{"id":"W1781051676","doi":"10.1002/atr.1317","title":"A multi‐objective subway timetable optimization approach with minimum passenger time and energy consumption","year":2015,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":90,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"State Key Laboratory of Rail Traffic Control and Safety; China Scholarship Council; National Natural Science Foundation of China","keywords":"Dwell time; Energy consumption; Schedule; Train; Beijing; Genetic algorithm; Computer science; Fuzzy logic; Mathematical optimization; Operations research; Simulation; Real-time computing; Engineering","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":[0.000804089,0.001095253,0.001202601,0.0008433653,0.0004655254,0.001236099,0.001103572,0.0009984358,0.004128687],"category_scores_gemma":[0.000847451,0.0006151206,0.001131287,0.0009678444,0.0003347359,0.0009730736,0.0006720172,0.0008268866,0.0002764045],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001365333,"about_ca_system_score_gemma":0.001792066,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01134331,"about_ca_topic_score_gemma":0.007413207,"domain_scores_codex":[0.9996194,0.0001124215,0.00001505696,0.00008473809,0.00009584189,0.000072683],"domain_scores_gemma":[0.9997321,0.0001110592,0.00004296235,0.00001318604,0.00006851074,0.00003217916],"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":[0.00001907564,0.00001328018,0.0001148603,0.00002695824,0.0000156058,0.00002177769,0.00001719625,0.991388,0.0005862063,0.002159471,0.000245413,0.005392039],"study_design_scores_gemma":[0.000004712941,0.00002134211,0.00006921931,0.000002782611,0.000005836001,0.000005147228,0.00001046142,0.9985051,0.0001438182,0.0009775299,0.0002507941,0.000003244051],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04680956,0.000323571,0.9441057,0.000219981,0.00004225177,0.0001017629,0.0001570235,0.0001695652,0.008070551],"genre_scores_gemma":[0.8052435,0.0003967029,0.1847923,0.00009447525,0.0000408132,0.0003514499,0.0002663127,0.0001251659,0.008689245],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01134331,"threshold_uncertainty_score":0.02255458,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009070703200376087,"score_gpt":0.1965674000854321,"score_spread":0.187496696885056,"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."}}