{"id":"W2060895377","doi":"10.1007/s11431-009-0260-8","title":"Equilibrium model and algorithm of urban transit assignment based on augmented network","year":2009,"lang":"en","type":"article","venue":"Science in China. Series E, Technological sciences/Science in China. Series E, Technological Sciences","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Urban rail transit; Assignment problem; Transit (satellite); Flow network; Computer science; Line (geometry); Simple (philosophy); Network planning and design; Transport engineering; Transit system; Mathematical optimization; Flow (mathematics); Urban transit; Public transport; Operations research; Computer network; Engineering; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006181982,0.0007177626,0.00146313,0.0007115473,0.0007457674,0.001269335,0.002242328,0.001141684,0.007525086],"category_scores_gemma":[0.001640944,0.0005163634,0.0007114612,0.0009544675,0.000678375,0.001625136,0.001349582,0.000876933,0.0006047901],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001676234,"about_ca_system_score_gemma":0.001849961,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02164882,"about_ca_topic_score_gemma":0.01394803,"domain_scores_codex":[0.9996585,0.0001106615,0.00001487195,0.0001027718,0.00004422819,0.00006902152],"domain_scores_gemma":[0.9993735,0.0003451093,0.00006833434,0.00003564636,0.0001364903,0.00004083988],"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":[0.00005898212,0.00001940521,0.0002389446,0.0000365292,0.00001425305,0.00003127887,0.00004096808,0.9788776,0.0002064905,0.01039889,0.0009606828,0.009115948],"study_design_scores_gemma":[0.00001349671,0.000008504124,0.00003859234,0.000003051792,0.000004683997,0.000006526989,0.000008885834,0.9953492,0.00006137655,0.004302225,0.0002004453,0.000002980296],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04183086,0.0001611184,0.9505293,0.0003253292,0.00005383417,0.0001143137,0.0003315557,0.0003984587,0.00625529],"genre_scores_gemma":[0.838106,0.0002396273,0.1505973,0.00007370178,0.00003861091,0.0004822561,0.0006988273,0.00007986226,0.009683921],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02164882,"threshold_uncertainty_score":0.04304564,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01901430617570447,"score_gpt":0.2869955231366159,"score_spread":0.2679812169609114,"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."}}