{"id":"W592595758","doi":"","title":"Neighbour Corridors Travel Time Estimation: Concept and a Case Study","year":2012,"lang":"en","type":"article","venue":"Advances in transportation studies","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Mean absolute percentage error; Downtown; Artificial neural network; Estimation; Travel time; Computer science; Statistics; Transport engineering; Data collection; Geography; Engineering; Mathematics; Machine learning","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009920855,0.0001146725,0.0001633768,0.00008518054,0.00005194273,0.000006730121,0.00002784363,0.00002141987,0.000007198885],"category_scores_gemma":[0.000006163345,0.0001121623,0.00001514015,0.0001367717,0.00004952524,0.0006118298,0.000002385688,0.00006443161,0.000003298082],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000213355,"about_ca_system_score_gemma":0.00000152152,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008638435,"about_ca_topic_score_gemma":0.0001420707,"domain_scores_codex":[0.9994356,0.00001257559,0.0002249686,0.0001015406,0.00008771635,0.0001376203],"domain_scores_gemma":[0.9998124,0.0000448371,0.00002574903,0.00006445291,0.00001860857,0.00003401747],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00005013106,0.0007521706,0.140099,0.0006487936,0.0005905763,0.001022793,0.2539777,0.1608919,0.00009299732,0.004569579,0.004798302,0.4325061],"study_design_scores_gemma":[0.007084085,0.0006827955,0.5558337,0.0003361476,0.0006221843,0.0003358449,0.3251213,0.09349758,0.0009178311,0.0007562566,0.01285024,0.001962094],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9102218,0.02217108,0.06347027,0.00003145074,0.00054932,0.0007080392,0.00002357257,0.001725138,0.001099313],"genre_scores_gemma":[0.9965244,0.001707666,0.001569316,0.00002215731,0.0000261989,0.0001054426,0.000007843035,0.0000115826,0.00002540284],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.430544,"threshold_uncertainty_score":0.4573844,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01294798073568071,"score_gpt":0.288513189506811,"score_spread":0.2755652087711303,"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."}}