{"id":"W2134115685","doi":"10.1139/l10-115","title":"Estimation of expected travel time using the method of moment","year":2011,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Science Foundation","keywords":"Moment (physics); Monte Carlo method; Piecewise; Range (aeronautics); Computer science; Trajectory; Second moment of area; Expected value; Variance (accounting); Travel time; Random variable; Time point; Mathematical optimization; Statistics; Mathematics; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000958473,0.0007614572,0.0009582471,0.002283624,0.0003913471,0.0008047904,0.001077942,0.0006425336,0.001948899],"category_scores_gemma":[0.006229074,0.0004028639,0.001149632,0.001339197,0.0003933099,0.001258481,0.0007373594,0.001068532,0.0005082401],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006820494,"about_ca_system_score_gemma":0.0009557159,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003872671,"about_ca_topic_score_gemma":0.002200961,"domain_scores_codex":[0.9992004,0.000340999,0.0000354746,0.0001425368,0.0002292503,0.00005129804],"domain_scores_gemma":[0.9976366,0.001529447,0.000315642,0.0001682819,0.0003045959,0.00004550061],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001136989,0.00003636298,0.003168371,0.0001247942,0.0001304647,0.0001296476,0.00007536712,0.8703392,0.003087197,0.03199748,0.002080437,0.08871704],"study_design_scores_gemma":[0.000006634315,0.00001780279,0.0006567888,0.000008434389,0.000009375981,0.00006172741,0.000008447292,0.9898428,0.0007517683,0.007305032,0.001307074,0.00002408012],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004133932,0.0001132499,0.9949675,0.00003237853,0.00002105871,0.00001804193,0.00008669664,0.0001983183,0.0004287627],"genre_scores_gemma":[0.3822658,0.0007674402,0.6133007,0.00004780373,0.00021832,0.0002595141,0.0008849052,0.0002496639,0.002005833],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9961273,"threshold_uncertainty_score":0.007700264,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02659597297351015,"score_gpt":0.2537242298162055,"score_spread":0.2271282568426954,"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."}}