{"id":"W3200994651","doi":"10.1109/tits.2021.3111855","title":"Distributed Dynamic Route Guidance and Signal Control for Mobile Edge Computing-Enhanced Connected Vehicle Environment","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Traffic control and management","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Real-time computing; Computation; Signal timing; Cruise control; Cloud computing; Edge computing; Enhanced Data Rates for GSM Evolution; Intelligent transportation system; Distributed computing; SIGNAL (programming language); Computer network; Control (management); Engineering; Traffic signal; Telecommunications; Transport engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.000199311,0.0004176491,0.0003435225,0.0002453082,0.000425621,0.0005937712,0.0008175977,0.000395506,0.001078168],"category_scores_gemma":[0.0004436234,0.0001140114,0.0002767713,0.000283109,0.0003377007,0.0005029725,0.0006405734,0.0004973258,0.000152173],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005892332,"about_ca_system_score_gemma":0.0008611189,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007319316,"about_ca_topic_score_gemma":0.007950858,"domain_scores_codex":[0.9997889,0.00003528887,0.000005907877,0.00006549212,0.00005489006,0.00004941357],"domain_scores_gemma":[0.9998338,0.00004089943,0.00002194446,0.00002944011,0.00004926531,0.00002465565],"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":[0.000122017,0.00006544806,0.0009919693,0.0000486365,0.00002268958,0.0001568247,0.00007080119,0.9176226,0.009130648,0.01244853,0.001349042,0.05797094],"study_design_scores_gemma":[0.000006420837,0.00002504734,0.0001477749,0.000001403105,0.000003725434,0.00001493143,0.00001733137,0.9966345,0.0007838019,0.00162459,0.0007372627,0.000003091797],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05821911,0.0001771907,0.9350321,0.000156396,0.00006906285,0.00004860319,0.00005390719,0.000606334,0.005637153],"genre_scores_gemma":[0.9696413,0.00006804655,0.02890078,0.00003230188,0.00001516307,0.00002794426,0.00004232422,0.00001615242,0.001255961],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007319316,"threshold_uncertainty_score":0.01455343,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007215364975284523,"score_gpt":0.2055847549950661,"score_spread":0.1983693900197816,"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."}}