{"id":"W3150493802","doi":"10.1109/tvt.2021.3069921","title":"A Novel Reinforcement Learning-Based Cooperative Traffic Signal System Through Max-Pressure Control","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Traffic control and management","field":"Engineering","cited_by":77,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Reinforcement learning; Computer science; Transmission (telecommunications); Traffic flow (computer networking); Data transmission; SIGNAL (programming language); Traffic generation model; Real-time computing; Network traffic control; Computer network; Artificial intelligence; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00008501005,0.0003083666,0.0004028123,0.0002247572,0.0002117046,0.00003885623,0.0001951779,0.0003134928,0.0001426943],"category_scores_gemma":[0.000004007632,0.0003149206,0.0001780012,0.0005325094,0.00008158795,0.00007830827,0.0000018517,0.0006601333,0.00007549996],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001406119,"about_ca_system_score_gemma":0.00005706095,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007481424,"about_ca_topic_score_gemma":0.00005018223,"domain_scores_codex":[0.9985806,0.00004356154,0.0003434959,0.0003822098,0.000235969,0.0004141834],"domain_scores_gemma":[0.9993235,0.00005037978,0.00004280663,0.0003956045,0.0001301523,0.00005754227],"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":[0.00003797828,0.0001282445,3.300526e-7,0.0001241133,0.0005606124,0.00009281219,0.00006026504,0.9742689,0.01656986,0.0008280741,0.00007269176,0.007256182],"study_design_scores_gemma":[0.00348319,0.0002391747,0.000003970875,0.0001037532,0.0003179529,0.00004965144,0.0004641092,0.9338595,0.03714593,0.000002843645,0.02399209,0.0003377873],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005965452,0.0005593252,0.9889543,0.0007146874,0.0004321742,0.0004975895,0.00002486505,0.002438247,0.0004133545],"genre_scores_gemma":[0.998302,0.00002974892,0.0006642454,0.0001342059,0.00002670104,0.0003667611,0.000009647804,0.00005050879,0.0004161369],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9923366,"threshold_uncertainty_score":0.9999303,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006261894909769767,"score_gpt":0.190822982440072,"score_spread":0.1845610875303022,"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."}}