ModeSense: Ubiquitous and Accurate Transportation Mode Detection using Serving Cell Tower Information - Sherif Mostafa (13548916) (2024)
Recent transportation mode detection systems propose leveraging signals from only the serving cell tower to ensure ubiquity and practical deployability across all phones. However, existing solutions employ limited statistical hand-engineered features and traditional machine learning classifiers, leading to low estimation accuracy. We present ModeSense, a novel ubiquitous and easily deployable transportation mode detection system that achieves high accuracy using handover and received signal strength information from only the serving cell tower. ModeSense introduces a hybrid deep learning-based model that fuses hand-engineered features with automatically extracted features from the serving tower data. This provides ModeSense with a comprehensive set of features, allowing it to overcome the limited information available when using the serving tower only. ModeSense effectively addresses several design challenges, including cellular data issues, absence of motion information, loss of temporal context in long-term dependencies, and optimizing the fusion of features. We extensively evaluate ModeSense using a real-world dataset comprising 395 hours of data. Our results indicate that ModeSense improves average precision and recall by up to 23% and 22%, respectively, compared to state-of-the-art systems. Moreover, ModeSense achieves these enhancements while remaining robust to phone placement variations and user heterogeneity, and requiring over five times less power consumption than GPS. Other Information Published in: Proceedings of the 32nd ACM International Conference on Advances in Geographic Information Systems License: https://creativecommons.org/licenses/by/4.0/ See article on publisher's website: https://dx.doi.org/10.1145/3678717.3691250