{"id":"W4313429600","doi":"10.1155/2022/2000835","title":"A Travel Demand Response Model in MaaS Based on Spatiotemporal Preference Clustering","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation and Mobility Innovations","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Science Foundation of Gansu Province; National Natural Science Foundation of China","keywords":"Reservation; Cluster analysis; Computer science; Preference; DBSCAN; Hierarchical clustering; Mathematical optimization; Data mining; Fuzzy clustering; Mathematics; Statistics; Machine learning; CURE data clustering algorithm","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":[0.0007301804,0.000872203,0.0007270724,0.0006385721,0.0008375543,0.001349636,0.002420779,0.001064802,0.003939538],"category_scores_gemma":[0.001533535,0.0004164973,0.001016713,0.001440644,0.0004891906,0.001795795,0.001080771,0.0009848985,0.000688995],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002050416,"about_ca_system_score_gemma":0.001410307,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0529248,"about_ca_topic_score_gemma":0.025835,"domain_scores_codex":[0.9992238,0.0001499311,0.00004812513,0.000251658,0.0001632455,0.0001632273],"domain_scores_gemma":[0.9993973,0.0001185883,0.0000751187,0.00005145274,0.0002911926,0.00006633793],"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":[0.00006002388,0.0000260509,0.001423666,0.00003690805,0.00002671379,0.00007826977,0.0000802614,0.9776682,0.001200245,0.01046299,0.001098123,0.007838482],"study_design_scores_gemma":[0.000002678797,0.000009035708,0.0001644292,0.000001370927,0.000004642418,0.00001225795,0.00002808444,0.9980082,0.0001149296,0.001287866,0.0003618665,0.000004736371],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1045296,0.0003178964,0.8824577,0.0006938779,0.0001036575,0.0001710088,0.0006720317,0.0006433864,0.01041097],"genre_scores_gemma":[0.9513366,0.0002512207,0.03871275,0.0000923459,0.00003010863,0.0001816444,0.0005190513,0.00006828023,0.008808107],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0529248,"threshold_uncertainty_score":0.1052335,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02234420411370676,"score_gpt":0.2452062077695381,"score_spread":0.2228620036558313,"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."}}