{"id":"W2929926241","doi":"10.1177/0361198119838260","title":"Multi-Objective Stochastic Optimization Algorithms to Calibrate Microsimulation Models","year":2019,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Traffic control and management","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"A priori and a posteriori; Mathematical optimization; Particle swarm optimization; VisSim; Calibration; Optimization problem; Computer science; Algorithm; Process (computing); Stochastic optimization; Microsimulation; Engineering; Mathematics; Statistics","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":[0.001307895,0.00124512,0.0007890242,0.0009442762,0.0004198882,0.0007802726,0.0009072812,0.0008693637,0.001909922],"category_scores_gemma":[0.003417643,0.000719182,0.0009586812,0.0006336445,0.0004670304,0.0007263733,0.001018129,0.001348147,0.0003801443],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007821173,"about_ca_system_score_gemma":0.001542027,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005636256,"about_ca_topic_score_gemma":0.004598711,"domain_scores_codex":[0.9995897,0.0001365633,0.00002752132,0.00006261992,0.0001479043,0.00003569068],"domain_scores_gemma":[0.9986734,0.0007454543,0.0002226021,0.00009955181,0.000226791,0.00003221382],"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":[0.000005324897,0.000009038022,0.0001827482,0.00002753812,0.00001361183,0.000007596079,0.00001139107,0.9904763,0.0005347615,0.002043066,0.0001043288,0.006584394],"study_design_scores_gemma":[0.000002005676,0.000005547808,0.00004111679,0.000004508487,0.00000229429,0.000002283992,0.000002465135,0.9986585,0.0002936973,0.0006866503,0.0002985774,0.00000232308],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008567185,0.0001229069,0.9890042,0.00005288802,0.00001950702,0.00005470917,0.00004433175,0.0004486513,0.001685562],"genre_scores_gemma":[0.4515524,0.0003971557,0.5443887,0.00009818019,0.00003243019,0.0007356601,0.0003400952,0.0003500254,0.002105469],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005636256,"threshold_uncertainty_score":0.01120687,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04969159787982447,"score_gpt":0.3227138882963731,"score_spread":0.2730222904165486,"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."}}