{"id":"W4404605349","doi":"10.59400/issc1737","title":"Data-driven insights: Unravelling traffic dynamics with k-means clustering and vehicle type differentiation","year":2024,"lang":"en","type":"article","venue":"Information System and Smart City","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Cluster analysis; Traffic flow (computer networking); Computer science; Traffic congestion; Categorization; Field (mathematics); Preprocessor; Data mining; Transport engineering; Machine learning; Engineering; Artificial intelligence; Computer network; Mathematics","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":[0.001214534,0.0007902989,0.0007459966,0.002205966,0.000577805,0.001853727,0.001365585,0.0008934826,0.0006441305],"category_scores_gemma":[0.007913956,0.000500072,0.0008657182,0.002333476,0.0007581059,0.002463958,0.001168199,0.001564404,0.0003066428],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001263292,"about_ca_system_score_gemma":0.00192211,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01700886,"about_ca_topic_score_gemma":0.01846397,"domain_scores_codex":[0.999365,0.0002043875,0.00004256148,0.0001599398,0.0001688542,0.00005930954],"domain_scores_gemma":[0.9974002,0.001360563,0.0002647424,0.0003324819,0.0005537463,0.00008827663],"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":[0.0001710813,0.0002583941,0.02737412,0.0003565323,0.0002074295,0.0001890965,0.001045153,0.7532564,0.003453833,0.06156063,0.004922012,0.1472052],"study_design_scores_gemma":[0.0000039964,0.00001508035,0.002184257,0.00002281096,0.00001067329,0.00002493892,0.0002004116,0.9647267,0.0006951123,0.03053547,0.001560708,0.00001978395],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06518579,0.0003681697,0.9300494,0.0007820981,0.00008401461,0.0001097517,0.0008542547,0.0005889932,0.001977647],"genre_scores_gemma":[0.6196168,0.0005341631,0.3764778,0.0001842576,0.00008455654,0.0001805697,0.001778288,0.0001382032,0.001005258],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01700886,"threshold_uncertainty_score":0.03381974,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.013487788069889,"score_gpt":0.2004951039592253,"score_spread":0.1870073158893363,"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."}}