{"id":"W3137448256","doi":"10.5383/jttm.03.01.003","title":"Machine Learning and statistic predictive modeling for road traffic flow","year":2021,"lang":"en","type":"article","venue":"International Journal of Traffic and Transportation Management","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Context (archaeology); Computer science; Autoregressive integrated moving average; Traffic flow (computer networking); Artificial neural network; Traffic congestion; Statistic; Mean absolute percentage error; Autoregressive model; Multilayer perceptron; Intelligent transportation system; Machine learning; Artificial intelligence; Transport engineering; Time series; Engineering; Econometrics; Statistics; Geography; Computer security","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":[0.0001544621,0.0001151432,0.0001431635,0.0001857625,0.00004977626,0.00005769931,0.00007500935,0.00003311095,0.0000124124],"category_scores_gemma":[0.000007042217,0.0001191426,0.00006076131,0.00005764461,0.00001844604,0.0001861179,0.000005096073,0.0001197514,3.389403e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003254905,"about_ca_system_score_gemma":0.000008685644,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":7.208629e-7,"about_ca_topic_score_gemma":0.00001416927,"domain_scores_codex":[0.9991446,0.00001326381,0.0003732247,0.000125625,0.0002426491,0.0001006821],"domain_scores_gemma":[0.9996455,0.0000257056,0.00007127802,0.0000333323,0.0001636167,0.00006056106],"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":[0.00005725793,0.00003310438,0.00002793674,0.00009167793,0.0003986566,0.0000642017,0.0003658279,0.76645,0.00001289892,0.001063183,0.0002821116,0.2311531],"study_design_scores_gemma":[0.001201005,0.00007317534,0.0008704377,0.00009103658,0.0001586271,0.00002088769,0.0005679657,0.9911227,0.00001763548,0.00008677388,0.005684646,0.0001051491],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08697889,0.001305931,0.9101873,0.0002083002,0.0005145232,0.0001726603,0.00007507097,0.0003046272,0.0002526942],"genre_scores_gemma":[0.9770558,0.005486945,0.01714221,0.00003651739,0.00006455673,0.00001411579,0.0001159335,0.00001768462,0.00006626988],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8930451,"threshold_uncertainty_score":0.4858495,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007351217630148736,"score_gpt":0.2313326467833026,"score_spread":0.2239814291531539,"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."}}