Predicting Personal Transportation Modes Using Uncertain Smartphone Sensor Data

摘要

This paper describes a method for inferring user destinations and routes based on logs collected by smartphones. A challenging problem is coping with the uncertainty of smartphone sensor data. In this study, we represent a user transportation model with probabilistic models based on temporal smartphone sensor data, including GPS and accelerometer data. In our model, the travel behavior and spatio-temporal information of users are factors that affect route decisions. We propose hierarchical particle filters to enhance the performance and efficiency by sampling the route model based on hierarchical and semantic relationships.