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This paper describes a method for automatic transportation mode detection based on smartphone sensors. Our approach is designed to work in real-time as it only requires 5s of sensor readings for the detection. Because we used accelerometer instead of GPS signal it uses less battery power and is ...

This paper addresses transportation mode detection for a mobile phone user using machine learning and based on mobile phone sensor data. We describe our approach to data collection, preprocessing and feature extraction. We evaluate our approach using random forest classification with focus on featur ...

This thesis addresses transportation mode detection based primarily on mobile phone data using machine learning methods. Our approach uses short samples of accelerometer readings taken while traveling in a vehicle to distinguish between three modalities --- car, bus, and train. We use gravity esti ...