The expected growth in air travel demand and the positive correlation with the economic factors highlight the significant contribution of the aviation community to the U.S. economy. On‐time operations play a key role in airline performance and passenger satisfaction. Thus, an accurate investigation of the variables that cause delays is of major importance. The application of machine learning techniques in data mining has seen explosive growth in recent years and has garnered interest from a broadening variety of research domains including aviation. This study employed a support vector machine (SVM) model to explore the non-linear relationship between flight delay outcomes. Individual flight data were gathered from 20 days in 2018 to investigate causes and patterns of air traffic delay at three major New York City airports. Considering the black box characteristic of the SVM, a sensitivity analysis was performed to assess the relationship between dependent and explanatory variables. The impacts of various explanatory variables are examined in relation to delay, weather information, airport ground operation, demand-capacity, and flow management characteristics. The variable impact analysis reveals that factors such as pushback delay, taxi-out delay, ground delay program, and demand-capacity imbalance with the probabilities of 0.506, 0.478, 0.339, and 0.338, respectively, are significantly associated with flight departure delay. These findings provide insight for better understanding of the causes of departure delays and the impacts of various explanatory factors on flight delay patterns.

航空旅行需求的预期增长以及与经济因素的正相关关系突显了航空界对美国经济的重大贡献。准时运营对航空公司的绩效和乘客满意度至关重要。因此，对导致延迟的变量进行准确调查非常重要。近年来，机器学习技术在数据挖掘中的应用呈爆炸式增长，并引起了包括航空在内的广泛研究领域的关注。这项研究采用了支持向量机（SVM）模型来探索航班延误结果之间的非线性关系。从2018年的20天开始收集个人航班数据，以调查纽约市三个主要机场空中交通延误的原因和模式。考虑到SVM的黑盒特性，进行了敏感性分析，以评估因变量和解释变量之间的关系。与延误，天气信息，机场地面运营，需求能力和流量管理特征相关的各种解释变量的影响进行了检查。可变影响分析表明，诸如推后延误，滑行延误，地面延误计划和需求容量失衡等因素的概率分别为0.506、0.478、0.339和0.338，与航班起飞延误显着相关。这些发现为更好地了解离港延误的原因以及各种解释性因素对航班延误模式的影响提供了见识。进行敏感性分析以评估因变量和解释变量之间的关系。与延误，天气信息，机场地面运营，需求能力和流量管理特征相关的各种解释变量的影响进行了检查。可变影响分析表明，诸如推后延误，滑行延误，地面延误计划和需求容量失衡等因素的概率分别为0.506、0.478、0.339和0.338，与航班起飞延误显着相关。这些发现为更好地了解离港延误的原因以及各种解释性因素对航班延误模式的影响提供了见识。进行敏感性分析以评估因变量和解释变量之间的关系。与延误，天气信息，机场地面运营，需求能力和流量管理特征相关的各种解释变量的影响进行了研究。可变影响分析表明，诸如推后延误，滑行延误，地面延误计划和需求容量失衡等因素的概率分别为0.506、0.478、0.339和0.338，与航班起飞延误显着相关。这些发现为更好地了解离港延误的原因以及各种解释性因素对航班延误模式的影响提供了见识。天气信息，机场地面运营，需求容量和流量管理特征。可变影响分析表明，推后延误，滑行延误，地面延误计划和需求容量失衡等因素的概率分别为0.506、0.478、0.339和0.338，与航班起飞延误显着相关。这些发现为更好地了解离港延误的原因以及各种解释性因素对航班延误模式的影响提供了见识。天气信息，机场地面运营，需求容量和流量管理特征。可变影响分析表明，诸如推后延误，滑行延误，地面延误计划和需求容量失衡等因素的概率分别为0.506、0.478、0.339和0.338，与航班起飞延误显着相关。这些发现为更好地了解离港延误的原因以及各种解释性因素对航班延误模式的影响提供了见识。与航班起飞延误密切相关。这些发现为更好地了解离港延误的原因以及各种解释性因素对航班延误模式的影响提供了见识。与航班起飞延误密切相关。这些发现为更好地了解离港延误的原因以及各种解释性因素对航班延误模式的影响提供了见识。