{"id":"W2007922317","doi":"10.1002/atr.122","title":"A risk‐averse user equilibrium model for route choice problem in signal‐controlled networks","year":2010,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Variational inequality; Path (computing); Computer science; Mathematical optimization; Sensitivity (control systems); Accident (philosophy); Travel time; Simulation; Operations research; Transport engineering; Engineering; Mathematics; Computer network","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":[0.0009482137,0.0001229862,0.0003086329,0.000178523,0.0001461726,0.00003823634,0.0001445861,0.000151939,0.00002244191],"category_scores_gemma":[0.0001176903,0.0001170333,0.0001795987,0.0002773274,0.00005100177,0.001015293,4.493483e-7,0.0003792027,4.484432e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004162098,"about_ca_system_score_gemma":0.0002303586,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001210404,"about_ca_topic_score_gemma":0.0109531,"domain_scores_codex":[0.9984652,0.00005674777,0.0007502639,0.0001507847,0.0003235858,0.000253374],"domain_scores_gemma":[0.9982834,0.000289924,0.000801556,0.00006854023,0.0004382631,0.0001183572],"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":[0.0009452496,0.00007498114,0.0216651,0.00001043686,0.00002259616,0.000003474622,0.008461029,0.9655005,0.0006795162,0.001289241,0.0000349521,0.001312955],"study_design_scores_gemma":[0.03247792,0.0003852556,0.2586125,0.0002372538,0.0005113938,0.000001706838,0.003557483,0.6900486,0.0001483829,0.006753602,0.006587804,0.0006781275],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6533834,0.00005125526,0.3449432,0.0003260623,0.0004404782,0.0006492102,0.00002906817,0.00003147476,0.0001458378],"genre_scores_gemma":[0.9272389,0.0001098208,0.07201642,0.00004892352,0.000214685,0.00003148244,0.00005440756,0.00001787348,0.0002674875],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2754519,"threshold_uncertainty_score":0.6112089,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01038180177763899,"score_gpt":0.2795068703442811,"score_spread":0.2691250685666421,"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."}}