{"id":1365,"date":"2024-11-13T15:22:07","date_gmt":"2024-11-13T15:22:07","guid":{"rendered":"https:\/\/crp.trb.org\/acrpwebresource18\/bibliography\/"},"modified":"2024-11-13T17:08:30","modified_gmt":"2024-11-13T17:08:30","slug":"bibliography","status":"publish","type":"page","link":"https:\/\/crp.trb.org\/acrpwebresource18\/bibliography\/","title":{"rendered":"Bibliography"},"content":{"rendered":"
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Bibliography<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Davenport, T. 2018. DELTA Plus Model and Five Stages of Analytics<\/a> Maturity: A Primer<\/em>. Research Brief. International Institute for Analytics<\/a>. IIAnalytics.com<\/a>.<\/span><\/p>

Davenport, T.H., J.G. Harris, and R. Morrison. 2010. Analytics<\/a> at Work: Smarter Decisions, Better Results.<\/em> Harvard Business School Publishing. Boston, MA.<\/span> <\/span><\/p>

Degas, A., M.R. Islam, C. Hurter, S. Barua, H. Rahman, M. Poudel, D. Ruscio, …, and P. Aricó. 2022. A survey on artificial intelligence (AI)<\/a> and eXplainable AI in air traffic management: Current trends and development with future research trajectory. <\/span>Applied Sciences, 12(3),<\/span><\/i> 1295. <\/span>https:\/\/doi.org\/10.3390\/app12031295<\/span><\/a>. <\/span> <\/span><\/p>

Gartner Inc. <\/span>Information Technology Glossary<\/span><\/i>. Accessed March 24, 2023. <\/span>https:\/\/www.gartner.com\/en\/information-technology\/glossary<\/span><\/a>. <\/span><\/p>

Global Aerospace. 2020. <\/span>The Benefits of Aviation Data Sharing and How to Get Started<\/span><\/i>. <\/span>https:\/\/www.global-aero.com\/the-benefits-of-aviation-data-sharing-and-how-to-get-started\/<\/span><\/a>. <\/span> <\/span><\/p>

Lunacek, M., L. Williams, J. Severino, K. Ficenec, J. Ugirumurera, M. Eash, Y. Ge, and C. Phillips. 2021. A data-driven operational model for traffic at the Dallas Fort Worth International Airport. <\/span>Journal of Air Transport Management, 94,<\/span><\/i> 102061. <\/span>https:\/\/doi.org\/10.1016\/j.jairtraman.2021.102061<\/span><\/a>. <\/span> <\/span><\/p>

Mullan, M. 2019. The data-driven airport: How daa created data and analytics<\/a> capabilities to drive business growth, improve the passenger experience and deliver operational efficiency. <\/span>Journal of Airport Management, 13(3)<\/span><\/i>, 362-379. Henry Stewart Publications.<\/span> <\/span><\/p>

Provost, F. and T. Fawcett. 2013. <\/span>Data Science<\/a> for Business<\/span><\/i>. O’Reilly Media Inc. Sebastopol, CA. <\/span>https:\/\/learning.oreilly.com\/library\/view\/data-science-for\/9781449374273\/<\/span><\/a>. <\/span> <\/span><\/p>

Sadiku, M.N.O., J. Foreman, and S.M. Musa. 2018. Big data analytics<\/span>: A primer. <\/span>International Journal of Engineering Technologies and Management Research, 5(9)<\/span><\/i>, 44-49. <\/span>https:\/\/doi.org\/10.29121\/ijetmr.v5.i9.2018.287<\/span><\/a>.<\/span><\/p>

Salinas, D., V. Flunkert, J. Gasthaus, and T. Januschowski. 2020. DeepAR: Probabilistic forecasting<\/a> with autoregressive recurrent networks. International Journal of Forecasting<\/a>, 36(3),<\/em> 1181-1191. https:\/\/doi.org\/10.1016\/j.ijforecast.2019.07.001<\/a>. <\/span><\/p>

Schultz, M., S. Reitmann, and S. Alam. 2021. Predictive classification and understanding of weather impact on airport performance through machine learning. <\/span>Transportation Research Part C: Emerging Technologies, 131,<\/span><\/i> 103119. <\/span>https:\/\/doi.org\/10.1016\/j.trc.2021.103119<\/span><\/a>.<\/span> <\/span><\/p>

