{"id":"W3208830559","doi":"10.1155/2021/9805560","title":"Trajectory Optimization of CAVs in Freeway Work Zone considering Car-Following Behaviors Using Online Multiagent Reinforcement Learning","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Traffic control and management","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Bottleneck; Headway; Reinforcement learning; Computer science; Trajectory; Traffic simulation; Computation; Trajectory optimization; Simulation; Real-time computing; Engineering; Microsimulation; Optimal control; Mathematical optimization; Transport engineering; Artificial intelligence; Algorithm","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":[0.0001268213,0.0001178888,0.000275146,0.0001792165,0.00002518768,0.000007998743,0.00004458453,0.00004295114,0.000018263],"category_scores_gemma":[0.00001731519,0.0001323165,0.0001281488,0.0002601599,0.000008245303,0.0002627321,0.000002033926,0.0002027525,8.125231e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001267052,"about_ca_system_score_gemma":0.00003798558,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001529941,"about_ca_topic_score_gemma":0.0002176543,"domain_scores_codex":[0.9987844,0.00002073145,0.000729366,0.00009040331,0.0002311033,0.0001439957],"domain_scores_gemma":[0.9995477,0.0000305452,0.0002058394,0.00006735277,0.0001029661,0.00004561503],"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":[0.00003749633,0.00004692539,0.001947119,0.00007631802,0.00005273989,0.0001460033,0.001712535,0.9430104,0.04755905,0.000003140608,5.84038e-7,0.00540772],"study_design_scores_gemma":[0.01568969,0.000391468,0.4155914,0.003466928,0.001151182,0.00002868239,0.01696397,0.515777,0.02930984,0.0000114034,0.0005583431,0.001060101],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7876146,0.0006705764,0.2111847,0.000009093028,0.0004019508,0.00008833539,0.000001459704,0.00002182701,0.00000736774],"genre_scores_gemma":[0.9440667,0.0002379472,0.05559579,0.000005249266,0.00003060687,0.000002097297,0.00003060082,0.00002077482,0.00001023115],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4272333,"threshold_uncertainty_score":0.5395712,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01065776264533223,"score_gpt":0.2304636591252916,"score_spread":0.2198058964799594,"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."}}