{"id":"W4387522652","doi":"10.1155/2023/8962283","title":"BiLSTM- and GNN-Based Spatiotemporal Traffic Flow Forecasting with Correlated Weather Data","year":2023,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Majmaah University","keywords":"Computer science; Traffic flow (computer networking); Artificial neural network; Mean squared error; Graph; Data mining; Traffic volume; Transport engineering; Artificial intelligence; Statistics; Engineering; Mathematics","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":[0.0001506559,0.00009431223,0.0001259087,0.0001932156,0.00003436899,0.00001488154,0.00008930814,0.00003817653,0.000004547599],"category_scores_gemma":[0.000006581434,0.00008043933,0.0000217455,0.0002751885,0.00001759097,0.0004263879,0.000001427998,0.0001327717,7.667543e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001609064,"about_ca_system_score_gemma":0.0000146799,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":9.272158e-7,"about_ca_topic_score_gemma":0.00004825625,"domain_scores_codex":[0.9993583,0.000007040831,0.0002753244,0.00009375441,0.000161301,0.0001042885],"domain_scores_gemma":[0.9996707,0.00002645455,0.00009931454,0.000109421,0.00004706952,0.00004707698],"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":[0.00005550078,0.00001181875,0.0005802642,0.00005827716,0.00003642785,0.00004549315,0.0002948581,0.9118462,0.0002629194,0.000007311362,0.0009004262,0.0859005],"study_design_scores_gemma":[0.001236758,0.0001680288,0.02534165,0.0001939108,0.00007320525,0.000009810626,0.0002501439,0.9698268,0.0002524109,0.00001489305,0.002507325,0.0001250671],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7912611,0.0001058527,0.2068699,0.0001009078,0.0002575799,0.0001590266,0.00003382476,0.001160436,0.00005141072],"genre_scores_gemma":[0.9810525,0.0001420982,0.01854812,0.00001392998,0.00003299227,0.000002970258,0.0001742071,0.00002480467,0.000008407893],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1897914,"threshold_uncertainty_score":0.3280221,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01933937876570633,"score_gpt":0.2240810659226753,"score_spread":0.204741687156969,"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."}}