{"id":"W4416997254","doi":"10.3390/ai6120315","title":"Optimizing Urban Travel Time Using Genetic Algorithms for Intelligent Transportation Systems","year":2025,"lang":"en","type":"article","venue":"AI","topic":"Traffic control and management","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Weighting; Traffic congestion; Genetic algorithm; Microsimulation; Latency (audio); Software deployment; Set (abstract data type); Routing (electronic design automation); Fuel efficiency","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009360241,0.0006304123,0.0004790222,0.0006260202,0.0002825985,0.0007548929,0.0007368311,0.0006431196,0.0007997444],"category_scores_gemma":[0.00210538,0.0002270343,0.0005095184,0.0007376636,0.0005387276,0.0004110711,0.0003901312,0.0005389472,0.0001115326],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001112437,"about_ca_system_score_gemma":0.001144398,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01086829,"about_ca_topic_score_gemma":0.006427962,"domain_scores_codex":[0.9995927,0.0002087869,0.00001551838,0.0000685948,0.00007420623,0.00004012582],"domain_scores_gemma":[0.9992893,0.0004719823,0.00007795589,0.00004884966,0.00009450104,0.00001754333],"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":[0.000006466502,0.00001149019,0.0002116338,0.00001067011,0.00001136141,0.000005729609,0.000007731452,0.9927747,0.0003255812,0.001511307,0.00004998048,0.00507317],"study_design_scores_gemma":[0.000003464972,0.00001262085,0.00007597371,0.000002437947,0.000003739899,0.000002045921,0.000004680857,0.9984643,0.0001950868,0.001064143,0.0001695613,0.000001875415],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08071543,0.0003487413,0.9140318,0.0001987115,0.00003245059,0.000102402,0.00008920495,0.0003347894,0.004146452],"genre_scores_gemma":[0.81879,0.0002604666,0.1790523,0.0000572973,0.00001904047,0.0002233841,0.0001256388,0.00006159143,0.00141031],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01086829,"threshold_uncertainty_score":0.02161002,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01177203058830353,"score_gpt":0.2241673034858461,"score_spread":0.2123952728975426,"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."}}