{"id":"W4382982125","doi":"10.3390/app13137810","title":"A Microscopic Traffic Model Considering Driver Reaction and Sensitivity","year":2023,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Traffic control and management","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Headway; Sensitivity (control systems); Platoon; Acceleration; Traffic flow (computer networking); Simulation; Exponent; String (physics); Computer science; Traffic model; Microscopic traffic flow model; Real-time computing; Traffic generation model; Mathematics; Engineering; Physics; Artificial intelligence; Computer network; Electronic engineering; Classical mechanics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.0002906728,0.0008444633,0.0006562199,0.0006838536,0.0003390992,0.0007881434,0.001329984,0.0008666533,0.002105025],"category_scores_gemma":[0.0008955876,0.0003296794,0.0007426951,0.0004616886,0.0005538458,0.001310319,0.0007082553,0.0007540567,0.0003758155],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009169976,"about_ca_system_score_gemma":0.001038233,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00969427,"about_ca_topic_score_gemma":0.003636984,"domain_scores_codex":[0.9996958,0.00004489638,0.000012214,0.00009382193,0.00009389067,0.00005950221],"domain_scores_gemma":[0.9996732,0.00008087188,0.00005957253,0.00003522772,0.0001173704,0.0000336587],"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":[0.00002306473,0.00003084392,0.0008898082,0.00002650423,0.00001682618,0.00006059149,0.0000293931,0.9746401,0.004524764,0.01536045,0.0004795081,0.003918287],"study_design_scores_gemma":[0.000003198657,0.00001264031,0.0002033334,0.000001390479,0.000005687158,0.00001674831,0.000004003362,0.9975923,0.0001821949,0.001657663,0.0003140846,0.000006861939],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1304158,0.0002154478,0.8503119,0.0003778385,0.0002139744,0.0001098929,0.0005477996,0.0005185388,0.01728878],"genre_scores_gemma":[0.9706807,0.0003189557,0.01846738,0.0000694847,0.00006966072,0.0001652068,0.0003133766,0.00006676857,0.009848454],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00969427,"threshold_uncertainty_score":0.01927572,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0166465993439332,"score_gpt":0.2150371285696991,"score_spread":0.1983905292257659,"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."}}