{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002952323,0.0003151324,0.0003547766,0.0009100434,0.0001421625,0.0003848416,0.0003615018,0.0002539563,0.0006199947],"category_scores_gemma":[0.001199584,0.0001450751,0.000373417,0.0008229548,0.0001989787,0.0003296093,0.0001477939,0.000319248,0.0002740632],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002842208,"about_ca_system_score_gemma":0.0005750237,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01297528,"about_ca_topic_score_gemma":0.01039783,"domain_scores_codex":[0.9998413,0.00002884677,0.0000112832,0.00005155101,0.00004881977,0.0000180673],"domain_scores_gemma":[0.9994054,0.0002484554,0.00009481491,0.00006552603,0.0001626445,0.00002323517],"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":[0.0002751067,0.0001872779,0.02918991,0.00005562475,0.00009954625,0.0001341053,0.0001030856,0.6239893,0.02176707,0.001571568,0.001193757,0.3214335],"study_design_scores_gemma":[0.000002441703,0.00002348921,0.002399413,0.000001388878,0.000006125137,0.00001544283,0.000005607662,0.9958124,0.001388077,0.0002274536,0.0001150963,0.000003113957],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3302965,0.0001366024,0.6657926,0.0001171705,0.00003126762,0.00003582663,0.0002369252,0.002058457,0.001294635],"genre_scores_gemma":[0.9506477,0.00004432345,0.04833381,0.00001166164,0.00001201021,0.00002724679,0.0001897039,0.00002283953,0.0007107619],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01297528,"threshold_uncertainty_score":0.02579951,"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."}}