{"id":"W2296239200","doi":"","title":"Application of RP/SP Data to the Joint Estimation of Mode Choice Models: Lessons Learned from an Empirical Investigation into Cross-Regional Commuting Trips in the Greater Toronto and Hamilton Area","year":2016,"lang":"en","type":"article","venue":"Transportation Research Board 95th Annual MeetingTransportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Mode choice; Multinomial logistic regression; TRIPS architecture; Revealed preference; Econometrics; Nested logit; Choice set; Estimation; Mode (computer interface); Logit; Discrete choice; Economics; Regional science; Computer science; Operations research; Geography; Transport engineering; Statistics; Mathematics; Public transport; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01620245,0.0007009375,0.0009981357,0.00130499,0.0005608791,0.001187521,0.001921504,0.001109359,0.002288484],"category_scores_gemma":[0.07171692,0.0008294482,0.001341445,0.002856655,0.001141616,0.002164982,0.00191056,0.001875665,0.0003124293],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002127253,"about_ca_system_score_gemma":0.002057472,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1971106,"about_ca_topic_score_gemma":0.1703524,"domain_scores_codex":[0.9864084,0.01114977,0.0003365182,0.0009618645,0.0008205755,0.0003229303],"domain_scores_gemma":[0.9093772,0.07504398,0.00495297,0.007030654,0.003014543,0.0005807039],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005247693,0.0004111674,0.534331,0.0003295034,0.001265704,0.0007665613,0.003687632,0.350887,0.0006107283,0.03708888,0.002572232,0.06752492],"study_design_scores_gemma":[0.00006183785,0.0002100454,0.1222858,0.00007138437,0.0001616475,0.0001426368,0.001956991,0.85908,0.0004422133,0.01276393,0.00274731,0.00007617575],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8714265,0.0003684856,0.1231718,0.0004493548,0.00001441795,0.0001889961,0.001531897,0.0001264364,0.002722034],"genre_scores_gemma":[0.9740138,0.0001237471,0.02357656,0.00003868152,0.00001240497,0.0001133657,0.001514836,0.00002312788,0.0005836117],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8028894,"threshold_uncertainty_score":0.3919266,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3288023387579384,"score_gpt":0.4909816055855734,"score_spread":0.162179266827635,"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."}}