{"id":"W2493442683","doi":"10.1002/atr.1401","title":"Survey and empirical evaluation of nonhomogeneous arrival process models with taxi data","year":2016,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Canada Excellence Research Chairs, Government of Canada; Canada Research Chairs","keywords":"Autoregressive integrated moving average; Computer science; Process (computing); Count data; Operations research; Set (abstract data type); Piecewise linear function; Data set; Time series; Data mining; Engineering; Statistics; Mathematics; Machine learning; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.02261288,0.001137793,0.001285183,0.002593536,0.0005517474,0.002177867,0.002917045,0.001533595,0.001477393],"category_scores_gemma":[0.06201569,0.0007467559,0.001637608,0.004483978,0.001199651,0.003905057,0.001227194,0.00231027,0.0003836351],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00240901,"about_ca_system_score_gemma":0.001698186,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03203015,"about_ca_topic_score_gemma":0.01182829,"domain_scores_codex":[0.9916855,0.005526592,0.0005402771,0.0009329803,0.001044056,0.000270638],"domain_scores_gemma":[0.8452104,0.1367747,0.005223456,0.007040584,0.004889855,0.0008609912],"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":[0.0004506544,0.0007987995,0.1005666,0.0008614373,0.0004662036,0.0001755704,0.0003893036,0.8113526,0.0004065253,0.01295943,0.004212938,0.0673601],"study_design_scores_gemma":[0.000021453,0.0001951939,0.01108281,0.00008193151,0.00004710687,0.00003900376,0.0001946348,0.983652,0.0003065242,0.003159273,0.001192124,0.00002790551],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8653797,0.009575289,0.1138095,0.003570247,0.0002329907,0.0001435524,0.00252726,0.0008550035,0.003906423],"genre_scores_gemma":[0.976076,0.003498106,0.01666728,0.0001248569,0.0001721566,0.00004181787,0.003028224,0.00006569645,0.0003258064],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03203015,"threshold_uncertainty_score":0.1195897,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1005746586635398,"score_gpt":0.3843592861116986,"score_spread":0.2837846274481588,"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."}}