{"id":"W2808030106","doi":"10.1016/j.retrec.2018.06.004","title":"The usage of location based big data and trip planning services for the estimation of a long-distance travel demand model. Predicting the impacts of a new high speed rail corridor","year":2018,"lang":"en","type":"article","venue":"Research in Transportation Economics","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Seventh Framework Programme; Technische Universität München; European Commission","keywords":"Transport engineering; TRIPS architecture; Multinomial logistic regression; Destinations; Trip generation; Travel survey; Modal; Mode choice; Estimation; Travel behavior; Service (business); Level of service; Computer science; Public transport; Business; Geography; Engineering; Tourism; Marketing","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":[0.001063278,0.001330738,0.000744706,0.003204096,0.000612599,0.001608201,0.001396682,0.0007741983,0.006014199],"category_scores_gemma":[0.005233331,0.0007032808,0.001282781,0.006217255,0.0003045262,0.001399686,0.0009148381,0.0009853006,0.002984124],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002823544,"about_ca_system_score_gemma":0.003940596,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.3678928,"about_ca_topic_score_gemma":0.4492974,"domain_scores_codex":[0.9993153,0.0001365167,0.00006566593,0.0001876079,0.0002467636,0.00004822993],"domain_scores_gemma":[0.9965311,0.001098665,0.000417217,0.0009163167,0.0008704369,0.0001663144],"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":[0.0001580135,0.0003799994,0.06890129,0.001176868,0.0006561385,0.000383902,0.0003824721,0.6477334,0.003776904,0.00938757,0.04543222,0.2216311],"study_design_scores_gemma":[0.00002562111,0.00005362709,0.02009903,0.000124619,0.00008033461,0.0001000494,0.0004522923,0.9464433,0.001681765,0.006208006,0.02465547,0.00007596781],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1350472,0.001346095,0.5611955,0.001878669,0.0003198124,0.0009937934,0.2613233,0.01314791,0.02474755],"genre_scores_gemma":[0.5826401,0.001517539,0.238838,0.0002358347,0.0001050242,0.0006594534,0.1688767,0.0004492713,0.0066781],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.3678928,"threshold_uncertainty_score":0.7315029,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.133145669900912,"score_gpt":0.3821944858251187,"score_spread":0.2490488159242067,"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."}}