{"id":"W4229066385","doi":"10.1155/2022/4100704","title":"Understanding the Shortest Route Selection Behavior for Private Cars Using Trajectory Data and Navigation Information","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Major Science and Technology Projects in Anhui Province; Fundamental Research Funds for the Central Universities; Chinese University of Hong Kong; University of Toronto; National Natural Science Foundation of China","keywords":"TRIPS architecture; Shortest path problem; Selection (genetic algorithm); Trajectory; Transport engineering; Global Positioning System; Computer science; Preference; Travel behavior; Standard deviation; Travel time; Operations research; Geography; Statistics; Mathematics; Engineering; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":true,"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":[0.0007053145,0.0004333643,0.0002916783,0.001806457,0.0002858088,0.001032991,0.0003670402,0.0004003524,0.001197188],"category_scores_gemma":[0.004291102,0.000201383,0.0004290522,0.002582741,0.0002008827,0.00229,0.0005020605,0.0004633843,0.0003897446],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007785195,"about_ca_system_score_gemma":0.0009857697,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04513426,"about_ca_topic_score_gemma":0.06049576,"domain_scores_codex":[0.9994833,0.0001220926,0.00005924594,0.0001446797,0.0001227155,0.00006794511],"domain_scores_gemma":[0.997956,0.0006090845,0.0004499527,0.0002084943,0.0006884247,0.00008800667],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000177737,0.0001715737,0.7714313,0.0003050869,0.0002275017,0.0002755024,0.001590601,0.08484652,0.002860331,0.004124119,0.003003356,0.1309864],"study_design_scores_gemma":[0.00001393152,0.0002009534,0.4687219,0.00009984146,0.0001835555,0.0002753205,0.006001759,0.5050064,0.002663092,0.006784382,0.009934778,0.0001141195],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9558117,0.0003842616,0.03700649,0.0003608575,0.0000226942,0.00005694651,0.003637736,0.0001653658,0.002553944],"genre_scores_gemma":[0.9853291,0.0002860597,0.0104681,0.00001454591,0.000005034125,0.000023433,0.003303448,0.00001923381,0.0005511004],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04513426,"threshold_uncertainty_score":0.08974308,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08074246274490716,"score_gpt":0.3307824938943759,"score_spread":0.2500400311494687,"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."}}