{"id":"W2600505068","doi":"10.1109/vtcfall.2016.7880881","title":"A Solution to the Congestion Problem: Profiles Driven Trip Planning","year":2016,"lang":"en","type":"article","venue":"","topic":"Transportation and Mobility Innovations","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"TRIPS architecture; Computer science; Traffic congestion; Personalization; Plan (archaeology); Transportation planning; GRASP; Operations research; Transport engineering; Engineering; World Wide Web","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":[0.001544464,0.0008227171,0.0008879915,0.0007009512,0.00084684,0.001636372,0.002287254,0.001489352,0.004974511],"category_scores_gemma":[0.005395803,0.0007055179,0.0009341497,0.001266309,0.0007339341,0.002475431,0.002179652,0.001958603,0.0008710104],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001081607,"about_ca_system_score_gemma":0.002317629,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008383767,"about_ca_topic_score_gemma":0.008334371,"domain_scores_codex":[0.9989466,0.0004861489,0.00004121267,0.0002039375,0.0001937779,0.0001283801],"domain_scores_gemma":[0.9983909,0.0007675677,0.0001330283,0.0002271325,0.0002853888,0.0001959811],"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":[0.0002717108,0.000221246,0.001737399,0.0002009268,0.000113978,0.0001963244,0.0005598109,0.7679939,0.001670297,0.09547975,0.009890276,0.1216643],"study_design_scores_gemma":[0.00002701202,0.00005251696,0.0001648341,0.00001364596,0.00001646435,0.00006547671,0.0001107204,0.9571127,0.0003705714,0.0388129,0.003237452,0.00001559432],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01701344,0.0002056983,0.9751507,0.0008901726,0.00009362846,0.000156055,0.0002033257,0.0003676921,0.005919253],"genre_scores_gemma":[0.5569583,0.0004874733,0.434713,0.0002932611,0.00009928369,0.0002823362,0.0004918676,0.0002112485,0.00646328],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008383767,"threshold_uncertainty_score":0.01666999,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02031532668537636,"score_gpt":0.2379485943805602,"score_spread":0.2176332676951838,"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."}}