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Vehicular speed estimation using received signal strength from mobile phones

Published: 26 September 2010 Publication History

Abstract

This paper introduces an algorithm that estimates the speed of a mobile phone by matching time-series signal strength data to a known signal strength trace from the same road. Knowing a mobile phone's speed is useful, for example, to estimate traffic congestion or other transportation performancemetrics. The proposed algorithmcan be implemented in the carrier's infrastructure with Network Measurement Reports obtained by a base station or on a mobile phone with signal strength readings obtained by the handset and depending on implementation choices, promises lower energy consumption than Global Positioning System (GPS) receivers. We evaluate the effectiveness of our algorithm on highway and arterial roads using GSM signal strength traces obtained from several phones over a one month period. The results show that the Correlation algorithm is significantly more accurate than existing techniques based on handoffs or phone localization.

References

[1]
}}AirSage Inc. http://tinyurl.com/c782oz.
[2]
}}Bureau of Transportation Statistics. http://www.bts.gov.
[3]
}}Mobile millennium. http://traffic.berkeley.edu/.
[4]
}}Privacy observant location system (pols). http://pols.sourceforge.net/.
[5]
}}Transportation Research Laboratory. http://www.trl.co.uk/.
[6]
}}Keith G. Calkins. E-Book, An Introduction to Statistics. http://tinyurl.com/2jlrsu, 2008.
[7]
}}Mike Y. Chen, Timothy Sohn, Dmitri Chmelev, Dirk Haehnel, Jeffrey Hightower, Jeff Hughes, Anthony LaMarca, Fred Potter, Ian Smith, and Alex Varshavsky. Practical metropolitan-scale positioning for gsm phones. In Ubicomp, pages 225--242. Springer-Verlag, 2006.
[8]
}}Benjamin Coifman. Improved velocity estimation using single loop detectors. In Transportation Research Part A, pages 863--880, 2001.
[9]
}}D. Gundlegard and J.M. Karlsson. Handover location accuracy for travel time estimation in gsm and umts. In IEEE ITSC, pages 87--94, 2009.
[10]
}}Baik Hoh, Marco Gruteser, Ryan Herring, Jeff Ban, Daniel Work, Juan-Carlos Herrera, Alexandre M. Bayen, Murali Annavaram, and Quinn Jacobson. Virtual trip lines for distributed privacy-preserving traffic monitoring. In MobiSys, pages 15--28. ACM, 2008.
[11]
}}Kaisen Lin, Aman Kansal, Dimitrios Lymberopoulos, and Feng Zhao. Energy-accuracy trade-off for continuous mobile device location. In MobiSys, pages 285--298, 2010.
[12]
}}R. Sankar and L. Civil. Traffic monitoring and congestion prediction using handoffs in wireless cellular communications. In IEEE VTC, pages 520--524, 1997.
[13]
}}Timothy Sohn, Alex Varshavsky, Anthony LaMarca, Mike Y. Chen, Tanzeem Choudhury, Ian Smith, Sunny Consolvo, Jeffrey Hightower, William G. Griswold, and Eyal de Lara. Mobility detection using everyday gsm traces. In Ubicomp, pages 212--224, 2006.
[14]
}}Arvind Thiagarajan, Lenin Ravindranath Sivalingam, Katrina LaCurts, Sivan Toledo, Jakob Eriksson, Samuel Madden, and Hari Balakrishnan. VTrack: Accurate, Energy-Aware Traffic Delay Estimation Using Mobile Phones. In SenSys, pages 85--98. ACM, 2009.
[15]
}}C. Xiao, K. D. Mann, and J. C. Olivier. Mobile speed estimation for tdma-based hierarchical cellular systems. In IEEE VTC, pages 2456--2460, 1999.
[16]
}}Yahong Rosa Zheng and Chengshan Xiao. Mobile speed estimation for broadband wireless communications over rician fading channels. Trans. Wireless. Comm., 8(1):1--5, 2009.

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    cover image ACM Conferences
    UbiComp '10: Proceedings of the 12th ACM international conference on Ubiquitous computing
    September 2010
    366 pages
    ISBN:9781605588438
    DOI:10.1145/1864349
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    Published: 26 September 2010

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    Author Tags

    1. correlation
    2. received signal strength (rss)

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    Ubicomp '10
    Ubicomp '10: The 2010 ACM Conference on Ubiquitous Computing
    September 26 - 29, 2010
    Copenhagen, Denmark

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    UbiComp '10 Paper Acceptance Rate 39 of 202 submissions, 19%;
    Overall Acceptance Rate 764 of 2,912 submissions, 26%

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    • (2025)A systematic review of generative adversarial networks for traffic state prediction: Overview, taxonomy, and future prospectsInformation Fusion10.1016/j.inffus.2024.102915117(102915)Online publication date: May-2025
    • (2023)Creation of Signals Database for the Development of Speed Estimation in an Axle Counter SystemApplied Sciences10.3390/app1305293813:5(2938)Online publication date: 24-Feb-2023
    • (2022)CTTE: Customized Travel Time Estimation via Mobile CrowdsensingIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2022.316046823:10(19335-19347)Online publication date: Oct-2022
    • (2022)Commodity WiFi Sensing in Ten Years: Status, Challenges, and OpportunitiesIEEE Internet of Things Journal10.1109/JIOT.2022.31645699:18(17832-17843)Online publication date: 15-Sep-2022
    • (2020)Train Speed Estimation from Track Structure Vibration MeasurementsApplied Sciences10.3390/app1014474210:14(4742)Online publication date: 9-Jul-2020
    • (2020)Distributed Estimation Framework for Beyond 5G Intelligent Vehicular NetworksIEEE Open Journal of Vehicular Technology10.1109/OJVT.2020.29895341(190-214)Online publication date: 2020
    • (2019)Deep-Learning-Based Real-Time Road Traffic Prediction Using Long-Term Evolution Access DataSensors10.3390/s1923532719:23(5327)Online publication date: 3-Dec-2019
    • (2019)Estimating the Relative Speed of RF Jammers in VANETsSecurity and Communication Networks10.1155/2019/20643482019Online publication date: 1-Jan-2019
    • (2019)A Machine Learning Approach for SNR Prediction in 5G Systems2019 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS)10.1109/ANTS47819.2019.9118097(1-6)Online publication date: Dec-2019
    • (2018)A Survey of Enabling Technologies for Network Localization, Tracking, and NavigationIEEE Communications Surveys & Tutorials10.1109/COMST.2018.285506320:4(3607-3644)Online publication date: Dec-2019
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