Survey of Machine Learning and Deep Learning Techniques for Travel Demand Forecasting
Abstract: There is limited diversity in methods used to forecast today’s travel industry demand. Accompanied with constantly-evolving dynamics influencing the accommodation sector and new innovative technologies reducing the cost of alternatives to airline travel, future demand in these areas is becoming increasingly difficult to predict. More advanced and hybrid forecasting models are continually being sought after. This paper aims to collect and organize significant documents related to modern-day travel industry demand forecasting, incorporating classical techniques that stem from time series analysis and revenue management to state-of-the-art machine learning and deep learning models.
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