Modeling and Forecasting U.S. Outbound Travel Demand Across Regions Using Time Series Model and Machine Learning: A Comparative Study

Forecasting of travel demand has become increasingly important in the context of evolving mobility patterns and structural disruptions, including economic fluctuations and public health crises. Classical time series models, although well established in travel-demand analysis, are often limited in their ability to capture non-linear dependencies or adapt to abrupt regime shifts. This study develops and evaluates forecasting techniques drawn from both traditional statistical modeling and machine learning approaches. Their predictive performance and adaptability are benchmarked for U.S. outbound air travel demand across eight global destination regions, Europe, the Caribbean, Asia, South America, Central America, Oceania, the Middle East, and Africa, respectively. Using historical outbound passenger data, six forecasting models are constructed and assessed through multiple forecasting accuracy measures, including the Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). Empirical results demonstrate that machine-learning-based models, particularly those incorporating adaptive learning components, consistently outperform conventional approaches in modeling structural changes in travel demand data. The study further contributes a generalizable methodological framework that enhances robustness under uncertainty and offers broad applicability to forecasting problems in transportation, tourism, and related domains.