{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004854591,0.0006305336,0.000826749,0.0009508817,0.0004284122,0.0007305232,0.001349741,0.0007348932,0.001297997],"category_scores_gemma":[0.001169745,0.0003379577,0.0004835247,0.0008196302,0.0002671196,0.0009415008,0.0007045587,0.0006144585,0.0004250588],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004039572,"about_ca_system_score_gemma":0.0008313836,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009408598,"about_ca_topic_score_gemma":0.01031596,"domain_scores_codex":[0.9995847,0.00006193553,0.00002123215,0.0001039064,0.0001880389,0.00004023762],"domain_scores_gemma":[0.9994308,0.0001911913,0.00004125641,0.00006959267,0.0002366141,0.00003056638],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000263731,0.0002307653,0.003257797,0.0001108781,0.0001416579,0.0001184884,0.0000947899,0.4129246,0.03203954,0.005931376,0.003847362,0.541039],"study_design_scores_gemma":[0.000002868151,0.00001215362,0.0001514916,0.000001537295,0.000005019233,0.00001403317,0.000003872921,0.9978907,0.001263884,0.0003642339,0.0002857792,0.000004583467],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01198002,0.0001355671,0.9863968,0.00003767981,0.0000729977,0.00002639078,0.00006116171,0.0006241571,0.0006651669],"genre_scores_gemma":[0.3842708,0.0002298008,0.6100438,0.0000941931,0.000118844,0.0001568032,0.0003351655,0.0001113406,0.004639419],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009408598,"threshold_uncertainty_score":0.01870763,"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."}}