{"id":"W2994771130","doi":"10.1155/2019/7878042","title":"Exploring the Performance of Different On-Demand Transit Services Provided by a Fleet of Shared Automated Vehicles: An Agent-Based Model","year":2019,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation and Mobility Innovations","field":"Engineering","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Public transport; Service (business); Transport engineering; Transit (satellite); TRIPS architecture; Kilometer; Computer science; Intelligent transportation system; Operations research; Travel time; Service system; Level of service; Fleet management; Simulation; Engineering; Business","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":[0.0001187734,0.0001442092,0.000271576,0.0001369254,0.00003016805,0.00000786913,0.0001514111,0.00003849451,0.00001061155],"category_scores_gemma":[0.000001428259,0.0001106373,0.00009633658,0.0002278008,0.00001914009,0.0007362099,3.230288e-7,0.0001518741,5.145363e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002734125,"about_ca_system_score_gemma":0.00003143944,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003327872,"about_ca_topic_score_gemma":0.00005406646,"domain_scores_codex":[0.9986693,0.00001451849,0.000776879,0.00009796808,0.000322154,0.0001191632],"domain_scores_gemma":[0.9992435,0.00003659252,0.0002852517,0.0001623978,0.0002250992,0.00004718762],"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":[0.0002091209,0.0001083082,0.002017796,0.0005039226,0.00004117202,4.070527e-7,0.003420604,0.774294,0.2187657,0.00005213825,0.000002755243,0.0005840934],"study_design_scores_gemma":[0.002265804,0.0005201383,0.3238572,0.0004666827,0.00009627685,5.140353e-7,0.001044855,0.5116354,0.1599051,0.00002334779,0.00002414085,0.0001605121],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9956264,0.00003314011,0.003661498,0.00004542669,0.0001396834,0.0002927617,0.0001168295,0.00007580226,0.000008426514],"genre_scores_gemma":[0.9985904,0.00007175304,0.001109662,0.00002978186,0.000008619068,0.00001831176,0.0001452349,0.0000225518,0.00000366009],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3218394,"threshold_uncertainty_score":0.451166,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01866950745911894,"score_gpt":0.234221779963214,"score_spread":0.2155522725040951,"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."}}