{"id":"W3087780323","doi":"10.1155/2020/8867404","title":"Equity-Oriented Train Timetabling with Collaborative Passenger Flow Control: A Spatial Rebalance of Service on an Oversaturated Urban Rail Transit Line","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Train; Solver; Weighting; Computer science; Tabu search; Service (business); Service level; Integer programming; Heuristic; Transport engineering; Urban rail transit; Operations research; Mathematical optimization; Engineering; Algorithm","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":[0.0007497454,0.0007708975,0.0006849535,0.0004080503,0.0004837121,0.0008077503,0.0008870697,0.0006218667,0.001259251],"category_scores_gemma":[0.000806354,0.0002766462,0.0004214206,0.0006330361,0.0004411066,0.0006708279,0.000707785,0.0004471632,0.00006649701],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001204711,"about_ca_system_score_gemma":0.001271991,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02581507,"about_ca_topic_score_gemma":0.01437309,"domain_scores_codex":[0.9995753,0.0001095286,0.00001388852,0.00009934653,0.00007299029,0.0001287802],"domain_scores_gemma":[0.9996493,0.0001136638,0.00007960546,0.00002661533,0.0000636199,0.00006718563],"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":[0.0000639532,0.00003296065,0.0006776235,0.00002130587,0.0000139531,0.00007894004,0.00002786556,0.9880172,0.001648433,0.001412923,0.0001501357,0.007854651],"study_design_scores_gemma":[0.000008089384,0.00003686146,0.0002190518,0.000001267349,0.00000644372,0.000007444092,0.00001655608,0.9989892,0.0003567834,0.0002604787,0.00009507234,0.000002753915],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5085351,0.000210574,0.484643,0.0002213112,0.00003973348,0.0001312659,0.0001024745,0.0003292635,0.005787309],"genre_scores_gemma":[0.989256,0.00003107273,0.01008176,0.00001129933,0.000005349769,0.00001866957,0.00002278704,0.000007882128,0.0005652902],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02581507,"threshold_uncertainty_score":0.05132961,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007321100585265899,"score_gpt":0.2177197759545639,"score_spread":0.210398675369298,"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."}}