{"id":"W4405754221","doi":"10.1109/access.2024.3522107","title":"An Ensemble Convolutional Recursive Neural Network Based on Deep Reinforcement Learning for Traffic Volume Forecasting","year":2024,"lang":"en","type":"article","venue":"IEEE Access","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Education and Child Care","funders":"","keywords":"Computer science; Reinforcement learning; Artificial intelligence; Convolutional neural network; Ensemble learning; Machine learning; Deep learning; Volume (thermodynamics); Traffic volume; Artificial neural network; Recurrent neural network; Engineering","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":[0.0005532212,0.0005874049,0.0006739657,0.0004119769,0.000213226,0.0004701033,0.00100517,0.0004870263,0.0008457288],"category_scores_gemma":[0.0009358155,0.0003262587,0.0004786699,0.0004577708,0.0002145669,0.0007622833,0.0004776293,0.0008258876,0.0002216136],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007226677,"about_ca_system_score_gemma":0.0007694894,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02026508,"about_ca_topic_score_gemma":0.01937734,"domain_scores_codex":[0.9998339,0.00002545398,0.000009582311,0.00004980227,0.00004811375,0.00003312245],"domain_scores_gemma":[0.9997608,0.00006885061,0.00002786234,0.00002527547,0.0001010744,0.00001617484],"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":[0.00006998732,0.00007897959,0.002480367,0.00003026961,0.00007830483,0.00005905141,0.00003216328,0.8576427,0.003671621,0.002325321,0.00157855,0.1319528],"study_design_scores_gemma":[0.000001104349,0.000007167807,0.00009975931,0.000001058872,0.000004232763,0.000002914056,8.18934e-7,0.9993654,0.0002119718,0.0002058991,0.00009813713,0.000001541796],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1130335,0.001199035,0.8793437,0.0003365915,0.0001689398,0.00004175764,0.0001905004,0.001638308,0.004047712],"genre_scores_gemma":[0.9357565,0.0004038396,0.05993079,0.0001027896,0.00003789403,0.00005048364,0.0003321669,0.0000424849,0.003342934],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02026508,"threshold_uncertainty_score":0.04029423,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02646105834581164,"score_gpt":0.2683278626940012,"score_spread":0.2418668043481896,"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."}}