Detecting flight trajectory anomalies and predicting diversions in freight transportation
作者：
Highlights：
• An approach for the automated prediction of flight diversions is proposed.
• No prior knowledge of the flight route is required.
• The prediction is made by analyzing the flight data anomalies detected by a one-class classifier.
• The anomaly detection is based on one-class Support Vector Machines with Gaussian kernel.
• Input data is gathered from public flight tracking services available online.
摘要
Timely identifying flight diversions is a crucial aspect of efficient multi-modal transportation. When an airplane diverts, logistics providers must promptly adapt their transportation plans in order to ensure proper delivery despite such an unexpected event. In practice, the different parties in a logistics chain do not exchange real-time information related to flights. This calls for a means to detect diversions that just requires publicly available data, thus being independent of the communication between different parties.
论文关键词：Air transportation,Airplane trajectory,Aircraft navigation,Logistics,Machine learning,Prediction methods
论文评审过程：Received 31 January 2015, Revised 7 April 2016, Accepted 18 May 2016, Available online 31 May 2016, Version of Record 1 July 2016.
论文官网地址：https://doi.org/10.1016/j.dss.2016.05.004
