{"id":"W605113028","doi":"","title":"Neighbour Links Travel Time Estimation Using Probe Vehicles and Buses Data","year":2011,"lang":"en","type":"article","venue":"18th ITS World CongressTransCoreITS AmericaERTICO - ITS EuropeITS Asia-Pacific","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Travel time; VisSim; Market penetration; Computer science; Data collection; Estimation; Floating car data; Real-time data; Transport engineering; Transit (satellite); Value of time; Arrival time; Real-time computing; Data mining; Simulation; Statistics; Engineering; Public transport; Traffic congestion; Mathematics; Intersection (aeronautics)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003548023,0.000505493,0.0004511553,0.001364883,0.0001877913,0.00052727,0.0004864536,0.0004883647,0.0004625529],"category_scores_gemma":[0.002781763,0.0003245104,0.0003481571,0.001301459,0.000149317,0.001070922,0.0004809306,0.0003263922,0.0001706803],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000408429,"about_ca_system_score_gemma":0.0004052223,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01332859,"about_ca_topic_score_gemma":0.01161026,"domain_scores_codex":[0.9996834,0.0000837297,0.00001736102,0.00008639301,0.00009511537,0.00003401182],"domain_scores_gemma":[0.9990906,0.0004271017,0.0001811235,0.0001178306,0.0001517523,0.00003164701],"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":[0.0002539729,0.00008610226,0.0351188,0.00006590813,0.00009170654,0.00009622704,0.0001262923,0.8810238,0.009346469,0.001157791,0.000312326,0.07232054],"study_design_scores_gemma":[0.000003363172,0.0000443762,0.00493481,0.000002260508,0.00001200179,0.00002505671,0.00003169713,0.9920996,0.002303219,0.0003409288,0.0001945354,0.000008168569],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7182931,0.0001060965,0.2794238,0.00004024335,0.00001788283,0.00003702159,0.0004118269,0.0004758694,0.001194124],"genre_scores_gemma":[0.97073,0.00004286761,0.02842212,0.000003653068,0.000004375219,0.00001990524,0.0003738183,0.000015292,0.0003879536],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01332859,"threshold_uncertainty_score":0.02650201,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05357714256279777,"score_gpt":0.2477757143448799,"score_spread":0.1941985717820821,"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."}}