Shmueli, G., P.C. Bruce, P. Gedeck, and N.R. Patel. 2020. <\/span>Data Mining<\/span> for Business Analytics<\/span>: Concepts, Techniques, and Applications in Python<\/span><\/i>. Wiley & Sons. Hoboken, NJ. <\/span>https:\/\/learning.oreilly.com\/library\/view\/data-mining-for\/9781119549840\/<\/span><\/a>. <\/span> <\/span><\/p>

Simon, P. 2017. <\/span>Analytics<\/a>: The Agile Way.<\/span><\/i> Wiley & Sons Inc. Hoboken, NJ. <\/span>https:\/\/www.wiley.com\/en-us\/Analytics:+The+Agile+Way-p-9781119423478<\/span><\/a>. <\/span> <\/span><\/p>

U.S. Geological Survey. n.d. <\/span>Data Sharing Agreements<\/span><\/i>. <\/span>https:\/\/www.usgs.gov\/data-management\/data-sharing-agreements<\/span><\/a>.<\/p>

U.S. National Archives. 1984. <\/span>Airport Noise Compatibility <\/em>Planning<\/span><\/i>, 14 C.F.R. § 150. Code of Federal Regulations. <\/span>https:\/\/www.ecfr.gov\/current\/title-14\/chapter-I\/subchapter-I\/part-150.<\/span><\/a> <\/span> <\/span><\/p>

U.S. National Archives. 2012. <\/span>Airport De-icing Point Source Category<\/span><\/i>, 40 C.F.R. § 449. Code of Federal Regulations. <\/span>https:\/\/www.ecfr.gov\/current\/title-40\/chapter-I\/subchapter-N\/part-449.<\/span><\/a>  <\/span><\/p>

Yang, Z., U. Wang, J. Li, L. Liu, J. Ma, and Y. Zhong. 2020. Airport arrival flow prediction considering meteorological factors based on deep-learning methods. <\/span>Complexity: Special Edition on Deep Learning Models Applied to Complex Big Data<\/span> Analysis,<\/span><\/i> 2020, 6309272. Wiley: Hindawi. <\/span>https:\/\/doi.org\/10.1155\/2020\/6309272<\/span><\/a>.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"

Bibliography Davenport, T. 2018. DELTA Plus Model and Five Stages of Analytics<\/a> Maturity: A Primer. Research Brief. International Institute for Analytics<\/a>. IIAnalytics.com. Davenport, T.H., J.G. Harris, and R. Morrison. 2010. Analytics<\/a> at Work: Smarter Decisions, Better Results. Harvard Business School Publishing. Boston, MA.  Degas, A., M.R. Islam, C. Hurter, S. Barua, H. Rahman, M. Poudel, D. Ruscio, …, and … Continue reading “Bibliography”<\/span><\/a><\/p>\n","protected":false},"author":95,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"footnotes":""},"categories":[],"tags":[],"class_list":["post-1365","page","type-page","status-publish","hentry"],"acf":[],"_links":{"self":[{"href":"https:\/\/crp.trb.org\/acrpwebresource18\/wp-json\/wp\/v2\/pages\/1365","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/crp.trb.org\/acrpwebresource18\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/crp.trb.org\/acrpwebresource18\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/crp.trb.org\/acrpwebresource18\/wp-json\/wp\/v2\/users\/95"}],"replies":[{"embeddable":true,"href":"https:\/\/crp.trb.org\/acrpwebresource18\/wp-json\/wp\/v2\/comments?post=1365"}],"version-history":[{"count":7,"href":"https:\/\/crp.trb.org\/acrpwebresource18\/wp-json\/wp\/v2\/pages\/1365\/revisions"}],"predecessor-version":[{"id":1590,"href":"https:\/\/crp.trb.org\/acrpwebresource18\/wp-json\/wp\/v2\/pages\/1365\/revisions\/1590"}],"wp:attachment":[{"href":"https:\/\/crp.trb.org\/acrpwebresource18\/wp-json\/wp\/v2\/media?parent=1365"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/crp.trb.org\/acrpwebresource18\/wp-json\/wp\/v2\/categories?post=1365"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/crp.trb.org\/acrpwebresource18\/wp-json\/wp\/v2\/tags?post=1365"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}