{"id":"W3038876156","doi":"10.1109/tmc.2020.3006713","title":"Prediction of Traffic Flow via Connected Vehicles","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; Polytechnique Montréal","funders":"","keywords":"Mean squared error; Autoregressive integrated moving average; Computer science; Artificial neural network; Trajectory; Traffic flow (computer networking); Time series; Deep learning; Measure (data warehouse); Artificial intelligence; Intelligent transportation system; Flow (mathematics); Recurrent neural network; Machine learning; Data mining; Statistics; Mathematics; Engineering","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":[0.00006827943,0.0001462063,0.0001772822,0.000116814,0.00008772487,0.00001462017,0.0001166003,0.00007744756,0.00003010572],"category_scores_gemma":[0.000001846922,0.0001659254,0.00009614869,0.0003195186,0.00003160866,0.0000876745,0.000001222998,0.0002256979,0.00001634457],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003461408,"about_ca_system_score_gemma":0.000006557653,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001281925,"about_ca_topic_score_gemma":0.000001632965,"domain_scores_codex":[0.9991685,0.00002587504,0.0003102167,0.0001850994,0.0001480736,0.0001622128],"domain_scores_gemma":[0.9996624,0.00005099772,0.00003519001,0.0001346521,0.00003403387,0.00008271228],"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":[0.000009165516,0.00004045148,0.000001907109,0.00006650259,0.00004374204,0.0000010966,0.0003277307,0.76421,0.007702438,0.000003039786,0.0009876899,0.2266062],"study_design_scores_gemma":[0.0003496591,0.0001854712,0.00006181826,0.0000418928,0.00003183465,0.000002527085,0.0000932895,0.9618399,0.03655306,0.000001393883,0.0007319173,0.0001072619],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1769629,0.00003426157,0.8161685,0.00002742224,0.0004328054,0.0002802522,0.00003249368,0.005873711,0.0001875928],"genre_scores_gemma":[0.9968892,0.00004605552,0.002858746,0.0000671236,0.00006864294,0.00002708537,0.000006507521,0.00003232443,0.000004252828],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8199263,"threshold_uncertainty_score":0.6766241,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01596605853523558,"score_gpt":0.2019522358382751,"score_spread":0.1859861773030395,"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."}}