{"id":"W4382463422","doi":"10.1155/2023/4419907","title":"Coordinated Variable Speed Limit Control for Consecutive Bottlenecks on Freeways Using Multiagent Reinforcement Learning","year":2023,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Traffic control and management","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Government of Jiangsu Province; National Natural Science Foundation of China","keywords":"Bottleneck; Reinforcement learning; Speed limit; Computer science; Variable (mathematics); Simulation; Limit (mathematics); Traffic flow (computer networking); Engineering; Transport engineering; Artificial intelligence; Mathematics; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002434185,0.0001466061,0.0002661425,0.0002047335,0.00007687264,0.0000163036,0.00007247103,0.00004869222,0.00001648676],"category_scores_gemma":[0.00003923235,0.0001425833,0.0001191602,0.0002089016,0.00001244472,0.0001817398,0.000001116267,0.0001682551,0.000003430819],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001069442,"about_ca_system_score_gemma":0.00002424891,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004689424,"about_ca_topic_score_gemma":0.000007672367,"domain_scores_codex":[0.9989458,0.00001392232,0.0005008676,0.0001041451,0.0001985289,0.0002367533],"domain_scores_gemma":[0.9993504,0.0001409775,0.0002014957,0.00006668737,0.0001695624,0.00007085728],"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":[0.0004198254,0.00002038428,0.00003922376,0.00007534845,0.0002055728,0.00002303532,0.0005221574,0.9634144,0.0311358,0.0005260213,0.0001480124,0.003470206],"study_design_scores_gemma":[0.01515052,0.001048915,0.01684928,0.0003936959,0.0004520418,0.000004141607,0.001680406,0.9476552,0.003490382,0.0003164842,0.01254876,0.0004101864],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4759213,0.0001270092,0.5214642,0.0001126995,0.001140703,0.0007509544,0.00002747227,0.000247023,0.0002086605],"genre_scores_gemma":[0.9968827,0.00009798612,0.002712614,0.00003961641,0.00008394292,0.00001133626,0.00004444621,0.00003049236,0.00009688155],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5209614,"threshold_uncertainty_score":0.5814378,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01300667751478074,"score_gpt":0.2326561254731483,"score_spread":0.2196494479583676,"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."}}