{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001729986,0.001254868,0.001297006,0.002427474,0.0005132062,0.001605727,0.002942385,0.002202419,0.004383285],"category_scores_gemma":[0.01055102,0.000896991,0.001493157,0.003816805,0.0007169293,0.003534572,0.001497203,0.003681331,0.001835587],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001896985,"about_ca_system_score_gemma":0.001526212,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02001764,"about_ca_topic_score_gemma":0.01943525,"domain_scores_codex":[0.9989642,0.0002844283,0.00005726291,0.0003812647,0.0002025011,0.0001104288],"domain_scores_gemma":[0.9968054,0.002089095,0.0002612742,0.0003781002,0.0003761523,0.00008990462],"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":[0.0001440456,0.0001405894,0.001946175,0.0001228659,0.0001099036,0.00007458137,0.00009115062,0.8210089,0.0007152192,0.01266044,0.008028058,0.154958],"study_design_scores_gemma":[0.000004777134,0.000006947382,0.0001238597,0.000004887576,0.000006553655,0.00001116661,0.000004983081,0.9910983,0.0001551092,0.00826276,0.0003151626,0.000005380288],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01805963,0.000457894,0.9748984,0.0004236712,0.00005251743,0.00004422626,0.001193893,0.003752969,0.00111674],"genre_scores_gemma":[0.7349163,0.0007801941,0.2484819,0.0004640137,0.0002421594,0.0003752999,0.008437942,0.0005204852,0.005781732],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02001764,"threshold_uncertainty_score":0.03980219,"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."}}