{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006567229,0.0007563352,0.0008704673,0.0005481876,0.0002285182,0.0006649604,0.001479884,0.0009078351,0.001464259],"category_scores_gemma":[0.001050652,0.0005189612,0.0006912552,0.0006363986,0.0003541839,0.0009182517,0.0008349992,0.001247561,0.0003692987],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008614201,"about_ca_system_score_gemma":0.001182167,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02340198,"about_ca_topic_score_gemma":0.02219374,"domain_scores_codex":[0.9997428,0.00005725925,0.00001527318,0.00007214348,0.00006730851,0.00004524123],"domain_scores_gemma":[0.9997497,0.0001000912,0.00002417913,0.00001476337,0.00009552312,0.00001560783],"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":[0.00003708508,0.00003377407,0.0006057489,0.00002171394,0.00003637028,0.0000256632,0.00001613533,0.9590559,0.0007597913,0.002818779,0.000916398,0.03567258],"study_design_scores_gemma":[0.000001033057,0.00000232653,0.00002890338,8.698333e-7,0.0000016207,0.000001110141,5.228572e-7,0.99954,0.00005867804,0.0003134115,0.00005056815,0.000001015061],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03648605,0.0006830162,0.9584568,0.0003285913,0.00005765052,0.00002779952,0.00028321,0.0008272354,0.002849695],"genre_scores_gemma":[0.8644634,0.0005135157,0.1254541,0.0002594957,0.00006542147,0.0002026965,0.0009565808,0.00008840069,0.007996332],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02340198,"threshold_uncertainty_score":0.0465315,"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."}}