{"id":"W4220933714","doi":"10.1155/2022/5929725","title":"Spatiotemporal Traffic Density Estimation Based on ADAS Probe Data","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ministry of Science and ICT, South Korea; National Research Foundation of Korea; National Research Foundation","keywords":"Headway; Computer science; Trajectory; Sampling (signal processing); Density estimation; Reliability (semiconductor); Range (aeronautics); Data collection; Statistics; Simulation; Mathematics; Engineering; Estimator; Computer vision","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":[0.0003236157,0.0005187357,0.000420713,0.001669428,0.0002150218,0.0004832422,0.0006440261,0.0003181651,0.0005699595],"category_scores_gemma":[0.002184462,0.0001995749,0.0003426544,0.00124149,0.0001736486,0.0009421656,0.0007270441,0.0003853716,0.0003801691],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003647385,"about_ca_system_score_gemma":0.000464803,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006644612,"about_ca_topic_score_gemma":0.004762619,"domain_scores_codex":[0.9995556,0.0000700604,0.00002659642,0.0001125051,0.000185717,0.00004945832],"domain_scores_gemma":[0.9992867,0.0001157547,0.0001145543,0.00008745215,0.0003625074,0.00003299674],"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":[0.0005527642,0.0002814482,0.1940213,0.0004174573,0.0001702107,0.0006045748,0.0005984232,0.3899732,0.04943319,0.006849985,0.004252823,0.3528446],"study_design_scores_gemma":[0.00001226627,0.0001154894,0.03049104,0.00003137141,0.00003234053,0.0002029668,0.0002439693,0.9561088,0.008514623,0.00146129,0.002748227,0.00003759066],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5082552,0.0002824039,0.4832986,0.0001247776,0.00008868117,0.0001297386,0.00249205,0.001394964,0.003933731],"genre_scores_gemma":[0.9526848,0.0001451764,0.0445169,0.00002030055,0.00002101715,0.00009668939,0.001880619,0.00002345782,0.0006110081],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006644612,"threshold_uncertainty_score":0.01321185,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01314445536524165,"score_gpt":0.2382167315256547,"score_spread":0.2250722761604131,"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."}}