A Hybrid Deep Neural Network for Classifying Transportation Modes based on Human Activity Vibration

Abstract

Sensor advanced technologies have facilitated the growth of various solutions for recognizing human movement through wearable devices. Characterization of the means of transportation has become beneficial applications in an intelligent transportation system since it enables context-aware support for the implementation of systems such as driver assistance and intelligent transportation management. Smartphone sensing technology has been employed to capture accurate real-time transportation information to improve urban transportation planning. Recently, several studies introduced machine learning and deep learning techniques to investigate transportation utilization from multimodal sensors, including accelerometer, gyroscope, and magnetometer sensors. However, prior work has been constrained by impractical mobile computing with a large number of model parameters. We tackle this issue in this study by providing a hybrid deep learning model for identifying vehicle usages utilizing data from smartphone sensors. We conducted experiments on a publicly available dataset of human activity vibrations called the HAV dataset. The proposed model is evaluated with a variety of conventional deep learning algorithms. The performance assessment demonstrates that the proposed hybrid deep learning model classifies people's transportation behaviors more accurately than previous studies.