Abstract
The signaling traffic of a cellular network is rich of information related to the movement of devices across cell boundaries. Thus, passive monitoring of anonymized signaling traffic enables the observation of the devices’ mobility patterns. This approach is intrinsically more powerful and accurate than previous studies based exclusively on Call Data Records as significantly more devices can be included for investigation, but it is also more challenging to implement due to a number of artifacts implicitly present in the network signaling. In this study we tackle the problem of estimating vehicular trajectories from 3G signaling traffic with particular focus on crucial elements of the data processing chain. The work is based on a sample set of anonymous traces from a large operational 3G network, including both the circuit-switched and packet-switched domains. We first investigate algorithms and procedures for preprocessing the raw dataset to make it suitable for mobility studies. Second, we present a preliminary analysis and characterization of the mobility signaling traffic. Finally, we present an algorithm for exploiting the refined data for road traffic monitoring, i.e., route detection. The work shows the potential of leveraging the 3G cellular network as a complementary “sensor” to existing solutions for road traffic monitoring.
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References
Valerio, D., et al.: Road traffic estimation from cellular network monitoring: a hands-on investigation. In: IEEE Personal Indoor Mobile Radio Communication Simposium, IEEE PIMRC 2009 (September 2009)
Valerio, D., et al.: Exploiting cellular networks for road traffic estimation: a survey and a research roadmap. In: IEEE VTC 2009 Spring (April 2009)
Holma, H., Toskala, A.: WCDMA for UMTS: Radio Access for Third Generation Mobile Communications. John Wiley and Sons, Ltd. (2000)
Ricciato, F., Pilz, R., Hasenleithner, E.: Measurement-Based Optimization of a 3G Core Network: A Case Study. In: Koucheryavy, Y., Harju, J., Iversen, V.B. (eds.) NEW2AN 2006. LNCS, vol. 4003, pp. 70–82. Springer, Heidelberg (2006)
González, M.C., Hidalgo, C.A., Barabási, A.L.: Understanding individual human mobility patterns. Nature 453(5) (June 2008)
Song, C., Qu, Z., Blumm, N., Barabási, A.L.: Limits of predictability in human mobility. Science 327(5968) (February 2010)
Halepovic, E., Williamson, C.: Characterizing and Modeling User Mobility in a Cellular Data Network. In: 2nd ACM Int’l. Workshop on Performance Evaluation of Wireless Ad Hoc, Sensor, and Ubiquitous Networks (PE-WASUN) (October 2005)
Isaacman, S., et al.: A Tale of Two Cities. In: 11th Workshop on Mobile Computing Systems and Applications (HotMobile), Annapolis, Maryland (February 2010)
Isaacman, S., et al.: Ranges of Human Mobility in Los Angeles and New York. In: 8th Int’l Workshop on Managing Ubiquitous Comm. and Services (March 2011)
Coluccia, A., et al.: On robust estimation of network-wide packet loss in 3g cellular networks. In: 5th IEEE Broadband Wireless Access Workshop (BWA 2009) (December 2009)
Coluccia, A., Ricciato, F., Romirer-Maierhofer, P.: Bayesian Estimation of Network-Wide Mean Failure Probability in 3G Cellular Networks. In: Hummel, K.A., Hlavacs, H., Gansterer, W. (eds.) PERFORM 2010 Workshop. LNCS, vol. 6821, pp. 167–178. Springer, Heidelberg (2011)
D’Alconzo, A., et al.: A distribution-based approach to anomaly detection for 3g mobile networks. In: IEEE GLOBECOM (December 2009)
Trestian, I., et al.: Measuring serendipity: connecting people, locations and interests in a mobile 3g network. In: Internet Measurement Conference (IMC 2009), Chicago, IL (November 2009)
Trestian, I., et al.: Taming user-generated content in mobile networks via drop zones. In: IEEE INFOCOM 2011, Shanghai, China (April 2011)
Tanimoto: Internal report. Technical report, IBM (November 1957)
TomTom Traffic, http://www.tomtom.com/landing_pages/traffic_solutions/
Cellint Traffic Solutions, http://www.cellint.com
Decell Technologies Ltd., http://www.decell.com
Poolsawat, A., et al.: Acquiring road traffic information through mobile phones. In: 8th Int’l Conference on ITS Telecommunications (October 2008)
Minh, Q.T., Kamioka, E.: Granular quantifying traffic states using mobile probes. In: IEEE VTC 2010-Fall (October 2010)
Sricharan, M.S., Vaidehi, V.: A Pragmatic Analysis of User Mobility Patterns in Macrocellular Wireless Networks. In: World of Wireless, Mobile and Multimedia Networks (WoWMoM), Espoo, Finland (June 2007)
Mobile Millennium, Project, http://traffic.berkeley.edu
Hummel, K.A., Hess, A.: Estimating Human Movement Activities for Opportunistic Networking: A Study of Movement Features. In: IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM 2011), Lucca, Italy, pp. 1–7 (June 2011)
Traffic.online, http://www.its-munich.de/pdf/DynInfoBY/4-Birle.pdf
Do-iT T-Mobile D1
Gundlegard, D., Karlsson, J.M.: Route Classification in Travel Time Estimation Based on Cellular Network Signaling. In: 12th Int’l IEEE Conference on ITS (2009)
Gundlegard, D., Karlsson, J.M.: Handover location accuracy for travel time estimation in GSM and UMTS. In: IET Intelligent Transport System (March 2008)
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Fiadino, P., Valerio, D., Ricciato, F., Hummel, K.A. (2012). Steps towards the Extraction of Vehicular Mobility Patterns from 3G Signaling Data. In: Pescapè, A., Salgarelli, L., Dimitropoulos, X. (eds) Traffic Monitoring and Analysis. TMA 2012. Lecture Notes in Computer Science, vol 7189. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28534-9_7
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DOI: https://doi.org/10.1007/978-3-642-28534-9_7
Publisher Name: Springer, Berlin, Heidelberg
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