{"id":"W3008591799","doi":"10.1155/2020/1902162","title":"Agent-Based Simulation to Improve Policy Sensitivity of Trip-Based Models","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":62,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Seventh Framework Programme; Technische Universität München; Deutsche Forschungsgemeinschaft; European Commission","keywords":"Microsimulation; Nested logit; TRIPS architecture; Mode choice; Trip generation; Computer science; Aggregate (composite); Operations research; Travel behavior; Transport engineering; Mode (computer interface); Sensitivity (control systems); Trip distribution; Econometrics; Economics; Engineering; Public transport","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":[0.002813656,0.001031508,0.001330909,0.001256511,0.000572825,0.001379286,0.001217834,0.001157145,0.004192804],"category_scores_gemma":[0.01125791,0.0008085018,0.000988862,0.000937834,0.0004246287,0.001187711,0.001671088,0.001681728,0.0003430263],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001553728,"about_ca_system_score_gemma":0.001547079,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02503941,"about_ca_topic_score_gemma":0.01090336,"domain_scores_codex":[0.9988468,0.0007372155,0.00005344738,0.0001295197,0.000138798,0.00009412127],"domain_scores_gemma":[0.9927605,0.005709736,0.0003675828,0.0004390284,0.000552698,0.0001704446],"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":[0.0000135313,0.00001269575,0.0002532282,0.000009187778,0.0000144161,0.000009378341,0.00000766196,0.9971258,0.00005457215,0.001206511,0.00009887604,0.001194105],"study_design_scores_gemma":[0.000002678939,0.000005376466,0.00002995394,0.000002268641,0.000003085181,0.000001505055,0.000003498066,0.9989952,0.0000493372,0.0007276932,0.000177766,0.000001617766],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1524898,0.0004899078,0.825889,0.0006820898,0.0002060354,0.0003363088,0.0009407054,0.00195837,0.01700776],"genre_scores_gemma":[0.9248633,0.000202797,0.07228344,0.00009465283,0.00002798933,0.0002260953,0.0003689112,0.0001749994,0.001757797],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02503941,"threshold_uncertainty_score":0.04978734,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0261542466896772,"score_gpt":0.3159541246372538,"score_spread":0.2897998779475766,"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."}}