Bus Scheduling in Dynamical Urban Transport Networks with the use of Genetic Algorithms and High Performance Computing Technologies

  • V. A. Shmelev
  • A. V. Dukhanov
  • K. V. Knyazkov
  • S. V. Ivanov
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 416)


Public transport is one of the main infrastructures in any city. It facilitates the smooth running of everyday life for ordinary people. Public transport services require constant improvement, and current methods of problem solving are not sufficient for dealing with high traffic congestion. In this paper, we present a genetic algorithm to optimize bus routes. We achieved a reduction of passengers’ waiting times at bus stops.


Public transport scheduling Genetic algorithm Urban transport networks 



This paper is supported by the Russian Scientific Foundation, grant #14-21-00137 “Supercomputer simulation of critical phenomena in complex social systems”.


  1. 1.
    Berlingerio, M., Calabrese, F., Di Lorenzo G., Nair, R., Sbodio, M.L.: AllAboard : a system for exploring urban mobility and optimizing public transport using cellphone data. Mach. Learn. Knowl. Discov. Databases, pp. 663–666 (2013)Google Scholar
  2. 2.
    Ceylan, H., Bell, M.G.: Traffic signal timing optimisation based on genetic algorithm approach, including drivers’ routing. Transp. Res. Part B Methodol. 38(4), 329–342 (2004)CrossRefGoogle Scholar
  3. 3.
    Amoroso, S., Migliore, M., Catalano, M., Galatioto, F.: A demand-based methodology for planning the bus network of a small or medium town. European Transport Trasporti Europei n 44, 41–56 (2010)Google Scholar
  4. 4.
    Bielli, M., Caramia, M., Carotenuto, P.: Genetic algorithms in bus network optimization. Transport. Res. Part C: Emerg. Technol. June 1998, 10, pp. 19–34 (2002)Google Scholar
  5. 5.
    Kidwai, F.A.: A genetic algorithm based bus scheduling model for transit network. In: Proceedings of the Eastern Asia Society for Transportation Studies 5, 477–489 (2005)Google Scholar
  6. 6.
    Ivanov, S.V., Knyazkov, K. V., Churov, T.N., Dukhanov, A.V., Boukhanovsky, A.V.: Modelng and optimization of city public transport in the CLAVIRE cloud computing environment. 3(17), 1–11 (2013)Google Scholar
  7. 7.
    Huang, K.-C., Wang, F.-J., Tsai, J.-H.: Two design patterns for data-parallel computation based on master-slave model. Inf. Process. Lett. 70(4), 197–204 (1999)CrossRefGoogle Scholar
  8. 8.
    Knyazkov, K.V., Kovalchuk, S.V., Tchurov, T.N., Maryin, S.V., Boukhanovsky, A.V.: CLAVIRE: e-Science infrastructure for data-driven computing. J. Comput. Sci. 3(6), 504–510 (2012)CrossRefGoogle Scholar
  9. 9.
    Shmelev, V.A., Dukhanov, A.V., Knyazkov, K.V., Ivanov, S.V.: Bus scheduling in dynamical urban transport networks with the use of genetic algorithms and high performance computing technologies. In: 9th International Conference on Knowledge,Information and Creativity Support Systems.“KICSS’2014. Proceedings”, pp. 86– 92 (2014)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • V. A. Shmelev
    • 1
  • A. V. Dukhanov
    • 1
  • K. V. Knyazkov
    • 1
  • S. V. Ivanov
    • 1
  1. 1.ITMO UniversitySaint PetersburgRussian Federation

Personalised recommendations