{"id":"W2981722124","doi":"10.1109/ithings/greencom/cpscom/smartdata.2019.00167","title":"A Dynamic Traffic Awareness System for Urban Driving","year":2019,"lang":"en","type":"article","venue":"","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Computer science; Cluster analysis; Traffic flow (computer networking); Internet of Things; Floating car data; Traffic congestion; Intelligent transportation system; Big data; Vehicle Information and Communication System; Traffic congestion reconstruction with Kerner's three-phase theory; Real-time computing; Distributed computing; Computer network; Data mining; Road traffic; Computer security; Artificial intelligence; Transport engineering; 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":[0.00006736287,0.00008579276,0.0001020799,0.00008050068,0.00002294267,0.00002485437,0.00009828037,0.00004320882,0.00001369436],"category_scores_gemma":[0.000001422379,0.0000826348,0.00004843745,0.00006604346,0.000003959298,0.0000799163,0.00001184524,0.00003706068,0.00004922718],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007297241,"about_ca_system_score_gemma":0.000004111811,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002020658,"about_ca_topic_score_gemma":0.00001521896,"domain_scores_codex":[0.999566,0.000003604588,0.0001135692,0.0001123696,0.00005938721,0.0001450685],"domain_scores_gemma":[0.9997805,0.00001581824,0.000009717793,0.0001532288,0.00001102753,0.00002964465],"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":[0.00003123229,0.0001324062,0.00457506,0.007143953,0.0005301621,0.000008764394,0.001125555,0.4422131,0.01435987,0.08108289,0.302369,0.1464281],"study_design_scores_gemma":[0.0002255822,0.00001985475,0.0007701927,0.00005319112,0.00001011894,0.000001598711,0.0001390565,0.983385,0.0003265857,0.000002052057,0.01494883,0.0001179032],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3669043,0.00004482657,0.5948466,0.00001941275,0.0009329472,0.0006352551,0.00000442669,0.02195934,0.01465293],"genre_scores_gemma":[0.9971856,0.000006726658,0.001935224,0.00001174519,0.00001768325,0.00008705096,0.000009572525,0.00002677064,0.0007195538],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6302814,"threshold_uncertainty_score":0.3369749,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00460652306038827,"score_gpt":0.2027499661799716,"score_spread":0.1981434431195833,"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."}}