Toward Understanding Multimodal Transport Classification Using Features From RINEX Data Extracted From Android Phones

Multimodal transport refers to multiple transportation means (e.g., car, plane) that can be used to transport people or goods. Classifying the mode of transportation can have multiple usages towards sustainable transport solutions, such as optimizing routes, reducing transit times, having efficient logistics operations, reducing transportation costs by strategically combining different modes, or understanding how people move within cities for migration studies. Multimodal transport classification has traditionally relied on data collected from various movement sensors (e.g., accelerometers, pedometers, gyroscopes); yet, with the opening of the access to raw Global Navigation Satellite Systems (GNSS) data on mobile devices, new avenues of multimodal analysis have been created, when GNSS signals alone (without additional sensors) could be used to classify the mode of transport. This paper introduces a novel RINEX-based framework for multimodal transport classification that operates exclusively on instantaneous raw GNSS observables, without relying on position estimates or auxiliary motion sensors. Unlike traditional approaches that require at least four satellites for positioning, the proposed method achieves classification using data from as little as one strongest satellite in view. By leveraging machine -learning algorithms, transportation modes are inferred directly from single differences and double differences of pseudorange, Doppler, and carrier-to-noise ratio features extracted from raw RINEX data. The framework was validated using an extensive dataset collected from 18 volunteers across five European countries, using 409 tracks and ten transportation modes. The results show that accurate and stable classification is possible even with limited satellite visibility, demonstrating the feasibility of low-power, privacy-preserving, and geometry-aware mobility analytics based solely on raw GNSS measurements.