Tracking motion context of railway passengers by fusion of low-power sensors in mobile devices

Abstract

In this paper we develop StationSense, a novel mobile sensing solution for precisely tracking temporal stop-and-go patterns of railway passengers. While such motion context serves as a promising enabler of various traveler support systems, we found through experiments in a major railway network in Japan that existing accelerometer-based passenger tracking systems can poorly work in modern trains, where jolts during motion have been dramatically reduced. Towards robust motion tracking, StationSense harnesses characteristic features in ambient magnetic fields in trains to find candidates of stationary periods, and subsequently filters out false positive detections by a tailored acceleration fusion mechanism. Then it finds optimal boundaries between adjacent moving/stationary periods, employing unique signatures in accelerometer readings. Through field experiments around 16 railway lines, we show that StationSense can identify periods of train stops with accuracy of 81%, which is almost 2 times higher than the existing accelerometer-based solutions.