{"id":"W4226278669","doi":"10.1155/2022/5681234","title":"A Reinforcement Learning Based Traffic Control Strategy in a Macroscopic Fundamental Diagram Region","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Traffic control and management","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Science and Technology Commission of Shanghai Municipality; National Natural Science Foundation of China","keywords":"Reinforcement learning; Marl; Intersection (aeronautics); Traffic flow (computer networking); Testbed; Computer science; Control (management); Simulation; Control theory (sociology); Engineering; Control engineering; Transport engineering; Artificial intelligence; Computer network","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003270101,0.0003057553,0.0003192392,0.0002546262,0.0003142597,0.0004689617,0.000607106,0.000343604,0.0008671543],"category_scores_gemma":[0.0008304869,0.0001202736,0.0002341599,0.0001493836,0.0004497405,0.0005437627,0.0005327754,0.000354523,0.0001011369],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006176638,"about_ca_system_score_gemma":0.0006757163,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004341669,"about_ca_topic_score_gemma":0.002349786,"domain_scores_codex":[0.9997965,0.00004786733,0.000008443028,0.00006962405,0.00004717769,0.00003038874],"domain_scores_gemma":[0.9996367,0.0001023077,0.00007395994,0.00003165604,0.0001037024,0.00005167601],"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":[0.0001787339,0.0001184126,0.002319021,0.00007362538,0.00002772408,0.0001689432,0.000155738,0.8687851,0.03214926,0.01652463,0.0007276051,0.07877125],"study_design_scores_gemma":[0.00001272616,0.0000873054,0.0003636879,0.000002439018,0.00000544726,0.00002353527,0.00001048066,0.9960784,0.001489354,0.001393413,0.0005260587,0.000007143768],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2095661,0.0001117684,0.7841868,0.0001815053,0.00004230979,0.00007630717,0.00003624588,0.0005410769,0.005257829],"genre_scores_gemma":[0.9833152,0.0000272906,0.01586294,0.00002088967,0.000005574361,0.0000265961,0.00001832485,0.000005982207,0.0007171314],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004341669,"threshold_uncertainty_score":0.008632779,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00602854834356898,"score_gpt":0.211696579573691,"score_spread":0.205668031230122,"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."}}