{"id":"W4414161157","doi":"10.1139/cjce-2025-0202","title":"Multi-agent deep reinforcement learning-based decentralized adaptive traffic signal control for corridor-level safety optimization","year":2025,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Traffic control and management","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Reinforcement learning; SIGNAL (programming language); Crash; Traffic signal; Focus (optics); Control (management); Adaptive control","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0007904202,0.0006460582,0.0008908899,0.0002461441,0.0003055228,0.0005263574,0.001138469,0.0007760692,0.001314278],"category_scores_gemma":[0.00197883,0.0003270435,0.0003818031,0.000239915,0.0008445185,0.0006149266,0.0011998,0.001317282,0.0001967574],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007890746,"about_ca_system_score_gemma":0.001324441,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004821608,"about_ca_topic_score_gemma":0.004408466,"domain_scores_codex":[0.9996471,0.0001054761,0.00001263297,0.00008145127,0.00008716415,0.00006622908],"domain_scores_gemma":[0.9992668,0.0003349802,0.000121102,0.00005001007,0.0001505876,0.00007652763],"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":[0.00002177728,0.00002237395,0.0002098066,0.00001403397,0.00001448391,0.00001879426,0.0000148506,0.9864426,0.0007039026,0.003410887,0.0002905549,0.008835943],"study_design_scores_gemma":[0.000002565434,0.000007584438,0.00001570358,6.256905e-7,0.000001074391,0.000001606729,8.99944e-7,0.9991654,0.00006010783,0.0006900877,0.00005346488,9.333062e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01696257,0.00008681624,0.9811168,0.000137367,0.00002924868,0.00001812879,0.00001723766,0.0001944127,0.001437401],"genre_scores_gemma":[0.9499522,0.00006692166,0.04791442,0.0001029416,0.00003199923,0.00006330253,0.00003997066,0.0000327486,0.001795479],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004821608,"threshold_uncertainty_score":0.009587049,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009503332439264647,"score_gpt":0.1860397314048205,"score_spread":0.1765363989655559,"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."}}