{"id":"W4319339328","doi":"10.1155/2023/5070504","title":"A Hybrid Deep Learning Model for Link Dynamic Vehicle Count Forecasting with Bayesian Optimization","year":2023,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Key Research and Development Program of China; China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Hyperparameter; Benchmark (surveying); Computer science; Artificial intelligence; Dynamic Bayesian network; Deep learning; Machine learning; Bayesian probability; Artificial neural network; Bayesian optimization","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":[0.0001261155,0.00008471801,0.00011448,0.0001726227,0.0000567237,0.00001537033,0.0000497109,0.00002561845,0.000001284018],"category_scores_gemma":[0.000008494543,0.0000814025,0.00004846676,0.0001486168,0.000007552369,0.0003912463,6.640895e-7,0.0001229734,2.540711e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005003556,"about_ca_system_score_gemma":0.000009099242,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":2.31158e-7,"about_ca_topic_score_gemma":0.00001325628,"domain_scores_codex":[0.9993823,0.000004218403,0.0002780917,0.00007042679,0.0001410967,0.0001239049],"domain_scores_gemma":[0.9996715,0.00002262038,0.0001219531,0.00003967296,0.0001068386,0.0000373877],"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":[0.00005044678,0.000005120096,0.00003059551,0.00007202318,0.00002553326,0.000007994832,0.0004419516,0.9495917,0.0004672938,0.00003850245,0.00003826927,0.04923056],"study_design_scores_gemma":[0.0006947027,0.0001260245,0.0006463592,0.00008712914,0.00004429717,0.000004134186,0.0001585636,0.9976172,0.0001764698,0.0001705171,0.0001878289,0.00008681656],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04940279,0.0000378986,0.9493673,0.00005456903,0.00009745911,0.0001709907,0.000005440057,0.000828216,0.00003534892],"genre_scores_gemma":[0.8845043,0.0002612833,0.1150485,0.00000981534,0.00002809741,0.0000180914,0.0000808917,0.0000305912,0.00001844559],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8351015,"threshold_uncertainty_score":0.3319498,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007578582019429075,"score_gpt":0.2154674956824311,"score_spread":0.207888913663002,"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."}}