Use of GSM Technology as the Support to Manage the Modal Distribution in the Cities

  • Grzegorz SierpińskiEmail author
  • Ireneusz Celiński


The concept of use of GSM technology to manage the congestion in the agglomerations and metropolitan areas is described in the paper. The topic is reviewed from two different angles. Firstly, by the acquisition of dynamic information about relocations (with the accuracy of up to a single user and vehicle type) it becomes possible to better adapt the offer of the urban public transportation to the needs of the passengers. Secondly, the data on the identification of movements of the urban public transportation vehicles may, due to the application of GSM technology, be made available to the passengers in real time allowing them to make rational decisions on the choice of the mode of travel. The presented problem discussion is aimed at supporting the solutions reducing the congestion in the cities (in this case by changing the modal split of the traffic) and at reducing the negative influence of the transportation on the environment (the increase of the share of environmentally friendly means of transportation in the overall traffic).


Modal Distribution Congestion Management Variable Message Sign Mobile Phone Network Traffic Management System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Caceres, N., Wideberg, J.P., Benitez, F.G.: Deriving origin-destination data from a mobile phone network. IET Intelligent Transport Systems 1, 15–26 (2007)CrossRefGoogle Scholar
  2. 2.
    Celiński, I., Sierpiński, G.: The Study of Modal Distribution of the Travel Based on Mobile Phone Networks Data. In: IIIrd International Scientific Conference Transport Problems. Katowice - Tarnowskie Góry (June 20-22, 2011); International Scientific Journal Transport Problems (in press)Google Scholar
  3. 3.
    Fontaine, M.D., Yakkala, A.P., Smith, B.L.: Probe Sampling Strategies for Traffic Monitoring Systems Based on Wireless Location Technology. Final Contract Report FHWA/VTRC 07-CR12Google Scholar
  4. 4.
    Gómez-Torres, N.R., Valdés-Díaz, D.M.: GPS Capable Mobile Phones to Gather Traffic Data. In: Ninth LACCEI Latin American and Caribbean Conference (LACCEI 2011), Engineering for a Smart Planet, Innovation, Information Technology and Computational Tools for Sustainable Development, Medellín, Colombia, August 3-5 (2011)Google Scholar
  5. 5.
    Green Paper. Towards a new culture for urban mobility. COM, 551 (2007)Google Scholar
  6. 6.
    Privat, L.: Orange to Provide Road Traffic Data Based on GSM Signal,
  7. 7.
    Rutten, B., van der Vlist, M., de Wolff, P.: GSM as the Source for Traffic Information. In: European Transport Conference (2004)Google Scholar
  8. 8.
    Sierpiński, G.: Cars Navigation and Drivers Behavior in Roads Disturb Situation. vol. 6, Logistyka – Nauka (2009)Google Scholar
  9. 9.
    Sierpiński, G.: Modeling of City Traffic and Sustainable Development. In: 14th International Conference TRANSCOMP, Computer Systems Aided Science, Industry and Transport, Zakopane, December 6-9, vol. 6, pp. 3027–3034, Logistyka – Nauka, (2010)Google Scholar
  10. 10.
    Sierpiński, G.: Travel Behaviour and Alternative Modes of Transportation. In: Mikulski, J. (ed.) TST 2011. CCIS, vol. 239, pp. 86–93. Springer, Heidelberg (2011a)CrossRefGoogle Scholar
  11. 11.
    Sierpiński, G.: Integration of activities as a method to the sustainable mobility. In: Janecki, R., Sierpiński, G. (eds.) Contemporary Transportation Systems. Selected Theoretical and Practical Problems. New Culture of Mobility, Monography, nr. 324, pp. 93–102. Publishing House of Silesian University of Technology, Gliwice (2011b)Google Scholar
  12. 12.
    Sobh, T., Elleithy, K., Mahmood, A., Karim, M.: Innovative Algorithms and Techniques in Automation, Industrial Electronics and Telecommunications. Springer, Netherlands (2007)CrossRefGoogle Scholar
  13. 13.
    Ule, A., Boucherie, R.J.: Adaptive dynamic channel borrowing in road-covering mobile networks. Faculty of Mathematical Sciences, University of Twente, University for Technical and Social Sciences (2001)Google Scholar
  14. 14.
    Valerio, D.: Road Traffic Monitoring from Cellular Network Signaling. FTW-TR-2009-003, No. of Pages: 48 (2009)Google Scholar
  15. 15.
    Valerio, D., D’Alconzo, A., Ricciato, F., Wiedermann, W.: Exploiting Cellular Networks for Road Traffic Estimation: A Survey and a Research Roadmap. In: IEEE 69th Vehicular Technology Conference (IEEE VTC 2009-Spring), Barcelona, Spain, April 26-29 (2009)Google Scholar
  16. 16.
    Valerio, D., Witek, T., Ricciato, F., Pilz, R., Wiedermann, W.: Road Traffic Estimation from Cellular Network Monitoring: a Hands-on Investigation. In: IEEE 20th Personal Indoor Mobile Radio Communication Simposium 2009 (IEEE PIMRC 2009), Tokyo, Japan, September 13-16 (2009)Google Scholar
  17. 17.
    Keep Europe moving - Sustainable mobility for our continent. Mid-term review of the European Commission’s 2001 Transport White Paper, 314 (2006)Google Scholar

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© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Faculty of Transport, Department of Traffic EngineeringSilesian Technical UniversityKatowicePoland

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