{"id":"W2895118251","doi":"10.1155/2018/4218625","title":"Minimizing Metro Transfer Waiting Time with AFCS Data Using Simulated Annealing with Parallel Computing","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Headway; Simulated annealing; Transfer (computing); Computer science; Transfer station; Train; Travel time; Real-time computing; Transfer efficiency; Simulation; Operations research; Engineering; Transport engineering; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006889442,0.0001572395,0.0002844484,0.0001737133,0.0005484406,0.00006647286,0.0002357089,0.00007685493,0.00003585288],"category_scores_gemma":[0.00003585599,0.0001318419,0.00004314053,0.0005902383,0.0001595011,0.001398911,0.000001159737,0.0001971384,9.618232e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005352614,"about_ca_system_score_gemma":0.0002437186,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009251384,"about_ca_topic_score_gemma":0.0005900618,"domain_scores_codex":[0.9982046,0.00008152126,0.0006015512,0.000235935,0.0005894905,0.0002868421],"domain_scores_gemma":[0.9984127,0.0001539644,0.0004420818,0.0001400365,0.0007261961,0.0001249739],"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":[0.0006149847,0.00002991213,0.007512351,0.00001547077,0.00007248949,0.00003633127,0.01911101,0.9702014,0.0005638332,0.00007094255,0.00000360737,0.001767687],"study_design_scores_gemma":[0.01044,0.001638165,0.05305475,0.002646205,0.001321676,0.00003601276,0.04575879,0.8805703,0.001295934,0.0001378088,0.001866034,0.001234353],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7133051,0.00005496235,0.2861616,0.0001016568,0.00009070297,0.0001248369,0.00001895683,0.0000440707,0.00009811846],"genre_scores_gemma":[0.8285348,0.00001845135,0.1709886,0.00004288206,0.0002346031,1.326889e-7,0.0001458998,0.00002373399,0.00001088813],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1152297,"threshold_uncertainty_score":0.5376357,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0370050801069123,"score_gpt":0.3173161113539384,"score_spread":0.2803110312470262,"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."}}