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Open AccessArticle

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

1
Computer Learning Research Centre, Computer Science Department, Royal Holloway University of London, Surrey TW20 0EX, UK
2
State Key Laboratory of Industrial Control Technology, Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou 310027, China
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Sensors 2019, 19(19), 4184; https://doi.org/10.3390/s19194184
Received: 30 August 2019 / Revised: 20 September 2019 / Accepted: 23 September 2019 / Published: 26 September 2019
(This article belongs to the Special Issue Smart City and Smart Infrastructure)
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. View Full-Text
Keywords: underground transport tracking; inertial dead reckoning; sensor trace matching underground transport tracking; inertial dead reckoning; sensor trace matching
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MDPI and ACS Style

Nguyen, K.A.; Wang, Y.; Li, G.; Luo, Z.; Watkins, C. Realtime Tracking of Passengers on the London Underground Transport by Matching Smartphone Accelerometer Footprints. Sensors 2019, 19, 4184.

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