{"id":"W2035893651","doi":"10.1287/trsc.35.4.345.10433","title":"A Bilevel Model for Toll Optimization on a Multicommodity Transportation Network","year":2001,"lang":"en","type":"article","venue":"Transportation Science","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":199,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis; Université de Montréal","funders":"","keywords":"Toll; Bilevel optimization; Mathematical optimization; Flow network; Revenue; Set (abstract data type); Computer science; Mathematics; Operations research; Optimization problem; Economics","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":[0.001247971,0.001312879,0.002385564,0.001007348,0.0009370463,0.003811897,0.002119642,0.002807047,0.005464072],"category_scores_gemma":[0.004010227,0.0008806632,0.001226023,0.003241339,0.001630759,0.003372974,0.002235448,0.002738939,0.0009400186],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001967514,"about_ca_system_score_gemma":0.001811739,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009147476,"about_ca_topic_score_gemma":0.007445169,"domain_scores_codex":[0.9987198,0.000487895,0.00005023793,0.0002730672,0.000202811,0.0002662562],"domain_scores_gemma":[0.999023,0.0005001792,0.0001358996,0.00005810777,0.0001658693,0.0001169592],"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":[0.00004344544,0.00004292418,0.0002174054,0.00009044717,0.0000278511,0.00009493864,0.00006561586,0.9081237,0.00030798,0.08404746,0.0009323212,0.006005914],"study_design_scores_gemma":[0.00001589402,0.00002617531,0.00004196944,0.00001229273,0.00001043658,0.00002183245,0.00002861177,0.9434953,0.00007691546,0.05475046,0.001510932,0.000009211035],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02948159,0.0009417697,0.9539737,0.0008920732,0.00009092328,0.0000692204,0.0004428676,0.0001878801,0.01392012],"genre_scores_gemma":[0.7735924,0.002187644,0.198567,0.0002589464,0.0001814157,0.0004279581,0.0008895924,0.0001571909,0.02373793],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009147476,"threshold_uncertainty_score":0.01827914,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06522803791999959,"score_gpt":0.3404594592474697,"score_spread":0.2752314213274701,"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."}}