{"id":"W3114518607","doi":"10.1155/2020/8850123","title":"Traffic Status Evolution Trend Prediction Based on Congestion Propagation Effects under Rainy Weather","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Beijing; Computer science; Cluster analysis; Artificial neural network; Traffic congestion; Online and offline; Fuzzy logic; Predictive modelling; Traffic generation model; Data mining; Floating car data; Real-time computing; Machine learning; Artificial intelligence; Transport engineering; Engineering; Geography","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":[0.0002751038,0.000509991,0.0002251111,0.001070189,0.0002620656,0.0004203766,0.0003372816,0.0002492145,0.0006269266],"category_scores_gemma":[0.001216228,0.0001803104,0.0002539653,0.000556945,0.0001328911,0.000768256,0.0002452476,0.0003760952,0.0001251751],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000360247,"about_ca_system_score_gemma":0.0003773578,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02051458,"about_ca_topic_score_gemma":0.01989472,"domain_scores_codex":[0.9998393,0.00001220911,0.00001083577,0.00006139596,0.00005195692,0.00002421111],"domain_scores_gemma":[0.9996234,0.00007369901,0.00005710655,0.00002513823,0.000190755,0.0000298377],"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":[0.0003658425,0.0002287649,0.1670604,0.0001351865,0.000117214,0.0003057349,0.0002602977,0.5785655,0.01792158,0.001332222,0.002989454,0.2307176],"study_design_scores_gemma":[0.000003258427,0.00002979215,0.02496539,0.000004794934,0.00002088636,0.00002153587,0.00003820888,0.97256,0.00182936,0.0002505722,0.0002658092,0.00001041436],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8656835,0.0001829735,0.1292072,0.0001002824,0.00007909712,0.00004760285,0.000872877,0.0007364086,0.003090132],"genre_scores_gemma":[0.9943649,0.00008914175,0.004440988,0.000006312612,0.00001040222,0.00001264668,0.0005114644,0.00001060516,0.0005535993],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02051458,"threshold_uncertainty_score":0.04079032,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005845874267728388,"score_gpt":0.2027239365122541,"score_spread":0.1968780622445257,"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."}}