{"id":"W1974993124","doi":"10.1016/j.trpro.2014.09.104","title":"Mixed Logit Model of Vertical Transport Choice in Toronto Subway Stations and Application within Pedestrian Simulation","year":2014,"lang":"en","type":"article","venue":"Transportation research procedia","topic":"Evacuation and Crowd Dynamics","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Arup Group (Canada); University of Toronto","funders":"","keywords":"Pedestrian; Mixed logit; Transport engineering; Logit; Logistic regression; Computer science; Econometrics; Engineering; Statistics; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000590878,0.0001054956,0.0001488843,0.0001281079,0.00005007766,0.00001192661,0.00008841691,0.0001118155,0.00001361535],"category_scores_gemma":[0.00008958169,0.0001160816,0.00002322402,0.0002761443,0.00007372563,0.0003207213,0.000001416222,0.0001898562,0.000003274244],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001021615,"about_ca_system_score_gemma":0.00006285118,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005704958,"about_ca_topic_score_gemma":0.04432524,"domain_scores_codex":[0.998718,0.00003581541,0.0004395714,0.0001978909,0.0003971658,0.0002115332],"domain_scores_gemma":[0.999318,0.0001954023,0.00002638927,0.0001350755,0.0002210077,0.0001041064],"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":[0.00002962525,0.00004811622,0.01876802,0.0002763496,0.000007078211,1.964619e-7,0.001901278,0.9527446,0.004665649,0.01980899,0.000006202079,0.001743876],"study_design_scores_gemma":[0.0004654195,0.00003011419,0.2286033,0.00001684474,0.000007583584,3.466498e-8,0.0002460195,0.7689471,0.0004906532,0.001086165,0.00002169401,0.0000850276],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7253019,0.00003137978,0.2737183,0.00004501465,0.00001673805,0.0003792232,0.00002601788,0.000101412,0.0003800328],"genre_scores_gemma":[0.9977176,0.0000447911,0.001853864,0.000008969445,0.00001789846,0.0001159687,0.0001927319,0.00002970898,0.00001845874],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2724157,"threshold_uncertainty_score":0.9731134,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05720236145857382,"score_gpt":0.3519339784970772,"score_spread":0.2947316170385034,"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."}}