{"id":"W2743768825","doi":"10.1155/2017/6937385","title":"Predicting Freeway Work Zone Delays and Costs with a Hybrid Machine-Learning Model","year":2017,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Traffic control and management","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"New Jersey Department of Transportation; U.S. Department of Transportation","keywords":"Work zone; Schedule; Support vector machine; Work (physics); Artificial neural network; Mean squared error; Computer science; Incentive; Work schedule; Machine learning; Artificial intelligence; Transport engineering; Operations research; Simulation; Engineering; Statistics; Mathematics","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":[0.0005807503,0.0007186308,0.0005784869,0.0007007108,0.0002789698,0.000800334,0.0009150243,0.0008910036,0.001060046],"category_scores_gemma":[0.001292963,0.0003820682,0.0005681252,0.0006826496,0.0002094401,0.0008821133,0.0004244176,0.000564892,0.0002026619],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009253024,"about_ca_system_score_gemma":0.001001426,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03059941,"about_ca_topic_score_gemma":0.02226267,"domain_scores_codex":[0.9996538,0.00007875662,0.00002517028,0.0001063543,0.00008031377,0.00005554897],"domain_scores_gemma":[0.9993549,0.0003604468,0.00008267632,0.00002941274,0.0001451799,0.00002739044],"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":[0.00004208554,0.00005054544,0.002876497,0.00001420042,0.00001925323,0.00002106322,0.00001062743,0.9868167,0.000346156,0.0002731765,0.0001883474,0.009341241],"study_design_scores_gemma":[0.000001594,0.00001000901,0.0004176735,9.366408e-7,0.000002619564,0.000002134534,0.000002743963,0.9993501,0.00007801189,0.00009286829,0.00003918197,0.00000210616],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6029563,0.0003629483,0.3906066,0.0002998486,0.00009023803,0.00009575048,0.0009710907,0.0008277363,0.003789594],"genre_scores_gemma":[0.9821057,0.00006518952,0.01577118,0.00002031364,0.00001310296,0.00007750748,0.0003046976,0.00001066995,0.001631738],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03059941,"threshold_uncertainty_score":0.06084263,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004514194566914979,"score_gpt":0.1910865063848152,"score_spread":0.1865723118179002,"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."}}