{"id":"W3092099726","doi":"10.5121/civej.2020.7301","title":"Deep Learning Neural Network Approaches to Land Use-demographic- Temporal based Traffic Prediction","year":2020,"lang":"en","type":"article","venue":"Civil Engineering and Urban Planning An International Journal (CiVEJ)","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Deep learning; Computer science; Artificial neural network; Artificial intelligence; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0006964725,0.0007140519,0.0004613626,0.0009285693,0.000242882,0.0006161547,0.000913963,0.0007545437,0.001062138],"category_scores_gemma":[0.001342987,0.0003135854,0.0004160909,0.001323692,0.0002267504,0.0008816796,0.0004711636,0.001017167,0.0002378985],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009171728,"about_ca_system_score_gemma":0.0008267873,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0271847,"about_ca_topic_score_gemma":0.02919363,"domain_scores_codex":[0.9998149,0.00004806844,0.000015157,0.00005287262,0.00003203072,0.00003692255],"domain_scores_gemma":[0.9995552,0.000198324,0.00006723808,0.00002194803,0.0001323304,0.00002499462],"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":[0.00003848531,0.0001149009,0.005300547,0.00006059696,0.00007563749,0.00006195233,0.00003297111,0.9077644,0.0008989227,0.00375664,0.001258025,0.0806369],"study_design_scores_gemma":[8.065352e-7,0.000004256903,0.0004045247,0.000002881617,0.000003574263,0.000002997685,0.000005338888,0.9980197,0.0001252715,0.001271275,0.0001575448,0.000001933949],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1578358,0.003847398,0.8281405,0.001456361,0.0002309686,0.00006995955,0.001126022,0.001047392,0.006245649],"genre_scores_gemma":[0.9231157,0.001754573,0.06871658,0.0001500495,0.0001434465,0.00008279608,0.001040394,0.00003215284,0.004964348],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0271847,"threshold_uncertainty_score":0.05405295,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03476315543293621,"score_gpt":0.2033669659692483,"score_spread":0.1686038105363121,"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."}}