Comparison of Artificial Intelligence Techniques for The UK Air Passenger Short-Term Demand Forecasting: A Destination Insight Study

Abstract

Web search queries become essential drivers to forecast air passenger demand for operational benefits. Scholars and marketing experts. Forecasting passenger demand is one of the most important marketing problems that experts frequently encounter, but there are very few studies in the literature using search queries. The main novelty of this study is to show that Destination Insight (DI) can be useful as an air passenger demand proxy in the UK. To prove this primary objective, this work uses several machine and deep learning multi-layer perceptron (MLP) methods based on a big-data framework. The findings indicate that DI is a crucial predictor of the UK air passenger demand. Besides, popular error metrics (RMSE, MAPE, MAD and AIC) were compared to find the best model in this study. Specifically, results indicate that MLP following feed forward neural networks works better for the UK air passenger market.

Keywords

• Destination insight
• air passenger demand forecasting
• artificial intelligence
• consumer search behavior
• big data analytics

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