{"id":"W1543690589","doi":"10.1002/atr.205","title":"A multi‐class mean‐excess traffic equilibrium model with elastic demand","year":2012,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"China Scholarship Council; Tongji University; National Science Foundation","keywords":"Price elasticity of demand; Travel time; Variational inequality; Elasticity (physics); Computer science; Function (biology); Risk aversion (psychology); Class (philosophy); Mathematical optimization; Econometrics; Operations research; Economics; Mathematical economics; Microeconomics; Mathematics; Expected utility hypothesis; Transport engineering; Engineering; Artificial intelligence","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":[0.001090721,0.0007714197,0.001295579,0.0007836669,0.0005380087,0.001579717,0.002017703,0.002041983,0.005418226],"category_scores_gemma":[0.002187924,0.0005711684,0.001163018,0.0007206784,0.0009599394,0.00133181,0.001301046,0.001351374,0.0004473206],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001838149,"about_ca_system_score_gemma":0.001050266,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02600992,"about_ca_topic_score_gemma":0.01046801,"domain_scores_codex":[0.9993617,0.0002051705,0.00002145978,0.0001545925,0.00009261536,0.0001645313],"domain_scores_gemma":[0.9990369,0.0004371035,0.0001737971,0.0000449168,0.0001967142,0.0001106291],"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":[0.00005595653,0.00002428485,0.0005649187,0.00001738824,0.00001911169,0.00009234581,0.00003525992,0.9714999,0.0003379252,0.02567107,0.0004376911,0.001244113],"study_design_scores_gemma":[0.000005866395,0.0000060038,0.00008078006,0.000001750519,0.000003177114,0.000005932717,0.00001015627,0.9967097,0.00002095976,0.003027045,0.0001242011,0.000004468965],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3076651,0.0004704866,0.6635309,0.001703918,0.0001233744,0.0001106128,0.0009678879,0.0002428229,0.02518507],"genre_scores_gemma":[0.9770961,0.0001493346,0.01118201,0.00007321552,0.00002750385,0.0001222782,0.0002043267,0.0000282187,0.01111692],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02600992,"threshold_uncertainty_score":0.05171704,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01832041953930727,"score_gpt":0.2857894207781338,"score_spread":0.2674690012388266,"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."}}