{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002541294,0.0001138355,0.0001625563,0.0002634173,0.00003789317,0.000009849926,0.00007038979,0.00006411839,0.0000239587],"category_scores_gemma":[0.00001753284,0.0001004841,0.0000959761,0.0002937703,0.00002366101,0.0002661156,6.066981e-7,0.0001655775,0.000009853246],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005340381,"about_ca_system_score_gemma":0.00001857935,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":3.723631e-7,"about_ca_topic_score_gemma":0.000001856223,"domain_scores_codex":[0.999054,0.00001812735,0.0004287606,0.00009888138,0.0002775821,0.0001227138],"domain_scores_gemma":[0.9996039,0.00003966409,0.0001199669,0.00009947171,0.00007726883,0.00005971779],"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":[0.0001590327,0.00007639459,0.0002284139,0.0001250445,0.00002819223,0.00002402312,0.0002793618,0.932312,0.03216817,0.0002630081,0.005993303,0.02834302],"study_design_scores_gemma":[0.003350377,0.0009831291,0.4582971,0.0008046029,0.0001169858,0.00000678688,0.0003583649,0.507988,0.02296864,0.001013721,0.003774849,0.0003374808],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7408839,0.00004105574,0.2502975,0.0003732972,0.001374794,0.0006781697,0.0001297292,0.004385568,0.001835973],"genre_scores_gemma":[0.9956573,0.00009140185,0.004022131,0.00002432394,0.00005469587,0.00001153401,0.0000931284,0.00001905613,0.00002647489],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4580686,"threshold_uncertainty_score":0.4097621,"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."}}