{"id":"W2782254836","doi":"10.1155/2018/6197549","title":"Taxi Driver’s Operation Behavior and Passengers’ Demand Analysis Based on GPS Data","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation and Mobility Innovations","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; Natural Science Foundation of Heilongjiang Province; China Postdoctoral Science Foundation; National Natural Science Foundation of China; Arizona State University","keywords":"Global Positioning System; Transport engineering; Computer science; License; Beijing; Geographic coordinate system; Automatic vehicle location; Real-time data; Travel time; Duration (music); Operations research; China; Geography; Engineering; Telecommunications","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":[0.0001833327,0.0001122171,0.0001895579,0.0003303739,0.00007078512,0.00002429453,0.0001090182,0.00005463625,0.00007080045],"category_scores_gemma":[0.00001148592,0.0001086979,0.00005569251,0.0004692975,0.00004358181,0.0006055225,6.577587e-7,0.0001323567,0.000001946544],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002658931,"about_ca_system_score_gemma":0.00002803997,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003217457,"about_ca_topic_score_gemma":0.0005422203,"domain_scores_codex":[0.9990327,0.00001125449,0.0004909391,0.0001401313,0.0002263236,0.00009860536],"domain_scores_gemma":[0.9993048,0.00003528099,0.0001319603,0.000221345,0.0002392517,0.00006738786],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0001394094,0.0001329832,0.03675167,0.00004573112,0.0003309432,0.00002364275,0.001723486,0.9189873,0.02614455,0.0005483548,0.0001425876,0.01502928],"study_design_scores_gemma":[0.0009879724,0.0001812743,0.9736449,0.00002551296,0.0007912316,0.000001517781,0.0002077509,0.0199874,0.002689074,0.00003226539,0.001309637,0.0001415085],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.796649,0.00003518711,0.2026649,0.0001169717,0.0002261238,0.0001223883,0.0001102496,0.00003726126,0.00003788014],"genre_scores_gemma":[0.9860738,0.0000577151,0.01319441,0.00006394023,0.00009307432,0.000005786394,0.0004930375,0.00001353395,0.000004689949],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9368932,"threshold_uncertainty_score":0.443257,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01799102290776922,"score_gpt":0.2755851313875251,"score_spread":0.2575941084797559,"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."}}