{"id":"W4412403834","doi":"10.1109/ojits.2025.3589208","title":"Machine Learning Advancements in Urban Traffic Simulation: A Comprehensive Survey","year":2025,"lang":"en","type":"article","venue":"IEEE Open Journal of Intelligent Transportation Systems","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Computer science; Transport engineering; Engineering","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":[0.0005533061,0.0001493104,0.0003424762,0.000425617,0.00004053568,0.00009242356,0.0003023213,0.00005908365,0.00001521632],"category_scores_gemma":[0.00001140378,0.000148298,0.00006585263,0.0004454176,0.00001450186,0.0003736819,0.000003672306,0.0002608296,0.00000452738],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000111799,"about_ca_system_score_gemma":0.00002767288,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007914451,"about_ca_topic_score_gemma":0.0002668229,"domain_scores_codex":[0.9984032,0.0001224225,0.0009998024,0.0001182461,0.0002180472,0.0001382611],"domain_scores_gemma":[0.9993304,0.0001100219,0.0001916491,0.00009730585,0.0002186668,0.000051938],"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":[0.00009058126,0.00004803894,0.01029797,0.0001125265,0.000104986,0.00001701385,0.0004888183,0.9822107,0.00008446312,0.0001061006,0.001377535,0.005061262],"study_design_scores_gemma":[0.001856994,0.0001918706,0.0421049,0.001271935,0.00006321134,0.000004731218,0.001185163,0.8577863,0.0007280944,0.00001034354,0.0944727,0.0003237374],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2359794,0.002588883,0.7561842,0.00002364057,0.002682808,0.0009894536,0.00003384687,0.000342454,0.001175286],"genre_scores_gemma":[0.9988765,0.0006098389,0.0001715056,0.00002508475,0.00002779404,0.00001691925,0.00004320468,0.0000172064,0.0002119674],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7628971,"threshold_uncertainty_score":0.6047418,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03631186835334441,"score_gpt":0.30372936880848,"score_spread":0.2674175004551356,"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."}}