{"id":"W2470641485","doi":"10.1002/atr.1392","title":"Short‐term traffic flow prediction with linear conditional Gaussian Bayesian network","year":2016,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":133,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Categorical variable; Traffic flow (computer networking); Computer science; Traffic generation model; Spatial correlation; Data mining; Intelligent transportation system; Gaussian process; Bayesian probability; Gaussian; Bayesian network; Term (time); Time series; Flow (mathematics); Artificial intelligence; Machine learning; Engineering; Real-time computing; Mathematics; Transport engineering","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":[0.0001006837,0.0001317509,0.0001586626,0.000124111,0.00004874353,0.00001040378,0.00007545081,0.00005940305,0.00002686316],"category_scores_gemma":[0.000002196065,0.00009202208,0.0000736353,0.0001366134,0.0000315683,0.0006787034,6.122808e-7,0.0001256652,0.000001653246],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006137937,"about_ca_system_score_gemma":0.00001496383,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":8.435185e-8,"about_ca_topic_score_gemma":0.00001886512,"domain_scores_codex":[0.9990644,0.00001076897,0.0004104045,0.00009678177,0.0002582138,0.0001593726],"domain_scores_gemma":[0.9996327,0.00001978449,0.00009244717,0.00008079104,0.00008215467,0.00009214797],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0001096399,0.00002995942,0.0009519971,0.00003023763,0.00008953775,0.00003106587,0.0001420618,0.8982515,0.001268171,0.000210917,0.002092258,0.0967927],"study_design_scores_gemma":[0.005358854,0.001453569,0.9161358,0.001371521,0.0004183648,0.000140053,0.0002475297,0.04427686,0.002824904,0.0006187428,0.02645347,0.0007002897],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1403045,0.00006964395,0.8579689,0.0001336158,0.000454532,0.0001477799,0.00004845981,0.0007344648,0.0001380969],"genre_scores_gemma":[0.978043,0.0003604154,0.02111556,0.0000195005,0.0003447135,0.00001107113,0.00006265851,0.00002756562,0.00001548561],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9151838,"threshold_uncertainty_score":0.3752552,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00465174743202079,"score_gpt":0.2044209678535449,"score_spread":0.1997692204215241,"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."}}