{"id":"W4320726415","doi":"10.1177/03611981231152459","title":"Freeway Congestion Management With Reinforcement Learning Headway Control of Connected and Autonomous Vehicles","year":2023,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Traffic control and management","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Headway; Reinforcement learning; Bottleneck; Traffic flow (computer networking); Throughput; Controller (irrigation); Computer science; Traffic congestion; Cruise control; Automotive engineering; Simulation; Engineering; Control theory (sociology); Control (management); Transport engineering; Computer network; Telecommunications","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002455017,0.0001937236,0.0003804677,0.0009704445,0.0002711974,0.00006304406,0.0003838962,0.0000880687,0.00006065128],"category_scores_gemma":[0.00003482678,0.0001439546,0.0001311597,0.001436908,0.0003208185,0.0002756841,0.000005745796,0.0009874558,0.000008346722],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001152461,"about_ca_system_score_gemma":0.00008720105,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009135266,"about_ca_topic_score_gemma":0.00535401,"domain_scores_codex":[0.9960611,0.0003554488,0.0008955264,0.000231285,0.001858865,0.0005977869],"domain_scores_gemma":[0.9978157,0.0005308477,0.0001953505,0.0002420109,0.001013562,0.0002025687],"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":[0.001708926,0.00008923024,0.04186895,0.001194257,0.0008099485,0.0001923282,0.002441617,0.9024823,0.004681595,0.005956768,0.002013828,0.03656029],"study_design_scores_gemma":[0.004201024,0.0008885529,0.9671388,0.0004985937,0.0001193377,5.238379e-7,0.002422635,0.01467625,0.0005589736,0.0003695247,0.008938042,0.0001877177],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.990027,0.0002532722,0.006758207,0.00118132,0.0001953109,0.001128903,0.00001745153,0.0001265926,0.0003119296],"genre_scores_gemma":[0.9971875,0.001721019,0.0003355784,0.00001050053,0.00004584873,0.00008890588,0.00001693876,0.00004264533,0.0005510641],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9252699,"threshold_uncertainty_score":0.5870298,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02925834686143658,"score_gpt":0.28811206386806,"score_spread":0.2588537170066235,"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."}}