research-article

The Cellular Network as a Sensor: From Mobile Phone Data to Real-Time Road Traffic Monitoring

Authors:

Andreas Janecek,

Danilo Valerio,

Karin Anna Hummel,

Fabio Ricciato,

Helmut HlavacsAuthors Info & Claims

IEEE Transactions on Intelligent Transportation Systems, Volume 16, Issue 5

Pages 2551 - 2572

https://doi.org/10.1109/TITS.2015.2413215

Published: 25 September 2015 Publication History

Abstract

Mobile cellular networks can serve as ubiquitous sensors for physical mobility. We propose a method to infer vehicle travel times on highways and to detect road congestion in real-time, based solely on anonymized signaling data collected from a mobile cellular network. Most previous studies have considered data generated from mobile devices active in calls, namely Call Detail Records (CDR), an approach that limits the number of observable devices to a small fraction of the whole population. Our approach overcomes this drawback by exploiting the whole set of signaling events generated by both idle and active devices. While idle devices contribute with a large volume of spatially coarse-grained mobility data, active devices provide finer-grained spatial accuracy for a limited subset of devices. The combined use of data from idle and active devices improves congestion detection performance in terms of coverage, accuracy, and timeliness. We apply our method to real mobile signaling data obtained from an operational network during a one-month period on a sample highway segment in the proximity of a European city, and present an extensive validation study based on ground-truth obtained from a rich set of reference datasources - road sensor data, toll data, taxi floating car data, and radio broadcast messages.

References

[1]

D. Valerio, A. D'Alconzo, F. Ricciato, and W. Wiedermann, “ Exploiting cellular networks for road traffic estimation: A survey and a research roadmap,” in Proc. 69th IEEE VTC—Spring, 2009, pp. 1– 5.

[2]

F. Y. Wang, “ Parallel control and management for intelligent transportation systems: Concepts, architectures, and applications,” IEEE Trans. Intell. Transp. Syst., vol. 11, no. 3, pp. 630– 638, Sep. 2010.

Digital Library

[3]

J. Zhang et al., “ Data-driven intelligent transportation systems: A survey,” IEEE Trans. Intell. Transp. Syst., vol. 12, no. 4, pp. 1624– 1693, Dec. 2011.

Digital Library

[4]

C. de Fabritiis, R. Ragona, and G. Valenti, “ Traffic estimation and prediction based on real time floating car data,” in Proc. IEEE ITSC, Oct. 2008, pp. 197– 203.

[5]

B. Kerner, “ Empirical macroscopic features of spatial-temporal traffic patterns at highway bottlenecks,” Phys. Rev. E, Stat., Nonlin., Soft Matter Phys., vol. 65, no. 4, Apr. 2002,.

[6]

T. Wang, T. Fang, J. Han, and J. Wu, “ Traffic monitoring using floating car data in Hefei,” in Proc. IPTC, 2010, pp. 122– 124.

[7]

Y. Jing, Z. Yu, X. Xing, and G. Sun, “ Driving with knowledge from the physical world,” in Proc. 17th ACM SIGKDD Int. Conf. Knowl. Discov. Data Mining, 2011, pp. 316– 324.

[8]

H. Bar-Gera, “ Evaluation of a cellular phone-based system for measurements of traffic speeds and travel times: A case study from Israel,” Transp. Res. C, Emerging Technol., vol. 15, no. 6, pp. 380– 391, Dec. 2007.

[9]

N. Caceres, L. M. Romero, F. G. Benitez, and J. M. del Castillo, “ Traffic flow estimation models using cellular phone data,” IEEE Trans. Intell. Transp. Syst., vol. 13, no. 3, pp. 1430– 1441, Sep. 2012.

Digital Library

[10]

M. C. Gonzalez, C. A. Hidalgo, and A. L. Barabási, “ Understanding individual human mobility patterns,” Nature, vol. 453, no. 7196, pp. 779– 782, Jun. 2008.

[11]

C. Song, Z. Qu, N. Blumm, and A. L. Barabási, “ Limits of predictability in human mobility,” Science, vol. 327, no. 5968, pp. 1081– 1021, Feb. 2010.

[12]

P. Fiadino, D. Valerio, F. Ricciato, and K. A. Hummel, “ Steps towards the extraction of vehicular mobility patterns from 3G signaling data,” in Proc. TMA Workshop, 2012, pp. 66– 80.

[13]

A. Janecek, D. Valerio, K. A. Hummel, F. Ricciato, and H. Hlavacs, “ Cellular data meet vehicular traffic theory: Location area updates and cell transitions for travel time estimation,” in Proc. 14th ACM Conf. UbiComp, 2012, pp. 361– 370.

[14]

C. Chevallier et al. WCDMA (UMTS) Deployment Handbook: Planning and Optimization Aspects. Hoboken, NJ, USA: Wiley, 2006.

[15]

3rd Generation Partnership Project (3GPP), “ General Packet Radio Service (GPRS); Service description; Stage 2, v7.11.0,” Sophia-Antipolis, Jun. 2011.

[16]

H. Holma and A. Toskala. WCDMA for UMTS: HSPA Evolution and LTE. Hoboken, NJ, USA: Wiley, 2010.

Digital Library

[17]

E. S. Gardner, “ Exponential smoothing: The state of the art,” J. Forecast., vol. 4, no. 1, pp. 1– 28, Jan. 1985.

[18]

M. Aftabuzzaman, “ Measuring traffic congestion: A critical review,” in Proc. Australasian Transp. Res. Forum, 2007, pp. 1– 29.

