{"id":"W4412722038","doi":"10.1109/tits.2025.3590075","title":"Link Representation Learning for Probabilistic Travel Time Estimation","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Probabilistic logic; Computer science; Estimation; Representation (politics); Travel time; Artificial intelligence; Machine learning; Transport engineering; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001913542,0.0002028404,0.0002258014,0.0004154677,0.0001614153,0.00006991948,0.0001052837,0.0001353772,0.00002325698],"category_scores_gemma":[0.000007339148,0.0002266893,0.0001441613,0.0003918613,0.00002734783,0.0001895514,1.100024e-7,0.0001935717,0.0000463203],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000133037,"about_ca_system_score_gemma":0.00002005772,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002125424,"about_ca_topic_score_gemma":0.00001674686,"domain_scores_codex":[0.9987101,0.00003759108,0.0005960332,0.0002760135,0.0001905985,0.0001896723],"domain_scores_gemma":[0.9994259,0.0001358681,0.00005947657,0.0001833805,0.0001437464,0.00005156782],"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":[0.0000435896,0.00003886791,0.000005391159,0.0003526795,0.0001053348,5.505499e-7,0.0003897632,0.947924,0.001079491,0.001414763,0.001539049,0.04710648],"study_design_scores_gemma":[0.0003717365,0.0000829381,0.0001799936,0.0002138687,0.0001373484,7.812019e-7,0.0003578582,0.9728314,0.02202805,0.0001231906,0.003474196,0.0001986326],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002097863,0.00005353037,0.9901388,0.0000845674,0.00140065,0.00159573,0.0000496772,0.003422639,0.001156594],"genre_scores_gemma":[0.995626,0.0001031343,0.0009845573,0.00002932228,0.00003541528,0.001134567,0.0001453259,0.00003680847,0.001904834],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9935282,"threshold_uncertainty_score":0.9244121,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01625493107145377,"score_gpt":0.2588928269522777,"score_spread":0.242637895880824,"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."}}