{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007128647,0.0006570419,0.0005400476,0.001260436,0.0002614484,0.0006405129,0.0006872794,0.0006518597,0.0006757591],"category_scores_gemma":[0.003913909,0.0004083998,0.0003878181,0.001125892,0.0002736585,0.001130693,0.0002839643,0.0007728176,0.0002365159],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007936954,"about_ca_system_score_gemma":0.0006370249,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01660415,"about_ca_topic_score_gemma":0.01760054,"domain_scores_codex":[0.9997777,0.00005812417,0.00001924977,0.00005232774,0.00006440967,0.00002813217],"domain_scores_gemma":[0.9977538,0.00148467,0.0002204331,0.00008388301,0.0004038227,0.00005348322],"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":[0.00005714938,0.00006160045,0.00452662,0.00001417552,0.00002053976,0.00001675821,0.000007638988,0.9728581,0.0004742355,0.0006326424,0.000293523,0.021037],"study_design_scores_gemma":[6.181338e-7,0.000002049321,0.000261474,4.997329e-7,0.000001060611,0.000001074951,7.279271e-7,0.9994312,0.00006889043,0.0002211187,0.00001048735,7.71679e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5101609,0.0004834868,0.4848913,0.0004438093,0.0001420657,0.00003571169,0.0005022998,0.001171414,0.002169066],"genre_scores_gemma":[0.9747516,0.0001699267,0.02361033,0.00002120984,0.00004675909,0.00003479178,0.0003100567,0.00002312523,0.001032295],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01660415,"threshold_uncertainty_score":0.03301501,"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."}}