[19]

R. L. Bertini. You are the Traffic Jam: An Examination of Congestion Measures. Technical report, Department of Civil and Environmental Engineering, Portland State University (2005), 2005.

[20]

A. M. Rao and K. R. Rao, “ Measuring urban traffic congestion—A review,” Int. J. Traffic Transp. Eng., vol. 2, no. 4, pp. 286– 305, 2012.

[21]

M. Treiber, A. Kesting, and D. Helbing, “ Three-phase traffic theory and two-phase models with a fundamental diagram in the light of empirical stylized facts,” Transp. Res. B, Methodol., vol. 44, no. 8/9, pp. 983– 1000, Sep.–Nov. 2010.

[22]

B. Kerner et al. Traffic state detection with floating car data in road networks,” in Proc. IEEE ITSC, 2005, pp. 44– 49.

[23]

J. C. Herrera et al., “ Evaluation of traffic data obtained via GPS-enabled mobile phones: The mobile century field experiment,” Transp. Res. C, Emerging Technol., vol. 18, no. 4, pp. 568– 583, Aug. 2010.

[24]

W. Vandenberghe, E. Vanhauwaert, S. Verbrugge, I. Moerman, and P. Demeester, “ Feasibility of expanding traffic monitoring systems with floating car data technology,” IET Intell. Transp. Syst., vol. 6, no. 4, pp. 347– 354, Dec. 2012.

[25]

G. Ranjan, H. Zang, Z. L. Zhang, and J. Bolot, “ Are call detail records biased for sampling human mobility?” SIGMOBILE Mobile Comput. Commun. Rev., vol. 16, no. 3, pp. 33– 44, Dec. 2012.

Digital Library

[26]

F. Ricciato, “ Traffic monitoring and analysis for the optimization of a 3G network,” IEEE Wireless Commun., vol. 13, no. 6, pp. 42– 49, Dec. 2006.

Digital Library

[27]

I. Trestian et al. Measuring serendipity: Connecting people, locations and interests in a mobile 3G network,” in Proc. IMC, 2009, pp. 267– 279.

[28]

F. Calabrese, M. Colonna, P. Lovisolo, D. Parata, and C. Ratti, “ Real-time urban monitoring using cell phones: A case study in Rome,” IEEE Trans. Intell. Transp. Syst., vol. 12, no. 1, pp. 141– 151, Mar. 2011.

Digital Library

[29]

N. Caceres, J. P. Wideberg, and F. G. Benitez, “ Deriving origin-destination data from a mobile phone network,” IET Intell. Transp. Syst., vol. 1, no. 1, pp. 15– 26, Mar. 2007.

[30]

F. Calabrese, M. Diao, G. Di Lorenzo, J. Ferreira, Jr., and C. Ratti, “ Understanding individual mobility patterns from urban sensing data: A mobile phone trace example,” Transp. Res. C, Emerging Technol., vol. 26, pp. 303– 313, Jan. 2013.

[31]

K. Sohn and D. Kim, “ Dynamic origin-destination flow estimation using cellular communication system,” IEEE Trans. Veh. Technol., vol. 57, no. 5, pp. 2703– 2713, Sep. 2008.

[32]

A. Sridharan and J. Bolot, “ Location patterns of mobile users: A large-scale study,” in Proc. IEEE INFOCOM, Apr. 2013, pp. 1007– 1015.

[33]

T. Tettamanti and I. Varga, “ Mobile phone location area based traffic flow estimation in urban road traffic,” Columbia Int. Publ. Adv. Civil Environ. Eng., vol. 1, no. 1, pp. 1– 15, 2014.

[34]

P. Wang, T. Hunter, A. M. Bayen, K. Schechtner, and M. C. González, “ Understanding road usage patterns in urban areas,” Sci. Rep., vol. 2, Dec. 2012,.

[35]

M. Alger et al., “ Real-time traffic monitoring using mobile phone data, ” Vodafone Pilotentwicklung GmbH, Mönchen, Germany, 2004. [Online]. Available: http://www.maths-in-industry.org/miis/30

[36]

G. Rose, “ Mobile phones as traffic probes: Practices, prospects and issues,” Transp. Rev., vol. 26, no. 3, pp. 275– 291, May 2006.

[37]

K. Sohn and K. Hwang, “ Space-based passing time estimation on a freeway using cell phones as traffic probes,” IEEE Trans. Intell. Transp. Syst., vol. 9, no. 3, pp. 559– 568, Sep. 2008.

Digital Library

[38]

O. Qing, R. L. Bertini, J. W. C. Van Lint, and S. P. Hoogendoorn, “ A theoretical framework for traffic speed estimation by fusing low-resolution probe vehicle data,” IEEE Trans. Intell. Transp. Syst., vol. 12, no. 3, pp. 747– 756, Sep. 2011.

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cover image IEEE Transactions on Intelligent Transportation Systems

IEEE Transactions on Intelligent Transportation Systems Volume 16, Issue 5

Oct. 2015

651 pages

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Copyright © 2015.

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IEEE Press

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Published: 25 September 2015

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