{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T09:42:56Z","timestamp":1776678176635,"version":"3.51.2"},"reference-count":61,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2026,4,18]],"date-time":"2026-04-18T00:00:00Z","timestamp":1776470400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>This study proposes a scalable, AI-driven, and Geographic Information System (GIS)-integrated framework for intelligent route-level classification in large-scale airport transportation networks to support airport operations, logistics planning, and network-level decision-making. The framework addresses the need for practical artificial intelligence applications that combine spatial network analysis with supervised machine learning to improve route assessment and resource allocation in complex air transport systems. A structured dataset was developed using operational and traffic-related attributes, including route distance, aircraft capacity, weekly frequency, annual passenger volume, demand variability, and route performance indicators, with additional normalized features to improve data representation. A Gradient Boosting ensemble classifier was trained to categorize routes into high-, medium-, and low-priority classes. The model achieved strong predictive performance, with a testing area under the ROC curve of 0.961, accuracy of 0.922, F1-score of 0.915, precision of 0.918, and a recall of 0.922. Feature importance analysis identified demand variability and route-density indicators as the main drivers of classification, enhancing interpretability and practical trust. The proposed framework demonstrates the real-world potential of AI for scalable, explainable, and efficient decision support in airport logistics and transportation network management.<\/jats:p>","DOI":"10.3390\/computers15040255","type":"journal-article","created":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T08:24:23Z","timestamp":1776673463000},"page":"255","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine Learning and Geographic Information Systems for Aircraft Route Analysis in Large-Scale Airport Transportation Networks"],"prefix":"10.3390","volume":"15","author":[{"given":"Saadi Turied","family":"Kurdi","sequence":"first","affiliation":[{"name":"College of Engineering, Al-Bayan University, Baghdad 10066, Iraq"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7832-1048","authenticated-orcid":false,"given":"Luttfi A.","family":"Al-Haddad","sequence":"additional","affiliation":[{"name":"College of Mechanical Engineering, University of Technology-Iraq, Baghdad 19006, Iraq"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zeashan Hameed","family":"Khan","sequence":"additional","affiliation":[{"name":"Interdisciplinary Research Center for Intelligent Manufacturing and Robotics (IRC-IMR), King Fahd University of Petroleum and Minerals (KFUPM), Dhahran 31261, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"111689","DOI":"10.1016\/j.cie.2025.111689","article-title":"Optimization of Intelligent Logistics Strategies for Platform-Based Supply Chain Management Networks","volume":"212","author":"Zhang","year":"2026","journal-title":"Comput. 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