{"id":"W4411465327","doi":"10.1155/atr/1680317","title":"Optimization Methods for Customized Bus Routes in Random Environments","year":2025,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation and Mobility Innovations","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Randomness; Reservation; Computer science; Sorting; Genetic algorithm; Mathematical optimization; Heuristic; Path (computing); Service (business); Stochastic programming; Variable neighborhood search; Operations research; Algorithm; Engineering; Metaheuristic; Mathematics; Artificial intelligence; Computer network","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":[0.001613662,0.001324478,0.001277201,0.0009962764,0.0004418588,0.0009532016,0.001045621,0.001084423,0.002662557],"category_scores_gemma":[0.003051589,0.0007981308,0.001248955,0.001058763,0.0007392524,0.0008601279,0.001082535,0.0009505919,0.0003295838],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001171659,"about_ca_system_score_gemma":0.0015513,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008289141,"about_ca_topic_score_gemma":0.006094996,"domain_scores_codex":[0.9992675,0.0003600041,0.00002790176,0.000130688,0.0001211237,0.00009279962],"domain_scores_gemma":[0.9984713,0.001040426,0.0001912227,0.00005374177,0.0001884813,0.00005467842],"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":[0.000006269964,0.000006280095,0.00007933861,0.00001576004,0.00001220771,0.00001565069,0.000007270442,0.9937125,0.00009908931,0.003585001,0.0001350865,0.002325461],"study_design_scores_gemma":[0.000003149797,0.000008033584,0.00002822067,0.000002600964,0.000002547635,0.000004254585,0.000005445934,0.9976972,0.00003701212,0.00200884,0.000200308,0.000002343045],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009011864,0.000313824,0.9879821,0.0001210092,0.0000344819,0.00004796061,0.0000591102,0.0001016259,0.002328004],"genre_scores_gemma":[0.5936583,0.001082151,0.3952805,0.0001578748,0.00009468247,0.0005868776,0.0003466812,0.0002829352,0.008509936],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008289141,"threshold_uncertainty_score":0.01648176,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007357633920769358,"score_gpt":0.294825450585935,"score_spread":0.2874678166651656,"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."}}