{"id":"W2799787002","doi":"10.1177/0361198118777064","title":"Estimation of a Long-Distance Travel Demand Model using Trip Surveys, Location-Based Big Data, and Trip Planning Services","year":2018,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation and Mobility Innovations","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Technische Universität München; European Commission","keywords":"TRIPS architecture; Travel survey; Trip generation; Transport engineering; Mode choice; Destinations; Travel behavior; Attractiveness; Computer science; Modal; Demand forecasting; Transportation planning; Geography; Tourism; Public transport; Operations research; 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":[0.001077477,0.0006949599,0.0006026013,0.0008242688,0.0005310306,0.001077333,0.001305544,0.0008272227,0.001967244],"category_scores_gemma":[0.002698114,0.0008472557,0.0008106974,0.001450288,0.0005169082,0.000839264,0.0007218544,0.001065794,0.0003434235],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005884515,"about_ca_system_score_gemma":0.005064151,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.6368923,"about_ca_topic_score_gemma":0.558984,"domain_scores_codex":[0.9995332,0.0001666865,0.00002697194,0.000117466,0.00005508786,0.0001006786],"domain_scores_gemma":[0.9986116,0.0007718985,0.0001791557,0.00007009959,0.000260425,0.0001067368],"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":[0.00008860353,0.0001159253,0.0504121,0.00006850957,0.000112504,0.0001462721,0.0001622292,0.932115,0.0004043362,0.004954865,0.001967975,0.009451791],"study_design_scores_gemma":[0.00001355228,0.00001356772,0.006917063,0.000006734037,0.00001560606,0.00001018844,0.0001681378,0.9911464,0.00007210157,0.001031464,0.0005943594,0.0000108873],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8538386,0.0002403563,0.1328605,0.001025079,0.00003710318,0.0002059071,0.007810473,0.0003691765,0.003612781],"genre_scores_gemma":[0.9727061,0.0001575795,0.01896386,0.00005634821,0.00001755283,0.000138759,0.004635891,0.00003199181,0.003292004],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6368923,"threshold_uncertainty_score":0.7304923,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1659144685001015,"score_gpt":0.3948847344120476,"score_spread":0.2289702659119461,"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."}}