{"id":"W3129964354","doi":"10.1155/2021/3932627","title":"Optimum Equilibrium Passenger Flow Control Strategies with Delay Penalty Functions under Oversaturated Condition on Urban Rail Transit","year":2021,"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":"Fundamental Research Funds for the Central Universities; China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Urban rail transit; Simulated annealing; Flow (mathematics); Flow control (data); Line (geometry); Computer science; Control (management); Constant (computer programming); Control theory (sociology); Beijing; Function (biology); Sensitivity (control systems); Simulation; Transport engineering; Mathematical optimization; Engineering; Mathematics; Algorithm; Telecommunications","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":[0.0008629987,0.001159988,0.001031551,0.0005744866,0.000498803,0.0009952162,0.0007631483,0.0009238463,0.001118052],"category_scores_gemma":[0.001412676,0.0004956233,0.0006067939,0.0003838507,0.0005702263,0.0007070525,0.0007256748,0.0006411679,0.00007911729],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001230885,"about_ca_system_score_gemma":0.001225693,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0247038,"about_ca_topic_score_gemma":0.01014973,"domain_scores_codex":[0.9995897,0.000127663,0.0000145582,0.00007543484,0.0000503411,0.0001423883],"domain_scores_gemma":[0.9993857,0.0002959102,0.0001037382,0.00001651473,0.0001425601,0.00005548071],"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":[0.00005043011,0.00001790704,0.0002307324,0.00002202843,0.000009786044,0.00003776374,0.00003462275,0.9950354,0.000663814,0.001347569,0.00009229994,0.002457669],"study_design_scores_gemma":[0.000006606019,0.00002815904,0.0000901894,0.000001594105,0.0000052472,0.000002936745,0.00001280298,0.9993899,0.0001159217,0.0002957825,0.00004788872,0.00000295705],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4234113,0.0005117769,0.5688678,0.0002792712,0.00003711527,0.0001075287,0.00007869916,0.0002683529,0.006438083],"genre_scores_gemma":[0.991796,0.0001011948,0.0070379,0.00001736745,0.000004200967,0.00003438883,0.00002416298,0.00001018991,0.0009746117],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0247038,"threshold_uncertainty_score":0.04912001,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00998433937724205,"score_gpt":0.2629790754663867,"score_spread":0.2529947360891447,"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."}}