{"id":"W2053911168","doi":"10.1002/atr.147","title":"Bayesian predictive travel time methodology for advanced traveller information system","year":2010,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Travel time; Computer science; Real-time data; Bayesian probability; Operations research; Point (geometry); Time point; Predictive value; Data mining; Transport engineering; Artificial intelligence; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.003307662,0.000634809,0.0007492572,0.001478809,0.0003162234,0.001110097,0.001440635,0.0008271159,0.002590164],"category_scores_gemma":[0.0116936,0.0004810755,0.0006858746,0.001388679,0.0008173882,0.001958765,0.001097485,0.001401006,0.0004433755],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001095304,"about_ca_system_score_gemma":0.001093078,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006947391,"about_ca_topic_score_gemma":0.003106379,"domain_scores_codex":[0.9985226,0.0006527401,0.00005448831,0.000251862,0.0004328623,0.00008550236],"domain_scores_gemma":[0.9961455,0.002563351,0.0004177024,0.000239008,0.0005414288,0.0000931637],"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":[0.00004050874,0.00002591676,0.0007758935,0.00004642866,0.00003952151,0.00004295825,0.00007235202,0.9031567,0.0005937934,0.05741084,0.0004465458,0.03734856],"study_design_scores_gemma":[0.00000234048,0.00001144168,0.000121789,0.000003669835,0.000005308885,0.00001013543,0.0000054551,0.9846898,0.0001606017,0.01458267,0.0004005891,0.000006168941],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006448371,0.00007474483,0.9925454,0.00006125831,0.000008981161,0.0000191913,0.00005777066,0.0001144904,0.0006697601],"genre_scores_gemma":[0.6701869,0.0003894727,0.3255594,0.00006570148,0.00009345414,0.000271573,0.0004997438,0.0001231898,0.00281055],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006947391,"threshold_uncertainty_score":0.01749277,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007605473817066665,"score_gpt":0.2322824785881096,"score_spread":0.2246770047710429,"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."}}