{"id":"W2068798206","doi":"10.1002/atr.5670410304","title":"Using automatic passenger counter data in bus arrival time prediction","year":2007,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Arrival time; Artificial neural network; Travel time; Flexibility (engineering); Computer science; Transit (satellite); Test data; Variety (cybernetics); Real-time computing; Simulation; Data mining; Engineering; Public transport; Artificial intelligence; Transport engineering; Statistics; Mathematics","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":[0.0004063995,0.00008283012,0.0001309843,0.0002470711,0.00001515565,0.00001015613,0.0001187605,0.00004754308,0.00001361287],"category_scores_gemma":[0.000008039395,0.00008388841,0.00002966276,0.0001646372,0.00001049881,0.0008467665,0.000001829616,0.0001525604,0.000001679717],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008697969,"about_ca_system_score_gemma":0.00001067142,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001644996,"about_ca_topic_score_gemma":0.00003165076,"domain_scores_codex":[0.9990698,0.00000738408,0.0005164545,0.00007650814,0.0002104722,0.0001193743],"domain_scores_gemma":[0.9996525,0.00002007658,0.0001134128,0.0001298605,0.00004435384,0.00003977998],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0001163099,0.0001487593,0.004638828,0.000245246,0.0001390048,0.0002009676,0.001477288,0.8049914,0.08313697,0.0001090621,0.002150654,0.1026456],"study_design_scores_gemma":[0.001942641,0.0001263509,0.554279,0.0004718091,0.0001392424,0.00004147449,0.0004459843,0.4328266,0.003004654,0.0001427726,0.006327098,0.0002523496],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6201013,0.00008654338,0.3787059,0.00001533157,0.0004481301,0.0001047103,0.00001838929,0.0003770739,0.000142686],"genre_scores_gemma":[0.9760801,0.00009709352,0.02364967,0.00001483234,0.00009007335,7.310896e-7,0.0000470674,0.00001618323,0.000004287972],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5496402,"threshold_uncertainty_score":0.342087,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01559752692874761,"score_gpt":0.2654290727644583,"score_spread":0.2498315458357107,"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."}}