{"id":"W4408696480","doi":"10.1109/itsc58415.2024.10919660","title":"Urban Arterial Route Travel Time Prediction Using Connected Vehicle Trajectories by Integrating Cloud and Edge Resources","year":2024,"lang":"en","type":"article","venue":"","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Cloud computing; Enhanced Data Rates for GSM Evolution; Computer science; Travel time; Real-time computing; Transport engineering; Artificial intelligence; Engineering; Operating system","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0001935069,0.0007381148,0.0004044695,0.001067126,0.0003073534,0.000629129,0.0007300308,0.0003404759,0.0005112783],"category_scores_gemma":[0.0009274089,0.000258781,0.0003871274,0.001384914,0.000153209,0.0009005771,0.0004958442,0.0004069203,0.000226034],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007235386,"about_ca_system_score_gemma":0.0008564207,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06067591,"about_ca_topic_score_gemma":0.04467867,"domain_scores_codex":[0.9998222,0.00002184702,0.000009280018,0.00006061336,0.0000485066,0.00003760258],"domain_scores_gemma":[0.9996747,0.0000636109,0.0000559753,0.00003602925,0.0001279826,0.00004172242],"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":[0.00009713219,0.00008201001,0.03141303,0.00002608126,0.00004889934,0.0001068949,0.00003843736,0.9282069,0.001478154,0.001103664,0.0008916628,0.03650721],"study_design_scores_gemma":[8.472099e-7,0.000003936128,0.001043048,0.000001228697,0.000003104109,0.000004759831,0.000008438156,0.9985271,0.0001660517,0.000157359,0.00008253263,0.000001648167],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6873949,0.0003703638,0.3058588,0.0002592375,0.0001043931,0.00006047101,0.001578507,0.001249791,0.003123651],"genre_scores_gemma":[0.9891981,0.00008711751,0.009742975,0.00001163993,0.00001089905,0.00001257859,0.0005924169,0.00001451653,0.000329833],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06067591,"threshold_uncertainty_score":0.1206455,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006132941385203669,"score_gpt":0.1932753064400659,"score_spread":0.1871423650548622,"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."}}