{"id":"W2124264130","doi":"10.7307/ptt.v27i4.1577","title":"A Hybrid Model Based on Support Vector Machine for Bus Travel-Time Prediction","year":2015,"lang":"en","type":"article","venue":"PROMET - Traffic&Transportation","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Transportation of Ontario","funders":"Program for Changjiang Scholars and Innovative Research Team in University; Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Support vector machine; Computer science; Outlier; Data mining; Machine learning; Artificial intelligence","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000330144,0.0002841734,0.0002587988,0.0003384867,0.00005832896,0.00003711218,0.0001467353,0.0001077432,0.00003390448],"category_scores_gemma":[0.00001502137,0.0003028898,0.0001301129,0.0002069997,0.00002492764,0.00027715,0.000001254871,0.0001596568,0.00002730444],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001242679,"about_ca_system_score_gemma":0.00005401237,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003258486,"about_ca_topic_score_gemma":0.00001328661,"domain_scores_codex":[0.9985155,0.00001578545,0.0004350953,0.0003426979,0.0003763837,0.0003145701],"domain_scores_gemma":[0.9994004,0.00002630589,0.00006376871,0.0002307994,0.00009493229,0.0001837313],"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":[0.0001622747,0.0002186875,0.0000230125,0.0001952129,0.00005717656,0.000006183443,0.0003539234,0.9338526,0.001078788,0.0004262894,0.0509787,0.0126472],"study_design_scores_gemma":[0.001542373,0.0004987713,0.00100685,0.00003326561,0.0001144047,0.000001065092,0.00001505732,0.9895409,0.00316284,0.00009277668,0.003716994,0.0002747468],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0520902,0.00002680112,0.9331336,0.0002170088,0.0004570439,0.001781991,0.002007596,0.008636601,0.00164915],"genre_scores_gemma":[0.9842951,0.00001692187,0.01094361,0.00009841033,0.00008070985,0.0005737489,0.003676231,0.00008793872,0.0002273875],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9322048,"threshold_uncertainty_score":0.9999423,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01448648655083034,"score_gpt":0.2176286457273479,"score_spread":0.2031421591765175,"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."}}