{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004743695,0.0001011875,0.0002355578,0.0002050648,0.0001018067,0.00001513354,0.00007516462,0.00007431575,0.00001213529],"category_scores_gemma":[0.0002457653,0.0001064234,0.0001365967,0.0006484939,0.00004161382,0.0004186,3.445956e-7,0.0001112916,0.0000010388],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006563996,"about_ca_system_score_gemma":0.0004987107,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001427078,"about_ca_topic_score_gemma":0.0002539117,"domain_scores_codex":[0.9984741,0.0001207832,0.0005931621,0.0001336888,0.0005294349,0.000148896],"domain_scores_gemma":[0.9982838,0.0002077195,0.0005880694,0.00006075427,0.0006488579,0.0002107907],"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":[0.0006027234,0.00005251848,0.0008452679,0.00003551498,0.00001114358,0.000008450183,0.01042591,0.9740288,0.008734653,0.0006618398,0.000007354744,0.004585816],"study_design_scores_gemma":[0.009584443,0.001734845,0.1327627,0.0004347324,0.0003757185,3.817707e-7,0.007735614,0.8123822,0.02854991,0.001478511,0.004155959,0.0008049326],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3702804,0.00001512192,0.6266139,0.002633608,0.0001281445,0.0001978543,0.00005133504,0.00002266286,0.00005700137],"genre_scores_gemma":[0.972916,0.000008890892,0.02623454,0.0006198757,0.0001544183,0.000002042843,0.00004416327,0.00001263903,0.000007444101],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6026356,"threshold_uncertainty_score":0.433982,"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."}}