{"id":"W2133708669","doi":"10.1139/l08-129","title":"Investigating optimal aggregation interval sizes of loop detector data for freeway travel-time estimation and prediction","year":2009,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Interval (graph theory); Travel time; Computer science; Detector; Intelligent transportation system; Mean squared error; Induction loop; Estimation; Software deployment; Mathematical model; Simulation; Real-time computing; Statistics; Transport engineering; Engineering; Mathematics; Telecommunications","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002738038,0.0001059131,0.0001693207,0.0003225958,0.00003079257,0.00003807324,0.0001675744,0.00005925604,0.000006641956],"category_scores_gemma":[0.0001895821,0.0001187614,0.000033296,0.0001098911,0.00001892303,0.0004620749,0.000007144832,0.0001178646,1.999147e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006862335,"about_ca_system_score_gemma":0.00005416807,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002594228,"about_ca_topic_score_gemma":0.0007326258,"domain_scores_codex":[0.9993234,0.000006169645,0.000349793,0.00008344302,0.00008923752,0.0001480143],"domain_scores_gemma":[0.9995022,0.00003511408,0.00008547415,0.0001322434,0.00005820466,0.0001867564],"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":[0.000007067409,0.000007515705,0.0001095773,0.0002910014,0.0001136706,0.000005487827,0.0005121956,0.8642452,0.02863126,0.0002585363,0.01069371,0.09512481],"study_design_scores_gemma":[0.0002738735,0.0001287165,0.002125737,0.0003155692,0.00004243531,0.0000278698,0.00002727192,0.9928074,0.003607493,0.00004601624,0.0005064913,0.00009105544],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1054329,0.0006502555,0.89289,0.00007626203,0.0002952675,0.0001867353,0.00009308025,0.0002352782,0.0001402883],"genre_scores_gemma":[0.9811534,0.00002886889,0.01865992,0.00001058428,0.00009880674,0.00000228967,0.00002484135,0.00001708161,0.000004211329],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8757205,"threshold_uncertainty_score":0.484295,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01194422824260533,"score_gpt":0.2008733858693213,"score_spread":0.188929157626716,"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."}}