{"id":"W1858288926","doi":"10.5623/cig2015-304","title":"PREDICTION OF TRAFFIC COUNTS USING STATISTICAL AND NEURAL NETWORK MODELS","year":2015,"lang":"en","type":"article","venue":"GEOMATICA","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Bangladesh University of Engineering and Technology; Northwest University; University of Twente","keywords":"Negative binomial distribution; Artificial neural network; Count data; Binomial regression; Statistical model; Regression analysis; Statistics; Computer science; Variables; Population; Variable (mathematics); Machine learning; Mathematics; Medicine","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006104531,0.00003780794,0.00005823829,0.00002095536,0.000009033506,0.000006639124,0.00002158364,0.00002269644,0.000007829998],"category_scores_gemma":[0.000003856247,0.00003763801,0.000006028551,0.00003640802,0.00001822641,0.00005284021,0.000008877762,0.00002919027,0.000002207235],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001050071,"about_ca_system_score_gemma":0.000003498196,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":8.899799e-7,"about_ca_topic_score_gemma":3.50993e-7,"domain_scores_codex":[0.9997326,0.000006159771,0.00008993461,0.0000385555,0.00006477949,0.00006803588],"domain_scores_gemma":[0.9998872,0.000008122328,0.000008622264,0.0000505516,0.000009880598,0.00003561011],"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":[0.000002461386,0.000007345259,0.00001836235,0.00005227459,0.00001518983,0.000001057919,0.00009215264,0.9169559,0.00001152083,0.004321283,0.06651525,0.01200723],"study_design_scores_gemma":[0.00007663725,0.00001367269,0.0001160338,0.0000116134,0.00001339488,0.000002949618,0.00001450332,0.9983982,0.000002952677,0.0006554381,0.0006678223,0.00002679812],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1091153,0.000176269,0.8779162,0.00001193051,0.0003340571,0.0001385786,0.0000296354,0.001595378,0.01068265],"genre_scores_gemma":[0.9902977,0.00001552412,0.009621101,0.000008095017,0.0000338018,0.000002399757,0.000005714505,0.000006566812,0.000009041264],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8811824,"threshold_uncertainty_score":0.1534833,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0478329123206377,"score_gpt":0.227945864291891,"score_spread":0.1801129519712533,"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."}}