{"id":"W4381801933","doi":"10.1155/2023/9910142","title":"Travel Time Probability Prediction Based on Constrained LSTM Quantile Regression","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":"","keywords":"Quantile; Quantile regression; Predictability; Reliability (semiconductor); Computer science; Probabilistic logic; Probabilistic forecasting; Trajectory; Artificial neural network; Statistics; Artificial intelligence; Mathematics; Machine learning","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":[0.0004688145,0.0005785818,0.0005890115,0.0004263875,0.0001687029,0.0004881339,0.001024544,0.000532139,0.001611574],"category_scores_gemma":[0.002206035,0.0003068293,0.0005264944,0.0007611622,0.0002187393,0.0008132131,0.0004836658,0.001120543,0.0003305637],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005579561,"about_ca_system_score_gemma":0.0005354791,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01779532,"about_ca_topic_score_gemma":0.009425041,"domain_scores_codex":[0.9997639,0.00004247087,0.00001377894,0.00008973121,0.00004543815,0.00004469173],"domain_scores_gemma":[0.9994965,0.000218919,0.00007060431,0.00003790275,0.0001544437,0.0000215733],"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":[0.0000774873,0.00005032615,0.003989996,0.00002884261,0.00003030918,0.0000620739,0.00003040242,0.952694,0.001276435,0.001460012,0.0008971971,0.03940286],"study_design_scores_gemma":[8.309171e-7,0.000003339717,0.000269831,0.000001121134,0.00000171343,0.000003031616,0.000001812188,0.9992073,0.0001222768,0.0003513736,0.00003601474,0.000001371948],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.204484,0.0004858717,0.7900913,0.0003845813,0.00007360036,0.000031844,0.0007469444,0.001585953,0.002115943],"genre_scores_gemma":[0.9803489,0.0001548783,0.01765643,0.00004638486,0.00002268872,0.00003248194,0.0006100109,0.00004142748,0.001086858],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01779532,"threshold_uncertainty_score":0.03538346,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009511846655686579,"score_gpt":0.2323516448143431,"score_spread":0.2228397981586566,"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."}}