{"id":"W4384297406","doi":"10.1016/j.comcom.2023.07.019","title":"A novel hybrid method for achieving accurate and timeliness vehicular traffic flow prediction in road networks","year":2023,"lang":"en","type":"article","venue":"Computer Communications","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"National Natural Science Foundation of China","keywords":"Computer science; Scalability; Context (archaeology); Traffic flow (computer networking); Floating car data; Traffic generation model; Network traffic control; Traffic congestion; Advanced Traffic Management System; Traffic congestion reconstruction with Kerner's three-phase theory; Scale (ratio); Traffic simulation; Process (computing); Intelligent transportation system; Network traffic simulation; Distributed computing; Real-time computing; Transport engineering; Microsimulation; Computer network","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003647548,0.0001108495,0.0001359107,0.0002080988,0.0001320116,0.00005697347,0.0003517461,0.00005013363,8.422075e-7],"category_scores_gemma":[0.00000989669,0.0001257995,0.00004107792,0.000330286,0.00002432466,0.00014291,0.0002178244,0.0001744354,0.000003549959],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003124064,"about_ca_system_score_gemma":0.00000535809,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004745148,"about_ca_topic_score_gemma":0.00002614313,"domain_scores_codex":[0.9993525,0.00004308077,0.0002428263,0.0001458053,0.00005198341,0.0001638375],"domain_scores_gemma":[0.9992043,0.0001395858,0.00002561559,0.0005623746,0.00002986313,0.00003827891],"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":[0.000001543204,0.0000205747,0.00001078562,0.00002335047,0.00002221705,3.008527e-7,0.00009533712,0.6912138,0.00006440079,0.0001488824,0.009129737,0.2992691],"study_design_scores_gemma":[0.00033994,0.00001411157,0.00344938,0.00005941835,0.00001692944,0.000004747685,0.00001530964,0.9820891,0.000007055663,0.00001771215,0.01388383,0.0001024679],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003805286,0.0002953081,0.9905113,0.0004647024,0.0002225946,0.0004564134,0.00002786581,0.004124342,0.00009220617],"genre_scores_gemma":[0.6547492,0.0008572309,0.3434963,0.00008703093,0.00009379807,0.00034424,0.0003212132,0.00003206267,0.00001895902],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6509439,"threshold_uncertainty_score":0.5129955,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02965019819475488,"score_gpt":0.2785498543602004,"score_spread":0.2488996561654455,"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."}}