{"id":"W4408892502","doi":"10.61091/jcmcc125-09","title":"Analysis of travel mode selection behavior based on machine learning in context of big data","year":2025,"lang":"en","type":"article","venue":"Journal of Combinatorial Mathematics and Combinatorial Computing","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Big data; Computer science; Selection (genetic algorithm); Context (archaeology); Mode (computer interface); Data science; Machine learning; Artificial intelligence; Data mining; Human–computer interaction; Geography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.0006732923,0.0004143072,0.0004070157,0.001912467,0.0003190688,0.0005861072,0.000518133,0.0002918533,0.000864371],"category_scores_gemma":[0.002674388,0.000123613,0.0005078723,0.00148269,0.0002367072,0.0009989848,0.0003473985,0.0005343884,0.000258286],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005737456,"about_ca_system_score_gemma":0.0004565086,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008851529,"about_ca_topic_score_gemma":0.01057012,"domain_scores_codex":[0.9995437,0.000114027,0.00003360143,0.0001191469,0.0001129811,0.00007653022],"domain_scores_gemma":[0.9984223,0.0007970301,0.0001884573,0.0001575302,0.0003392743,0.00009548604],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004185528,0.0007993052,0.4494369,0.000216209,0.000326752,0.0006189179,0.0005293068,0.3018359,0.008127407,0.005199058,0.003881489,0.2286103],"study_design_scores_gemma":[0.000002083253,0.00004005458,0.03252086,0.000005080203,0.00001567414,0.00005325909,0.0001623912,0.9643849,0.00109379,0.001408931,0.0003030575,0.00001000808],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8411542,0.0001905877,0.1549104,0.0004024306,0.00003448845,0.00008566929,0.001006878,0.0004059651,0.001809394],"genre_scores_gemma":[0.9803227,0.00006828897,0.01818446,0.00002549575,0.00001273764,0.00003778337,0.0007285677,0.0000134243,0.0006065078],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008851529,"threshold_uncertainty_score":0.0176,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01812727024941179,"score_gpt":0.2626906908219259,"score_spread":0.2445634205725141,"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."}}