{"id":"W4409246081","doi":"10.1287/trsc.2024.0560","title":"Probabilistic Traffic Forecasting with Dynamic Regression","year":2025,"lang":"en","type":"article","venue":"Transportation Science","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Probabilistic logic; Probabilistic forecasting; Regression analysis; Regression; Computer science; Mathematical model; Transport engineering; Operations research; Econometrics; Engineering; Statistics; Artificial intelligence; Economics; Mathematics; Machine learning","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0009272834,0.0006729292,0.0008139772,0.0007133585,0.0002366023,0.0008275589,0.001365914,0.00083648,0.001394183],"category_scores_gemma":[0.003640287,0.0005381182,0.0007095179,0.0009842855,0.0004423648,0.001403903,0.0008080605,0.001410446,0.0004122177],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000719997,"about_ca_system_score_gemma":0.0007212637,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01220958,"about_ca_topic_score_gemma":0.007362981,"domain_scores_codex":[0.9995702,0.000122027,0.00001873558,0.0001519028,0.00009216984,0.00004491867],"domain_scores_gemma":[0.9991145,0.0004921019,0.0001224572,0.00008009862,0.0001608461,0.0000301227],"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":[0.00001358044,0.000008130351,0.0003594451,0.00001230622,0.00001480593,0.00001477419,0.00001066846,0.9703362,0.0002646816,0.006957788,0.0004584048,0.02154929],"study_design_scores_gemma":[9.937411e-7,0.0000022712,0.00004459421,0.000001352599,0.000001424301,0.000002716365,6.993996e-7,0.9976574,0.00004696528,0.002075298,0.0001646133,0.000001614503],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01870477,0.00037918,0.9779,0.0003708939,0.00005699395,0.00001247946,0.0001720448,0.0005778394,0.001825862],"genre_scores_gemma":[0.8666385,0.0007746107,0.1268503,0.0001592866,0.0002116755,0.00008378537,0.0007207235,0.0001515315,0.00440957],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01220958,"threshold_uncertainty_score":0.02427703,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008999947762259044,"score_gpt":0.2368553046550725,"score_spread":0.2278553568928135,"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."}}