{"@type": "dcat:Dataset", "accessLevel": "public", "accrualPeriodicity": "irregular", "bureauCode": ["026:00"], "contactPoint": {"@type": "vcard:Contact", "fn": "Vlad Popescu", "hasEmail": "mailto:vmpopescu@gmail.com"}, "description": "This paper takes an empirical approach\r\nto identify operational factors at busy airports that\r\nmay predate go-around maneuvers. Using four years\r\nof data from San Francisco International Airport, we\r\nbegin our investigation with a statistical approach\r\nto investigate which features of airborne, ground\r\noperations (e.g., number of inbound aircraft, number\r\nof aircraft taxiing from gate, etc.) or weather are\r\nmost likely to fluctuate, relative to nominal operations,\r\nin the minutes immediately preceding a missed\r\napproach. We analyze these findings both in terms\r\nof their implication on current airport operations\r\nand discuss how the antecedent factors may affect\r\nNextGen. Finally, as a means to assist air traffic controllers,\r\nwe draw upon techniques from the machine\r\nlearning community to develop a preliminary alert\r\nsystem for go-around prediction.", "identifier": "DASHLINK_308", "issued": "2011-02-07", "keyword": ["ames", "dashlink", "nasa"], "landingPage": "https://c3.nasa.gov/dashlink/resources/308/", "modified": "2025-07-17", "programCode": ["026:029"], "publisher": {"@type": "org:Organization", "name": "Dashlink"}, "title": "On the Statistics and Predictability of Go-Arounds"}