The objective of the current study is to examine monthly air passenger departures at the airport level considering spatial interactions between airports. In this study, we develop a novel spatial grouped generalized ordered probit (SGGOP) model system of monthly air passenger departures at the airport level. Specifically, we estimate two variants of spatial models including spatial lag model and spatial error model. In the presence of repeated demand measures for the airports, we also consider temporal variations of spatial correlation effects among proximally located airports by employing space and time-based weight matrix. The proposed model is estimated using monthly air passenger departures for five years for 369 airports across the US. The proposed spatial model is implemented using composite marginal likelihood (CML) approach that offers a computationally feasible framework. From the estimation results, it is evident that air passenger departures at the airport level are influenced by different factors including MSA specific demographic characteristics, built environment characteristics, airport specific factors, spatial factors, and temporal factors. Moreover, spatial autocorrelation parameter is found to be significant validating our hypothesis of the presence of common unobserved factors associated with the spatial unit of analysis. In this study, we also perform a validation analysis to examine the predictive performance of the proposed spatial models. The results highlight the superiority of spatial error model compared to spatial lag model and the independent model that ignores the spatial interactions. Finally, we undertake an elasticity analysis to quantify the impact of the independent variables.

中文翻译：

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适应航空公司需求建模中的时空依赖性

当前研究的目的是考虑机场之间的空间相互作用，检查机场级别的每月航空旅客出发量。在这项研究中，我们开发了一种新颖的机场级别每月航空旅客出发的空间分组广义有序概率（SGGOP）模型系统。具体来说，我们估计了空间模型的两种变体，包括空间滞后模型和空间误差模型。在存在重复的机场需求测量的情况下，我们还通过采用基于空间和时间的权重矩阵来考虑邻近机场之间空间相关效应的时间变化。拟议的模型使用美国 369 个机场五年内每月航空旅客出发量进行估算。所提出的空间模型是使用复合边际似然（CML）方法实现的，该方法提供了计算上可行的框架。从估计结果可以看出，机场层面的航空旅客出发受到不同因素的影响，包括 MSA 特定的人口特征、建筑环境特征、机场特定因素、空间因素和时间因素。此外，发现空间自相关参数对于验证我们关于存在与分析空间单位相关的常见未观察因素的假设具有重要意义。在本研究中，我们还进行了验证分析，以检查所提出的空间模型的预测性能。结果凸显了空间误差模型相对于空间滞后模型和忽略空间相互作用的独立模型的优越性。最后，我们进行弹性分析来量化自变量的影响。