Enhancing Transportation Mode Detection using Multi-scale Sensor Fusion and Spatial-topological Attention

Mobile sensors have improved traffic prediction through transportation mode recognition. Researchers are interested in exploring mobile sensor-based recognition methods for transportation modes. The SHL Recognition Challenge is a prominent competition in this field. SHL Challenge 2023 introduced a diverse dataset with GNSS-based and motion sensor data for transportation mode recognition. Our team, "we-can-fly," presents a fine-grained method using a multi-scale fusion approach that incorporates motion sensor and GNSS information. With temporal and topological attention mechanisms, we capture scene characteristics and enhance contextual understanding for transportation mode recognition. On the validation set, our method achieves an impressive 72.39% accuracy and 71.25% F1 score, confirming the effectiveness of our multimodal fusion transportation mode recognition algorithm.