{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001746159,0.0005280899,0.0004676959,0.0005442848,0.0002779838,0.000681973,0.001254143,0.0006505235,0.001382284],"category_scores_gemma":[0.0005394218,0.0002246615,0.0005418711,0.0004635376,0.0004625568,0.001174884,0.0005048123,0.0005967075,0.0002386866],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008026413,"about_ca_system_score_gemma":0.0007843942,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01201878,"about_ca_topic_score_gemma":0.00616521,"domain_scores_codex":[0.999836,0.00002220308,0.000006202816,0.00005859598,0.0000473953,0.0000295241],"domain_scores_gemma":[0.9998184,0.00004249373,0.00004510575,0.00002124139,0.00005077483,0.00002210198],"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":[0.00001982901,0.00003249866,0.00118891,0.00002149657,0.00001587581,0.0000625377,0.00002928884,0.9717107,0.00380145,0.01752624,0.0004396333,0.005151577],"study_design_scores_gemma":[0.000003257115,0.00001419757,0.0002856438,0.000001279301,0.000005194363,0.00001542918,0.000005404859,0.9976794,0.0001927042,0.00136599,0.0004266991,0.000004794151],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1762106,0.0002371457,0.8089196,0.0002924056,0.0001477193,0.0000886787,0.0005120802,0.0003429389,0.01324894],"genre_scores_gemma":[0.9667276,0.0003137533,0.02412649,0.0000563686,0.00005235839,0.00009885038,0.0002805855,0.00003401075,0.008309967],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01201878,"threshold_uncertainty_score":0.02389765,"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."}}