{"id":"W4363677470","doi":"10.1155/2023/2941035","title":"CPT-DF: Congestion Prediction on Toll-Gates Using Deep Learning and Fuzzy Evaluation for Freeway Network in China","year":2023,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Key Research and Development Program of China","keywords":"Bottleneck; Computer science; Traffic congestion; Fuzzy logic; Toll; Key (lock); Artificial intelligence; Data mining; Engineering; Transport engineering","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":[0.0006388714,0.00106746,0.0005074319,0.001262806,0.0004781334,0.0005456021,0.0008479059,0.0006445159,0.0008601749],"category_scores_gemma":[0.001108619,0.0002443166,0.0005905252,0.0007262633,0.0003338158,0.000872911,0.0006428981,0.0005630882,0.0001156424],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002779682,"about_ca_system_score_gemma":0.001708622,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.152612,"about_ca_topic_score_gemma":0.09278841,"domain_scores_codex":[0.9996743,0.00003544005,0.00002147814,0.00009161801,0.00009288549,0.00008440198],"domain_scores_gemma":[0.9996141,0.00007117627,0.00004796584,0.00002599078,0.0001797136,0.00006109433],"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":[0.0002539363,0.0002778106,0.03873479,0.00006401542,0.00007616853,0.0001976692,0.00005598395,0.8926534,0.0027844,0.0006941539,0.002338394,0.06186931],"study_design_scores_gemma":[0.000005840386,0.00002443862,0.003628553,0.000001612455,0.000006659977,0.000005499307,0.00001565298,0.9955003,0.0005988046,0.0001461716,0.00006010115,0.000006355516],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9462487,0.0002206113,0.04962994,0.000211639,0.00005358641,0.00006254642,0.000838486,0.0008154298,0.001919122],"genre_scores_gemma":[0.9937563,0.00005384769,0.004635717,0.00001764735,0.000006157131,0.00002237529,0.0007151384,0.000007599418,0.0007852162],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.152612,"threshold_uncertainty_score":0.3034475,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01173310793128738,"score_gpt":0.2650244777538091,"score_spread":0.2532913698225217,"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."}}