{"id":"W1992341494","doi":"10.1002/atr.136","title":"Hybrid model for prediction of bus arrival times at next station","year":2010,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":108,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Arrival time; Kalman filter; Support vector machine; Time of arrival; Artificial neural network; Baseline (sea); Computer science; Real-time computing; Travel time; Direction of arrival; Data mining; Simulation; Transport engineering; Engineering; Artificial intelligence; Telecommunications","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001099545,0.00007651466,0.0001247624,0.0001322686,0.00002545394,0.000005799032,0.00005743671,0.00003711764,0.000007911275],"category_scores_gemma":[0.000009359911,0.00007734078,0.00008124421,0.0000472045,0.00001514059,0.000532695,7.005925e-7,0.0001085259,3.261062e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002876167,"about_ca_system_score_gemma":0.00001227368,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":4.259597e-7,"about_ca_topic_score_gemma":0.00001696879,"domain_scores_codex":[0.9992918,0.000003002363,0.0004118191,0.00005872003,0.0001554422,0.00007918747],"domain_scores_gemma":[0.9995558,0.00001802173,0.0001777223,0.00006432132,0.0001449371,0.00003918476],"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":[0.0001001154,0.00002720879,0.00008518255,0.00009313864,0.00003575457,0.000001038761,0.0004172514,0.7874347,0.1902944,0.0007998009,0.001936432,0.01877496],"study_design_scores_gemma":[0.001980871,0.0002761096,0.02303517,0.00007384982,0.0001577576,0.000009043517,0.0001292707,0.8613366,0.1056956,0.002625542,0.004528778,0.0001514055],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4422626,0.00002753226,0.5567735,0.00001968639,0.0004290877,0.0001346528,0.0001049958,0.0001862393,0.00006172409],"genre_scores_gemma":[0.9558943,0.000178071,0.04369787,0.000007711506,0.00006240417,0.00001077745,0.0001027203,0.00001662699,0.0000294968],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5136317,"threshold_uncertainty_score":0.3153866,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00869408554000613,"score_gpt":0.2214554500788737,"score_spread":0.2127613645388675,"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."}}