{"title":"利用大数据方法分析航空公司网络和竞争特征","authors":"Emily Clemons, Richard Jordan, T. Reynolds","doi":"10.1109/DASC.2016.7777957","DOIUrl":null,"url":null,"abstract":"This study uses Big Data techniques to characterize the U.S. air transportation system over the years from 1998-2014, in an effort to capture the network's behavior and determine what internal and/or external drivers result in structural changes to the network. The metrics discussed in this study allow for the identification of trends in the network, along with capturing major events such as the merger of two airlines. In addition, the metrics shed light on the potential impact of data consistency issues using a dataset commonly used in airline network analysis.","PeriodicalId":340472,"journal":{"name":"2016 IEEE/AIAA 35th Digital Avionics Systems Conference (DASC)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Airline network and competition characterization using big data approaches\",\"authors\":\"Emily Clemons, Richard Jordan, T. Reynolds\",\"doi\":\"10.1109/DASC.2016.7777957\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This study uses Big Data techniques to characterize the U.S. air transportation system over the years from 1998-2014, in an effort to capture the network's behavior and determine what internal and/or external drivers result in structural changes to the network. The metrics discussed in this study allow for the identification of trends in the network, along with capturing major events such as the merger of two airlines. In addition, the metrics shed light on the potential impact of data consistency issues using a dataset commonly used in airline network analysis.\",\"PeriodicalId\":340472,\"journal\":{\"name\":\"2016 IEEE/AIAA 35th Digital Avionics Systems Conference (DASC)\",\"volume\":\"7 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 IEEE/AIAA 35th Digital Avionics Systems Conference (DASC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/DASC.2016.7777957\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE/AIAA 35th Digital Avionics Systems Conference (DASC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DASC.2016.7777957","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Airline network and competition characterization using big data approaches
This study uses Big Data techniques to characterize the U.S. air transportation system over the years from 1998-2014, in an effort to capture the network's behavior and determine what internal and/or external drivers result in structural changes to the network. The metrics discussed in this study allow for the identification of trends in the network, along with capturing major events such as the merger of two airlines. In addition, the metrics shed light on the potential impact of data consistency issues using a dataset commonly used in airline network analysis.