Analysis and prediction of performance indicators of Brazilian air traffic by machine learning

The Brazilian air transport sector plays a significant role in the country's economy. For example, in 2018, this sector contributed 1.1% to the Gross Domestic Product (GDP) and generated 839,000 direct jobs. Although the industry was severely affected by the pandemic, growth rates are gradually returning, making it essential to analyze the performance of airports and airlines. One way to perform this analysis is through performance indicators such as the Key Performance Indicators (KPIs) of the International Civil Aviation Organization's (ICAO) Global Air Navigation Plan (GANP). This article analyzes departure punctuality (KPI 01) and identifies the key factors affecting punctuality to improve airport and airline management. To conduct this analysis, the Extreme Gradient Boosting (XGBOOST) computational algorithm was used to predict whether a flight would be classified as punctual or not. Additionally, the XGBClassifier algorithm was used to assess the input variables' importance in the supervised machine learning model. The variables employed in the machine learning analysis include local meteorological variables at the time of departure, operational data on aircraft demand and capacity, specific geolocation information, and temporal data collected from public databases. The XGBoost model used to evaluate the punctuality of Brazilian airports achieved an accuracy of 92.96\%. For the analyzed airlines, the departure accuracy rates were 91.6% for GOL, 93.9% for TAM, and 94.0% for AZUL. Furthermore, the results indicated that atmospheric pressure, the number of flights departing simultaneously, and the weight of checked baggage were the main factors affecting airport departure operations. For airlines, the variables of atmospheric pressure, free baggage, and paid baggage had the most significant impact on punctuality.

巴西航空运输业在国民经济中占据着举足轻重的地位。以2018年为例，该行业对国内生产总值（GDP）的贡献达到了1.1%，并创造了83.9万个直接就业岗位。尽管该行业遭受了疫情的严重冲击，但其增长速度正逐渐恢复，因此分析机场和航空公司的运营绩效变得尤为关键。进行此类分析的一种途径是利用国际民用航空组织（ICAO）的全球航空导航计划（GANP）的关键绩效指标（KPI）。本文旨在分析航班起飞准时性（KPI 01）以及识别影响准时的关键因素，以提升机场和航空公司的管理水平。为实现这一目标，本研究采用了极梯度提升（XGBOOST）计算算法，以预测航班是否被归类为准时。此外，XGBClassifier算法被用于评估输入变量在监督机器学习模型中的重要性。在机器学习分析中使用的变量包括起飞时的局部气象变量、飞机需求与产能的运营数据、特定的地理定位信息，以及从公共数据库收集的时间数据。用于评估巴西机场准时的XGBoost模型达到了92.96%的准确率。对于所分析的航空公司，起飞准时率分别为GOL的91.6%、TAM的93.9%和AZUL的94.0%。进一步分析结果显示，大气压力、同时起飞的航班数量以及托运行李的重量是影响机场出发运营的主要因素。对于航空公司而言，大气压力、免费行李和付费行李的变量对准时的影響最为显著。