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.

巴西航空运输业在该国经济中发挥着举足轻重的作用。以2018年为例，该行业贡献了国内生产总值（Gross Domestic Product，GDP）的1.1%，并创造了83.9万个直接就业岗位。尽管该行业受疫情严重冲击，但其增长速率正逐步回升，因此对机场与航空公司的运营绩效开展分析显得至关重要。

开展此类分析的途径之一，便是借助国际民用航空组织（International Civil Aviation Organization，ICAO）全球航空导航计划（Global Air Navigation Plan，GANP）所提出的关键绩效指标（Key Performance Indicators，KPIs）这类绩效指标。本文针对起飞准点率（KPI 01）展开分析，并识别影响准点率的核心因素，以期优化机场与航空公司的运营管理。

为完成本次分析，研究采用了极限梯度提升（Extreme Gradient Boosting，XGBOOST）计算算法，以预测航班是否可被归类为准点航班。此外，还通过XGBClassifier算法评估了该监督式机器学习模型中各输入变量的重要性。

本次机器学习分析所采用的变量包括起飞时段的本地气象数据、飞机需求与运力的运营数据、特定地理定位信息，以及从公共数据库采集的时序数据。

用于评估巴西机场准点率的XGBoost模型准确率达到了92.96%。针对本次分析的三家航空公司，GOL的起飞准点率为91.6%，TAM为93.9%，AZUL为94.0%。

此外，研究结果表明，大气压力、同时起飞的航班数量以及托运行李重量，是影响机场起飞运营的核心因素。对于航空公司而言，大气压力、免费行李额度与付费行李额度这几个变量，对准点率的影响最为显著。