{"id":"W2151069056","doi":"10.1002/atr.146","title":"On seat capacity in traffic assignment to a transit network","year":2010,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Transit (satellite); Transport engineering; Probabilistic logic; Service (business); Variable (mathematics); Operations research; Computer science; Set (abstract data type); Level of service; Flow network; Transit system; Simulation; Mathematical optimization; Engineering; Public transport; Mathematics; Economics","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":[0.001005324,0.0006401995,0.0006370917,0.001017144,0.0006560186,0.001151307,0.001119825,0.0006738462,0.007438635],"category_scores_gemma":[0.004530773,0.0004962584,0.0004547442,0.001039453,0.001200684,0.001399327,0.001183726,0.0007540333,0.0003440919],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002350123,"about_ca_system_score_gemma":0.001003129,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01681894,"about_ca_topic_score_gemma":0.009940496,"domain_scores_codex":[0.9994562,0.0002528036,0.00001132966,0.00006800213,0.00006292607,0.0001487635],"domain_scores_gemma":[0.9985936,0.0009748579,0.0001441848,0.00005470427,0.0001258707,0.0001067742],"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":[0.00005751586,0.00002288115,0.0006235822,0.00001803062,0.000008644827,0.00002652519,0.00002816086,0.9673281,0.0004081267,0.02715034,0.0003386064,0.003989505],"study_design_scores_gemma":[0.000003367122,0.00001544968,0.000191817,0.000003614331,0.000004727407,0.000007369868,0.00001485524,0.9890444,0.0001083666,0.01045209,0.0001505119,0.00000342619],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4000727,0.0003004511,0.584538,0.0004899408,0.00003994407,0.0001231575,0.0004494252,0.0002107537,0.0137756],"genre_scores_gemma":[0.9760649,0.0002146864,0.01797212,0.00003083756,0.00001946066,0.00008493373,0.0001245583,0.0000457584,0.005442907],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01681894,"threshold_uncertainty_score":0.03344208,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01609193256671307,"score_gpt":0.2725808457173303,"score_spread":0.2564889131506173,"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."}}