{"id":"W4393225365","doi":"10.1177/03611981241233282","title":"Using Deep Neural Networks and Big Data to Predict Microscopic Travel Time in Work Zones","year":2024,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Artificial neural network; Work (physics); Big data; Travel time; Work zone; Transport engineering; Computer science; Time travel; Deep time; Operations research; Engineering; Artificial intelligence; Geology; Data mining; Mechanical engineering","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":[0.0006030328,0.000989484,0.0003799517,0.001278245,0.0003195309,0.001054144,0.0009388498,0.0007418105,0.0008000191],"category_scores_gemma":[0.002919998,0.0004042508,0.000564907,0.001443206,0.0003103,0.001484549,0.0007133618,0.001297516,0.0003593149],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001222031,"about_ca_system_score_gemma":0.000876425,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04011579,"about_ca_topic_score_gemma":0.05647475,"domain_scores_codex":[0.9997477,0.00005208421,0.00002027638,0.00007130374,0.0000675089,0.00004114468],"domain_scores_gemma":[0.9991211,0.0003489896,0.0001381434,0.000100267,0.0002379735,0.00005344105],"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":[0.0001253909,0.0002421094,0.04263411,0.00007634323,0.0001196889,0.0001104488,0.00007989083,0.8484408,0.001434918,0.00132517,0.002698088,0.102713],"study_design_scores_gemma":[0.000002663364,0.00001233205,0.004811895,0.00000785649,0.000006373912,0.000005413552,0.00002916118,0.9931825,0.000474614,0.001184141,0.0002772798,0.000005907855],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6074024,0.0008132333,0.3785251,0.001536489,0.0002680103,0.0001099613,0.00453656,0.002295656,0.004512683],"genre_scores_gemma":[0.9428769,0.00030925,0.05178815,0.00008912159,0.00005030969,0.00007029984,0.003426915,0.00003941159,0.001349624],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04011579,"threshold_uncertainty_score":0.0797646,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1280849518806288,"score_gpt":0.3666616066335641,"score_spread":0.2385766547529353,"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."}}