{"id":"W1987973803","doi":"10.3141/1895-13","title":"The Trouble with Intercity Travel Demand Models","year":2004,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Nested logit; TRIPS architecture; Transport engineering; Demand forecasting; Mode choice; Travel behavior; Trip generation; Computer science; Traffic congestion; Demand management; Operations research; Mathematical model; Economics; Engineering; Public transport; Econometrics","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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.007859478,0.0002305116,0.000351198,0.000535695,0.003463347,0.0003790004,0.001359313,0.000208274,0.00008079875],"category_scores_gemma":[0.0001709228,0.0001410874,0.000292128,0.002411129,0.001783183,0.001232672,0.00000331146,0.002018485,0.00001210138],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003923744,"about_ca_system_score_gemma":0.001947644,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.02747749,"about_ca_topic_score_gemma":0.3084331,"domain_scores_codex":[0.9912987,0.00137387,0.001155707,0.0003713258,0.004748811,0.001051561],"domain_scores_gemma":[0.9930931,0.001162903,0.0004739833,0.0004099078,0.004403847,0.000456298],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.008354993,0.0009077232,0.1823314,0.0002432738,0.0005826474,0.0002897315,0.1322492,0.3212238,0.0007800782,0.3380522,0.006044642,0.008940345],"study_design_scores_gemma":[0.00537464,0.001177815,0.8624581,0.0008509899,0.0001422702,0.00000125643,0.04771832,0.0003349737,0.001087421,0.0551687,0.02520193,0.0004835567],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9358432,0.0003291745,0.0445484,0.0156544,0.0005602325,0.00130869,0.00004211066,0.00005290817,0.001660831],"genre_scores_gemma":[0.9934691,0.002116987,0.002785165,0.00005736623,0.0002026615,0.00006778829,0.00001531287,0.00004529474,0.001240333],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6801267,"threshold_uncertainty_score":0.997834,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1009283992671204,"score_gpt":0.3882756239679462,"score_spread":0.2873472247008258,"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."}}