{"id":"W4297997730","doi":"10.1155/2022/2435643","title":"Adaptive Coordinated Variable Speed Limit between Highway Mainline and On-Ramp with Deep Reinforcement Learning","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Traffic control and management","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Science Research of Jiangsu Higher Education Institutions of China; National Natural Science Foundation of China","keywords":"Bottleneck; Speed limit; VisSim; Traffic flow (computer networking); Computer science; Variable (mathematics); Reliability (semiconductor); Reinforcement learning; Limit (mathematics); Transport engineering; Control theory (sociology); Simulation; Engineering; Control (management); Artificial intelligence; Microsimulation; 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":[0.0001471537,0.0001109767,0.0001953689,0.0001216688,0.00009052634,0.00001061133,0.00005771468,0.00001678284,0.00003904283],"category_scores_gemma":[0.000003749954,0.00009987526,0.00003121792,0.000167317,0.000009731439,0.000164296,0.000002476333,0.0002963926,4.722405e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000853501,"about_ca_system_score_gemma":0.00001311247,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003281403,"about_ca_topic_score_gemma":0.000009579433,"domain_scores_codex":[0.9992173,0.00001612129,0.0003044269,0.00008560271,0.0002388761,0.0001376511],"domain_scores_gemma":[0.9996362,0.00004785001,0.0001480721,0.00004914681,0.00005938698,0.00005930225],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0003798441,0.00001813272,0.0001857231,0.00002350963,0.0001528291,0.00003462306,0.0006957965,0.9862382,0.000698353,0.0005415439,0.00001269871,0.01101877],"study_design_scores_gemma":[0.03930412,0.01979439,0.4960881,0.0006393745,0.001853877,0.00006410014,0.01489338,0.3222968,0.001540829,0.001047233,0.1005186,0.001959158],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8106859,0.0002601844,0.1875703,0.0001806329,0.0002717072,0.0003302074,0.000008990102,0.0001227173,0.0005693696],"genre_scores_gemma":[0.9962031,0.00005274753,0.003506041,0.00002373233,0.00004963575,0.000006227054,0.00003976635,0.0000196163,0.00009913008],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6639414,"threshold_uncertainty_score":0.4072795,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0063934282786259,"score_gpt":0.1902774358915453,"score_spread":0.1838840076129194,"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."}}