{"id":"W2943771175","doi":"10.1155/2019/4212631","title":"Vehicle Scheduling Optimization considering the Passenger Waiting Cost","year":2019,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Beijing Municipal Natural Science Foundation; National Natural Science Foundation of China","keywords":"Beijing; Scheduling (production processes); Schedule; Public transport; Computer science; Operations research; Transport engineering; Bus priority; Engineering; Operations management","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":[0.0005481816,0.0008646498,0.0009704696,0.000457672,0.0003381975,0.0007963033,0.0007471766,0.0005593504,0.002222527],"category_scores_gemma":[0.000760405,0.0003919495,0.0005184742,0.0007252393,0.0003401362,0.0007336375,0.0004510087,0.0004845356,0.0001598737],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00126893,"about_ca_system_score_gemma":0.001672327,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01996654,"about_ca_topic_score_gemma":0.008956339,"domain_scores_codex":[0.9996703,0.00008937428,0.00001007811,0.00006190357,0.00005040453,0.0001179591],"domain_scores_gemma":[0.9997351,0.0001170765,0.00004273854,0.00001327482,0.00005160505,0.00004018975],"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":[0.00002216428,0.000007938556,0.0001454491,0.00001135773,0.000006242071,0.00001764203,0.000006067241,0.9964921,0.0004740456,0.0009775689,0.00008988802,0.001749534],"study_design_scores_gemma":[0.000003707208,0.00001755171,0.0001174827,8.063723e-7,0.000003463007,0.000003440248,0.000008461124,0.9990339,0.0001469471,0.0005626593,0.00009970568,0.000001818304],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.355236,0.0004777211,0.6362564,0.00023672,0.00006471745,0.00007542519,0.0003251234,0.0002257091,0.00710214],"genre_scores_gemma":[0.9770327,0.0002055021,0.01846323,0.00001967393,0.00001601435,0.0000491698,0.0001835293,0.00005062073,0.003979662],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01996654,"threshold_uncertainty_score":0.03970063,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01560347214376105,"score_gpt":0.2787820910500913,"score_spread":0.2631786189063302,"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."}}