{"id":"W3197367263","doi":"10.3390/fi13090225","title":"Spatiotemporal Traffic Prediction Using Hierarchical Bayesian Modeling","year":2021,"lang":"en","type":"article","venue":"Future Internet","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Autoregressive model; Gaussian process; Covariance; Bayesian probability; Computer science; Kriging; Gaussian; Bayesian inference; Covariance function; Gaussian network model; Artificial intelligence; Data mining; Pattern recognition (psychology); Machine learning; Mathematics; Statistics","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":[0.001619154,0.0006238011,0.0008732824,0.001678529,0.000472114,0.00117941,0.001446267,0.000815394,0.00162868],"category_scores_gemma":[0.005568105,0.0005747678,0.001002898,0.001971658,0.0003862316,0.001888438,0.000826469,0.001145304,0.0004157448],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00114298,"about_ca_system_score_gemma":0.001485988,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04892085,"about_ca_topic_score_gemma":0.04092654,"domain_scores_codex":[0.9992929,0.0002524985,0.00003875097,0.0001515187,0.0001893967,0.00007503109],"domain_scores_gemma":[0.9985769,0.0008242565,0.0001757652,0.00009474336,0.0002753377,0.00005300443],"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":[0.00003543497,0.00003429124,0.00326357,0.00003741009,0.00005443073,0.00004845244,0.00004864736,0.9356695,0.0003489963,0.01866272,0.001573709,0.04022279],"study_design_scores_gemma":[0.000001528725,0.000002321333,0.0001690823,0.000002908922,0.000003278553,0.000002942855,0.000003208508,0.9955454,0.00003109441,0.004095365,0.0001405865,0.000002320067],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03815459,0.0004047812,0.9567102,0.0003976829,0.00004485412,0.00004722636,0.0007578188,0.000651728,0.002831075],"genre_scores_gemma":[0.855729,0.0007919034,0.138761,0.0001065457,0.000112984,0.0001427734,0.001730554,0.00008937012,0.002535896],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04892085,"threshold_uncertainty_score":0.09727216,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01218702445997785,"score_gpt":0.2171545919510018,"score_spread":0.2049675674910239,"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."}}