{"id":"W2219062175","doi":"10.1139/cjce-2014-0513","title":"Optimal congestion pricing toll design for revenue maximization: comprehensive numerical results and implications","year":2015,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Toll; Congestion pricing; Revenue; Mathematical optimization; Maximization; Value of time; Genetic algorithm; Homogeneous; Sensitivity (control systems); Computer science; Optimal design; Operations research; Traffic congestion; Transport engineering; Engineering; Economics; Mathematics; Travel time","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00154103,0.0006458409,0.0008867693,0.0008755278,0.0005696458,0.001293585,0.0008890824,0.00156753,0.003386423],"category_scores_gemma":[0.006357829,0.0004667882,0.0005749752,0.001168383,0.0009012715,0.001119514,0.0007853845,0.001097429,0.000199141],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00184977,"about_ca_system_score_gemma":0.001811676,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01422142,"about_ca_topic_score_gemma":0.01196151,"domain_scores_codex":[0.9994203,0.0003030834,0.00001133003,0.00003726164,0.0001290758,0.00009905348],"domain_scores_gemma":[0.9980292,0.001561777,0.0001236866,0.00005750448,0.0001811789,0.00004662134],"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":[0.000008159328,0.00002314972,0.0001633334,0.00001508067,0.000003573982,0.00001952949,0.00000613134,0.993681,0.00007349758,0.004571788,0.0001731545,0.001261647],"study_design_scores_gemma":[0.000008706799,0.00001113945,0.00005968824,0.000005710581,0.000003097257,0.000005811623,0.00001615532,0.9971014,0.00008733365,0.002580581,0.0001175495,0.000002880624],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4406828,0.001836543,0.473817,0.001924609,0.00009237969,0.0002669365,0.0004115551,0.0003103327,0.08065802],"genre_scores_gemma":[0.9529567,0.0004967326,0.04397154,0.00008830233,0.00002208331,0.00009942936,0.00007860336,0.00003287656,0.002253782],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01422142,"threshold_uncertainty_score":0.02827728,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04483570377264248,"score_gpt":0.2653922229875016,"score_spread":0.2205565192148591,"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."}}