Enhancing Passenger Experience through Real-Time Onboard Comfort Estimation using Artificial Intelligence

An ongoing challenge faced by airlines is to enhance passenger comfort, thereby improving the overall travel experience. This research delves into the potential of artificial intelligence (AI) to predict and enhance comfort levels in real time.By collecting data from 42 passengers on a flight from Istanbul to Rome, information was collected on variables such as temperature, location and passenger demographics. This data is enriched using a powerful language model (GPT-3.5) before being analyzed by three prominent AI frameworks: TensorFlow, PyTorch, and XGBoost.The study evaluated the effectiveness of these frameworks in predicting comfort levels, with XGBoost emerging as the most successful. It achieved the highest accuracy (92.16%) and lowest error rates, surpassing PyTorch (71.55%) and TensorFlow (81.10%).The effect of input attributes on the output was analyzed using XAI. These results provide valuable insights into selecting appropriate libraries in occupant comfort estimates. The study showed that vibration and noise are the two factors that most influence customer satisfaction.These findings provide airlines with actionable insights. By adopting the right AI framework (such as XGBoost) and focusing on noise and vibration mitigation, airlines can significantly enhance passenger comfort and overall satisfaction.