Realtime tracking of passengers on the London underground transport by matching smartphone accelerometer footprints

Khuong An Nguyen, You Wang, Guang Li, Zhiyuan Luo, Christopher Watkins

Research output: Contribution to journalArticle

Abstract

Passengers travelling on the London underground tubes currently have no means of knowing their whereabouts between stations. The challenge for providing such service is that the London underground tunnels have no GPS, WiFi, Bluetooth or any kind of terrestrial signals to leverage.

This paper presents a novel, yet practical idea to track passengers in realtime using the smartphone accelerometer and a training database of the entire London underground network. Our rationales are that London tubes are self-driving transports with predictable accelerations, decelerations and travelling time, that they always travel on the same fixed rail lines between stations with distinctive bumps and vibrations, which permit us to generate an accelerometer map of the tubes' movements on each line. Given the passenger's accelerometer data, we identify in realtime what line they are travelling on, and what station they depart from, using pattern matching algorithm, with an accuracy of up to about 90% when the sampling length is equivalent to at least 3 station stops. We incorporate Principal Component Analysis to perform inertial tracking of passenger's position along the line, when trains break away from scheduled movements during rush hours.

Our proposal was painstakingly assessed on the entire London underground covering approximately 940 kilometres of travelling distance, spanning across 381 stations on 11 different lines.
Original languageEnglish
Article number4184
JournalSensors
Volume19
Issue number19
DOIs
Publication statusPublished - 26 Sep 2019

Bibliographical note

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Keywords

  • underground transport tracking
  • contact tracing
  • sensor trace matching

Fingerprint Dive into the research topics of 'Realtime tracking of passengers on the London underground transport by matching smartphone accelerometer footprints'. Together they form a unique fingerprint.

  • Cite this