Predicting Space Tourism Demand Using Explainable AI

TL;DR

This work develops SpaceNet, a transformer-based model for predicting space tourism demand across four travel types using US survey data, framed within a trustworthy and explainable AI pipeline guided by NIST principles. It leverages SHAP for global, local, and instance explanations and employs data diversification and SMOTE to address biases, achieving a ROC-AUC of 0.82 ± 0.088 with the top-25 features. Key drivers identified include travel price, age, income, gender, and fatality probability, providing actionable insights for marketing and service design. The study demonstrates that explainable AI can reveal not only accurate predictions but also the reasoning behind individual and class-level decisions, offering practical impact for a rapidly evolving space tourism market.

Abstract

Comprehensive forecasts of space tourism demand are crucial for businesses to optimize strategies and customer experiences in this burgeoning industry. Traditional methods struggle to capture the complex factors influencing an individual's decision to travel to space. In this paper, we propose an explainable and trustworthy artificial intelligence framework to address the challenge of predicting space tourism demand by following the National Institute of Standards and Technology guidelines. We develop a novel machine learning network, called SpaceNet, capable of learning wide-range dependencies in data and allowing us to analyze the relationships between various factors such as age, income, and risk tolerance. We investigate space travel demand in the US, categorizing it into four types: no travel, moon travel, suborbital, and orbital travel. To this end, we collected 1860 data points in many states and cities with different ages and then conducted our experiment with the data. From our experiments, the SpaceNet achieves an average ROC-AUC of 0.82 $\pm$ 0.088, indicating strong classification performance. Our investigation demonstrated that travel price, age, annual income, gender, and fatality probability are important features in deciding whether a person wants to travel or not. Beyond demand forecasting, we use explainable AI to provide interpretation for the travel-type decisions of an individual, offering insights into the factors driving interest in space travel, which is not possible with traditional classification methods. This knowledge enables businesses to tailor marketing strategies and optimize service offerings in this rapidly evolving market. To the best of our knowledge, this is the first work to implement an explainable and interpretable AI framework for investigating the factors influencing space tourism.

Figures (7)

• Figure 1: Distribution of representative attributes in our collected data.
• Figure 2: Trustworthy and responsible AI.
• Figure 3: Tranformer network.
• Figure 4: Comparison of ROC AUC when using all features and top 25 features.
• Figure 5: Global feature contribution.