Ubiquitous Transportation Mode Estimation using Limited Cell Tower Information

The need for a ubiquitous and accurate transportation mode estimation system has recently risen. Unfortunately, GPS-based and inertial sensor-based solutions lack this needed ubiquity and large-scale deployability, especially in developing countries. Thus, novel systems have proposed leveraging the more ubiquitous cellular technology. However, these systems either require cell tower locations or rely on information from multiple towers, which limits their deployability. We propose AutoSense, a ubiquitous and easily deployable transportation mode estimation system that works on all phones by relying on handover and received signal strength (RSS) information from only the serving cell tower. AutoSense offers a novel domain-specific deep learning-based system to perform automatic feature extraction and time-series processing. Our system handles several challenges, including limitations in cellular data, lack of location information, overfitting, and information decay in longterm dependencies. We extensively evaluate AutoSense using a real-world public dataset composed of 395 hours of data collected over seven months. Our results show that, compared to state-of-the-art systems, AutoSense can achieve enhancements in average precision and recall of 12.36% and 14.93%, respectively, while providing a highly ubiquitous and deployable solution using only the serving cell tower information.