{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007523104,0.0008179254,0.0008807103,0.0004146142,0.0003978864,0.0008832671,0.001257887,0.001040439,0.004171821],"category_scores_gemma":[0.001948282,0.0004159751,0.0006667929,0.0007223065,0.0003570024,0.0007951349,0.0004303614,0.001448626,0.0009290494],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00131039,"about_ca_system_score_gemma":0.00150351,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05472209,"about_ca_topic_score_gemma":0.02573725,"domain_scores_codex":[0.9995172,0.00009946618,0.00002801022,0.000157043,0.0001256531,0.00007260477],"domain_scores_gemma":[0.9994592,0.0002385961,0.00007824251,0.00002559219,0.000183033,0.00001533621],"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":[0.00003850014,0.00002044914,0.0006288882,0.00002804449,0.00001565359,0.00002732392,0.00002411295,0.9885002,0.0003494773,0.002885538,0.000535166,0.006946601],"study_design_scores_gemma":[0.000003860726,0.00001083023,0.0002487521,0.000001889789,0.000005895724,0.000004529566,0.000002740527,0.998833,0.0001021687,0.0004919515,0.0002910327,0.000003339845],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07805651,0.0004595774,0.9087055,0.0004891389,0.0001581903,0.0001505063,0.001536785,0.001524641,0.008919206],"genre_scores_gemma":[0.9481857,0.000454582,0.03205352,0.00007825728,0.00006336996,0.0005655673,0.001920477,0.00007242186,0.01660613],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05472209,"threshold_uncertainty_score":0.1088071,"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."}}