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.

巴西航空运输业在本国经济中占据重要地位。以2018年为例，该行业对国内生产总值（Gross Domestic Product，GDP）的贡献占比达1.1%，并创造了83.9万个直接就业岗位。尽管该行业受疫情冲击严重，但增速正逐步回暖，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。开展此类分析的可行路径之一，便是采用国际民用航空组织（International Civil Aviation Organization，ICAO）全球航空导航计划（Global Air Navigation Plan，GANP）所定义的关键绩效指标（Key Performance Indicators，KPIs）等绩效评估维度。本文针对起飞准点率（KPI 01）展开研究，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，本研究还借助XGBClassifier算法，评估该监督式机器学习模型中各输入变量的重要性程度。本次机器学习分析所采用的变量涵盖航班起飞时段的本地气象数据、航空器需求与运力运营数据、特定地理定位信息，以及从公开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92.96%。针对本次分析涉及的航空公司，GOL的起飞准点准确率为91.6%，TAM为93.9%，AZUL为94.0%。此外，研究结果显示，大气压力、同时起飞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；而就航空公司而言，大气压力、免费行李额度与付费行李额度变量对准点率的影响最为显著。