{"id":"W4389194826","doi":"10.1177/03611981231205881","title":"Enhancing the Spatial Transferability of Direct Demand Models for Estimating Pedestrian Volumes at Intersections","year":2023,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Pedestrian; Transferability; Calibration; Computer science; Estimation; Statistics; Transport engineering; Mathematics; Engineering; Machine learning","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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":[0.002795606,0.00109967,0.0007476605,0.001028648,0.0003283513,0.001119039,0.00152669,0.0009797558,0.001530133],"category_scores_gemma":[0.009801886,0.0006945659,0.001541607,0.0009224314,0.0005736442,0.001641591,0.001636536,0.001518465,0.0004384571],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00139858,"about_ca_system_score_gemma":0.001208699,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03722951,"about_ca_topic_score_gemma":0.02061997,"domain_scores_codex":[0.9986678,0.0006028414,0.00008044542,0.0003582241,0.0002014133,0.00008922968],"domain_scores_gemma":[0.9959928,0.002308302,0.0004031713,0.0004997976,0.0007210799,0.00007480741],"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":[0.00003260247,0.00004259388,0.004645795,0.00002350658,0.00004446372,0.00003296819,0.0000785872,0.9767902,0.0005360922,0.0008422465,0.0001532867,0.01677759],"study_design_scores_gemma":[0.000004348219,0.00001737325,0.0008750164,0.000004590901,0.000007504642,0.00001183058,0.00002344352,0.9977167,0.0003325053,0.0007678574,0.0002306749,0.000008092567],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2720604,0.0002116844,0.7226164,0.0003101034,0.00004010188,0.0001094229,0.0004571855,0.001280679,0.002914104],"genre_scores_gemma":[0.9298691,0.00009836314,0.0680084,0.00007074852,0.00001953501,0.00009140829,0.00060029,0.00008541097,0.001156872],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03722951,"threshold_uncertainty_score":0.07402563,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1321051300270147,"score_gpt":0.4237371702067209,"score_spread":0.2916320401797062,"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."}}