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CTS: A Cellular-based Trajectory Tracking System with GPS-level Accuracy

Published: 08 January 2018 Publication History

Abstract

GPS has been widely used for locating mobile devices on the road map. Due to its high power consumption and poor signal penetration, GPS is unfortunately unsuitable to be used for continuously tracking low-power devices. Compared with GPS-based positioning, cellular-infrastructure-based positioning consumes much less energy, and works in any place covered by the cellular networks. However, the challenges of cellular positioning come from the relatively low accuracy and sampling rate. In this paper, we propose a novel cellular-based trajectory tracking system, namely CTS. It achieves GPS-level accuracy by combining trilateration-based cellular positioning, stationary state detection, and Hidden-Markov-Model-based path recovery. In particular, CTS utilizes basic characteristics of cellular sectors to produce more credible inferences for device locations.
To evaluate the performance of CTS, we collaborated with a mobile operator and deployed the system the city of Urumchi, Xinjiang Province of China. We collected the location data of 489,032 anonymous mobile subscribers from cellular networks during 24 hours, and retrieved 201 corresponding GPS trajectories. Our experimental results show that CTS achieves GPS-level accuracy in 95.7% of cases, which significantly outperforms the state-of-the-art solutions.

References

[1]
M. Lin and W.-J. Hsu, “Mining gps data for mobility patterns: A survey,” Pervasive and Mobile Computing, vol. 12, pp. 1--16, 2014.
[2]
J. Wang, C. Jiang, L. Gao, S. Yu, Z. Han, and Y. Ren, “Complex network theoretical analysis on information dissemination over vehicular networks,” in IEEE International Conference on Communications (ICC), Kuala Lumpur, Malaysia, May 2016, pp. 1--6.
[3]
L. Stenneth, O. Wolfson, P. S. Yu, and B. Xu, “Transportation mode detection using mobile phones and gis information,” in Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. ACM, 2011, pp. 54--63.
[4]
E. Come, N. A. Randriamanamihaga, L. Oukhellou, and P. Aknin, “Spatio-temporal analysis of dynamic origin-destination data using latent dirichlet allocation: Application to vélib'bike sharing system of paris,” in TRB 93rd Annual meeting. TRANSPORTATION RESEARCH BOARD, 2014, p. 19p.
[5]
P. A. Zandbergen, “Accuracy of iphone locations: A comparison of assisted gps, wifi and cellular positioning,” Transactions in GIS, vol. 13, no. s1, pp. 5--25, 2009.
[6]
F. Alizadeh-Shabdiz, “Methods and systems for determining location using a cellular and wlan positioning system by selecting the best cellular positioning system solution,” Apr. 10 2012, uS Patent 8,155,666.
[7]
Y. Zhao, “Standardization of mobile phone positioning for 3g systems,” IEEE Communications Magazine, vol. 40, no. 7, pp. 108--116, 2002.
[8]
S. Wakamiya, R. Lee, and K. Sumiya, “Crowd-sourced cartography: Measuring socio-cognitive distance for urban areas based on crowd's movement,” in Proceedings of the 2012 ACM Conference on Ubiquitous Computing, ser. UbiComp ‘12. New York, NY, USA: ACM, 2012, pp. 935--942. [Online]. Available: http://doi.acm.org/10.1145/2370216.2370424
[9]
Y. Zhao, “Mobile phone location determination and its impact on intelligent transportation systems,” IEEE Transactions on intelligent transportation systems, vol. 1, no. 1, pp. 55--64, 2000.
[10]
R. Mohamed, H. Aly, and M. Youssef, “Accurate real-time map matching for challenging environments,” IEEE Transactions on Intelligent Transportation Systems, 2016.
[11]
------, “Accurate and efficient map matching for challenging environments,” in ACM Sigspatial International Conference on Advances in Geographic Information Systems, 2014, pp. 401--404.
[12]
C. E. White, D. Bernstein, and A. L. Kornhauser, “Some map matching algorithms for personal navigation assistants,” Transportation research part c: emerging technologies, vol. 8, no. 1, pp. 91--108, 2000.
[13]
W. Chen, M. Yu, Z. Li, and Y. Chen, “Integrated vehicle navigation system for urban applications,” 2003.
[14]
D. Bernstein and A. Kornhauser, “An introduction to map matching for personal navigation assistants,” 1998.
[15]
J. S. Greenfeld, “Matching gps observations to locations on a digital map,” in Transportation Research Board 81st Annual Meeting, 2002.
[16]
W. Y. Ochieng, M. A. Quddus, and R. B. Noland, “Map-matching in complex urban road networks,” 2003.
[17]
P. Newson and J. Krumm, “Hidden markov map matching through noise and sparseness,” in Proceedings of the 17th ACM SIGSPATIAL international conference on advances in geographic information systems. ACM, 2009, pp. 336--343.
[18]
A. Thiagarajan, L. Ravindranath, H. Balakrishnan, S. Madden, L. Girod et al., “Accurate, low-energy trajectory mapping for mobile devices.” in NSDI, 2011.
[19]
I. Leontiadis, A. Lima, H. Kwak, R. Stanojevic, D. Wetherall, and K. Papagiannaki, “From cells to streets: Estimating mobile paths with cellular-side data,” in Proceedings of the 10th ACM International on Conference on emerging Networking Experiments and Technologies. ACM, 2014, pp. 121--132.
[20]
K. Perera, T. Bhattacharya, L. Kulik, and J. Bailey, “Trajectory inference for mobile devices using connected cell towers,” in Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems. ACM, 2015, p. 23.
[21]
F. Izquierdo, M. Ciurana, F. Barceló, J. Paradells, and E. Zola, “Performance evaluation of a toa-based trilateration method to locate terminals in wlan,” in Wireless Pervasive Computing, 2006 1st International Symposium on. IEEE, 2006, pp. 1--6.
[22]
C. Y. Goh, J. Dauwels, N. Mitrovic, M. Asif, A. Oran, and P. Jaillet, “Online map-matching based on hidden markov model for real-time traffic sensing applications,” in Intelligent Transportation Systems (ITSC), 2012 15th International IEEE Conference on. IEEE, 2012, pp. 776--781.
[23]
L. R. Rabiner, “A tutorial on hidden markov models and selected applications in speech recognition,” Proceedings of the IEEE, vol. 77, no. 2, pp. 257--286, 1989.
[24]
E. Hepsaydir, “Mobile positioning in cdma cellular networks,” in Vehicular Technology Conference, 1999. VTC 1999-Fall. IEEE VTS 50th, vol. 2. IEEE, 1999, pp. 795--799.
[25]
D. W. Lefever, “Measuring geographic concentration by means of the standard deviational ellipse,” American Journal of Sociology, vol. 32, no. 1, pp. 88--94, 1926.

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    cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
    Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies  Volume 1, Issue 4
    December 2017
    1298 pages
    EISSN:2474-9567
    DOI:10.1145/3178157
    Issue’s Table of Contents
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 08 January 2018
    Accepted: 01 October 2017
    Revised: 01 August 2017
    Received: 01 May 2017
    Published in IMWUT Volume 1, Issue 4

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    Author Tags

    1. Cellular networks
    2. cellular positioning
    3. system implementation
    4. trajectory tracking

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    • (2024)DMM: A Deep Reinforcement Learning Based Map Matching Framework for Cellular DataIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.338388136:10(5120-5137)Online publication date: 1-Oct-2024
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