{"id":"W2335575388","doi":"10.1061/9780784413036.085","title":"Comparison Between Traditional Four-Step &amp; Activity-Based Travel Demand Modeling - A Case Study of Tampa, Florida","year":2013,"lang":"en","type":"article","venue":"","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"TRIPS architecture; Trip generation; Trip distribution; Transport engineering; Salient; Strengths and weaknesses; Travel behavior; Operations research; Computer science; Demand forecasting; Bay; Sensitivity (control systems); Mode (computer interface); Mode choice; Quarter (Canadian coin); Fidelity; Microsimulation; Travel time; Engineering; Geography; Public transport; Artificial intelligence; Civil engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007792453,0.0006783456,0.0003595056,0.0006317322,0.0003540843,0.0008118345,0.001099071,0.0007639618,0.001850463],"category_scores_gemma":[0.001619115,0.0003327603,0.0007205053,0.0006252778,0.00023201,0.0007926864,0.0003942496,0.0005111256,0.0001958686],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002939497,"about_ca_system_score_gemma":0.001437247,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.183931,"about_ca_topic_score_gemma":0.1327603,"domain_scores_codex":[0.9996972,0.0001538279,0.00001340695,0.00005812016,0.00003967406,0.00003784964],"domain_scores_gemma":[0.9990951,0.0005774374,0.00005642523,0.00005325732,0.0001802985,0.00003746145],"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":[0.00009853594,0.0002326076,0.01457007,0.00007058479,0.00005851467,0.0001442238,0.0001329015,0.9721159,0.0006287202,0.001595626,0.0005145107,0.009837684],"study_design_scores_gemma":[0.00001434181,0.0001032992,0.004361509,0.000009257033,0.00001707541,0.00001934709,0.0001976689,0.9940421,0.0002747775,0.000311926,0.0006368239,0.00001192781],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9800317,0.00007370934,0.01229424,0.0002710345,0.000008050928,0.0001301025,0.001076851,0.0001038166,0.006010544],"genre_scores_gemma":[0.9875363,0.0001026046,0.009963603,0.00003397023,0.000003328179,0.0001245777,0.0007016838,0.00001776508,0.001516259],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.183931,"threshold_uncertainty_score":0.3657208,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2106892258647766,"score_gpt":0.3653565876344915,"score_spread":0.154667361769715,"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."}}