{"id":"W190534554","doi":"10.1002/atr.166","title":"Comparison of pedestrian trip generation models","year":2011,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Maryland Department of Transportation; U.S. Department of Transportation","keywords":"Poisson regression; Negative binomial distribution; Akaike information criterion; Econometrics; Pedestrian; Regression analysis; Deviance (statistics); Statistics; Count data; Poisson distribution; Variables; Regression; Mathematics; Transport engineering; Engineering; Population","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":[0.009352943,0.0006505715,0.0008740116,0.001921189,0.0003598373,0.001248739,0.001811896,0.0005482798,0.007347468],"category_scores_gemma":[0.02279487,0.0003106632,0.002049058,0.00174865,0.0003360361,0.00118496,0.0007902266,0.0008552053,0.001131387],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001122073,"about_ca_system_score_gemma":0.001307447,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01457356,"about_ca_topic_score_gemma":0.01034198,"domain_scores_codex":[0.9952711,0.003715506,0.0001529095,0.0003320564,0.0003846845,0.0001437226],"domain_scores_gemma":[0.9871802,0.01022093,0.0005626614,0.0006224363,0.001235597,0.0001781208],"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":[0.0005654221,0.0002107788,0.03411603,0.0003556444,0.0005241597,0.0002595137,0.0006012645,0.8294796,0.0002570503,0.05567573,0.006564534,0.07139028],"study_design_scores_gemma":[0.00003074455,0.0001240155,0.006558585,0.00006570514,0.00008586687,0.0001026445,0.00030279,0.9740512,0.0001529623,0.01618419,0.002300845,0.00004036865],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6199867,0.001936865,0.3480994,0.001556532,0.0002733153,0.000431767,0.003425259,0.000904109,0.0233861],"genre_scores_gemma":[0.9537861,0.001001167,0.03635606,0.0001266688,0.00006766625,0.0002555615,0.002582932,0.0001861231,0.005637723],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01457356,"threshold_uncertainty_score":0.04946363,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1191239377107848,"score_gpt":0.3651329663428854,"score_spread":0.2460090286321006,"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."}}