{"id":"W4229672879","doi":"10.1109/itsc.2014.6957864","title":"Floating car and camera data fusion for non-parametric route travel time estimation","year":2014,"lang":"en","type":"article","venue":"","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Transport Canada","funders":"","keywords":"Computer science; Sensor fusion; Floating car data; Real-time computing; Global Positioning System; Robustness (evolution); Data collection; Taxis; Parametric statistics; Artificial intelligence; Data mining; Telecommunications; Transport engineering; Traffic congestion; Engineering; Statistics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001895741,0.00007135495,0.00008306809,0.0001052674,0.00004171555,0.00003437946,0.0001017551,0.00003667683,0.000007764238],"category_scores_gemma":[0.00004510901,0.00006880025,0.0000107843,0.00009758583,0.000006835572,0.0001790491,0.00004775637,0.00003812595,0.000009721482],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001368642,"about_ca_system_score_gemma":0.000001949913,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001752216,"about_ca_topic_score_gemma":0.000003923141,"domain_scores_codex":[0.9995959,0.000004873988,0.0001125026,0.0001313203,0.00005988515,0.00009556443],"domain_scores_gemma":[0.9996865,0.00004672379,0.00001443407,0.0002102665,0.00001055864,0.00003151364],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00000533161,0.00001877946,0.0000746423,0.0001999722,0.00003692107,2.650472e-7,0.0001183278,0.01060125,0.006906982,0.001753913,0.1398282,0.8404554],"study_design_scores_gemma":[0.0001907154,0.00002305786,0.00099416,0.00001193332,0.00001664897,7.583533e-7,0.00001043327,0.9930087,0.001052026,0.0000357422,0.004575399,0.00008042235],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01379319,0.00001210561,0.978389,0.00003946185,0.00008149405,0.0002514938,0.00001835479,0.001597296,0.005817586],"genre_scores_gemma":[0.8914576,0.00002220369,0.1081025,0.00004530909,0.00003615924,0.00001055921,0.0001461417,0.00001345174,0.0001660539],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9824075,"threshold_uncertainty_score":0.2805593,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01290011642345022,"score_gpt":0.2307913263020243,"score_spread":0.217891209878574,"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."}}