.2037122118571793,"score_spread":0.1973727974464311,"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."}}