{"id":"W3147678316","doi":"10.1177/03611981211003593","title":"Inferring the Purposes of using Ride-Hailing Services through Data Fusion of Trip Trajectories, Secondary Travel Surveys, and Land Use Data","year":2021,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"TRIPS architecture; Multinomial logistic regression; Transport engineering; Trip generation; Trip distribution; Estimation; Travel behavior; Context (archaeology); Discrete choice; Destinations; Travel survey; Computer science; Mode choice; Public transport; Geography; Econometrics; Economics; Engineering; Tourism","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":[0.001538094,0.0007589691,0.0004781678,0.003199024,0.0004515139,0.001025932,0.0007551652,0.0004644888,0.000785081],"category_scores_gemma":[0.008833884,0.0004358659,0.0007718116,0.002865809,0.000447153,0.0009841012,0.001483752,0.0006540113,0.0003606419],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001865817,"about_ca_system_score_gemma":0.001975322,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1839124,"about_ca_topic_score_gemma":0.2575869,"domain_scores_codex":[0.9988834,0.0003092745,0.00009073346,0.0003117772,0.0002813845,0.0001233778],"domain_scores_gemma":[0.9963487,0.001180579,0.0007399505,0.0006686696,0.0008679551,0.000194008],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002059589,0.00009547063,0.8717242,0.000260851,0.000318519,0.0002925742,0.002007694,0.05671134,0.00274634,0.002510714,0.001801732,0.06132466],"study_design_scores_gemma":[0.00001551193,0.0001019772,0.6181719,0.0001121275,0.0001657935,0.0001949786,0.002397263,0.3647642,0.004496254,0.004224999,0.005270573,0.00008447246],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.85202,0.000397293,0.1297252,0.0003081093,0.00003130952,0.0002242475,0.01409377,0.0004301323,0.002769934],"genre_scores_gemma":[0.9609207,0.0001224431,0.03164692,0.00001790377,0.000009371603,0.00006464988,0.006678949,0.0000199131,0.0005191545],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1839124,"threshold_uncertainty_score":0.3656837,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3292296607541524,"score_gpt":0.4577364625330899,"score_spread":0.1285068017789374,"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."}}