Real world performance of an RNN model for sustainable mobility metrics

This study presents the development, deployment, and testing of a Recurrent Neural Network (RNN) model for real-time transportation mode classification using smartphone sensor data. The system integrates a mobile app and cloud-based backend, enabling continuous data collection and model inference. Experimental results show that the model achieves high categorical accuracy (91.8%) and F1-score (78.0%) on the test set, with excellent precision and recall for common modes such as car , foot , and still . The model inference takes ∼ 130 ms per block of points. However, classification of the metro mode remains challenging due to limited GPS availability in underground environments. The analysis highlights that GPS-based data triggering is suboptimal for such contexts, suggesting the adoption of Human Activity Recognition (HAR) state transitions as a more effective alternative. Future improvements will focus on optimizing data collection strategies and extending the dataset to multiple urban areas to improve generalizability. These findings contribute to the advancement of mobile transport mode recognition for transportation behavior analysis and policy-making.