# Predicting Space Tourism Demand Using Explainable AI

arXiv: https://arxiv.org/abs/2503.03113 · HTML: https://mlsanity.com/paper/2503.03113/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 69 references that are in the map.
- First posted on arXiv 5 Mar 2025.

## Builds on (most cited first, 11 of 11)

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- [Explainable Artificial Intelligence for Human Decision-Support System in Medical Domain](https://mlsanity.com/paper/2105.02357/) (2021)
- [Challenging the Performance-Interpretability Trade-off: An Evaluation of Interpretable Machine Learning Models](https://mlsanity.com/paper/2409.14429/) (2024)
- [SeUNet-Trans: A Simple yet Effective UNet-Transformer Model for Medical Image Segmentation](https://mlsanity.com/paper/2310.09998/) (2023)
- [Deep-Learning Framework for Optimal Selection of Soil Sampling Sites](https://mlsanity.com/paper/2309.00974/) (2023)
- [Adaptive Compensation for Robotic Joint Failures Using Partially Observable Reinforcement Learning](https://mlsanity.com/paper/2409.14435/) (2024)
- [Enhanced Droplet Analysis Using Generative Adversarial Networks](https://mlsanity.com/paper/2402.15909/) (2024)

## Abstract (arXiv)

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 ± 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.