{"id":"W3000056055","doi":"10.1155/2020/9628957","title":"Evaluation of Short-Term Freeway Speed Prediction Based on Periodic Analysis Using Statistical Models and Machine Learning Models","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":65,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Key Research and Development Program of China; Science and Technology Commission of Shanghai Municipality; China Postdoctoral Science Foundation; Central South University; National Natural Science Foundation of China","keywords":"Autoregressive integrated moving average; Component (thermodynamics); Support vector machine; Computer science; Artificial neural network; Residual; Perceptron; Artificial intelligence; Autoregressive model; Machine learning; Traffic flow (computer networking); Multilayer perceptron; Time series; Intelligent transportation system; Statistical model; Data mining; Algorithm; Engineering; Mathematics; Statistics","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":[0.0003922428,0.0001100579,0.0002391235,0.000285808,0.00003398067,0.0000130614,0.00004523028,0.00004654072,0.00001235675],"category_scores_gemma":[0.00001718305,0.000110827,0.00008163564,0.0002762019,0.00002008914,0.000374215,0.000001091148,0.0001813574,5.450403e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006371491,"about_ca_system_score_gemma":0.00002306829,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002398691,"about_ca_topic_score_gemma":0.000009455031,"domain_scores_codex":[0.9986583,0.0000522426,0.0004867624,0.000111042,0.0006099364,0.00008173734],"domain_scores_gemma":[0.9994915,0.00002473874,0.0001375074,0.00005495451,0.0002130878,0.00007819322],"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":[0.00007901064,0.00002395173,0.0007230813,0.00005562922,0.0001746567,0.000003009717,0.0007145525,0.9788268,0.007382773,0.0001038179,0.000004713291,0.01190801],"study_design_scores_gemma":[0.0007117935,0.0002150641,0.01498388,0.00004880012,0.001495569,6.751523e-7,0.000127206,0.9814661,0.0006828893,0.0001863625,0.000005256021,0.00007643598],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3701724,0.00009463575,0.6293945,0.00001377533,0.00004017004,0.0001049252,0.00003963236,0.00009734106,0.00004264517],"genre_scores_gemma":[0.9848306,0.0001561428,0.01487716,0.0000128209,0.00002709019,0.000001996543,0.00007828458,0.00001561697,2.470018e-7],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6146583,"threshold_uncertainty_score":0.4519394,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04723101905749663,"score_gpt":0.2750805891023155,"score_spread":0.2278495700448189,"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."}}