Cross-Domain Travel Mode Detection for Electric Micro-Mobility Using Semi-Supervised Learning

Electric micro-mobility modes, such as e-scooters and e-bikes, are increasingly used in urban areas, posing challenges for accurate travel mode detection in mobility studies. Traditional supervised learning approaches require large labeled datasets, which are costly and time-consuming to generate. To address this, we propose xSeCA, a semi-supervised convolutional autoencoder that leverages both labeled and unlabeled trajectory data to detect electric micro-mobility travel modes. The model architecture integrates representation learning and classification in a compact and efficient manner, enabling accurate detection even with limited annotated samples. We evaluate xSeCA on multi-city datasets, including Copenhagen, Tel Aviv, Beijing and San Francisco, and benchmark it against supervised baselines such as XGBoost. Results demonstrate that xSeCA achieves high classification accuracy while exhibiting strong generalization capabilities across different urban contexts. In addition to validating model performance, we examine key travel properties relevant to micro-mobility behavior. This research highlights the benefits of semi-supervised learning for scalable and transferable travel mode detection, offering practical implications for urban planning and smart mobility systems.