Stress-Aware Urban Mobility: Predicting User Comfort with Physiological and Geo-Semantic Features

Abstract

Human comfort and stress in urban mobility are increasingly recognized as critical dimensions for designing adaptive and user-centered transport systems. While most mobility research focuses on efficiency, reliability, and safety, the experiential quality of travel remains underexplored. This study contributes to closing this gap by developing and empirically validating machine learning models capable of predicting passenger stress in real-world on-demand public transport scenarios through a unique integration of physiological, mobility and semantic geodata. A field study was conducted in Neustrelitz (Germany) with 18 participants to capture naturalistic mobility behavior. Trajectory data were collected using the DLR MovingLab smartphone app and synchronized with physiological signals recorded by Garmin smartwatch sensors. In addition, qualitative interviews and standardized stress inventories were conducted before, during, and after the trips to better understand daily mobility routines and to interpret the physiological measurements. After preprocessing, 28,831 data points were enriched with more than 70 features covering transport modes, weather conditions and semantically annotated geodata such as road categories, intersection density and land-use characteristics. Machine learning models, including XGBoost and neural networks, were applied to predict stress levels. Results showed that semantic environmental factors such as proximity to intersections, traffic signals, or commercial areas emerged as significant predictors, highlighting the value of semantic awareness in transport system design. By linking physiological stress markers with contextual geodata, this study establishes a foundation for stress-aware mobility services that adapt dynamically to human needs and support the design of healthier, more inclusive, and more sustainable transport environments.