Exploring the usefulness of bluetooth and WiFi proximity for transportation mode recognition

Abstract

Understanding the mobility patterns of large groups of people is essential in transport planning. Today’s assessments rely on questionnaires or self-reported data, which are cumbersome, expensive, and prone to errors. With recent developments in mobile and ubiquitous computing, it has become feasible to automate this process and classify transportation modes using data collected by users’ smartphones. Previous work has mainly considered GPS and accelerometers; however, the achieved accuracies were often insufficient. We propose a novel method which also considers the proximity patterns of WiFi and Bluetooth (BT) devices in the environment, which are expected to be quite specific to the different transportation modes. In this poster, we present the promising results of a preliminary study in Zurich.