{"id":"W645282532","doi":"","title":"BUS TRAVEL TIME PREDICTION MODEL FOR DYNAMIC OPERATIONS CONTROL AND PASSENGER INFORMATION SYSTEMS","year":2003,"lang":"en","type":"article","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":93,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Microsimulation; Schedule; Kalman filter; Real-time computing; Real-time data; Computer science; Automatic vehicle location; VisSim; Train; Dwell time; Software; Control (management); Transit (satellite); Engineering; Public transport; Transport engineering; Global Positioning System","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0003160704,0.00004806935,0.0000657222,0.00005173085,0.0003745184,0.0001116635,0.00002267352,0.00006990977,0.00001212857],"category_scores_gemma":[0.00006636841,0.00004589826,0.00001550978,0.00007023742,0.00002653604,0.000661179,3.478035e-7,0.00002675796,0.00000696855],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002864321,"about_ca_system_score_gemma":0.00008479482,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001270546,"about_ca_topic_score_gemma":0.0002040432,"domain_scores_codex":[0.9995104,0.00003836772,0.0001704892,0.00006725197,0.0001137954,0.00009970846],"domain_scores_gemma":[0.9996945,0.00004314146,0.00003078231,0.00003740725,0.0001467385,0.00004740778],"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":[0.000009224482,0.00001245461,0.0003426094,0.00001107131,0.00001091221,2.941801e-8,0.006825793,0.8839663,0.0000951176,0.1080484,0.0004734196,0.0002046006],"study_design_scores_gemma":[0.0004951492,0.00001287194,0.001235535,0.000005077938,0.00001872794,3.149188e-7,0.001454017,0.9952543,0.000002110482,0.00008324292,0.001383245,0.00005535474],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007021558,0.00002360497,0.9840536,0.000256994,0.0001171,0.0005498303,0.0001157309,0.00008654915,0.007775082],"genre_scores_gemma":[0.9920815,0.00002414692,0.004023311,0.00007911959,0.00001323199,0.00007318058,0.0001618369,0.000003606083,0.003540056],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.98506,"threshold_uncertainty_score":0.2880531,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008072739529346196,"score_gpt":0.2390658842926562,"score_spread":0.23099314476331,"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."}}