{"id":"W3192327502","doi":"10.1155/2021/3328202","title":"Traffic Light Optimization Based on Modified Webster Function","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Intersection (aeronautics); Signal timing; Traffic signal; SIGNAL (programming language); Genetic algorithm; Computer science; Scheme (mathematics); Mathematical optimization; Function (biology); Traffic congestion; Control theory (sociology); Algorithm; Simulation; Real-time computing; Engineering; Mathematics; Transport engineering; Artificial intelligence","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":[0.0000715569,0.00009456874,0.0001216934,0.0001650098,0.00002668698,0.00001719888,0.00004244668,0.00005364386,0.00003132674],"category_scores_gemma":[0.000005884046,0.00009457659,0.00008648296,0.0001902967,0.000005078037,0.0003276958,3.567126e-7,0.0001320217,0.000001663663],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004603087,"about_ca_system_score_gemma":0.00001726061,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":4.844167e-8,"about_ca_topic_score_gemma":0.000003622639,"domain_scores_codex":[0.9992836,0.00001367622,0.0003327394,0.0000803302,0.0002048132,0.00008483864],"domain_scores_gemma":[0.9996396,0.00001439352,0.00009205355,0.00008387832,0.0001258136,0.00004427547],"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":[0.00007201343,0.00004936516,0.000006425295,0.00003390446,0.00002252844,0.00001850016,0.00008593565,0.982249,0.001751483,0.0001282534,0.000938508,0.01464407],"study_design_scores_gemma":[0.002247791,0.0003372383,0.005949378,0.0001980228,0.0001423153,0.000005657918,0.0002072834,0.9689148,0.008832283,0.00005259674,0.0128835,0.000229116],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02885424,0.000105646,0.9678428,0.0002165968,0.0009489924,0.0000889403,0.000004019015,0.0005801135,0.001358707],"genre_scores_gemma":[0.9870855,0.0001632238,0.01245299,0.0001270364,0.00007251587,0.000005171901,0.00004940417,0.00001988179,0.00002426616],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9582313,"threshold_uncertainty_score":0.3856721,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005566819219292139,"score_gpt":0.1973722717309632,"score_spread":0.191805452511671,"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."}}