Using Smartphones to Capture Personal Travel Behavior

Abstract

Embedded with a wide variety of sensors, such as GPSs, accelerometers, gyroscopes, and microphones, smartphones have become a very useful tool in the context of travel surveys. In this chapter, the authors present an innovative tool to estimate individuals' mobility patterns using an application for smartphones that records GPS and accelerometer data from trips annotated by the user. The authors also present a neural network model for the classification of trips into four transportation modes, based on features extracted from the accelerometer signal. A small sample was collected in Valencia (Spain) to train and evaluate the model. The best classification results were achieved for detecting walking trips (98.2%) and bike rides (99.3%).