{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002053097,0.0006931706,0.0003882457,0.001222238,0.0007796841,0.00155154,0.001447143,0.001962251,0.0007663764],"category_scores_gemma":[0.005567833,0.0003087784,0.0006126824,0.003357732,0.001169675,0.002195889,0.0009350949,0.0009157582,0.0001270149],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001618831,"about_ca_system_score_gemma":0.0007344962,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03611085,"about_ca_topic_score_gemma":0.02703582,"domain_scores_codex":[0.998655,0.0007333529,0.00006328711,0.000197701,0.0002712086,0.00007948807],"domain_scores_gemma":[0.9959596,0.002935239,0.0002702603,0.0002377132,0.0004974457,0.0000998054],"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":[0.0004021583,0.0004131902,0.0512843,0.0005144294,0.0001095469,0.00440497,0.003181974,0.7308819,0.003928022,0.04487061,0.001948452,0.1580604],"study_design_scores_gemma":[0.0000232654,0.0003755064,0.01249531,0.0001046477,0.00004931151,0.001719704,0.002731681,0.9633904,0.003709556,0.008565477,0.006705559,0.0001296131],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5142679,0.001324395,0.4747072,0.0006308186,0.00005203785,0.0003009163,0.0003875167,0.0002494535,0.008079789],"genre_scores_gemma":[0.8622016,0.0009250645,0.1346,0.00002659314,0.0000323164,0.0001456678,0.00020859,0.00003030274,0.001829907],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03611085,"threshold_uncertainty_score":0.0718013,"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."}}