{"id":"W2809439561","doi":"10.1061/jtepbs.0000157","title":"Applicability Analysis of an Extended METANET Model in Traffic-State Prediction for Congested Freeway Corridors","year":2018,"lang":"en","type":"article","venue":"Journal of Transportation Engineering Part A Systems","topic":"Traffic control and management","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Traffic flow (computer networking); Calibration; Computer science; Microscopic traffic flow model; Diagram; Transport engineering; Traffic congestion reconstruction with Kerner's three-phase theory; Simple (philosophy); Flow (mathematics); Traffic generation model; Simulation; Traffic congestion; Engineering; Real-time computing; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001003626,0.0008409671,0.0006904812,0.000524136,0.0003857018,0.0009233455,0.001151811,0.001054788,0.0008447265],"category_scores_gemma":[0.002221403,0.0003454922,0.0008000542,0.0003478707,0.0005204625,0.001204368,0.0007982605,0.0008488468,0.0001174645],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008855754,"about_ca_system_score_gemma":0.0009027116,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02315124,"about_ca_topic_score_gemma":0.009902228,"domain_scores_codex":[0.9997311,0.00009792431,0.00001359882,0.0000687775,0.00004315507,0.0000454338],"domain_scores_gemma":[0.9990495,0.0004868116,0.0001026723,0.00009096507,0.0002103421,0.00005968714],"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":[0.00002442096,0.00001540785,0.001479606,0.000007028772,0.00001077666,0.00002365487,0.000009085104,0.9959323,0.0003007191,0.0005285869,0.0000403761,0.001628011],"study_design_scores_gemma":[0.000001318336,0.00001062416,0.0001754225,0.000001533141,0.000002542867,0.000002480487,0.000003017205,0.9995702,0.00005522435,0.0001491126,0.00002708566,0.00000146925],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8163752,0.0004034957,0.1754871,0.0003757462,0.00008342791,0.00007094954,0.000348904,0.0004271711,0.006428008],"genre_scores_gemma":[0.9931995,0.00008491396,0.006000707,0.00002091774,0.00001094745,0.00002400885,0.0001200694,0.00001436174,0.0005246548],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02315124,"threshold_uncertainty_score":0.04603297,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01052587940186086,"score_gpt":0.2134078276778485,"score_spread":0.2028819482759877,"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."}}