{"id":"W4313361105","doi":"10.3390/math11010184","title":"A Microscopic Heterogeneous Traffic Flow Model Considering Distance Headway","year":2022,"lang":"en","type":"article","venue":"Mathematics","topic":"Traffic control and management","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Headway; Platoon; Traffic flow (computer networking); Acceleration; Traffic wave; Microscopic traffic flow model; Simulation; Computer science; Flow (mathematics); Constant (computer programming); Traffic model; Traffic generation model; Three-phase traffic theory; Road traffic; Traffic congestion reconstruction with Kerner's three-phase theory; Control theory (sociology); Engineering; Transport engineering; Real-time computing; Physics; Mechanics; Traffic congestion; Artificial intelligence; Computer network; Control (management)","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007639853,0.0001334856,0.0001798299,0.00004243417,0.0001247886,0.00003098621,0.0001447825,0.00001633575,0.00008924194],"category_scores_gemma":[0.000004464938,0.0001446952,0.00005979561,0.00007371278,0.00001372963,0.00002587155,0.00007192411,0.00011354,0.00001909064],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008893492,"about_ca_system_score_gemma":0.000009028983,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":3.995895e-7,"about_ca_topic_score_gemma":0.00001351123,"domain_scores_codex":[0.9992759,0.000007409575,0.0002085079,0.0001228328,0.0001536242,0.0002317203],"domain_scores_gemma":[0.9996576,0.00002663505,0.00002197189,0.0002440331,0.000006372657,0.00004336752],"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":[0.000001473429,0.00004621267,4.118113e-7,0.0001882507,0.00003131862,0.00001582517,0.001159868,0.9941135,0.000506885,0.0006199038,0.001080238,0.002236088],"study_design_scores_gemma":[0.0002662214,0.00001473009,9.270557e-7,0.00001319511,0.00001907701,0.00001821045,0.0001438087,0.9927282,0.00009271497,0.0006663166,0.005885045,0.0001515545],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4852312,0.001327822,0.5085956,0.000140421,0.0005322969,0.0005548409,0.00006690907,0.001303423,0.0022474],"genre_scores_gemma":[0.9805717,0.00001353857,0.01872066,0.00006314878,0.00001818213,0.0001176127,0.000004688684,0.00004028165,0.0004502158],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4953404,"threshold_uncertainty_score":0.59005,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01207715087890281,"score_gpt":0.1971172709514262,"score_spread":0.1850401200725234,"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."}}