{"id":"W2102586410","doi":"10.1111/0885-9507.00200","title":"Traffic Volume Time‐Series Analysis According to the Type of Road Use","year":2000,"lang":"en","type":"article","venue":"Computer-Aided Civil and Infrastructure Engineering","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":58,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Regina; Saint Mary's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Transport engineering; Traffic volume; Computer science; Trip distribution; Time series; Volume (thermodynamics); Floating car data; Traffic congestion; Term (time); Advanced Traffic Management System; TRIPS architecture; Intelligent transportation system; Traffic analysis; Plan (archaeology); Operations research; Engineering; Geography; Machine learning; Computer network","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007739002,0.0002049082,0.0002765737,0.0002592054,0.00005150793,0.00008845117,0.0002010533,0.00006846577,0.0001046927],"category_scores_gemma":[0.000009106199,0.0001720074,0.0000811546,0.0007721329,0.0000201265,0.000325207,0.00005187823,0.0001527321,0.000009568068],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002376735,"about_ca_system_score_gemma":0.00000422907,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004982262,"about_ca_topic_score_gemma":0.000004504217,"domain_scores_codex":[0.9992243,0.00001095644,0.0002478301,0.0001828103,0.0001191807,0.0002148876],"domain_scores_gemma":[0.9995811,0.00002214422,0.00002087281,0.0002651867,0.00002779103,0.00008293287],"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":[0.000005465546,0.000003020487,0.0002099828,0.0000311379,0.0002230975,0.000001489693,0.000193206,0.9177517,0.0004387504,0.00005551241,0.006814744,0.07427194],"study_design_scores_gemma":[0.00008688062,0.00005490227,0.04868474,0.00003180101,0.0001110697,0.000007177565,0.00001325871,0.918096,0.0001124596,0.000004016907,0.0326081,0.0001896087],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.693283,0.0001639621,0.3035927,0.0000484124,0.0003189212,0.0001813068,0.00001263304,0.002097746,0.0003013782],"genre_scores_gemma":[0.9907587,0.0001471686,0.008753827,0.00005057209,0.0001425779,0.000007628214,0.00001274676,0.00002659038,0.0001002383],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2974757,"threshold_uncertainty_score":0.7014259,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003955246215684002,"score_gpt":0.1753286373948867,"score_spread":0.1713733911792027,"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."}}