{"id":"W2143138030","doi":"10.1002/atr.1331","title":"Welfare maximization for bus transit systems with timed transfers and financial constraints","year":2015,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Universidad del Atlántico; Morgan State University; Pennsylvania State University","keywords":"Price elasticity of demand; Subsidy; Bounded function; Maximization; Mathematical optimization; Welfare; Service (business); Computer science; Transfer (computing); Elasticity (physics); Economic surplus; Genetic algorithm; Public transport; Service level; Operations research; Economics; Microeconomics; Business; Transport engineering; Engineering; Mathematics; Economy; Marketing","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":[0.001968067,0.001430285,0.00136192,0.0009866962,0.0007592986,0.00185855,0.0009405764,0.001272978,0.003680925],"category_scores_gemma":[0.004079725,0.0007088218,0.001051671,0.0009903598,0.001396006,0.001262828,0.001720218,0.001010984,0.0002937568],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002921959,"about_ca_system_score_gemma":0.001461927,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009696555,"about_ca_topic_score_gemma":0.005499222,"domain_scores_codex":[0.9991667,0.0004377884,0.00001313148,0.00006985398,0.00005450059,0.0002579423],"domain_scores_gemma":[0.9981813,0.001286829,0.0001747034,0.00004094871,0.00017381,0.0001423654],"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":[0.0001300662,0.00005934445,0.0006375908,0.00005852678,0.00004731767,0.0001568424,0.00004785466,0.9779564,0.0007781017,0.0163733,0.001007529,0.002747077],"study_design_scores_gemma":[0.0000344774,0.00004806387,0.0004871228,0.00001205484,0.00001708041,0.00002201055,0.00007653655,0.9868286,0.0002025242,0.01186053,0.0004016203,0.000009368724],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5681576,0.001112448,0.399362,0.001681902,0.00007311445,0.0002566839,0.001009646,0.0001839077,0.02816273],"genre_scores_gemma":[0.9805821,0.0005324032,0.01274917,0.00008893831,0.00003313274,0.0001646682,0.0002458843,0.00004584159,0.005557987],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009696555,"threshold_uncertainty_score":0.02120036,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01490309527466653,"score_gpt":0.2578230168629149,"score_spread":0.2429199215882484,"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."}}