{"id":"W2018845946","doi":"10.1080/03081060.2010.512225","title":"Bus running time prediction using a statistical pattern recognition technique","year":2010,"lang":"en","type":"article","venue":"Transportation Planning and Technology","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Basis (linear algebra); Smoothing; Predictive modelling; Public transport; Data mining; Intelligent transportation system; Arrival time; Real-time data; Machine learning; Automatic vehicle location; Travel time; Artificial intelligence; Real-time computing; Pattern recognition (psychology); Simulation; Engineering; Transport engineering; Computer vision; Global Positioning System","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001036465,0.0001261756,0.0001230728,0.0003732095,0.00008011987,0.00001819954,0.00005596133,0.0002442335,0.00003297745],"category_scores_gemma":[0.000007092595,0.0001431384,0.00001372098,0.0002103094,0.00007163551,0.0001215221,0.000003241647,0.000369697,0.000007594893],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001192716,"about_ca_system_score_gemma":0.000006258183,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008673662,"about_ca_topic_score_gemma":0.000007518721,"domain_scores_codex":[0.9993539,0.000004949071,0.0002188226,0.000176621,0.00007992347,0.0001657732],"domain_scores_gemma":[0.999779,0.00001478958,0.00003304157,0.0001002121,0.00003133935,0.00004165836],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000337646,0.0001074418,0.05183436,0.0003460675,0.0001591006,0.0001460839,0.0007420186,0.002147389,0.4571703,0.002404176,0.004190514,0.4807188],"study_design_scores_gemma":[0.002092529,0.0004392762,0.1043522,0.0006006559,0.0003872346,0.0003558155,0.0005200591,0.7890021,0.08307514,0.006730259,0.01099361,0.001451201],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4007171,0.00002740876,0.5914845,0.00003352706,0.0001327965,0.0001968163,0.00008817941,0.006982588,0.0003370871],"genre_scores_gemma":[0.9786021,0.00001549399,0.02096428,0.00001444002,0.00003019474,0.00007062042,0.0002720796,0.00002488329,0.000005971474],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7868546,"threshold_uncertainty_score":0.5837014,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00839327067652494,"score_gpt":0.2164958723375518,"score_spread":0.2081026016610268,"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."}}