Realtime Tracking of Passengers on the London Underground Transport by Matching Smartphone Accelerometer Footprints. / Nguyen, Khuong An; Wang, You; Li, Guang; Luo, Zhiyuan; Watkins, Chris.

In: Sensors (Basel, Switzerland), Vol. 19, No. 19, 4184, 26.09.2019, p. 1-26.

Research output: Contribution to journalArticle

Published

Standard

Realtime Tracking of Passengers on the London Underground Transport by Matching Smartphone Accelerometer Footprints. / Nguyen, Khuong An; Wang, You; Li, Guang; Luo, Zhiyuan; Watkins, Chris.

In: Sensors (Basel, Switzerland), Vol. 19, No. 19, 4184, 26.09.2019, p. 1-26.

Research output: Contribution to journalArticle

Harvard

APA

Vancouver

Author

BibTeX

@article{c26adc6cf9694272adc1dfd02cfb5e66,
title = "Realtime Tracking of Passengers on the London Underground Transport by Matching Smartphone Accelerometer Footprints",
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, Wi-Fi, 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 and 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{\textquoteright} movements on each line. Given the passenger{\textquoteright}s accelerometer data, we identify in realtime what line they are travelling on and what station they depart from, using a 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 passengers{\textquoteright} positions 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 km of travelling distance, spanning across 381 stations on 11 different lines.",
author = "Nguyen, {Khuong An} and You Wang and Guang Li and Zhiyuan Luo and Chris Watkins",
year = "2019",
month = sep,
day = "26",
doi = "10.3390/s19194184",
language = "English",
volume = "19",
pages = "1--26",
journal = "Sensors",
issn = "1424-8220",
publisher = "Multidisciplinary Digital Publishing Institute (MDPI)",
number = "19",

}

RIS

TY - JOUR

T1 - Realtime Tracking of Passengers on the London Underground Transport by Matching Smartphone Accelerometer Footprints

AU - Nguyen, Khuong An

AU - Wang, You

AU - Li, Guang

AU - Luo, Zhiyuan

AU - Watkins, Chris

PY - 2019/9/26

Y1 - 2019/9/26

N2 - 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, Wi-Fi, 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 and 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 a 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 passengers’ positions 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 km of travelling distance, spanning across 381 stations on 11 different lines.

AB - 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, Wi-Fi, 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 and 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 a 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 passengers’ positions 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 km of travelling distance, spanning across 381 stations on 11 different lines.

U2 - 10.3390/s19194184

DO - 10.3390/s19194184

M3 - Article

VL - 19

SP - 1

EP - 26

JO - Sensors

JF - Sensors

SN - 1424-8220

IS - 19

M1 - 4184

ER -