{"id":"W3210120538","doi":"10.1287/msom.2021.1023","title":"Urban Bike Lane Planning with Bike Trajectories: Models, Algorithms, and a Real-World Case Study","year":2021,"lang":"en","type":"article","venue":"Manufacturing & Service Operations Management","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Heuristics; Computer science; Multinomial logistic regression; Bike sharing; TRIPS architecture; Transportation planning; Variance (accounting); Metropolitan area; Operations research; Transport engineering; Mathematical optimization; Business; Machine learning; Engineering; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001914314,0.001528543,0.001227473,0.001146783,0.001268321,0.002953116,0.002272784,0.003642446,0.006136821],"category_scores_gemma":[0.004926993,0.0009493229,0.00112747,0.003729151,0.001770034,0.003246687,0.002115922,0.002733711,0.000451184],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004569581,"about_ca_system_score_gemma":0.002607838,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04449239,"about_ca_topic_score_gemma":0.04424937,"domain_scores_codex":[0.9986659,0.0006454602,0.0000427198,0.0002717598,0.0001406114,0.0002335078],"domain_scores_gemma":[0.9966784,0.002412606,0.0003212068,0.0001356296,0.0002379469,0.0002142663],"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":[0.00004034446,0.0001184881,0.0009686131,0.00007231526,0.00001775042,0.0001758088,0.00007344019,0.9768844,0.00008425092,0.016088,0.001066324,0.00441036],"study_design_scores_gemma":[0.00001858794,0.00002047882,0.0002050116,0.00001128996,0.000009772371,0.00003534698,0.0001353231,0.9902557,0.00008847992,0.008287418,0.0009230156,0.000009620956],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3242083,0.002520042,0.6332307,0.00483195,0.0001268103,0.0008700235,0.002164858,0.0006306671,0.03141659],"genre_scores_gemma":[0.8404826,0.001129931,0.1503358,0.000137369,0.00004842381,0.000540761,0.0008362067,0.00008194748,0.006407003],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04449239,"threshold_uncertainty_score":0.08846682,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03751734610616693,"score_gpt":0.3043572411316996,"score_spread":0.2668398950255327,"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."}}