{"id":"W4247204284","doi":"10.1109/ijcnn.2006.1716669","title":"Clustering Vehicle Trajectories with Hidden Markov Models Application to Automated Traffic Safety Analysis","year":2006,"lang":"en","type":"article","venue":"The 2006 IEEE International Joint Conference on Neural Network Proceedings","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"University of British Columbia; Clemson University","keywords":"Cluster analysis; Computer science; Hidden Markov model; Heuristic; Markov chain; Data mining; Traffic analysis; Machine learning; Artificial intelligence; Computer security","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":[0.0009490131,0.0005371612,0.0006360957,0.00132674,0.0004889351,0.0005664161,0.0007293755,0.0006828425,0.0004956796],"category_scores_gemma":[0.00476051,0.0004600057,0.000584111,0.001223462,0.000445925,0.0006867311,0.0005847809,0.0006859021,0.0001592143],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001019252,"about_ca_system_score_gemma":0.0009653672,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01649366,"about_ca_topic_score_gemma":0.01067005,"domain_scores_codex":[0.9994863,0.0002121738,0.00002995957,0.0001054416,0.0001237129,0.00004245519],"domain_scores_gemma":[0.9977391,0.001489204,0.0002288779,0.0001965574,0.0002765343,0.0000698162],"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":[0.00006575013,0.0000335076,0.001788172,0.00002413736,0.0000440589,0.00004804706,0.00009713608,0.942557,0.001352363,0.002991081,0.0002731871,0.05072559],"study_design_scores_gemma":[0.000001602799,0.000005227236,0.000179478,0.000001643142,0.000002035413,0.000007540339,0.000005911564,0.9977356,0.0003227456,0.001651634,0.00008326952,0.000003285568],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0624847,0.0002042705,0.9358361,0.0001323635,0.00002516838,0.00004006127,0.00008239071,0.0007119997,0.0004829223],"genre_scores_gemma":[0.7827799,0.0002087162,0.2156816,0.00002992978,0.0000315949,0.00007131765,0.0002945299,0.00006592025,0.0008364587],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01649366,"threshold_uncertainty_score":0.03279531,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01987366171158034,"score_gpt":0.2470067333121537,"score_spread":0.2271330716005734,"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."}}