Artificial Intelligence and Machine Learning in the Travel Industry

Over the past decade, Artificial Intelligence has proved invaluable in a range of industry verticals such as automotive and assembly, life sciences, retail, oil and gas, and travel. The leading sectors adopting AI rapidly are Financial Services, Automotive and Assembly, High Tech and Telecommunications. Travel has been slow in adoption, but the opportunity for generating incremental value by leveraging AI to augment traditional analytics driven solutions is extremely high.

The contributions in this book, originally published as a special issue for the Journal of Revenue and Pricing Management, showcase the breadth and scope of the technological advances that have the potential to transform the travel experience, as well as the individuals who are already putting them into practice.

Includes case studies of successful innovation in organisations Addresses why AI has been less quickly adopted in the travel industry Explores technological innovation in the travel industry

Autorentext

Ben Vinod is a co-founder of Charter and Go, a dynamic offer, order management, and dispatch solution for air charter operators. He served as vice president of pricing, yield management, and reservations inventory control at American Airlines Decision Technologies (1993-1999) and was senior vice president and chief scientist at Sabre (2008-2020), focused on innovation and thought leadership in pioneering advanced solutions across the travel value chain for travel suppliers and intermediaries. He has published over 50 articles in academic and trade journals, is a member of AGIFORS, and serves on the editorial board of the Journal of Revenue and Pricing Management.

Inhalt

1. Special issue on artificial intelligence/machine learning in travel.- 2. Price elasticity estimation for deep learning-based choice models: an application to air itinerary choices.- 3. An integrated reinforced learning and network competition analysis for calibrating airline itinerary choice models with constrained demand.- 4. Decoupling the individual effects of multiple marketing channels with state space models.- 5. Competitive revenue management models with loyal and fully flexible customers.- 6. Demand estimation from sales transaction data: practical extensions.- 7. How recommender systems can transform airline offer construction and retailing.- 8. A note on the advantage of context in Thompson sampling.- 9. Shelf placement optimization for air products.- 10. Applying reinforcement learning to estimating apartment reference rents.- 11. Machine learning approach to market behavior estimation with applications in revenue management.- 12. Multi-layered market forecast framework for hotel revenue management by continuously learning market dynamics.- 13. Artificial Intelligence in travel.- 14. The key to leveraging AI at scale.- 15. The future of AI is the market.