{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009124294,0.0006150324,0.0007371265,0.0007132524,0.000220231,0.0007126501,0.0007061037,0.0006233366,0.001237049],"category_scores_gemma":[0.003029793,0.000233249,0.0006025496,0.0012056,0.0003085012,0.0006909602,0.0003138746,0.001076949,0.0003731435],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007190021,"about_ca_system_score_gemma":0.000821051,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01630381,"about_ca_topic_score_gemma":0.009272905,"domain_scores_codex":[0.9996235,0.0001340617,0.00002151886,0.00007778878,0.0001014162,0.00004158769],"domain_scores_gemma":[0.9991348,0.0005801956,0.00009737417,0.00004546593,0.0001272878,0.00001471741],"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":[0.0000291771,0.00003451312,0.001210332,0.00003786882,0.00003227856,0.00002610908,0.00001417259,0.9631765,0.0003051437,0.003932175,0.0007036283,0.03049797],"study_design_scores_gemma":[4.612729e-7,0.000003977485,0.0001774438,0.000002118996,0.000001536921,0.000002339345,0.00000148237,0.9983014,0.00005264326,0.001327719,0.0001273132,0.000001517326],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06432484,0.002846707,0.926542,0.0007801785,0.0001665072,0.00004340854,0.000519759,0.0008403618,0.003936279],"genre_scores_gemma":[0.9573745,0.001503001,0.03607414,0.00009506652,0.0001897162,0.00008858031,0.000816411,0.0000487808,0.003809852],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01630381,"threshold_uncertainty_score":0.03241783,"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."}}