解读航空旅行中断：一种机器学习方法
Deciphering Air Travel Disruptions: A Machine Learning Approach
AI总结：
该研究面向航班延误预测，比较 LSTM 类时间序列模型与多种回归基线，并独立分析天气、安保等延误成分以识别关键特征。
摘要：
本研究通过考察起飞时间、航空公司和机场等因素来调查航班延误趋势。研究采用回归机器学习方法，预测各类来源对延误的贡献。研究将包括 LSTM、Hybrid LSTM 和 Bi-LSTM 在内的时间序列模型，与 Multiple Regression、Decision Tree Regression、Random Forest Regression 和 Neural Network 等基线回归模型进行比较。尽管基线模型存在相当大的误差，本研究仍旨在识别延误预测中的有影响特征，从而可能为航班规划策略提供参考。与以往工作不同，本研究聚焦于回归任务，并探索利用时间序列模型预测航班延误。研究通过独立分析每个延误组成部分（例如安保、天气），为航空运营提供洞见。
英文摘要：
This research investigates flight delay trends by examining factors such as departure time, airline, and airport. It employs regression machine learning methods to predict the contributions of various sources to delays. Time-series models, including LSTM, Hybrid LSTM, and Bi-LSTM, are compared with baseline regression models such as Multiple Regression, Decision Tree Regression, Random Forest Regression, and Neural Network. Despite considerable errors in the baseline models, the study aims to identify influential features in delay prediction, potentially informing flight planning strategies. Unlike previous work, this research focuses on regression tasks and explores the use of time-series models for predicting flight delays. It offers insights into aviation operations by independently analyzing each delay component (e.g., security, weather).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
