{"id":"W2939896982","doi":"10.1155/2019/5207814","title":"Hybrid Dynamic Route Planning Model for Pedestrian Microscopic Simulation at Subway Station","year":2019,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Evacuation and Crowd Dynamics","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Crowds; Visibility; Pedestrian; Computer science; Route planning; Motion planning; Process (computing); Multipath propagation; Simulation; Selection (genetic algorithm); Visibility graph; Ticket; Real-time computing; Transport engineering; Social force model; Graph; Robot; Artificial intelligence; Engineering; 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":[0.0002224613,0.0005848068,0.0005963174,0.0004531287,0.0005665784,0.0007947724,0.001255299,0.001036873,0.004019767],"category_scores_gemma":[0.0005962998,0.0003832224,0.0009549004,0.0004005765,0.0003944867,0.0006575154,0.0007675666,0.0006964552,0.0003477845],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000860277,"about_ca_system_score_gemma":0.001158974,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03926467,"about_ca_topic_score_gemma":0.02105591,"domain_scores_codex":[0.9998441,0.00003884026,0.000007708306,0.00003856948,0.00003493502,0.00003580525],"domain_scores_gemma":[0.9997668,0.00007962872,0.00004155107,0.00001521362,0.00005983889,0.00003700982],"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":[0.0000146179,0.000008719892,0.0004675664,0.00001076444,0.000007800554,0.00003723141,0.00002494647,0.9957373,0.0003396445,0.00204574,0.0001048105,0.001200904],"study_design_scores_gemma":[0.000003012203,0.000005426917,0.00007716026,0.000001078484,0.000003353281,0.000004035101,0.000006439745,0.9993351,0.00006190701,0.0003380426,0.0001622526,0.000002085912],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2371725,0.0003682579,0.7399121,0.0004016993,0.0001342358,0.0001064177,0.0007745046,0.0009097298,0.02022057],"genre_scores_gemma":[0.9513937,0.000223429,0.04125717,0.00004373333,0.00001886065,0.0001768705,0.0002914348,0.00005066565,0.00654411],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03926467,"threshold_uncertainty_score":0.07807225,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01012780057779429,"score_gpt":0.2743708881623956,"score_spread":0.2642430875846013,"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."}}