{"id":"W3183768995","doi":"10.1109/itsc48978.2021.9564847","title":"A Deep Reinforcement Learning Approach for Fair Traffic Signal Control","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Traffic control and management","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Intersection (aeronautics); Reinforcement learning; Computer science; Throughput; Exploit; Traffic flow (computer networking); Control (management); Fairness measure; SIGNAL (programming language); Real-time computing; Artificial intelligence; Computer network; Transport engineering; Computer security; Telecommunications; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001453432,0.0008275455,0.0009968398,0.0004624462,0.0003798121,0.0008646935,0.001461486,0.001157347,0.002434982],"category_scores_gemma":[0.003573397,0.0003627783,0.0004644062,0.0003896436,0.0009952816,0.0009368111,0.001100189,0.001863105,0.0003066074],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001505845,"about_ca_system_score_gemma":0.001646321,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008518605,"about_ca_topic_score_gemma":0.006337075,"domain_scores_codex":[0.9993506,0.0001759646,0.00002679193,0.0001470586,0.0001586225,0.0001409701],"domain_scores_gemma":[0.99885,0.0005950522,0.0001127353,0.00007707729,0.0002605058,0.0001045948],"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":[0.00007387419,0.00005134959,0.0003637891,0.00002887409,0.00002194075,0.00003135395,0.0000262227,0.9567603,0.001173282,0.008168251,0.0009526339,0.03234802],"study_design_scores_gemma":[0.000003089805,0.000006207309,0.00001795494,0.000001315824,0.000001533827,0.000001983061,7.554971e-7,0.9983252,0.0001159988,0.00143072,0.00009396066,0.000001272246],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01383177,0.0003037661,0.9828953,0.0002811819,0.00008147523,0.0000273263,0.00003835463,0.0004956309,0.002045248],"genre_scores_gemma":[0.9060517,0.0001948986,0.08870149,0.0002804669,0.0001059033,0.00009168445,0.0001018268,0.00008322154,0.004389024],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008518605,"threshold_uncertainty_score":0.01693803,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009803746949851781,"score_gpt":0.1941613936154297,"score_spread":0.1843576466655779,"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."}}